diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md
new file mode 100644
index 00000000..d78a3dc3
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE.md
@@ -0,0 +1,19 @@
+Please use the [caffe-users list](https://groups.google.com/forum/#!forum/caffe-users) for usage, installation, or modeling questions, or other requests for help.
+_Do not post such requests to Issues._ Doing so interferes with the development of Caffe.
+
+Please read the [guidelines for contributing](https://github.com/BVLC/caffe/blob/master/CONTRIBUTING.md) before submitting this issue.
+
+### Issue summary
+
+
+### Steps to reproduce
+
+If you are having difficulty building Caffe or training a model, please ask the caffe-users mailing list. If you are reporting a build error that seems to be due to a bug in Caffe, please attach your build configuration (either Makefile.config or CMakeCache.txt) and the output of the make (or cmake) command.
+
+### Your system configuration
+Operating system:
+Compiler:
+CUDA version (if applicable):
+CUDNN version (if applicable):
+BLAS:
+Python or MATLAB version (for pycaffe and matcaffe respectively):
diff --git a/.gitignore b/.gitignore
index 53c1fb05..eff292b7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -47,6 +47,9 @@
 # PyCharm files
 .idea
 
+# Visual Studio Code files
+.vscode
+
 # OSX dir files
 .DS_Store
 
@@ -81,6 +84,7 @@ cmake_build
 
 # Generated documentation
 docs/_site
+docs/_includes
 docs/gathered
 _site
 doxygen
diff --git a/.travis.yml b/.travis.yml
index 4dc7ed72..32979547 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,40 +1,52 @@
-# Use a build matrix to do two builds in parallel:
-# one using CMake, and one using make.
+dist: trusty
+sudo: required
+
+language: cpp
+compiler: gcc
+
 env:
+  global:
+    - NUM_THREADS=4
   matrix:
-    - WITH_CUDA=false WITH_CMAKE=false WITH_IO=true
-    - WITH_CUDA=false WITH_CMAKE=true WITH_IO=true PYTHON_VERSION=3
-    - WITH_CUDA=true WITH_CMAKE=false WITH_IO=true
-    - WITH_CUDA=true WITH_CMAKE=true WITH_IO=true
-    - WITH_CUDA=false WITH_CMAKE=false WITH_IO=false
-    - WITH_CUDA=false WITH_CMAKE=true WITH_IO=false PYTHON_VERSION=3
+    # Use a build matrix to test many builds in parallel
+    # envvar defaults:
+    #   WITH_CMAKE: false
+    #   WITH_PYTHON3: false
+    #   WITH_IO: true
+    #   WITH_CUDA: false
+    #   WITH_CUDNN: false
+    - BUILD_NAME="default-make"
+#   - BUILD_NAME="python3-make" WITH_PYTHON3=true
+    - BUILD_NAME="no-io-make" WITH_IO=false
+    - BUILD_NAME="cuda-make" WITH_CUDA=true
+    - BUILD_NAME="cudnn-make" WITH_CUDA=true WITH_CUDNN=true
 
-language: cpp
+    - BUILD_NAME="default-cmake" WITH_CMAKE=true
+    - BUILD_NAME="python3-cmake" WITH_CMAKE=true WITH_PYTHON3=true
+    - BUILD_NAME="no-io-cmake" WITH_CMAKE=true WITH_IO=false
+    - BUILD_NAME="cuda-cmake" WITH_CMAKE=true WITH_CUDA=true
+    - BUILD_NAME="cudnn-cmake" WITH_CMAKE=true WITH_CUDA=true WITH_CUDNN=true
 
-# Cache Ubuntu apt packages.
 cache:
   apt: true
   directories:
-  - /home/travis/miniconda
-  - /home/travis/miniconda2
-  - /home/travis/miniconda3
-
-compiler: gcc
+    - ~/protobuf3
 
 before_install:
-  - export NUM_THREADS=4
-  - export SCRIPTS=./scripts/travis
-  - export CONDA_DIR="/home/travis/miniconda$PYTHON_VERSION"
+  - source ./scripts/travis/defaults.sh
 
 install:
-  - sudo -E $SCRIPTS/travis_install.sh
+  - sudo -E ./scripts/travis/install-deps.sh
+  - ./scripts/travis/setup-venv.sh ~/venv
+  - source ~/venv/bin/activate
+  - ./scripts/travis/install-python-deps.sh
 
 before_script:
-  - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib:/usr/local/cuda/lib64:$CONDA_DIR/lib
-  - export PATH=$CONDA_DIR/bin:$PATH
-  - if ! $WITH_CMAKE; then $SCRIPTS/travis_setup_makefile_config.sh; fi
+  - ./scripts/travis/configure.sh
 
-script: $SCRIPTS/travis_build_and_test.sh
+script:
+  - ./scripts/travis/build.sh
+  - ./scripts/travis/test.sh
 
 notifications:
 # Emails are sent to the committer's git-configured email address by default,
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 32cc42ac..08f56a33 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -10,14 +10,15 @@ endif()
 project(Caffe C CXX)
 
 # ---[ Caffe version
-set(CAFFE_TARGET_VERSION "1.0.0-rc3")
-set(CAFFE_TARGET_SOVERSION "1.0.0-rc3")
+set(CAFFE_TARGET_VERSION "1.0.0" CACHE STRING "Caffe logical version")
+set(CAFFE_TARGET_SOVERSION "1.0.0" CACHE STRING "Caffe soname version")
 add_definitions(-DCAFFE_VERSION=${CAFFE_TARGET_VERSION})
 
 # ---[ Using cmake scripts and modules
 list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake/Modules)
 
 include(ExternalProject)
+include(GNUInstallDirs)
 
 include(cmake/Utils.cmake)
 include(cmake/Targets.cmake)
@@ -28,6 +29,7 @@ include(cmake/ConfigGen.cmake)
 # ---[ Options
 caffe_option(CPU_ONLY  "Build Caffe without CUDA support" OFF) # TODO: rename to USE_CUDA
 caffe_option(USE_CUDNN "Build Caffe with cuDNN library support" ON IF NOT CPU_ONLY)
+caffe_option(USE_NCCL "Build Caffe with NCCL library support" OFF)
 caffe_option(BUILD_SHARED_LIBS "Build shared libraries" ON)
 caffe_option(BUILD_python "Build Python wrapper" ON)
 set(python_version "2" CACHE STRING "Specify which Python version to use")
@@ -38,6 +40,7 @@ caffe_option(USE_OPENCV "Build with OpenCV support" ON)
 caffe_option(USE_LEVELDB "Build with levelDB" ON)
 caffe_option(USE_LMDB "Build with lmdb" ON)
 caffe_option(ALLOW_LMDB_NOLOCK "Allow MDB_NOLOCK when reading LMDB files (only if necessary)" OFF)
+caffe_option(USE_OPENMP "Link with OpenMP (when your BLAS wants OpenMP and you get linker errors)" OFF)
 
 # ---[ Dependencies
 include(cmake/Dependencies.cmake)
@@ -47,13 +50,13 @@ if(UNIX OR APPLE)
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -Wall")
 endif()
 
+caffe_set_caffe_link()
+
 if(USE_libstdcpp)
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -stdlib=libstdc++")
   message("-- Warning: forcing libstdc++ (controlled by USE_libstdcpp option in cmake)")
 endif()
 
-add_definitions(-DGTEST_USE_OWN_TR1_TUPLE)
-
 # ---[ Warnings
 caffe_warnings_disable(CMAKE_CXX_FLAGS -Wno-sign-compare -Wno-uninitialized)
 
@@ -62,8 +65,26 @@ configure_file(cmake/Templates/caffe_config.h.in "${PROJECT_BINARY_DIR}/caffe_co
 
 # ---[ Includes
 set(Caffe_INCLUDE_DIR ${PROJECT_SOURCE_DIR}/include)
-include_directories(${Caffe_INCLUDE_DIR} ${PROJECT_BINARY_DIR})
-include_directories(BEFORE src) # This is needed for gtest.
+set(Caffe_SRC_DIR ${PROJECT_SOURCE_DIR}/src)
+include_directories(${PROJECT_BINARY_DIR})
+
+# ---[ Includes & defines for CUDA
+
+# cuda_compile() does not have per-call dependencies or include pathes
+# (cuda_compile() has per-call flags, but we set them here too for clarity)
+#
+# list(REMOVE_ITEM ...) invocations remove PRIVATE and PUBLIC keywords from collected definitions and include pathes
+if(HAVE_CUDA)
+  # pass include pathes to cuda_include_directories()
+  set(Caffe_ALL_INCLUDE_DIRS ${Caffe_INCLUDE_DIRS})
+  list(REMOVE_ITEM Caffe_ALL_INCLUDE_DIRS PRIVATE PUBLIC)
+  cuda_include_directories(${Caffe_INCLUDE_DIR} ${Caffe_SRC_DIR} ${Caffe_ALL_INCLUDE_DIRS})
+
+  # add definitions to nvcc flags directly
+  set(Caffe_ALL_DEFINITIONS ${Caffe_DEFINITIONS})
+  list(REMOVE_ITEM Caffe_ALL_DEFINITIONS PRIVATE PUBLIC)
+  list(APPEND CUDA_NVCC_FLAGS ${Caffe_ALL_DEFINITIONS})
+endif()
 
 # ---[ Subdirectories
 add_subdirectory(src/gtest)
@@ -83,8 +104,19 @@ if(BUILD_python)
   add_dependencies(pytest pycaffe)
 endif()
 
+# ---[ uninstall target
+configure_file(
+    ${CMAKE_CURRENT_SOURCE_DIR}/cmake/Uninstall.cmake.in
+    ${CMAKE_CURRENT_BINARY_DIR}/cmake/Uninstall.cmake
+    IMMEDIATE @ONLY)
+
+add_custom_target(uninstall
+    COMMAND ${CMAKE_COMMAND} -P
+    ${CMAKE_CURRENT_BINARY_DIR}/cmake/Uninstall.cmake)
+
 # ---[ Configuration summary
 caffe_print_configuration_summary()
 
 # ---[ Export configs generation
 caffe_generate_export_configs()
+
diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index 8db66ea8..3fd76781 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -1,6 +1,6 @@
 # Contributors
 
-Caffe is developed by a core set of BVLC members and the open-source community.
+Caffe is developed by a core set of BAIR members and the open-source community.
 
 We thank all of our [contributors](https://github.com/BVLC/caffe/graphs/contributors)!
 
diff --git a/LICENSE b/LICENSE
index d69d16f5..0c99adc1 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,11 +1,11 @@
 COPYRIGHT
 
 All contributions by the University of California:
-Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
+Copyright (c) 2014-2017 The Regents of the University of California (Regents)
 All rights reserved.
 
 All other contributions:
-Copyright (c) 2014, 2015, the respective contributors
+Copyright (c) 2014-2017, the respective contributors
 All rights reserved.
 
 Caffe uses a shared copyright model: each contributor holds copyright over
diff --git a/Makefile b/Makefile
index 598d28d5..c6d5685b 100644
--- a/Makefile
+++ b/Makefile
@@ -34,7 +34,7 @@ LIB_BUILD_DIR := $(BUILD_DIR)/lib
 STATIC_NAME := $(LIB_BUILD_DIR)/lib$(LIBRARY_NAME).a
 DYNAMIC_VERSION_MAJOR 		:= 1
 DYNAMIC_VERSION_MINOR 		:= 0
-DYNAMIC_VERSION_REVISION 	:= 0-rc3
+DYNAMIC_VERSION_REVISION 	:= 0
 DYNAMIC_NAME_SHORT := lib$(LIBRARY_NAME).so
 #DYNAMIC_SONAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR)
 DYNAMIC_VERSIONED_NAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR).$(DYNAMIC_VERSION_MINOR).$(DYNAMIC_VERSION_REVISION)
@@ -192,12 +192,12 @@ ifeq ($(USE_LMDB), 1)
 	LIBRARIES += lmdb
 endif
 ifeq ($(USE_OPENCV), 1)
-	LIBRARIES += opencv_core opencv_highgui opencv_imgproc 
+	LIBRARIES += opencv_core opencv_highgui opencv_imgproc
 
 	ifeq ($(OPENCV_VERSION), 3)
 		LIBRARIES += opencv_imgcodecs
 	endif
-		
+
 endif
 PYTHON_LIBRARIES ?= boost_python python2.7
 WARNINGS := -Wall -Wno-sign-compare
@@ -248,6 +248,8 @@ ifeq ($(UNAME), Linux)
 	LINUX := 1
 else ifeq ($(UNAME), Darwin)
 	OSX := 1
+	OSX_MAJOR_VERSION := $(shell sw_vers -productVersion | cut -f 1 -d .)
+	OSX_MINOR_VERSION := $(shell sw_vers -productVersion | cut -f 2 -d .)
 endif
 
 # Linux
@@ -270,22 +272,29 @@ endif
 ifeq ($(OSX), 1)
 	CXX := /usr/bin/clang++
 	ifneq ($(CPU_ONLY), 1)
-		CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release \d' | grep -o '\d')
+		CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release [0-9.]*' | tr -d '[a-z ]')
 		ifeq ($(shell echo | awk '{exit $(CUDA_VERSION) < 7.0;}'), 1)
 			CXXFLAGS += -stdlib=libstdc++
 			LINKFLAGS += -stdlib=libstdc++
 		endif
 		# clang throws this warning for cuda headers
 		WARNINGS += -Wno-unneeded-internal-declaration
+		# 10.11 strips DYLD_* env vars so link CUDA (rpath is available on 10.5+)
+		OSX_10_OR_LATER   := $(shell [ $(OSX_MAJOR_VERSION) -ge 10 ] && echo true)
+		OSX_10_5_OR_LATER := $(shell [ $(OSX_MINOR_VERSION) -ge 5 ] && echo true)
+		ifeq ($(OSX_10_OR_LATER),true)
+			ifeq ($(OSX_10_5_OR_LATER),true)
+				LDFLAGS += -Wl,-rpath,$(CUDA_LIB_DIR)
+			endif
+		endif
 	endif
 	# gtest needs to use its own tuple to not conflict with clang
 	COMMON_FLAGS += -DGTEST_USE_OWN_TR1_TUPLE=1
 	# boost::thread is called boost_thread-mt to mark multithreading on OS X
 	LIBRARIES += boost_thread-mt
 	# we need to explicitly ask for the rpath to be obeyed
-	DYNAMIC_FLAGS := -install_name @rpath/libcaffe.so
 	ORIGIN := @loader_path
-	VERSIONFLAGS += -Wl,-install_name,$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../../build/lib
+	VERSIONFLAGS += -Wl,-install_name,@rpath/$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../../build/lib
 else
 	ORIGIN := \$$ORIGIN
 endif
@@ -319,6 +328,12 @@ ifeq ($(USE_CUDNN), 1)
 	COMMON_FLAGS += -DUSE_CUDNN
 endif
 
+# NCCL acceleration configuration
+ifeq ($(USE_NCCL), 1)
+	LIBRARIES += nccl
+	COMMON_FLAGS += -DUSE_NCCL
+endif
+
 # configure IO libraries
 ifeq ($(USE_OPENCV), 1)
 	COMMON_FLAGS += -DUSE_OPENCV
@@ -355,9 +370,9 @@ ifeq ($(BLAS), mkl)
 	# MKL
 	LIBRARIES += mkl_rt
 	COMMON_FLAGS += -DUSE_MKL
-	MKL_DIR ?= /opt/intel/mkl
-	BLAS_INCLUDE ?= $(MKL_DIR)/include
-	BLAS_LIB ?= $(MKL_DIR)/lib $(MKL_DIR)/lib/intel64
+	MKLROOT ?= /opt/intel/mkl
+	BLAS_INCLUDE ?= $(MKLROOT)/include
+	BLAS_LIB ?= $(MKLROOT)/lib $(MKLROOT)/lib/intel64
 else ifeq ($(BLAS), open)
 	# OpenBLAS
 	LIBRARIES += openblas
@@ -373,8 +388,11 @@ else
 		LIBRARIES += cblas
 		# 10.10 has accelerate while 10.9 has veclib
 		XCODE_CLT_VER := $(shell pkgutil --pkg-info=com.apple.pkg.CLTools_Executables | grep 'version' | sed 's/[^0-9]*\([0-9]\).*/\1/')
+		XCODE_CLT_GEQ_7 := $(shell [ $(XCODE_CLT_VER) -gt 6 ] && echo 1)
 		XCODE_CLT_GEQ_6 := $(shell [ $(XCODE_CLT_VER) -gt 5 ] && echo 1)
-		ifeq ($(XCODE_CLT_GEQ_6), 1)
+		ifeq ($(XCODE_CLT_GEQ_7), 1)
+			BLAS_INCLUDE ?= /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/$(shell ls /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/ | sort | tail -1)/System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/Headers
+		else ifeq ($(XCODE_CLT_GEQ_6), 1)
 			BLAS_INCLUDE ?= /System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/
 			LDFLAGS += -framework Accelerate
 		else
@@ -552,7 +570,7 @@ $(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
 
 $(DYNAMIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
 	@ echo LD -o $@
-	$(Q)$(CXX) -shared -o $@ $(OBJS) $(VERSIONFLAGS) $(LINKFLAGS) $(LDFLAGS) $(DYNAMIC_FLAGS)
+	$(Q)$(CXX) -shared -o $@ $(OBJS) $(VERSIONFLAGS) $(LINKFLAGS) $(LDFLAGS)
 	@ cd $(BUILD_DIR)/lib; rm -f $(DYNAMIC_NAME_SHORT);   ln -s $(DYNAMIC_VERSIONED_NAME_SHORT) $(DYNAMIC_NAME_SHORT)
 
 $(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
@@ -676,6 +694,6 @@ $(DISTRIBUTE_DIR): all py | $(DISTRIBUTE_SUBDIRS)
 	install -m 644 $(DYNAMIC_NAME) $(DISTRIBUTE_DIR)/lib
 	cd $(DISTRIBUTE_DIR)/lib; rm -f $(DYNAMIC_NAME_SHORT);   ln -s $(DYNAMIC_VERSIONED_NAME_SHORT) $(DYNAMIC_NAME_SHORT)
 	# add python - it's not the standard way, indeed...
-	cp -r python $(DISTRIBUTE_DIR)/python
+	cp -r python $(DISTRIBUTE_DIR)/
 
 -include $(DEPS)
diff --git a/Makefile.config.example b/Makefile.config.example
index 8fd49c9c..d552b38a 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -31,13 +31,17 @@ CUDA_DIR := /usr/local/cuda
 # CUDA_DIR := /usr
 
 # CUDA architecture setting: going with all of them.
-# For CUDA < 6.0, comment the *_50 lines for compatibility.
+# For CUDA < 6.0, comment the *_50 through *_61 lines for compatibility.
+# For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.
 CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
 		-gencode arch=compute_20,code=sm_21 \
 		-gencode arch=compute_30,code=sm_30 \
 		-gencode arch=compute_35,code=sm_35 \
 		-gencode arch=compute_50,code=sm_50 \
-		-gencode arch=compute_50,code=compute_50
+		-gencode arch=compute_52,code=sm_52 \
+		-gencode arch=compute_60,code=sm_60 \
+		-gencode arch=compute_61,code=sm_61 \
+		-gencode arch=compute_61,code=compute_61
 
 # BLAS choice:
 # atlas for ATLAS (default)
@@ -68,7 +72,7 @@ PYTHON_INCLUDE := /usr/include/python2.7 \
 # ANACONDA_HOME := $(HOME)/anaconda
 # PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
 		# $(ANACONDA_HOME)/include/python2.7 \
-		# $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \
+		# $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include
 
 # Uncomment to use Python 3 (default is Python 2)
 # PYTHON_LIBRARIES := boost_python3 python3.5m
@@ -94,10 +98,15 @@ LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
 # INCLUDE_DIRS += $(shell brew --prefix)/include
 # LIBRARY_DIRS += $(shell brew --prefix)/lib
 
+# NCCL acceleration switch (uncomment to build with NCCL)
+# https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0)
+# USE_NCCL := 1
+
 # Uncomment to use `pkg-config` to specify OpenCV library paths.
 # (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
 # USE_PKG_CONFIG := 1
 
+# N.B. both build and distribute dirs are cleared on `make clean`
 BUILD_DIR := build
 DISTRIBUTE_DIR := distribute
 
diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake
index 05637111..09bb09b4 100644
--- a/cmake/ConfigGen.cmake
+++ b/cmake/ConfigGen.cmake
@@ -1,31 +1,4 @@
 
-################################################################################################
-# Helper function to fetch caffe includes which will be passed to dependent projects
-# Usage:
-#   caffe_get_current_includes(<includes_list_variable>)
-function(caffe_get_current_includes includes_variable)
-  get_property(current_includes DIRECTORY PROPERTY INCLUDE_DIRECTORIES)
-  caffe_convert_absolute_paths(current_includes)
-
-  # remove at most one ${PROJECT_BINARY_DIR} include added for caffe_config.h
-  list(FIND current_includes ${PROJECT_BINARY_DIR} __index)
-  list(REMOVE_AT current_includes ${__index})
-
-  # removing numpy includes (since not required for client libs)
-  set(__toremove "")
-  foreach(__i ${current_includes})
-    if(${__i} MATCHES "python")
-      list(APPEND __toremove ${__i})
-    endif()
-  endforeach()
-  if(__toremove)
-    list(REMOVE_ITEM current_includes ${__toremove})
-  endif()
-
-  caffe_list_unique(current_includes)
-  set(${includes_variable} ${current_includes} PARENT_SCOPE)
-endfunction()
-
 ################################################################################################
 # Helper function to get all list items that begin with given prefix
 # Usage:
@@ -47,60 +20,24 @@ endfunction()
 function(caffe_generate_export_configs)
   set(install_cmake_suffix "share/Caffe")
 
-  # ---[ Configure build-tree CaffeConfig.cmake file ]---
-  caffe_get_current_includes(Caffe_INCLUDE_DIRS)
-
-  set(Caffe_DEFINITIONS "")
   if(NOT HAVE_CUDA)
     set(HAVE_CUDA FALSE)
-    list(APPEND Caffe_DEFINITIONS -DCPU_ONLY)
-  endif()
-
-  if(USE_OPENCV)
-    list(APPEND Caffe_DEFINITIONS -DUSE_OPENCV)
-  endif()
-
-  if(USE_LMDB)
-    list(APPEND Caffe_DEFINITIONS -DUSE_LMDB)
-    if (ALLOW_LMDB_NOLOCK)
-        list(APPEND Caffe_DEFINITIONS -DALLOW_LMDB_NOLOCK)
-    endif()
-  endif()
-
-  if(USE_LEVELDB)
-    list(APPEND Caffe_DEFINITIONS -DUSE_LEVELDB)
   endif()
 
   if(NOT HAVE_CUDNN)
     set(HAVE_CUDNN FALSE)
-  else()
-    list(APPEND DEFINITIONS -DUSE_CUDNN)
   endif()
 
-  if(BLAS STREQUAL "MKL" OR BLAS STREQUAL "mkl")
-    list(APPEND Caffe_DEFINITIONS -DUSE_MKL)
-  endif()
+  # ---[ Configure build-tree CaffeConfig.cmake file ]---
 
   configure_file("cmake/Templates/CaffeConfig.cmake.in" "${PROJECT_BINARY_DIR}/CaffeConfig.cmake" @ONLY)
 
   # Add targets to the build-tree export set
-  export(TARGETS caffe proto FILE "${PROJECT_BINARY_DIR}/CaffeTargets.cmake")
+  export(TARGETS caffe caffeproto FILE "${PROJECT_BINARY_DIR}/CaffeTargets.cmake")
   export(PACKAGE Caffe)
 
   # ---[ Configure install-tree CaffeConfig.cmake file ]---
 
-  # remove source and build dir includes
-  caffe_get_items_with_prefix(${PROJECT_SOURCE_DIR} Caffe_INCLUDE_DIRS __insource)
-  caffe_get_items_with_prefix(${PROJECT_BINARY_DIR} Caffe_INCLUDE_DIRS __inbinary)
-  list(REMOVE_ITEM Caffe_INCLUDE_DIRS ${__insource} ${__inbinary})
-
-  # add `install` include folder
-  set(lines
-     "get_filename_component(__caffe_include \"\${Caffe_CMAKE_DIR}/../../include\" ABSOLUTE)\n"
-     "list(APPEND Caffe_INCLUDE_DIRS \${__caffe_include})\n"
-     "unset(__caffe_include)\n")
-  string(REPLACE ";" "" Caffe_INSTALL_INCLUDE_DIR_APPEND_COMMAND ${lines})
-
   configure_file("cmake/Templates/CaffeConfig.cmake.in" "${PROJECT_BINARY_DIR}/cmake/CaffeConfig.cmake" @ONLY)
 
   # Install the CaffeConfig.cmake and export set to use with install-tree
@@ -109,7 +46,7 @@ function(caffe_generate_export_configs)
 
   # ---[ Configure and install version file ]---
 
-  # TODO: Lines below are commented because Caffe does't declare its version in headers.
+  # TODO: Lines below are commented because Caffe doesn't declare its version in headers.
   # When the declarations are added, modify `caffe_extract_caffe_version()` macro and uncomment
 
   # configure_file(cmake/Templates/CaffeConfigVersion.cmake.in "${PROJECT_BINARY_DIR}/CaffeConfigVersion.cmake" @ONLY)
diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake
index 286a4280..b2b19e8b 100644
--- a/cmake/Cuda.cmake
+++ b/cmake/Cuda.cmake
@@ -4,7 +4,7 @@ endif()
 
 # Known NVIDIA GPU achitectures Caffe can be compiled for.
 # This list will be used for CUDA_ARCH_NAME = All option
-set(Caffe_known_gpu_archs "20 21(20) 30 35 50")
+set(Caffe_known_gpu_archs "20 21(20) 30 35 50 60 61")
 
 ################################################################################################
 # A function for automatic detection of GPUs installed  (if autodetection is enabled)
@@ -56,7 +56,7 @@ endfunction()
 #   caffe_select_nvcc_arch_flags(out_variable)
 function(caffe_select_nvcc_arch_flags out_variable)
   # List of arch names
-  set(__archs_names "Fermi" "Kepler" "Maxwell" "All" "Manual")
+  set(__archs_names "Fermi" "Kepler" "Maxwell" "Pascal" "All" "Manual")
   set(__archs_name_default "All")
   if(NOT CMAKE_CROSSCOMPILING)
     list(APPEND __archs_names "Auto")
@@ -89,6 +89,8 @@ function(caffe_select_nvcc_arch_flags out_variable)
     set(__cuda_arch_bin "30 35")
   elseif(${CUDA_ARCH_NAME} STREQUAL "Maxwell")
     set(__cuda_arch_bin "50")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Pascal")
+    set(__cuda_arch_bin "60 61")
   elseif(${CUDA_ARCH_NAME} STREQUAL "All")
     set(__cuda_arch_bin ${Caffe_known_gpu_archs})
   elseif(${CUDA_ARCH_NAME} STREQUAL "Auto")
@@ -174,11 +176,18 @@ function(detect_cuDNN)
             PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT} ${CUDA_TOOLKIT_INCLUDE}
             DOC "Path to cuDNN include directory." )
 
-  get_filename_component(__libpath_hist ${CUDA_CUDART_LIBRARY} PATH)
-  find_library(CUDNN_LIBRARY NAMES libcudnn.so # libcudnn_static.a
-                             PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT} ${CUDNN_INCLUDE} ${__libpath_hist}
-                             DOC "Path to cuDNN library.")
+  # dynamic libs have different suffix in mac and linux
+  if(APPLE)
+    set(CUDNN_LIB_NAME "libcudnn.dylib")
+  else()
+    set(CUDNN_LIB_NAME "libcudnn.so")
+  endif()
 
+  get_filename_component(__libpath_hist ${CUDA_CUDART_LIBRARY} PATH)
+  find_library(CUDNN_LIBRARY NAMES ${CUDNN_LIB_NAME}
+   PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT} ${CUDNN_INCLUDE} ${__libpath_hist} ${__libpath_hist}/../lib
+   DOC "Path to cuDNN library.")
+  
   if(CUDNN_INCLUDE AND CUDNN_LIBRARY)
     set(HAVE_CUDNN  TRUE PARENT_SCOPE)
     set(CUDNN_FOUND TRUE PARENT_SCOPE)
@@ -231,17 +240,17 @@ endif()
 
 set(HAVE_CUDA TRUE)
 message(STATUS "CUDA detected: " ${CUDA_VERSION})
-include_directories(SYSTEM ${CUDA_INCLUDE_DIRS})
-list(APPEND Caffe_LINKER_LIBS ${CUDA_CUDART_LIBRARY}
-                              ${CUDA_curand_LIBRARY} ${CUDA_CUBLAS_LIBRARIES})
+list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${CUDA_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS PUBLIC ${CUDA_CUDART_LIBRARY}
+                                     ${CUDA_curand_LIBRARY} ${CUDA_CUBLAS_LIBRARIES})
 
 # cudnn detection
 if(USE_CUDNN)
   detect_cuDNN()
   if(HAVE_CUDNN)
-    add_definitions(-DUSE_CUDNN)
-    include_directories(SYSTEM ${CUDNN_INCLUDE})
-    list(APPEND Caffe_LINKER_LIBS ${CUDNN_LIBRARY})
+    list(APPEND Caffe_DEFINITIONS PUBLIC -DUSE_CUDNN)
+    list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${CUDNN_INCLUDE})
+    list(APPEND Caffe_LINKER_LIBS PUBLIC ${CUDNN_LIBRARY})
   endif()
 endif()
 
@@ -275,7 +284,7 @@ mark_as_advanced(CUDA_SDK_ROOT_DIR CUDA_SEPARABLE_COMPILATION)
 if(APPLE)
   caffe_detect_darwin_version(OSX_VERSION)
 
-  # OSX 10.9 and higher uses clang/libc++ by default which is incompartible with old CUDA toolkits
+  # OSX 10.9 and higher uses clang/libc++ by default which is incompatible with old CUDA toolkits
   if(OSX_VERSION VERSION_GREATER 10.8)
     # enabled by default if and only if CUDA version is less than 7.0
     caffe_option(USE_libstdcpp "Use libstdc++ instead of libc++" (CUDA_VERSION VERSION_LESS 7.0))
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index c7b6a17a..c48255c8 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -1,57 +1,76 @@
-# This list is required for static linking and exported to CaffeConfig.cmake
+# These lists are later turned into target properties on main caffe library target
 set(Caffe_LINKER_LIBS "")
+set(Caffe_INCLUDE_DIRS "")
+set(Caffe_DEFINITIONS "")
+set(Caffe_COMPILE_OPTIONS "")
 
 # ---[ Boost
-find_package(Boost 1.46 REQUIRED COMPONENTS system thread filesystem)
-include_directories(SYSTEM ${Boost_INCLUDE_DIR})
-list(APPEND Caffe_LINKER_LIBS ${Boost_LIBRARIES})
+find_package(Boost 1.54 REQUIRED COMPONENTS system thread filesystem)
+list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${Boost_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS PUBLIC ${Boost_LIBRARIES})
 
 # ---[ Threads
 find_package(Threads REQUIRED)
-list(APPEND Caffe_LINKER_LIBS ${CMAKE_THREAD_LIBS_INIT})
+list(APPEND Caffe_LINKER_LIBS PRIVATE ${CMAKE_THREAD_LIBS_INIT})
+
+# ---[ OpenMP
+if(USE_OPENMP)
+  # Ideally, this should be provided by the BLAS library IMPORTED target. However,
+  # nobody does this, so we need to link to OpenMP explicitly and have the maintainer
+  # to flick the switch manually as needed.
+  #
+  # Moreover, OpenMP package does not provide an IMPORTED target as well, and the
+  # suggested way of linking to OpenMP is to append to CMAKE_{C,CXX}_FLAGS.
+  # However, this naïve method will force any user of Caffe to add the same kludge
+  # into their buildsystem again, so we put these options into per-target PUBLIC
+  # compile options and link flags, so that they will be exported properly.
+  find_package(OpenMP REQUIRED)
+  list(APPEND Caffe_LINKER_LIBS PRIVATE ${OpenMP_CXX_FLAGS})
+  list(APPEND Caffe_COMPILE_OPTIONS PRIVATE ${OpenMP_CXX_FLAGS})
+endif()
 
 # ---[ Google-glog
 include("cmake/External/glog.cmake")
-include_directories(SYSTEM ${GLOG_INCLUDE_DIRS})
-list(APPEND Caffe_LINKER_LIBS ${GLOG_LIBRARIES})
+list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${GLOG_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS PUBLIC ${GLOG_LIBRARIES})
 
 # ---[ Google-gflags
 include("cmake/External/gflags.cmake")
-include_directories(SYSTEM ${GFLAGS_INCLUDE_DIRS})
-list(APPEND Caffe_LINKER_LIBS ${GFLAGS_LIBRARIES})
+list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${GFLAGS_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS PUBLIC ${GFLAGS_LIBRARIES})
 
 # ---[ Google-protobuf
 include(cmake/ProtoBuf.cmake)
 
 # ---[ HDF5
 find_package(HDF5 COMPONENTS HL REQUIRED)
-include_directories(SYSTEM ${HDF5_INCLUDE_DIRS} ${HDF5_HL_INCLUDE_DIR})
-list(APPEND Caffe_LINKER_LIBS ${HDF5_LIBRARIES})
+list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${HDF5_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS PUBLIC ${HDF5_LIBRARIES} ${HDF5_HL_LIBRARIES})
 
 # ---[ LMDB
 if(USE_LMDB)
   find_package(LMDB REQUIRED)
-  include_directories(SYSTEM ${LMDB_INCLUDE_DIR})
-  list(APPEND Caffe_LINKER_LIBS ${LMDB_LIBRARIES})
-  add_definitions(-DUSE_LMDB)
+  list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${LMDB_INCLUDE_DIR})
+  list(APPEND Caffe_LINKER_LIBS PUBLIC ${LMDB_LIBRARIES})
+  list(APPEND Caffe_DEFINITIONS PUBLIC -DUSE_LMDB)
   if(ALLOW_LMDB_NOLOCK)
-    add_definitions(-DALLOW_LMDB_NOLOCK)
+    list(APPEND Caffe_DEFINITIONS PRIVATE -DALLOW_LMDB_NOLOCK)
   endif()
 endif()
 
 # ---[ LevelDB
 if(USE_LEVELDB)
   find_package(LevelDB REQUIRED)
-  include_directories(SYSTEM ${LevelDB_INCLUDE})
-  list(APPEND Caffe_LINKER_LIBS ${LevelDB_LIBRARIES})
-  add_definitions(-DUSE_LEVELDB)
+  list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${LevelDB_INCLUDES})
+  list(APPEND Caffe_LINKER_LIBS PUBLIC ${LevelDB_LIBRARIES})
+  list(APPEND Caffe_DEFINITIONS PUBLIC -DUSE_LEVELDB)
 endif()
 
 # ---[ Snappy
 if(USE_LEVELDB)
   find_package(Snappy REQUIRED)
-  include_directories(SYSTEM ${Snappy_INCLUDE_DIR})
-  list(APPEND Caffe_LINKER_LIBS ${Snappy_LIBRARIES})
+  list(APPEND Caffe_INCLUDE_DIRS PRIVATE ${Snappy_INCLUDE_DIR})
+  list(APPEND Caffe_LINKER_LIBS PRIVATE ${Snappy_LIBRARIES})
 endif()
 
 # ---[ CUDA
@@ -63,8 +82,14 @@ if(NOT HAVE_CUDA)
     message(WARNING "-- CUDA is not detected by cmake. Building without it...")
   endif()
 
-  # TODO: remove this not cross platform define in future. Use caffe_config.h instead.
-  add_definitions(-DCPU_ONLY)
+  list(APPEND Caffe_DEFINITIONS PUBLIC -DCPU_ONLY)
+endif()
+
+if(USE_NCCL)
+  find_package(NCCL REQUIRED)
+  include_directories(SYSTEM ${NCCL_INCLUDE_DIR})
+  list(APPEND Caffe_LINKER_LIBS ${NCCL_LIBRARIES})
+  add_definitions(-DUSE_NCCL)
 endif()
 
 # ---[ OpenCV
@@ -73,10 +98,10 @@ if(USE_OPENCV)
   if(NOT OpenCV_FOUND) # if not OpenCV 3.x, then imgcodecs are not found
     find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc)
   endif()
-  include_directories(SYSTEM ${OpenCV_INCLUDE_DIRS})
-  list(APPEND Caffe_LINKER_LIBS ${OpenCV_LIBS})
+  list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${OpenCV_INCLUDE_DIRS})
+  list(APPEND Caffe_LINKER_LIBS PUBLIC ${OpenCV_LIBS})
   message(STATUS "OpenCV found (${OpenCV_CONFIG_PATH})")
-  add_definitions(-DUSE_OPENCV)
+  list(APPEND Caffe_DEFINITIONS PUBLIC -DUSE_OPENCV)
 endif()
 
 # ---[ BLAS
@@ -86,22 +111,28 @@ if(NOT APPLE)
 
   if(BLAS STREQUAL "Atlas" OR BLAS STREQUAL "atlas")
     find_package(Atlas REQUIRED)
-    include_directories(SYSTEM ${Atlas_INCLUDE_DIR})
-    list(APPEND Caffe_LINKER_LIBS ${Atlas_LIBRARIES})
+    list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${Atlas_INCLUDE_DIR})
+    list(APPEND Caffe_LINKER_LIBS PUBLIC ${Atlas_LIBRARIES})
   elseif(BLAS STREQUAL "Open" OR BLAS STREQUAL "open")
     find_package(OpenBLAS REQUIRED)
-    include_directories(SYSTEM ${OpenBLAS_INCLUDE_DIR})
-    list(APPEND Caffe_LINKER_LIBS ${OpenBLAS_LIB})
+    list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${OpenBLAS_INCLUDE_DIR})
+    list(APPEND Caffe_LINKER_LIBS PUBLIC ${OpenBLAS_LIB})
   elseif(BLAS STREQUAL "MKL" OR BLAS STREQUAL "mkl")
     find_package(MKL REQUIRED)
-    include_directories(SYSTEM ${MKL_INCLUDE_DIR})
-    list(APPEND Caffe_LINKER_LIBS ${MKL_LIBRARIES})
-    add_definitions(-DUSE_MKL)
+    list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${MKL_INCLUDE_DIR})
+    list(APPEND Caffe_LINKER_LIBS PUBLIC ${MKL_LIBRARIES})
+    list(APPEND Caffe_DEFINITIONS PUBLIC -DUSE_MKL)
   endif()
 elseif(APPLE)
   find_package(vecLib REQUIRED)
-  include_directories(SYSTEM ${vecLib_INCLUDE_DIR})
-  list(APPEND Caffe_LINKER_LIBS ${vecLib_LINKER_LIBS})
+  list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${vecLib_INCLUDE_DIR})
+  list(APPEND Caffe_LINKER_LIBS PUBLIC ${vecLib_LINKER_LIBS})
+
+  if(VECLIB_FOUND)
+    if(NOT vecLib_INCLUDE_DIR MATCHES "^/System/Library/Frameworks/vecLib.framework.*")
+      list(APPEND Caffe_DEFINITIONS PUBLIC -DUSE_ACCELERATE)
+    endif()
+  endif()
 endif()
 
 # ---[ Python
@@ -113,18 +144,18 @@ if(BUILD_python)
     find_package(NumPy 1.7.1)
     # Find the matching boost python implementation
     set(version ${PYTHONLIBS_VERSION_STRING})
-    
+
     STRING( REGEX REPLACE "[^0-9]" "" boost_py_version ${version} )
     find_package(Boost 1.46 COMPONENTS "python-py${boost_py_version}")
     set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND})
-    
+
     while(NOT "${version}" STREQUAL "" AND NOT Boost_PYTHON_FOUND)
       STRING( REGEX REPLACE "([0-9.]+).[0-9]+" "\\1" version ${version} )
-      
+
       STRING( REGEX REPLACE "[^0-9]" "" boost_py_version ${version} )
       find_package(Boost 1.46 COMPONENTS "python-py${boost_py_version}")
       set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND})
-      
+
       STRING( REGEX MATCHALL "([0-9.]+).[0-9]+" has_more_version ${version} )
       if("${has_more_version}" STREQUAL "")
         break()
@@ -143,9 +174,9 @@ if(BUILD_python)
   if(PYTHONLIBS_FOUND AND NUMPY_FOUND AND Boost_PYTHON_FOUND)
     set(HAVE_PYTHON TRUE)
     if(BUILD_python_layer)
-      add_definitions(-DWITH_PYTHON_LAYER)
-      include_directories(SYSTEM ${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR} ${Boost_INCLUDE_DIRS})
-      list(APPEND Caffe_LINKER_LIBS ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
+      list(APPEND Caffe_DEFINITIONS PRIVATE -DWITH_PYTHON_LAYER)
+      list(APPEND Caffe_INCLUDE_DIRS PRIVATE ${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR} PUBLIC ${Boost_INCLUDE_DIRS})
+      list(APPEND Caffe_LINKER_LIBS PRIVATE ${PYTHON_LIBRARIES} PUBLIC ${Boost_LIBRARIES})
     endif()
   endif()
 endif()
diff --git a/cmake/External/glog.cmake b/cmake/External/glog.cmake
index a44672f2..f9d0549c 100644
--- a/cmake/External/glog.cmake
+++ b/cmake/External/glog.cmake
@@ -37,6 +37,7 @@ if (NOT __GLOG_INCLUDED)
       GIT_TAG "v0.3.4"
       UPDATE_COMMAND ""
       INSTALL_DIR ${gflags_INSTALL}
+      PATCH_COMMAND autoreconf -i ${glog_PREFIX}/src/glog
       CONFIGURE_COMMAND env "CFLAGS=${GLOG_C_FLAGS}" "CXXFLAGS=${GLOG_CXX_FLAGS}" ${glog_PREFIX}/src/glog/configure --prefix=${glog_INSTALL} --enable-shared=no --enable-static=yes --with-gflags=${GFLAGS_LIBRARY_DIRS}/..
       LOG_DOWNLOAD 1
       LOG_CONFIGURE 1
diff --git a/cmake/Modules/FindAtlas.cmake b/cmake/Modules/FindAtlas.cmake
index 6e156435..7ffa6393 100644
--- a/cmake/Modules/FindAtlas.cmake
+++ b/cmake/Modules/FindAtlas.cmake
@@ -26,9 +26,9 @@ set(Atlas_LIB_SEARCH_PATHS
 find_path(Atlas_CBLAS_INCLUDE_DIR   NAMES cblas.h   PATHS ${Atlas_INCLUDE_SEARCH_PATHS})
 find_path(Atlas_CLAPACK_INCLUDE_DIR NAMES clapack.h PATHS ${Atlas_INCLUDE_SEARCH_PATHS})
 
-find_library(Atlas_CBLAS_LIBRARY NAMES  ptcblas_r ptcblas cblas_r cblas PATHS ${Atlas_LIB_SEARCH_PATHS})
-find_library(Atlas_BLAS_LIBRARY NAMES   atlas_r   atlas                 PATHS ${Atlas_LIB_SEARCH_PATHS})
-find_library(Atlas_LAPACK_LIBRARY NAMES alapack_r alapack lapack_atlas  PATHS ${Atlas_LIB_SEARCH_PATHS})
+find_library(Atlas_CBLAS_LIBRARY NAMES  ptcblas_r ptcblas cblas_r cblas       PATHS ${Atlas_LIB_SEARCH_PATHS})
+find_library(Atlas_BLAS_LIBRARY NAMES   atlas_r   atlas                       PATHS ${Atlas_LIB_SEARCH_PATHS})
+find_library(Atlas_LAPACK_LIBRARY NAMES lapack alapack_r alapack lapack_atlas atllapack PATHS ${Atlas_LIB_SEARCH_PATHS})
 
 set(LOOKED_FOR
   Atlas_CBLAS_INCLUDE_DIR
@@ -47,6 +47,6 @@ if(ATLAS_FOUND)
   set(Atlas_LIBRARIES ${Atlas_LAPACK_LIBRARY} ${Atlas_CBLAS_LIBRARY} ${Atlas_BLAS_LIBRARY})
   mark_as_advanced(${LOOKED_FOR})
 
-  message(STATUS "Found Atlas (include: ${Atlas_CBLAS_INCLUDE_DIR}, library: ${Atlas_BLAS_LIBRARY})")
+  message(STATUS "Found Atlas (include: ${Atlas_CBLAS_INCLUDE_DIR} library: ${Atlas_BLAS_LIBRARY} lapack: ${Atlas_LAPACK_LIBRARY}")
 endif(ATLAS_FOUND)
 
diff --git a/cmake/Modules/FindMKL.cmake b/cmake/Modules/FindMKL.cmake
index d2012db5..5ab93b2d 100644
--- a/cmake/Modules/FindMKL.cmake
+++ b/cmake/Modules/FindMKL.cmake
@@ -20,7 +20,7 @@ caffe_option(MKL_MULTI_THREADED  "Use multi-threading"   ON IF NOT MKL_USE_SINGL
 
 # ---[ Root folders
 set(INTEL_ROOT "/opt/intel" CACHE PATH "Folder contains intel libs")
-find_path(MKL_ROOT include/mkl.h PATHS $ENV{MKL_ROOT} ${INTEL_ROOT}/mkl
+find_path(MKL_ROOT include/mkl.h PATHS $ENV{MKLROOT} ${INTEL_ROOT}/mkl
                                    DOC "Folder contains MKL")
 
 # ---[ Find include dir
diff --git a/cmake/Modules/FindNCCL.cmake b/cmake/Modules/FindNCCL.cmake
new file mode 100644
index 00000000..c8845934
--- /dev/null
+++ b/cmake/Modules/FindNCCL.cmake
@@ -0,0 +1,26 @@
+set(NCCL_INC_PATHS
+    /usr/include
+    /usr/local/include
+    $ENV{NCCL_DIR}/include
+    )
+
+set(NCCL_LIB_PATHS
+    /lib
+    /lib64
+    /usr/lib
+    /usr/lib64
+    /usr/local/lib
+    /usr/local/lib64
+    $ENV{NCCL_DIR}/lib
+    )
+
+find_path(NCCL_INCLUDE_DIR NAMES nccl.h PATHS ${NCCL_INC_PATHS})
+find_library(NCCL_LIBRARIES NAMES nccl PATHS ${NCCL_LIB_PATHS})
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(NCCL DEFAULT_MSG NCCL_INCLUDE_DIR NCCL_LIBRARIES)
+
+if (NCCL_FOUND)
+  message(STATUS "Found NCCL    (include: ${NCCL_INCLUDE_DIR}, library: ${NCCL_LIBRARIES})")
+  mark_as_advanced(NCCL_INCLUDE_DIR NCCL_LIBRARIES)
+endif ()
diff --git a/cmake/Modules/FindvecLib.cmake b/cmake/Modules/FindvecLib.cmake
index 9600da43..4d44e613 100644
--- a/cmake/Modules/FindvecLib.cmake
+++ b/cmake/Modules/FindvecLib.cmake
@@ -12,11 +12,13 @@ endif()
 
 set(__veclib_include_suffix "Frameworks/vecLib.framework/Versions/Current/Headers")
 
+exec_program(xcode-select ARGS -print-path OUTPUT_VARIABLE CMAKE_XCODE_DEVELOPER_DIR)
 find_path(vecLib_INCLUDE_DIR vecLib.h
           DOC "vecLib include directory"
-          PATHS /System/Library/${__veclib_include_suffix}
-                /System/Library/Frameworks/Accelerate.framework/Versions/Current/${__veclib_include_suffix}
-                /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.9.sdk/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/)
+          PATHS /System/Library/Frameworks/Accelerate.framework/Versions/Current/${__veclib_include_suffix}
+                /System/Library/${__veclib_include_suffix}
+                ${CMAKE_XCODE_DEVELOPER_DIR}/Platforms/MacOSX.platform/Developer/SDKs/MacOSX.sdk/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/
+          NO_DEFAULT_PATH)
 
 include(FindPackageHandleStandardArgs)
 find_package_handle_standard_args(vecLib DEFAULT_MSG vecLib_INCLUDE_DIR)
diff --git a/cmake/ProtoBuf.cmake b/cmake/ProtoBuf.cmake
index fc799bd3..8005b448 100644
--- a/cmake/ProtoBuf.cmake
+++ b/cmake/ProtoBuf.cmake
@@ -2,8 +2,8 @@
 # the standard cmake script with version and python generation support
 
 find_package( Protobuf REQUIRED )
-include_directories(SYSTEM ${PROTOBUF_INCLUDE_DIR})
-list(APPEND Caffe_LINKER_LIBS ${PROTOBUF_LIBRARIES})
+list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${PROTOBUF_INCLUDE_DIR})
+list(APPEND Caffe_LINKER_LIBS PUBLIC ${PROTOBUF_LIBRARIES})
 
 # As of Ubuntu 14.04 protoc is no longer a part of libprotobuf-dev package
 # and should be installed separately as in: sudo apt-get install protobuf-compiler
@@ -23,7 +23,7 @@ endif()
 
 # place where to generate protobuf sources
 set(proto_gen_folder "${PROJECT_BINARY_DIR}/include/caffe/proto")
-include_directories(SYSTEM "${PROJECT_BINARY_DIR}/include")
+include_directories("${PROJECT_BINARY_DIR}/include")
 
 set(PROTOBUF_GENERATE_CPP_APPEND_PATH TRUE)
 
diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake
index ba025cf8..ed8c2526 100644
--- a/cmake/Summary.cmake
+++ b/cmake/Summary.cmake
@@ -117,6 +117,7 @@ function(caffe_print_configuration_summary)
   caffe_status("  USE_OPENCV        :   ${USE_OPENCV}")
   caffe_status("  USE_LEVELDB       :   ${USE_LEVELDB}")
   caffe_status("  USE_LMDB          :   ${USE_LMDB}")
+  caffe_status("  USE_NCCL          :   ${USE_NCCL}")
   caffe_status("  ALLOW_LMDB_NOLOCK :   ${ALLOW_LMDB_NOLOCK}")
   caffe_status("")
   caffe_status("Dependencies:")
diff --git a/cmake/Targets.cmake b/cmake/Targets.cmake
index 2401f252..090f86c5 100644
--- a/cmake/Targets.cmake
+++ b/cmake/Targets.cmake
@@ -1,16 +1,17 @@
 ################################################################################################
 # Defines global Caffe_LINK flag, This flag is required to prevent linker from excluding
 # some objects which are not addressed directly but are registered via static constructors
-if(BUILD_SHARED_LIBS)
-  set(Caffe_LINK caffe)
-else()
-  if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
-    set(Caffe_LINK -Wl,-force_load caffe)
-  elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
-    set(Caffe_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
+macro(caffe_set_caffe_link)
+  if(BUILD_SHARED_LIBS)
+    set(Caffe_LINK caffe)
+  else()
+    if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
+      set(Caffe_LINK -Wl,-force_load caffe)
+    elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
+      set(Caffe_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
+    endif()
   endif()
-endif()
-
+endmacro()
 ################################################################################################
 # Convenient command to setup source group for IDEs that support this feature (VS, XCode)
 # Usage:
@@ -87,13 +88,13 @@ function(caffe_pickup_caffe_sources root)
   file(GLOB_RECURSE proto_files ${root}/src/caffe/*.proto)
   list(APPEND srcs ${proto_files})
 
-  # convet to absolute paths
+  # convert to absolute paths
   caffe_convert_absolute_paths(srcs)
   caffe_convert_absolute_paths(cuda)
   caffe_convert_absolute_paths(test_srcs)
   caffe_convert_absolute_paths(test_cuda)
 
-  # propogate to parent scope
+  # propagate to parent scope
   set(srcs ${srcs} PARENT_SCOPE)
   set(cuda ${cuda} PARENT_SCOPE)
   set(test_srcs ${test_srcs} PARENT_SCOPE)
@@ -101,7 +102,7 @@ function(caffe_pickup_caffe_sources root)
 endfunction()
 
 ################################################################################################
-# Short command for setting defeault target properties
+# Short command for setting default target properties
 # Usage:
 #   caffe_default_properties(<target>)
 function(caffe_default_properties target)
@@ -110,7 +111,7 @@ function(caffe_default_properties target)
     ARCHIVE_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib"
     LIBRARY_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib"
     RUNTIME_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/bin")
-  # make sure we build all external depepdencies first
+  # make sure we build all external dependencies first
   if (DEFINED external_project_dependencies)
     add_dependencies(${target} ${external_project_dependencies})
   endif()
diff --git a/cmake/Templates/CaffeConfig.cmake.in b/cmake/Templates/CaffeConfig.cmake.in
index 73f57ac2..77c4059e 100644
--- a/cmake/Templates/CaffeConfig.cmake.in
+++ b/cmake/Templates/CaffeConfig.cmake.in
@@ -9,9 +9,9 @@
 # After successful configuration the following variables
 # will be defined:
 #
-#   Caffe_INCLUDE_DIRS - Caffe include directories
-#   Caffe_LIBRARIES    - libraries to link against
-#   Caffe_DEFINITIONS  - a list of definitions to pass to compiler
+#   Caffe_LIBRARIES    - IMPORTED targets to link against
+#                        (There is no Caffe_INCLUDE_DIRS and Caffe_DEFINITIONS
+#                         because they are specified in the IMPORTED target interface.)
 #
 #   Caffe_HAVE_CUDA    - signals about CUDA support
 #   Caffe_HAVE_CUDNN   - signals about cuDNN support
@@ -27,7 +27,7 @@ if(@USE_OPENCV@)
 
       if(EXISTS ${Caffe_OpenCV_CONFIG_PATH} AND NOT TARGET opencv_core)
         message(STATUS "Caffe: using OpenCV config from ${Caffe_OpenCV_CONFIG_PATH}")
-        include(${Caffe_OpenCV_CONFIG_PATH}/OpenCVModules.cmake)
+	include(${Caffe_OpenCV_CONFIG_PATH}/OpenCVConfig.cmake)
       endif()
 
     else()
@@ -39,9 +39,6 @@ endif()
 
 # Compute paths
 get_filename_component(Caffe_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
-set(Caffe_INCLUDE_DIRS "@Caffe_INCLUDE_DIRS@")
-
-@Caffe_INSTALL_INCLUDE_DIR_APPEND_COMMAND@
 
 # Our library dependencies
 if(NOT TARGET caffe AND NOT caffe_BINARY_DIR)
@@ -49,11 +46,9 @@ if(NOT TARGET caffe AND NOT caffe_BINARY_DIR)
 endif()
 
 # List of IMPORTED libs created by CaffeTargets.cmake
+# These targets already specify all needed definitions and include pathes
 set(Caffe_LIBRARIES caffe)
 
-# Definitions
-set(Caffe_DEFINITIONS "@Caffe_DEFINITIONS@")
-
 # Cuda support variables
 set(Caffe_CPU_ONLY @CPU_ONLY@)
 set(Caffe_HAVE_CUDA @HAVE_CUDA@)
diff --git a/cmake/Templates/caffe_config.h.in b/cmake/Templates/caffe_config.h.in
index 8a31b43c..2080c63d 100644
--- a/cmake/Templates/caffe_config.h.in
+++ b/cmake/Templates/caffe_config.h.in
@@ -4,35 +4,9 @@
 /* Binaries directory */
 #define BINARY_FOLDER "${PROJECT_BINARY_DIR}"
 
-/* NVIDA Cuda */
-#cmakedefine HAVE_CUDA
-
-/* NVIDA cuDNN */
-#cmakedefine HAVE_CUDNN
-#cmakedefine USE_CUDNN
-
-/* NVIDA cuDNN */
-#cmakedefine CPU_ONLY
+/* This is an absolute path so that we can run test from any build
+ * directory */
+#define ABS_TEST_DATA_DIR "${PROJECT_SOURCE_DIR}/src/caffe/test/test_data/"
 
 /* Test device */
 #define CUDA_TEST_DEVICE ${CUDA_TEST_DEVICE}
-
-/* Temporary (TODO: remove) */
-#if 1
-  #define CMAKE_SOURCE_DIR SOURCE_FOLDER "/src/"
-  #define EXAMPLES_SOURCE_DIR BINARY_FOLDER "/examples/"
-  #define CMAKE_EXT ".gen.cmake"
-#else
-  #define CMAKE_SOURCE_DIR "src/"
-  #define EXAMPLES_SOURCE_DIR "examples/"
-  #define CMAKE_EXT ""
-#endif
-
-/* Matlab */
-#cmakedefine HAVE_MATLAB
-
-/* IO libraries */
-#cmakedefine USE_OPENCV
-#cmakedefine USE_LEVELDB
-#cmakedefine USE_LMDB
-#cmakedefine ALLOW_LMDB_NOLOCK
diff --git a/cmake/Uninstall.cmake.in b/cmake/Uninstall.cmake.in
new file mode 100644
index 00000000..bb8e2964
--- /dev/null
+++ b/cmake/Uninstall.cmake.in
@@ -0,0 +1,26 @@
+if(NOT EXISTS "@CMAKE_CURRENT_BINARY_DIR@/install_manifest.txt")
+  message(FATAL_ERROR "Cannot find install manifest: @CMAKE_CURRENT_BINARY_DIR@/install_manifest.txt")
+endif(NOT EXISTS "@CMAKE_CURRENT_BINARY_DIR@/install_manifest.txt")
+
+if (NOT DEFINED CMAKE_INSTALL_PREFIX)
+  set (CMAKE_INSTALL_PREFIX "@CMAKE_INSTALL_PREFIX@")
+endif ()
+ message(${CMAKE_INSTALL_PREFIX})
+
+file(READ "@CMAKE_CURRENT_BINARY_DIR@/install_manifest.txt" files)
+string(REGEX REPLACE "\n" ";" files "${files}")
+foreach(file ${files})
+  message(STATUS "Uninstalling $ENV{DESTDIR}${file}")
+  if(IS_SYMLINK "$ENV{DESTDIR}${file}" OR EXISTS "$ENV{DESTDIR}${file}")
+    exec_program(
+      "@CMAKE_COMMAND@" ARGS "-E remove \"$ENV{DESTDIR}${file}\""
+      OUTPUT_VARIABLE rm_out
+      RETURN_VALUE rm_retval
+      )
+    if(NOT "${rm_retval}" STREQUAL 0)
+      message(FATAL_ERROR "Problem when removing $ENV{DESTDIR}${file}")
+    endif(NOT "${rm_retval}" STREQUAL 0)
+  else(IS_SYMLINK "$ENV{DESTDIR}${file}" OR EXISTS "$ENV{DESTDIR}${file}")
+    message(STATUS "File $ENV{DESTDIR}${file} does not exist.")
+  endif(IS_SYMLINK "$ENV{DESTDIR}${file}" OR EXISTS "$ENV{DESTDIR}${file}")
+endforeach(file)
\ No newline at end of file
diff --git a/cmake/Utils.cmake b/cmake/Utils.cmake
index a1bde1ae..653de5fd 100644
--- a/cmake/Utils.cmake
+++ b/cmake/Utils.cmake
@@ -346,10 +346,11 @@ function(caffe_parse_linker_libs Caffe_LINKER_LIBS_variable folders_var flags_va
     elseif(lib MATCHES "^-l.*")
       list(APPEND libflags ${lib})
     elseif(IS_ABSOLUTE ${lib})
-      get_filename_component(name_we ${lib} NAME_WE)
       get_filename_component(folder  ${lib} PATH)
+      get_filename_component(filename ${lib} NAME)
+      string(REGEX REPLACE "\\.[^.]*$" "" filename_without_shortest_ext ${filename})
 
-      string(REGEX MATCH "^lib(.*)" __match ${name_we})
+      string(REGEX MATCH "^lib(.*)" __match ${filename_without_shortest_ext})
       list(APPEND libflags -l${CMAKE_MATCH_1})
       list(APPEND folders    ${folder})
     else()
diff --git a/data/cifar10/get_cifar10.sh b/data/cifar10/get_cifar10.sh
index 623c8485..423f1098 100755
--- a/data/cifar10/get_cifar10.sh
+++ b/data/cifar10/get_cifar10.sh
@@ -2,7 +2,7 @@
 # This scripts downloads the CIFAR10 (binary version) data and unzips it.
 
 DIR="$( cd "$(dirname "$0")" ; pwd -P )"
-cd $DIR
+cd "$DIR"
 
 echo "Downloading..."
 
diff --git a/data/ilsvrc12/get_ilsvrc_aux.sh b/data/ilsvrc12/get_ilsvrc_aux.sh
index 90935f25..dc0d0a72 100755
--- a/data/ilsvrc12/get_ilsvrc_aux.sh
+++ b/data/ilsvrc12/get_ilsvrc_aux.sh
@@ -8,7 +8,7 @@
 # - the training splits with labels
 
 DIR="$( cd "$(dirname "$0")" ; pwd -P )"
-cd $DIR
+cd "$DIR"
 
 echo "Downloading..."
 
diff --git a/data/mnist/get_mnist.sh b/data/mnist/get_mnist.sh
index 8eb6aeed..ecadffa4 100755
--- a/data/mnist/get_mnist.sh
+++ b/data/mnist/get_mnist.sh
@@ -2,23 +2,14 @@
 # This scripts downloads the mnist data and unzips it.
 
 DIR="$( cd "$(dirname "$0")" ; pwd -P )"
-cd $DIR
+cd "$DIR"
 
 echo "Downloading..."
 
-wget --no-check-certificate http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
-wget --no-check-certificate http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
-wget --no-check-certificate http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
-wget --no-check-certificate http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
-
-echo "Unzipping..."
-
-gunzip train-images-idx3-ubyte.gz
-gunzip train-labels-idx1-ubyte.gz
-gunzip t10k-images-idx3-ubyte.gz
-gunzip t10k-labels-idx1-ubyte.gz
-
-# Creation is split out because leveldb sometimes causes segfault
-# and needs to be re-created.
-
-echo "Done."
+for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
+do
+    if [ ! -e $fname ]; then
+        wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz
+        gunzip ${fname}.gz
+    fi
+done
diff --git a/docker/README.md b/docker/README.md
new file mode 100644
index 00000000..f9c7c756
--- /dev/null
+++ b/docker/README.md
@@ -0,0 +1,47 @@
+### Running an official image
+
+You can run one of the automatic [builds](https://hub.docker.com/r/bvlc/caffe). E.g. for the CPU version:
+
+`docker run -ti bvlc/caffe:cpu caffe --version`
+
+or for GPU support (You need a CUDA 8.0 capable driver and
+[nvidia-docker](https://github.com/NVIDIA/nvidia-docker)):
+
+`nvidia-docker run -ti bvlc/caffe:gpu caffe --version`
+
+You might see an error about libdc1394, ignore it.
+
+### Docker run options
+
+By default caffe runs as root, thus any output files, e.g. snapshots, will be owned
+by root. It also runs by default in a container-private folder.
+
+You can change this using flags, like user (-u), current directory, and volumes (-w and -v).
+E.g. this behaves like the usual caffe executable:
+
+`docker run --rm -u $(id -u):$(id -g) -v $(pwd):$(pwd) -w $(pwd) bvlc/caffe:cpu caffe train --solver=example_solver.prototxt`
+
+Containers can also be used interactively, specifying e.g. `bash` or `ipython`
+instead of `caffe`.
+
+```
+docker run -ti bvlc/caffe:cpu ipython
+import caffe
+...
+```
+
+The caffe build requirements are included in the container, so this can be used to
+build and run custom versions of caffe. Also, `caffe/python` is in PATH, so python
+utilities can be used directly, e.g. `draw_net.py`, `classify.py`, or `detect.py`.
+
+### Building images yourself
+
+Examples:
+
+`docker build -t caffe:cpu cpu`
+
+`docker build -t caffe:gpu gpu`
+
+You can also build Caffe and run the tests in the image:
+
+`docker run -ti caffe:cpu bash -c "cd /opt/caffe/build; make runtest"`
diff --git a/docker/cpu/Dockerfile b/docker/cpu/Dockerfile
new file mode 100644
index 00000000..67e2e61b
--- /dev/null
+++ b/docker/cpu/Dockerfile
@@ -0,0 +1,46 @@
+FROM ubuntu:16.04
+LABEL maintainer caffe-maint@googlegroups.com
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        build-essential \
+        cmake \
+        git \
+        wget \
+        libatlas-base-dev \
+        libboost-all-dev \
+        libgflags-dev \
+        libgoogle-glog-dev \
+        libhdf5-serial-dev \
+        libleveldb-dev \
+        liblmdb-dev \
+        libopencv-dev \
+        libprotobuf-dev \
+        libsnappy-dev \
+        protobuf-compiler \
+        python-dev \
+        python-numpy \
+        python-pip \
+        python-setuptools \
+        python-scipy && \
+    rm -rf /var/lib/apt/lists/*
+
+ENV CAFFE_ROOT=/opt/caffe
+WORKDIR $CAFFE_ROOT
+
+# FIXME: use ARG instead of ENV once DockerHub supports this
+# https://github.com/docker/hub-feedback/issues/460
+ENV CLONE_TAG=1.0
+
+RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \
+    pip install --upgrade pip && \
+    cd python && for req in $(cat requirements.txt) pydot; do pip install $req; done && cd .. && \
+    mkdir build && cd build && \
+    cmake -DCPU_ONLY=1 .. && \
+    make -j"$(nproc)"
+
+ENV PYCAFFE_ROOT $CAFFE_ROOT/python
+ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH
+ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH
+RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig
+
+WORKDIR /workspace
diff --git a/docker/gpu/Dockerfile b/docker/gpu/Dockerfile
new file mode 100644
index 00000000..dcdbdf32
--- /dev/null
+++ b/docker/gpu/Dockerfile
@@ -0,0 +1,47 @@
+FROM nvidia/cuda:8.0-cudnn6-devel-ubuntu16.04
+LABEL maintainer caffe-maint@googlegroups.com
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        build-essential \
+        cmake \
+        git \
+        wget \
+        libatlas-base-dev \
+        libboost-all-dev \
+        libgflags-dev \
+        libgoogle-glog-dev \
+        libhdf5-serial-dev \
+        libleveldb-dev \
+        liblmdb-dev \
+        libopencv-dev \
+        libprotobuf-dev \
+        libsnappy-dev \
+        protobuf-compiler \
+        python-dev \
+        python-numpy \
+        python-pip \
+        python-setuptools \
+        python-scipy && \
+    rm -rf /var/lib/apt/lists/*
+
+ENV CAFFE_ROOT=/opt/caffe
+WORKDIR $CAFFE_ROOT
+
+# FIXME: use ARG instead of ENV once DockerHub supports this
+# https://github.com/docker/hub-feedback/issues/460
+ENV CLONE_TAG=1.0
+
+RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \
+    pip install --upgrade pip && \
+    cd python && for req in $(cat requirements.txt) pydot; do pip install $req; done && cd .. && \
+    git clone https://github.com/NVIDIA/nccl.git && cd nccl && make -j install && cd .. && rm -rf nccl && \
+    mkdir build && cd build && \
+    cmake -DUSE_CUDNN=1 -DUSE_NCCL=1 .. && \
+    make -j"$(nproc)"
+
+ENV PYCAFFE_ROOT $CAFFE_ROOT/python
+ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH
+ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH
+RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig
+
+WORKDIR /workspace
diff --git a/docs/_layouts/default.html b/docs/_layouts/default.html
index b8efe60b..3799e95a 100644
--- a/docs/_layouts/default.html
+++ b/docs/_layouts/default.html
@@ -36,7 +36,7 @@
       <header>
         <h1 class="header"><a href="/">Caffe</a></h1>
         <p class="header">
-          Deep learning framework by the <a class="header name" href="http://bvlc.eecs.berkeley.edu/">BVLC</a>
+          Deep learning framework by <a class="header name" href="http://bair.berkeley.edu/">BAIR</a>
         </p>
         <p class="header">
           Created by
diff --git a/docs/development.md b/docs/development.md
index 107c2c3b..36cd3995 100644
--- a/docs/development.md
+++ b/docs/development.md
@@ -4,7 +4,7 @@ title: Developing and Contributing
 # Development and Contributing
 
 Caffe is developed with active participation of the community.<br>
-The [BVLC](http://bvlc.eecs.berkeley.edu/) brewers welcome all contributions!
+The [BAIR](http://bair.berkeley.edu/)/BVLC brewers welcome all contributions!
 
 The exact details of contributions are recorded by versioning and cited in our [acknowledgements](http://caffe.berkeleyvision.org/#acknowledgements).
 This method is impartial and always up-to-date.
@@ -37,7 +37,7 @@ We absolutely appreciate any contribution to this effort!
 
 The `master` branch receives all new development including community contributions.
 We try to keep it in a reliable state, but it is the bleeding edge, and things do get broken every now and then.
-BVLC maintainers will periodically make releases by marking stable checkpoints as tags and maintenance branches. [Past releases](https://github.com/BVLC/caffe/releases) are catalogued online.
+BAIR maintainers will periodically make releases by marking stable checkpoints as tags and maintenance branches. [Past releases](https://github.com/BVLC/caffe/releases) are catalogued online.
 
 #### Issues & Pull Request Protocol
 
@@ -116,5 +116,5 @@ To get a list of all options `googletest` provides, simply pass the `--help` fla
 
 - **Run `make lint` to check C++ code.**
 - Wrap lines at 80 chars.
-- Follow [Google C++ style](http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml) and [Google python style](http://google-styleguide.googlecode.com/svn/trunk/pyguide.html) + [PEP 8](http://legacy.python.org/dev/peps/pep-0008/).
+- Follow [Google C++ style](https://google.github.io/styleguide/cppguide.html) and [Google python style](https://google.github.io/styleguide/pyguide.html) + [PEP 8](http://legacy.python.org/dev/peps/pep-0008/).
 - Remember that “a foolish consistency is the hobgoblin of little minds,” so use your best judgement to write the clearest code for your particular case.
diff --git a/docs/index.md b/docs/index.md
index 932b3b58..b633f7cf 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -5,7 +5,7 @@ title: Deep Learning Framework
 # Caffe
 
 Caffe is a deep learning framework made with expression, speed, and modularity in mind.
-It is developed by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and by community contributors.
+It is developed by Berkeley AI Research ([BAIR](http://bair.berkeley.edu)) and by community contributors.
 [Yangqing Jia](http://daggerfs.com) created the project during his PhD at UC Berkeley.
 Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE).
 
@@ -23,21 +23,20 @@ Thanks to these contributors the framework tracks the state-of-the-art in both c
 
 **Speed** makes Caffe perfect for research experiments and industry deployment.
 Caffe can process **over 60M images per day** with a single NVIDIA K40 GPU\*.
-That's 1 ms/image for inference and 4 ms/image for learning.
-We believe that Caffe is the fastest convnet implementation available.
+That's 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still.
+We believe that Caffe is among the fastest convnet implementations available.
 
 **Community**: Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia.
 Join our community of brewers on the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) and [Github](https://github.com/BVLC/caffe/).
 
 <p class="footnote" markdown="1">
-\* With the ILSVRC2012-winning [SuperVision](http://www.image-net.org/challenges/LSVRC/2012/supervision.pdf) model and caching IO.
-Consult performance [details](/performance_hardware.html).
+\* With the ILSVRC2012-winning [SuperVision](http://www.image-net.org/challenges/LSVRC/2012/supervision.pdf) model and prefetching IO.
 </p>
 
 ## Documentation
 
-- [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p)<br>
-Tutorial presentation.
+- [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p) and [Caffe in a Day](https://docs.google.com/presentation/d/1HxGdeq8MPktHaPb-rlmYYQ723iWzq9ur6Gjo71YiG0Y/edit#slide=id.gc2fcdcce7_216_0)<br>
+Tutorial presentation of the framework and a full-day crash course.
 - [Tutorial Documentation](/tutorial)<br>
 Practical guide and framework reference.
 - [arXiv / ACM MM '14 paper](http://arxiv.org/abs/1408.5093)<br>
@@ -45,18 +44,13 @@ A 4-page report for the ACM Multimedia Open Source competition (arXiv:1408.5093v
 - [Installation instructions](/installation.html)<br>
 Tested on Ubuntu, Red Hat, OS X.
 * [Model Zoo](/model_zoo.html)<br>
-BVLC suggests a standard distribution format for Caffe models, and provides trained models.
+BAIR suggests a standard distribution format for Caffe models, and provides trained models.
 * [Developing & Contributing](/development.html)<br>
 Guidelines for development and contributing to Caffe.
 * [API Documentation](/doxygen/annotated.html)<br>
 Developer documentation automagically generated from code comments.
-
-### Examples
-
-{% assign examples = site.pages | where:'category','example' | sort: 'priority' %}
-{% for page in examples %}
-- <div><a href="{{page.url}}">{{page.title}}</a><br>{{page.description}}</div>
-{% endfor %}
+* [Benchmarking](https://docs.google.com/spreadsheets/d/1Yp4rqHpT7mKxOPbpzYeUfEFLnELDAgxSSBQKp5uKDGQ/edit#gid=0)<br>
+Comparison of inference and learning for different networks and GPUs.
 
 ### Notebook Examples
 
@@ -65,6 +59,13 @@ Developer documentation automagically generated from code comments.
 - <div><a href="http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/{{page.original_path}}">{{page.title}}</a><br>{{page.description}}</div>
 {% endfor %}
 
+### Command Line Examples
+
+{% assign examples = site.pages | where:'category','example' | sort: 'priority' %}
+{% for page in examples %}
+- <div><a href="{{page.url}}">{{page.title}}</a><br>{{page.description}}</div>
+{% endfor %}
+
 ## Citing Caffe
 
 Please cite Caffe in your publications if it helps your research:
@@ -76,8 +77,7 @@ Please cite Caffe in your publications if it helps your research:
       Year = {2014}
     }
 
-If you do publish a paper where Caffe helped your research, we encourage you to update the [publications wiki](https://github.com/BVLC/caffe/wiki/Publications).
-Citations are also tracked automatically by [Google Scholar](http://scholar.google.com/scholar?oi=bibs&hl=en&cites=17333247995453974016).
+If you do publish a paper where Caffe helped your research, we encourage you to cite the framework for tracking by [Google Scholar](https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=-ltRSM0AAAAJ:u5HHmVD_uO8C).
 
 ## Contacting Us
 
@@ -85,17 +85,12 @@ Join the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users
 
 Framework development discussions and thorough bug reports are collected on [Issues](https://github.com/BVLC/caffe/issues).
 
-Contact [caffe-dev](mailto:caffe-dev@googlegroups.com) if you have a confidential proposal for the framework *and the ability to act on it*.
-Requests for features, explanations, or personal help will be ignored; post to [caffe-users](https://groups.google.com/forum/#!forum/caffe-users) instead.
-
-The core Caffe developers offer [consulting services](mailto:caffe-coldpress@googlegroups.com) for appropriate projects.
-
 ## Acknowledgements
 
-The BVLC Caffe developers would like to thank NVIDIA for GPU donation, A9 and Amazon Web Services for a research grant in support of Caffe development and reproducible research in deep learning, and BVLC PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for guidance.
+The BAIR Caffe developers would like to thank NVIDIA for GPU donation, A9 and Amazon Web Services for a research grant in support of Caffe development and reproducible research in deep learning, and BAIR PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for guidance.
 
-The BVLC members who have contributed to Caffe are (alphabetical by first name):
-[Eric Tzeng](https://github.com/erictzeng), [Evan Shelhamer](http://imaginarynumber.net/), [Jeff Donahue](http://jeffdonahue.com/), [Jon Long](https://github.com/longjon), [Ross Girshick](http://www.cs.berkeley.edu/~rbg/), [Sergey Karayev](http://sergeykarayev.com/), [Sergio Guadarrama](http://www.eecs.berkeley.edu/~sguada/), and [Yangqing Jia](http://daggerfs.com/).
+The BAIR members who have contributed to Caffe are (alphabetical by first name):
+[Carl Doersch](http://www.carldoersch.com/), [Eric Tzeng](https://github.com/erictzeng), [Evan Shelhamer](http://imaginarynumber.net/), [Jeff Donahue](http://jeffdonahue.com/), [Jon Long](https://github.com/longjon), [Philipp Krähenbühl](http://www.philkr.net/), [Ronghang Hu](http://ronghanghu.com/), [Ross Girshick](http://www.cs.berkeley.edu/~rbg/), [Sergey Karayev](http://sergeykarayev.com/), [Sergio Guadarrama](http://www.eecs.berkeley.edu/~sguada/), [Takuya Narihira](https://github.com/tnarihi), and [Yangqing Jia](http://daggerfs.com/).
 
 The open-source community plays an important and growing role in Caffe's development.
 Check out the Github [project pulse](https://github.com/BVLC/caffe/pulse) for recent activity and the [contributors](https://github.com/BVLC/caffe/graphs/contributors) for the full list.
@@ -103,4 +98,4 @@ Check out the Github [project pulse](https://github.com/BVLC/caffe/pulse) for re
 We sincerely appreciate your interest and contributions!
 If you'd like to contribute, please read the [developing & contributing](development.html) guide.
 
-Yangqing would like to give a personal thanks to the NVIDIA Academic program for providing GPUs, [Oriol Vinyals](http://www1.icsi.berkeley.edu/~vinyals/) for discussions along the journey, and BVLC PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for advice.
+Yangqing would like to give a personal thanks to the NVIDIA Academic program for providing GPUs, [Oriol Vinyals](http://www1.icsi.berkeley.edu/~vinyals/) for discussions along the journey, and BAIR PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for advice.
diff --git a/docs/install_apt.md b/docs/install_apt.md
index 2976e3cd..e361a92d 100644
--- a/docs/install_apt.md
+++ b/docs/install_apt.md
@@ -1,35 +1,67 @@
 ---
-title: Installation: Ubuntu
+title: "Installation: Ubuntu"
 ---
 
 # Ubuntu Installation
 
+### For Ubuntu (>= 17.04)
+
+**Installing pre-compiled Caffe**
+
+Everything including caffe itself is packaged in 17.04 and higher versions.
+To install pre-compiled Caffe package, just do it by
+
+    sudo apt install caffe-cpu
+
+for CPU-only version, or
+
+    sudo apt install caffe-cuda
+
+for CUDA version. Note, the cuda version may break if your NVIDIA driver
+and CUDA toolkit are not installed by APT.
+
+[Package status of CPU-only version](https://launchpad.net/ubuntu/+source/caffe)
+
+[Package status of CUDA version](https://launchpad.net/ubuntu/+source/caffe-contrib)
+
+**Installing Caffe from source**
+
+We may install the dependencies by merely one line
+
+    sudo apt build-dep caffe-cpu        # dependencies for CPU-only version
+    sudo apt build-dep caffe-cuda       # dependencies for CUDA version
+
+It requires a `deb-src` line in your `sources.list`.
+Continue with [compilation](installation.html#compilation).
+
+### For Ubuntu (\< 17.04)
+
 **General dependencies**
 
     sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
     sudo apt-get install --no-install-recommends libboost-all-dev
+    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
 
-**CUDA**: Install via the NVIDIA package instead of `apt-get` to be certain of the library and driver versions.
-Install the library and latest driver separately; the driver bundled with the library is usually out-of-date.
+**CUDA**: Install by `apt-get` or the NVIDIA `.run` package.
+The NVIDIA package tends to follow more recent library and driver versions, but the installation is more manual.
+If installing from packages, install the library and latest driver separately; the driver bundled with the library is usually out-of-date.
 This can be skipped for CPU-only installation.
 
-**BLAS**: install ATLAS by `sudo apt-get install libatlas-base-dev` or install OpenBLAS or MKL for better CPU performance.
+**BLAS**: install ATLAS by `sudo apt-get install libatlas-base-dev` or install OpenBLAS by `sudo apt-get install libopenblas-dev` or MKL for better CPU performance.
 
 **Python** (optional): if you use the default Python you will need to `sudo apt-get install` the `python-dev` package to have the Python headers for building the pycaffe interface.
 
-**Remaining dependencies, 14.04**
+**Compatibility notes, 16.04**
 
-Everything is packaged in 14.04.
-
-    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
+CUDA 8 is required on Ubuntu 16.04.
 
 **Remaining dependencies, 12.04**
 
 These dependencies need manual installation in 12.04.
 
     # glog
-    wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
-    tar zxvf glog-0.3.3.tar.gz
+    wget https://github.com/google/glog/archive/v0.3.3.tar.gz
+    tar zxvf v0.3.3.tar.gz
     cd glog-0.3.3
     ./configure
     make && make install
diff --git a/docs/install_apt_debian.md b/docs/install_apt_debian.md
new file mode 100644
index 00000000..0a6a3b96
--- /dev/null
+++ b/docs/install_apt_debian.md
@@ -0,0 +1,163 @@
+---
+title: "Installation: Debian"
+---
+
+# Debian Installation
+
+Caffe packages are available for several Debian versions, as shown in the
+following chart:
+
+```
+Your Distro     |  CPU_ONLY  |  CUDA  | Codename
+----------------+------------+--------+-------------------
+Debian/oldstable|     ✘      |   ✘    | Jessie (8.0)
+Debian/stable   |     ✔      |   ✔    | Stretch (9.0)
+Debian/testing  |     ✔      |   ✔    | Buster
+Debian/unstable |     ✔      |   ✔    | Buster
+```
+
+* `✘ ` You should take a look at [Ubuntu installation instruction](install_apt.html).
+
+* `✔ ` You can install caffe with a single command line following this guide.
+
+* [Package status of CPU-only version](https://tracker.debian.org/pkg/caffe)
+
+* [Package status of CUDA version](https://tracker.debian.org/pkg/caffe-contrib)
+
+Last update: 2017-07-08
+
+## Binary installation with APT
+
+Apart from the installation methods based on source, Debian users can install
+pre-compiled Caffe packages from the official archive with APT.
+
+Make sure that your `/etc/apt/sources.list` contains `contrib` and `non-free`
+sections if you want to install the CUDA version, for instance:
+
+```
+deb http://ftp2.cn.debian.org/debian sid main contrib non-free
+```
+
+Then we update APT cache and directly install Caffe. Note, the cpu version and
+the cuda version cannot coexist.
+
+```
+$ sudo apt update
+$ sudo apt install [ caffe-cpu | caffe-cuda ]
+$ caffe                                              # command line interface working
+$ python3 -c 'import caffe; print(caffe.__path__)'   # python3 interface working
+```
+
+These Caffe packages should work for you out of box. However, the CUDA version
+may break if your NVIDIA driver and CUDA toolkit are not installed with APT.
+
+#### Customizing caffe packages
+
+Some users may need to customize the Caffe package. The way to customize
+the package is beyond this guide. Here is only a brief guide of producing
+the customized `.deb` packages. 
+
+Make sure that there is a `dec-src` source in your `/etc/apt/sources.list`,
+for instance:
+
+```
+deb http://ftp2.cn.debian.org/debian sid main contrib non-free
+deb-src http://ftp2.cn.debian.org/debian sid main contrib non-free
+```
+
+Then we build caffe deb files with the following commands:
+
+```
+$ sudo apt update
+$ sudo apt install build-essential debhelper devscripts  # standard package building tools
+$ sudo apt build-dep [ caffe-cpu | caffe-cuda ]          # the most elegant way to pull caffe build dependencies
+$ apt source [ caffe-cpu | caffe-cuda ]                  # download the source tarball and extract
+$ cd caffe-XXXX
+[ ... optional, customizing caffe code/build ... ]
+$ dch --local "Modified XXX"                             # bump package version and write changelog
+$ debuild -B -j4                                         # build caffe with 4 parallel jobs (similar to make -j4)
+[ ... building ...]
+$ debc                                                   # optional, if you want to check the package contents
+$ sudo debi                                              # optional, install the generated packages
+$ ls ../                                                 # optional, you will see the resulting packages
+```
+
+It is a BUG if the package failed to build without any change.
+The changelog will be installed at e.g. `/usr/share/doc/caffe-cpu/changelog.Debian.gz`.
+
+## Source installation
+
+Source installation under Debian/unstable and Debian/testing is similar to that of Ubuntu, but
+here is a more elegant way to pull caffe build dependencies:
+
+```
+$ sudo apt build-dep [ caffe-cpu | caffe-cuda ]
+```
+
+Note, this requires a `deb-src` entry in your `/etc/apt/sources.list`.
+
+#### Compiler Combinations
+
+Some users may find their favorate compiler doesn't work with CUDA.
+
+```
+CXX compiler |  CUDA 7.5  |  CUDA 8.0  |  CUDA 9.0  |
+-------------+------------+------------+------------+
+GCC-8        |     ?      |     ?      |     ?      |
+GCC-7        |     ?      |     ?      |     ?      |
+GCC-6        |     ✘      |     ✘      |     ✔      |
+GCC-5        |     ✔ [1]  |     ✔      |     ✔      |
+-------------+------------+------------+------------+
+CLANG-4.0    |     ?      |     ?      |     ?      |
+CLANG-3.9    |     ✘      |     ✘      |     ✔      |
+CLANG-3.8    |     ?      |     ✔      |     ✔      |
+```
+
+`[1]` CUDA 7.5 's `host_config.h` must be patched before working with GCC-5.
+
+`[2]` CUDA 9.0: https://devblogs.nvidia.com/parallelforall/cuda-9-features-revealed/
+
+BTW, please forget the GCC-4.X series, since its `libstdc++` ABI is not compatible with GCC-5's.
+You may encounter failure linking GCC-4.X object files against GCC-5 libraries.
+(See https://wiki.debian.org/GCC5 )
+
+## Notes
+
+* Consider re-compiling OpenBLAS locally with optimization flags for sake of
+performance. This is highly recommended for any kind of production use, including
+academic research.
+
+* If you are installing `caffe-cuda`, APT will automatically pull some of the
+CUDA packages and the nvidia driver packages. Please be careful if you have
+manually installed or hacked nvidia driver or CUDA toolkit or any other
+related stuff, because in this case APT may fail.
+
+* Additionally, a manpage (`man caffe`) and a bash complementation script
+(`caffe <TAB><TAB>`, `caffe train <TAB><TAB>`) are provided.
+Both of the two files are still not merged into caffe master.
+
+* The python interface is Python 3 version: `python3-caffe-{cpu,cuda}`.
+No plan to support python2.
+
+* If you encountered any problem related to the packaging system (e.g. failed to install `caffe-*`),
+please report bug to Debian via Debian's bug tracking system. See https://www.debian.org/Bugs/ .
+Patches and suggestions are also welcome.
+
+## FAQ
+
+* where is caffe-cudnn?
+
+CUDNN library seems not redistributable currently. If you really want the
+caffe-cudnn deb packages, the workaround is to install cudnn by yourself,
+and hack the packaging scripts, then build your customized package.
+
+* I installed the CPU version. How can I switch to the CUDA version?
+
+`sudo apt install caffe-cuda`, apt's dependency resolver is smart enough to deal with this.
+
+* Where are the examples, the models and other documentation stuff?
+
+```
+$ sudo apt install caffe-doc
+$ dpkg -L caffe-doc
+```
diff --git a/docs/install_osx.md b/docs/install_osx.md
index 6405d8ad..a2da82f0 100644
--- a/docs/install_osx.md
+++ b/docs/install_osx.md
@@ -1,5 +1,5 @@
 ---
-title: Installation: OS X
+title: "Installation: OS X"
 ---
 
 # OS X Installation
diff --git a/docs/install_yum.md b/docs/install_yum.md
index 2104912e..842fbd64 100644
--- a/docs/install_yum.md
+++ b/docs/install_yum.md
@@ -1,5 +1,5 @@
 ---
-title: Installation: RHEL / Fedora / CentOS
+title: "Installation: RHEL / Fedora / CentOS"
 ---
 
 # RHEL / Fedora / CentOS Installation
@@ -15,7 +15,7 @@ title: Installation: RHEL / Fedora / CentOS
 **Remaining dependencies, if not found**
 
     # glog
-    wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
+    wget https://storage.googleapis.com/google-code-archive-downloads/v2/code.google.com/google-glog/glog-0.3.3.tar.gz
     tar zxvf glog-0.3.3.tar.gz
     cd glog-0.3.3
     ./configure
diff --git a/docs/installation.md b/docs/installation.md
index ef781e8d..42f1d0ce 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -5,13 +5,25 @@ title: Installation
 # Installation
 
 Prior to installing, have a glance through this guide and take note of the details for your platform.
-We install and run Caffe on Ubuntu 14.04 and 12.04, OS X 10.10 / 10.9 / 10.8, and AWS.
-The official Makefile and `Makefile.config` build are complemented by an automatic CMake build from the community.
+We install and run Caffe on Ubuntu 16.04–12.04, OS X 10.11–10.8, and through Docker and AWS.
+The official Makefile and `Makefile.config` build are complemented by a [community CMake build](#cmake-build).
+
+**Step-by-step Instructions**:
+
+- [Docker setup](https://github.com/BVLC/caffe/tree/master/docker) *out-of-the-box brewing*
+- [Ubuntu installation](install_apt.html) *the standard platform*
+- [Debian installation](install_apt_debian.html) *install caffe with a single command*
+- [OS X installation](install_osx.html)
+- [RHEL / CentOS / Fedora installation](install_yum.html)
+- [Windows](https://github.com/BVLC/caffe/tree/windows) *see the Windows branch led by Guillaume Dumont*
+- [OpenCL](https://github.com/BVLC/caffe/tree/opencl) *see the OpenCL branch led by Fabian Tschopp*
+- [AWS AMI](https://github.com/bitfusionio/amis/tree/master/awsmrkt-bfboost-ubuntu14-cuda75-caffe) *pre-configured for AWS*
+
+**Overview**:
 
 - [Prerequisites](#prerequisites)
 - [Compilation](#compilation)
 - [Hardware](#hardware)
-- Platforms: [Ubuntu guide](install_apt.html), [OS X guide](install_osx.html), and [RHEL / CentOS / Fedora guide](install_yum.html)
 
 When updating Caffe, it's best to `make clean` before re-compiling.
 
@@ -20,7 +32,7 @@ When updating Caffe, it's best to `make clean` before re-compiling.
 Caffe has several dependencies:
 
 * [CUDA](https://developer.nvidia.com/cuda-zone) is required for GPU mode.
-    * library version 7.0 and the latest driver version are recommended, but 6.* is fine too
+    * library version 7+ and the latest driver version are recommended, but 6.* is fine too
     * 5.5, and 5.0 are compatible but considered legacy
 * [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) via ATLAS, MKL, or OpenBLAS.
 * [Boost](http://www.boost.org/) >= 1.55
@@ -30,14 +42,14 @@ Optional dependencies:
 
 * [OpenCV](http://opencv.org/) >= 2.4 including 3.0
 * IO libraries: `lmdb`, `leveldb` (note: leveldb requires `snappy`)
-* cuDNN for GPU acceleration (v3)
+* cuDNN for GPU acceleration (v6)
 
 Pycaffe and Matcaffe interfaces have their own natural needs.
 
 * For Python Caffe:  `Python 2.7` or `Python 3.3+`, `numpy (>= 1.7)`, boost-provided `boost.python`
 * For MATLAB Caffe: MATLAB with the `mex` compiler.
 
-**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. The current version is cuDNN v3; older versions are supported in older Caffe.
+**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. The current version is cuDNN v6; older versions are supported in older Caffe.
 
 **CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` flag in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.
 
@@ -52,9 +64,10 @@ Caffe requires BLAS as the backend of its matrix and vector computations.
 There are several implementations of this library. The choice is yours:
 
 * [ATLAS](http://math-atlas.sourceforge.net/): free, open source, and so the default for Caffe.
-* [Intel MKL](http://software.intel.com/en-us/intel-mkl): commercial and optimized for Intel CPUs, with a free trial and [student](http://software.intel.com/en-us/intel-education-offerings) licenses.
+* [Intel MKL](http://software.intel.com/en-us/intel-mkl): commercial and optimized for Intel CPUs, with [free](https://registrationcenter.intel.com/en/forms/?productid=2558) licenses.
     1. Install MKL.
-    2. Set `BLAS := mkl` in `Makefile.config`
+    2. Set up MKL environment (Details: [Linux](https://software.intel.com/en-us/node/528499), [OS X](https://software.intel.com/en-us/node/528659)). Example: *source /opt/intel/mkl/bin/mklvars.sh intel64*
+    3. Set `BLAS := mkl` in `Makefile.config`
 * [OpenBLAS](http://www.openblas.net/): free and open source; this optimized and parallel BLAS could require more effort to install, although it might offer a speedup.
     1. Install OpenBLAS
     2. Set `BLAS := open` in `Makefile.config`
@@ -81,10 +94,6 @@ Install MATLAB, and make sure that its `mex` is in your `$PATH`.
 
 *Caffe's MATLAB interface works with versions 2015a, 2014a/b, 2013a/b, and 2012b.*
 
-#### Windows
-
-There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https://github.com/niuzhiheng/caffe). Thanks [@niuzhiheng](https://github.com/niuzhiheng)!
-
 ## Compilation
 
 Caffe can be compiled with either Make or CMake. Make is officially supported while CMake is supported by the community.
@@ -112,7 +121,7 @@ Be sure to set your MATLAB and Python paths in `Makefile.config` first!
 
 Now that you have installed Caffe, check out the [MNIST tutorial](gathered/examples/mnist.html) and the [reference ImageNet model tutorial](gathered/examples/imagenet.html).
 
-### Compilation with CMake
+### CMake Build
 
 In lieu of manually editing `Makefile.config` to configure the build, Caffe offers an unofficial CMake build thanks to @Nerei, @akosiorek, and other members of the community. It requires CMake version >= 2.8.7.
 The basic steps are as follows:
@@ -128,9 +137,9 @@ See [PR #1667](https://github.com/BVLC/caffe/pull/1667) for options and details.
 
 ## Hardware
 
-**Laboratory Tested Hardware**: Berkeley Vision runs Caffe with K40s, K20s, and Titans including models at ImageNet/ILSVRC scale. We also run on GTX series cards (980s and 770s) and GPU-equipped MacBook Pros. We have not encountered any trouble in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.
+**Laboratory Tested Hardware**: Berkeley Vision runs Caffe with Titan Xs, K80s, GTX 980s, K40s, K20s, Titans, and GTX 770s including models at ImageNet/ILSVRC scale. We have not encountered any trouble in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.
 
-**CUDA compute capability**: devices with compute capability <= 2.0 may have to reduce CUDA thread numbers and batch sizes due to hardware constraints. Your mileage may vary.
+**CUDA compute capability**: devices with compute capability <= 2.0 may have to reduce CUDA thread numbers and batch sizes due to hardware constraints. Brew with caution; we recommend compute capability >= 3.0.
 
 Once installed, check your times against our [reference performance numbers](performance_hardware.html) to make sure everything is configured properly.
 
diff --git a/docs/model_zoo.md b/docs/model_zoo.md
index 06dc0a49..3f77e825 100644
--- a/docs/model_zoo.md
+++ b/docs/model_zoo.md
@@ -3,7 +3,7 @@ title: Model Zoo
 ---
 # Caffe Model Zoo
 
-Lots of researchers and engineers have made Caffe models for different tasks with all kinds of architectures and data.
+Lots of researchers and engineers have made Caffe models for different tasks with all kinds of architectures and data: check out the [model zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo)!
 These models are learned and applied for problems ranging from simple regression, to large-scale visual classification, to Siamese networks for image similarity, to speech and robotics applications.
 
 To help share these models, we introduce the model zoo framework:
@@ -14,17 +14,17 @@ To help share these models, we introduce the model zoo framework:
 
 ## Where to get trained models
 
-First of all, we bundle BVLC-trained models for unrestricted, out of the box use.
+First of all, we bundle BAIR-trained models for unrestricted, out of the box use.
 <br>
-See the [BVLC model license](#bvlc-model-license) for details.
+See the [BAIR model license](#bair-model-license) for details.
 Each one of these can be downloaded by running `scripts/download_model_binary.py <dirname>` where `<dirname>` is specified below:
 
-- **BVLC Reference CaffeNet** in `models/bvlc_reference_caffenet`: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in [ImageNet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) by Krizhevsky et al. in NIPS 2012. (Trained by Jeff Donahue @jeffdonahue)
-- **BVLC AlexNet** in `models/bvlc_alexnet`: AlexNet trained on ILSVRC 2012, almost exactly as described in [ImageNet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) by Krizhevsky et al. in NIPS 2012. (Trained by Evan Shelhamer @shelhamer)
-- **BVLC Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn) as described by Girshick et al. in CVPR 2014. (Trained by Ross Girshick @rbgirshick)
-- **BVLC GoogLeNet** in `models/bvlc_googlenet`: GoogLeNet trained on ILSVRC 2012, almost exactly as described in [Going Deeper with Convolutions](http://arxiv.org/abs/1409.4842) by Szegedy et al. in ILSVRC 2014. (Trained by Sergio Guadarrama @sguada)
+- **BAIR Reference CaffeNet** in `models/bvlc_reference_caffenet`: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in [ImageNet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) by Krizhevsky et al. in NIPS 2012. (Trained by Jeff Donahue @jeffdonahue)
+- **BAIR AlexNet** in `models/bvlc_alexnet`: AlexNet trained on ILSVRC 2012, almost exactly as described in [ImageNet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) by Krizhevsky et al. in NIPS 2012. (Trained by Evan Shelhamer @shelhamer)
+- **BAIR Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn) as described by Girshick et al. in CVPR 2014. (Trained by Ross Girshick @rbgirshick)
+- **BAIR GoogLeNet** in `models/bvlc_googlenet`: GoogLeNet trained on ILSVRC 2012, almost exactly as described in [Going Deeper with Convolutions](http://arxiv.org/abs/1409.4842) by Szegedy et al. in ILSVRC 2014. (Trained by Sergio Guadarrama @sguada)
 
-**Community models** made by Caffe users are posted to a publicly editable [wiki page](https://github.com/BVLC/caffe/wiki/Model-Zoo).
+**Community models** made by Caffe users are posted to a publicly editable [model zoo wiki page](https://github.com/BVLC/caffe/wiki/Model-Zoo).
 These models are subject to conditions of their respective authors such as citation and license.
 Thank you for sharing your models!
 
@@ -42,6 +42,8 @@ A caffe model is distributed as a directory containing:
     - License information.
 - [optional] Other helpful scripts.
 
+This simple format can be handled through bundled scripts or manually if need be.
+
 ### Hosting model info
 
 Github Gist is a good format for model info distribution because it can contain multiple files, is versionable, and has in-browser syntax highlighting and markdown rendering.
@@ -55,14 +57,14 @@ Downloading model info is done just as easily with `scripts/download_model_from_
 ### Hosting trained models
 
 It is up to the user where to host the `.caffemodel` file.
-We host our BVLC-provided models on our own server.
+We host our BAIR-provided models on our own server.
 Dropbox also works fine (tip: make sure that `?dl=1` is appended to the end of the URL).
 
 `scripts/download_model_binary.py <dirname>` downloads the `.caffemodel` from the URL specified in the `<dirname>/readme.md` frontmatter and confirms SHA1.
 
-## BVLC model license
+## BAIR model license
 
-The Caffe models bundled by the BVLC are released for unrestricted use.
+The Caffe models bundled by the BAIR are released for unrestricted use.
 
 These models are trained on data from the [ImageNet project](http://www.image-net.org/) and training data includes internet photos that may be subject to copyright.
 
diff --git a/docs/multigpu.md b/docs/multigpu.md
index 01cfb893..e04ebb0b 100644
--- a/docs/multigpu.md
+++ b/docs/multigpu.md
@@ -13,14 +13,14 @@ The GPUs to be used for training can be set with the "-gpu" flag on the command
 # Hardware Configuration Assumptions
 
 The current implementation uses a tree reduction strategy.  e.g. if there are 4 GPUs in the system, 0:1, 2:3 will exchange gradients, then 0:2 (top of the tree) will exchange gradients, 0 will calculate
-updated model, 0\-\>2, and then 0\-\>1, 2\-\>3. 
+updated model, 0\-\>2, and then 0\-\>1, 2\-\>3.
 
 For best performance, P2P DMA access between devices is needed. Without P2P access, for example crossing PCIe root complex, data is copied through host and effective exchange bandwidth is greatly reduced.
 
-Current implementation has a "soft" assumption that the devices being used are homogeneous.  In practice, any devices of the same general class should work together, but performance and total size is limited by the smallest device being used.  e.g. if you combine a TitanX and a GTX980, peformance will be limited by the 980.  Mixing vastly different levels of boards, e.g. Kepler and Fermi, is not supported.
+Current implementation has a "soft" assumption that the devices being used are homogeneous.  In practice, any devices of the same general class should work together, but performance and total size is limited by the smallest device being used.  e.g. if you combine a TitanX and a GTX980, performance will be limited by the 980.  Mixing vastly different levels of boards, e.g. Kepler and Fermi, is not supported.
 
 "nvidia-smi topo -m" will show you the connectivity matrix.  You can do P2P through PCIe bridges, but not across socket level links at this time, e.g. across CPU sockets on a multi-socket motherboard.
 
 # Scaling Performance
 
-Performance is **heavily** dependent on the PCIe topology of the system, the configuration of the neural network you are training, and the speed of each of the layers.  Systems like the DIGITS DevBox have an optimized PCIe topology (X99-E WS chipset).  In general, scaling on 2 GPUs tends to be ~1.8X on average for networks like AlexNet, CaffeNet, VGG, GoogleNet.  4 GPUs begins to have falloff in scaling.  Generally with "weak scaling" where the batchsize increases with the number of GPUs you will see 3.5x scaling or so.  With "strong scaling", the system can become communication bound, especially with layer performance optimizations like those in [cuDNNv3](http://nvidia.com/cudnn), and you will likely see closer to mid 2.x scaling in performance.  Networks that have heavy computation compared to the number of parameters tend to have the best scaling performance.
\ No newline at end of file
+Performance is **heavily** dependent on the PCIe topology of the system, the configuration of the neural network you are training, and the speed of each of the layers.  Systems like the DIGITS DevBox have an optimized PCIe topology (X99-E WS chipset).  In general, scaling on 2 GPUs tends to be ~1.8X on average for networks like AlexNet, CaffeNet, VGG, GoogleNet.  4 GPUs begins to have falloff in scaling.  Generally with "weak scaling" where the batchsize increases with the number of GPUs you will see 3.5x scaling or so.  With "strong scaling", the system can become communication bound, especially with layer performance optimizations like those in [cuDNNv3](http://nvidia.com/cudnn), and you will likely see closer to mid 2.x scaling in performance.  Networks that have heavy computation compared to the number of parameters tend to have the best scaling performance.
diff --git a/docs/performance_hardware.md b/docs/performance_hardware.md
deleted file mode 100644
index cdd4b361..00000000
--- a/docs/performance_hardware.md
+++ /dev/null
@@ -1,73 +0,0 @@
----
-title: Performance and Hardware Configuration
----
-
-# Performance and Hardware Configuration
-
-To measure performance on different NVIDIA GPUs we use CaffeNet, the Caffe reference ImageNet model.
-
-For training, each time point is 20 iterations/minibatches of 256 images for 5,120 images total. For testing, a 50,000 image validation set is classified.
-
-**Acknowledgements**: BVLC members are very grateful to NVIDIA for providing several GPUs to conduct this research.
-
-## NVIDIA K40
-
-Performance is best with ECC off and boost clock enabled. While ECC makes a negligible difference in speed, disabling it frees ~1 GB of GPU memory.
-
-Best settings with ECC off and maximum clock speed in standard Caffe:
-
-* Training is 26.5 secs / 20 iterations (5,120 images)
-* Testing is 100 secs / validation set (50,000 images)
-
-Best settings with Caffe + [cuDNN acceleration](http://nvidia.com/cudnn):
-
-* Training is 19.2 secs / 20 iterations (5,120 images)
-* Testing is 60.7 secs / validation set (50,000 images)
-
-Other settings:
-
-* ECC on, max speed: training 26.7 secs / 20 iterations, test 101 secs / validation set
-* ECC on, default speed: training 31 secs / 20 iterations, test 117 secs / validation set
-* ECC off, default speed: training 31 secs / 20 iterations, test 118 secs / validation set
-
-### K40 configuration tips
-
-For maximum K40 performance, turn off ECC and boost the clock speed (at your own risk).
-
-To turn off ECC, do
-
-    sudo nvidia-smi -i 0 --ecc-config=0    # repeat with -i x for each GPU ID
-
-then reboot.
-
-Set the "persistence" mode of the GPU settings by
-
-    sudo nvidia-smi -pm 1
-
-and then set the clock speed with
-
-    sudo nvidia-smi -i 0 -ac 3004,875    # repeat with -i x for each GPU ID
-
-but note that this configuration resets across driver reloading / rebooting. Include these commands in a boot script to initialize these settings. For a simple fix, add these commands to `/etc/rc.local` (on Ubuntu).
-
-## NVIDIA Titan
-
-Training: 26.26 secs / 20 iterations (5,120 images).
-Testing: 100 secs / validation set (50,000 images).
-
-cuDNN Training: 20.25 secs / 20 iterations (5,120 images).
-cuDNN Testing: 66.3 secs / validation set (50,000 images).
-
-
-## NVIDIA K20
-
-Training: 36.0 secs / 20 iterations (5,120 images).
-Testing: 133 secs / validation set (50,000 images).
-
-## NVIDIA GTX 770
-
-Training: 33.0 secs / 20 iterations (5,120 images).
-Testing: 129 secs / validation set (50,000 images).
-
-cuDNN Training: 24.3 secs / 20 iterations (5,120 images).
-cuDNN Testing: 104 secs / validation set (50,000 images).
diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md
index d7ff3782..b5a4f1ad 100644
--- a/docs/tutorial/interfaces.md
+++ b/docs/tutorial/interfaces.md
@@ -91,7 +91,7 @@ In MatCaffe, you can
 * Run for a certain number of iterations and give back control to Matlab
 * Intermingle arbitrary Matlab code with gradient steps
 
-An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m (you need to download BVLC CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) to run it).
+An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m (you need to download BAIR CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) to run it).
 
 ### Build MatCaffe
 
@@ -114,7 +114,7 @@ You can save your Matlab search PATH by running `savepath` so that you don't hav
 
 MatCaffe is very similar to PyCaffe in usage.
 
-Examples below shows detailed usages and assumes you have downloaded BVLC CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) and started `matlab` from caffe root folder.
+Examples below shows detailed usages and assumes you have downloaded BAIR CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) and started `matlab` from caffe root folder.
 
     model = './models/bvlc_reference_caffenet/deploy.prototxt';
     weights = './models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';
diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 7362aac2..78a46f3a 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -1,186 +1,77 @@
 ---
 title: Layer Catalogue
 ---
+
 # Layers
 
 To create a Caffe model you need to define the model architecture in a protocol buffer definition file (prototxt).
 
 Caffe layers and their parameters are defined in the protocol buffer definitions for the project in [caffe.proto](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto).
 
-### Vision Layers
-
-* Header: `./include/caffe/vision_layers.hpp`
-
-Vision layers usually take *images* as input and produce other *images* as output.
-A typical "image" in the real-world may have one color channel ($$c = 1$$), as in a grayscale image, or three color channels ($$c = 3$$) as in an RGB (red, green, blue) image.
-But in this context, the distinguishing characteristic of an image is its spatial structure: usually an image has some non-trivial height $$h > 1$$ and width $$w > 1$$.
-This 2D geometry naturally lends itself to certain decisions about how to process the input.
-In particular, most of the vision layers work by applying a particular operation to some region of the input to produce a corresponding region of the output.
-In contrast, other layers (with few exceptions) ignore the spatial structure of the input, effectively treating it as "one big vector" with dimension $$chw$$.
-
-
-#### Convolution
-
-* Layer type: `Convolution`
-* CPU implementation: `./src/caffe/layers/convolution_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/convolution_layer.cu`
-* Parameters (`ConvolutionParameter convolution_param`)
-    - Required
-        - `num_output` (`c_o`): the number of filters
-        - `kernel_size` (or `kernel_h` and `kernel_w`): specifies height and width of each filter
-    - Strongly Recommended
-        - `weight_filler` [default `type: 'constant' value: 0`]
-    - Optional
-        - `bias_term` [default `true`]: specifies whether to learn and apply a set of additive biases to the filter outputs
-        - `pad` (or `pad_h` and `pad_w`) [default 0]: specifies the number of pixels to (implicitly) add to each side of the input
-        - `stride` (or `stride_h` and `stride_w`) [default 1]: specifies the intervals at which to apply the filters to the input
-        - `group` (g) [default 1]: If g > 1, we restrict the connectivity of each filter to a subset of the input. Specifically, the input and output channels are separated into g groups, and the $$i$$th output group channels will be only connected to the $$i$$th input group channels.
-* Input
-    - `n * c_i * h_i * w_i`
-* Output
-    - `n * c_o * h_o * w_o`, where `h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1` and `w_o` likewise.
-* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`)
-
-      layer {
-        name: "conv1"
-        type: "Convolution"
-        bottom: "data"
-        top: "conv1"
-        # learning rate and decay multipliers for the filters
-        param { lr_mult: 1 decay_mult: 1 }
-        # learning rate and decay multipliers for the biases
-        param { lr_mult: 2 decay_mult: 0 }
-        convolution_param {
-          num_output: 96     # learn 96 filters
-          kernel_size: 11    # each filter is 11x11
-          stride: 4          # step 4 pixels between each filter application
-          weight_filler {
-            type: "gaussian" # initialize the filters from a Gaussian
-            std: 0.01        # distribution with stdev 0.01 (default mean: 0)
-          }
-          bias_filler {
-            type: "constant" # initialize the biases to zero (0)
-            value: 0
-          }
-        }
-      }
-
-The `Convolution` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.
-
-#### Pooling
-
-* Layer type: `Pooling`
-* CPU implementation: `./src/caffe/layers/pooling_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/pooling_layer.cu`
-* Parameters (`PoolingParameter pooling_param`)
-    - Required
-        - `kernel_size` (or `kernel_h` and `kernel_w`): specifies height and width of each filter
-    - Optional
-        - `pool` [default MAX]: the pooling method. Currently MAX, AVE, or STOCHASTIC
-        - `pad` (or `pad_h` and `pad_w`) [default 0]: specifies the number of pixels to (implicitly) add to each side of the input
-        - `stride` (or `stride_h` and `stride_w`) [default 1]: specifies the intervals at which to apply the filters to the input
-* Input
-    - `n * c * h_i * w_i`
-* Output
-    - `n * c * h_o * w_o`, where h_o and w_o are computed in the same way as convolution.
-* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`)
-
-      layer {
-        name: "pool1"
-        type: "Pooling"
-        bottom: "conv1"
-        top: "pool1"
-        pooling_param {
-          pool: MAX
-          kernel_size: 3 # pool over a 3x3 region
-          stride: 2      # step two pixels (in the bottom blob) between pooling regions
-        }
-      }
-
-#### Local Response Normalization (LRN)
-
-* Layer type: `LRN`
-* CPU Implementation: `./src/caffe/layers/lrn_layer.cpp`
-* CUDA GPU Implementation: `./src/caffe/layers/lrn_layer.cu`
-* Parameters (`LRNParameter lrn_param`)
-    - Optional
-        - `local_size` [default 5]: the number of channels to sum over (for cross channel LRN) or the side length of the square region to sum over (for within channel LRN)
-        - `alpha` [default 1]: the scaling parameter (see below)
-        - `beta` [default 5]: the exponent (see below)
-        - `norm_region` [default `ACROSS_CHANNELS`]: whether to sum over adjacent channels (`ACROSS_CHANNELS`) or nearby spatial locaitons (`WITHIN_CHANNEL`)
+## Data Layers
 
-The local response normalization layer performs a kind of "lateral inhibition" by normalizing over local input regions. In `ACROSS_CHANNELS` mode, the local regions extend across nearby channels, but have no spatial extent (i.e., they have shape `local_size x 1 x 1`). In `WITHIN_CHANNEL` mode, the local regions extend spatially, but are in separate channels (i.e., they have shape `1 x local_size x local_size`). Each input value is divided by $$(1 + (\alpha/n) \sum_i x_i^2)^\beta$$, where $$n$$ is the size of each local region, and the sum is taken over the region centered at that value (zero padding is added where necessary).
-
-#### im2col
-
-`Im2col` is a helper for doing the image-to-column transformation that you most likely do not need to know about. This is used in Caffe's original convolution to do matrix multiplication by laying out all patches into a matrix.
-
-### Loss Layers
+Data enters Caffe through data layers: they lie at the bottom of nets. Data can come from efficient databases (LevelDB or LMDB), directly from memory, or, when efficiency is not critical, from files on disk in HDF5 or common image formats.
 
-Loss drives learning by comparing an output to a target and assigning cost to minimize. The loss itself is computed by the forward pass and the gradient w.r.t. to the loss is computed by the backward pass.
+Common input preprocessing (mean subtraction, scaling, random cropping, and mirroring) is available by specifying `TransformationParameter`s by some of the layers.
+The [bias](layers/bias.html), [scale](layers/scale.html), and [crop](layers/crop.html) layers can be helpful with transforming the inputs, when `TransformationParameter` isn't available.
 
-#### Softmax
+Layers:
 
-* Layer type: `SoftmaxWithLoss`
+* [Image Data](layers/imagedata.html) - read raw images.
+* [Database](layers/data.html) - read data from LEVELDB or LMDB.
+* [HDF5 Input](layers/hdf5data.html) - read HDF5 data, allows data of arbitrary dimensions.
+* [HDF5 Output](layers/hdf5output.html) - write data as HDF5.
+* [Input](layers/input.html) - typically used for networks that are being deployed.
+* [Window Data](layers/windowdata.html) - read window data file.
+* [Memory Data](layers/memorydata.html) - read data directly from memory.
+* [Dummy Data](layers/dummydata.html) - for static data and debugging.
 
-The softmax loss layer computes the multinomial logistic loss of the softmax of its inputs. It's conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.
+Note that the [Python](layers/python.html) Layer can be useful for create custom data layers.
 
-#### Sum-of-Squares / Euclidean
+## Vision Layers
 
-* Layer type: `EuclideanLoss`
+Vision layers usually take *images* as input and produce other *images* as output, although they can take data of other types and dimensions.
+A typical "image" in the real-world may have one color channel ($$c = 1$$), as in a grayscale image, or three color channels ($$c = 3$$) as in an RGB (red, green, blue) image.
+But in this context, the distinguishing characteristic of an image is its spatial structure: usually an image has some non-trivial height $$h > 1$$ and width $$w > 1$$.
+This 2D geometry naturally lends itself to certain decisions about how to process the input.
+In particular, most of the vision layers work by applying a particular operation to some region of the input to produce a corresponding region of the output.
+In contrast, other layers (with few exceptions) ignore the spatial structure of the input, effectively treating it as "one big vector" with dimension $$chw$$.
 
-The Euclidean loss layer computes the sum of squares of differences of its two inputs, $$\frac 1 {2N} \sum_{i=1}^N \| x^1_i - x^2_i \|_2^2$$.
+Layers:
 
-#### Hinge / Margin
+* [Convolution Layer](layers/convolution.html) - convolves the input image with a set of learnable filters, each producing one feature map in the output image.
+* [Pooling Layer](layers/pooling.html) - max, average, or stochastic pooling.
+* [Spatial Pyramid Pooling (SPP)](layers/spp.html)
+* [Crop](layers/crop.html) - perform cropping transformation.
+* [Deconvolution Layer](layers/deconvolution.html) - transposed convolution.
 
-* Layer type: `HingeLoss`
-* CPU implementation: `./src/caffe/layers/hinge_loss_layer.cpp`
-* CUDA GPU implementation: none yet
-* Parameters (`HingeLossParameter hinge_loss_param`)
-    - Optional
-        - `norm` [default L1]: the norm used. Currently L1, L2
-* Inputs
-    - `n * c * h * w` Predictions
-    - `n * 1 * 1 * 1` Labels
-* Output
-    - `1 * 1 * 1 * 1` Computed Loss
-* Samples
+* [Im2Col](layers/im2col.html) - relic helper layer that is not used much anymore.
 
-      # L1 Norm
-      layer {
-        name: "loss"
-        type: "HingeLoss"
-        bottom: "pred"
-        bottom: "label"
-      }
+## Recurrent Layers
 
-      # L2 Norm
-      layer {
-        name: "loss"
-        type: "HingeLoss"
-        bottom: "pred"
-        bottom: "label"
-        top: "loss"
-        hinge_loss_param {
-          norm: L2
-        }
-      }
+Layers:
 
-The hinge loss layer computes a one-vs-all hinge or squared hinge loss.
+* [Recurrent](layers/recurrent.html)
+* [RNN](layers/rnn.html)
+* [Long-Short Term Memory (LSTM)](layers/lstm.html)
 
-#### Sigmoid Cross-Entropy
+## Common Layers
 
-`SigmoidCrossEntropyLoss`
+Layers:
 
-#### Infogain
+* [Inner Product](layers/innerproduct.html) - fully connected layer.
+* [Dropout](layers/dropout.html)
+* [Embed](layers/embed.html) - for learning embeddings of one-hot encoded vector (takes index as input).
 
-`InfogainLoss`
+## Normalization Layers
 
-#### Accuracy and Top-k
+* [Local Response Normalization (LRN)](layers/lrn.html) - performs a kind of "lateral inhibition" by normalizing over local input regions.
+* [Mean Variance Normalization (MVN)](layers/mvn.html) - performs contrast normalization / instance normalization.
+* [Batch Normalization](layers/batchnorm.html) - performs normalization over mini-batches.
 
-`Accuracy` scores the output as the accuracy of output with respect to target -- it is not actually a loss and has no backward step.
+The [bias](layers/bias.html) and [scale](layers/scale.html) layers can be helpful in combination with normalization.
 
-### Activation / Neuron Layers
+## Activation / Neuron Layers
 
 In general, activation / Neuron layers are element-wise operators, taking one bottom blob and producing one top blob of the same size. In the layers below, we will ignore the input and out sizes as they are identical:
 
@@ -189,337 +80,56 @@ In general, activation / Neuron layers are element-wise operators, taking one bo
 * Output
     - n * c * h * w
 
-#### ReLU / Rectified-Linear and Leaky-ReLU
-
-* Layer type: `ReLU`
-* CPU implementation: `./src/caffe/layers/relu_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/relu_layer.cu`
-* Parameters (`ReLUParameter relu_param`)
-    - Optional
-        - `negative_slope` [default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
-* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`)
-
-      layer {
-        name: "relu1"
-        type: "ReLU"
-        bottom: "conv1"
-        top: "conv1"
-      }
-
-Given an input value x, The `ReLU` layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.
-
-#### Sigmoid
-
-* Layer type: `Sigmoid`
-* CPU implementation: `./src/caffe/layers/sigmoid_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/sigmoid_layer.cu`
-* Sample (as seen in `./examples/mnist/mnist_autoencoder.prototxt`)
-
-      layer {
-        name: "encode1neuron"
-        bottom: "encode1"
-        top: "encode1neuron"
-        type: "Sigmoid"
-      }
-
-The `Sigmoid` layer computes the output as sigmoid(x) for each input element x.
-
-#### TanH / Hyperbolic Tangent
-
-* Layer type: `TanH`
-* CPU implementation: `./src/caffe/layers/tanh_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/tanh_layer.cu`
-* Sample
-
-      layer {
-        name: "layer"
-        bottom: "in"
-        top: "out"
-        type: "TanH"
-      }
-
-The `TanH` layer computes the output as tanh(x) for each input element x.
-
-#### Absolute Value
-
-* Layer type: `AbsVal`
-* CPU implementation: `./src/caffe/layers/absval_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/absval_layer.cu`
-* Sample
-
-      layer {
-        name: "layer"
-        bottom: "in"
-        top: "out"
-        type: "AbsVal"
-      }
-
-The `AbsVal` layer computes the output as abs(x) for each input element x.
-
-#### Power
-
-* Layer type: `Power`
-* CPU implementation: `./src/caffe/layers/power_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/power_layer.cu`
-* Parameters (`PowerParameter power_param`)
-    - Optional
-        - `power` [default 1]
-        - `scale` [default 1]
-        - `shift` [default 0]
-* Sample
-
-      layer {
-        name: "layer"
-        bottom: "in"
-        top: "out"
-        type: "Power"
-        power_param {
-          power: 1
-          scale: 1
-          shift: 0
-        }
-      }
-
-The `Power` layer computes the output as (shift + scale * x) ^ power for each input element x.
-
-#### BNLL
-
-* Layer type: `BNLL`
-* CPU implementation: `./src/caffe/layers/bnll_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/bnll_layer.cu`
-* Sample
-
-      layer {
-        name: "layer"
-        bottom: "in"
-        top: "out"
-        type: BNLL
-      }
-
-The `BNLL` (binomial normal log likelihood) layer computes the output as log(1 + exp(x)) for each input element x.
-
-
-### Data Layers
-
-Data enters Caffe through data layers: they lie at the bottom of nets. Data can come from efficient databases (LevelDB or LMDB), directly from memory, or, when efficiency is not critical, from files on disk in HDF5 or common image formats.
-
-Common input preprocessing (mean subtraction, scaling, random cropping, and mirroring) is available by specifying `TransformationParameter`s.
-
-#### Database
+Layers:
 
-* Layer type: `Data`
-* Parameters
-    - Required
-        - `source`: the name of the directory containing the database
-        - `batch_size`: the number of inputs to process at one time
-    - Optional
-        - `rand_skip`: skip up to this number of inputs at the beginning; useful for asynchronous sgd
-        - `backend` [default `LEVELDB`]: choose whether to use a `LEVELDB` or `LMDB`
+* [ReLU / Rectified-Linear and Leaky-ReLU](layers/relu.html) - ReLU and Leaky-ReLU rectification.
+* [PReLU](layers/prelu.html) - parametric ReLU.
+* [ELU](layers/elu.html) - exponential linear rectification.
+* [Sigmoid](layers/sigmoid.html)
+* [TanH](layers/tanh.html)
+* [Absolute Value](layers/absval.html)
+* [Power](layers/power.html) - f(x) = (shift + scale * x) ^ power.
+* [Exp](layers/exp.html) - f(x) = base ^ (shift + scale * x).
+* [Log](layers/log.html) - f(x) = log(x).
+* [BNLL](layers/bnll.html) - f(x) = log(1 + exp(x)).
+* [Threshold](layers/threshold.html) - performs step function at user defined threshold.
+* [Bias](layers/bias.html) - adds a bias to a blob that can either be learned or fixed.
+* [Scale](layers/scale.html) - scales a blob by an amount that can either be learned or fixed.
 
+## Utility Layers
 
+Layers:
 
-#### In-Memory
+* [Flatten](layers/flatten.html)
+* [Reshape](layers/reshape.html)
+* [Batch Reindex](layers/batchreindex.html)
 
-* Layer type: `MemoryData`
-* Parameters
-    - Required
-        - `batch_size`, `channels`, `height`, `width`: specify the size of input chunks to read from memory
+* [Split](layers/split.html)
+* [Concat](layers/concat.html)
+* [Slicing](layers/slice.html)
+* [Eltwise](layers/eltwise.html) - element-wise operations such as product or sum between two blobs.
+* [Filter / Mask](layers/filter.html) - mask or select output using last blob.
+* [Parameter](layers/parameter.html) - enable parameters to be shared between layers.
+* [Reduction](layers/reduction.html) - reduce input blob to scalar blob using operations such as sum or mean.
+* [Silence](layers/silence.html) - prevent top-level blobs from being printed during training.
 
-The memory data layer reads data directly from memory, without copying it. In order to use it, one must call `MemoryDataLayer::Reset` (from C++) or `Net.set_input_arrays` (from Python) in order to specify a source of contiguous data (as 4D row major array), which is read one batch-sized chunk at a time.
+* [ArgMax](layers/argmax.html)
+* [Softmax](layers/softmax.html)
 
-#### HDF5 Input
+* [Python](layers/python.html) - allows custom Python layers.
 
-* Layer type: `HDF5Data`
-* Parameters
-    - Required
-        - `source`: the name of the file to read from
-        - `batch_size`
+## Loss Layers
 
-#### HDF5 Output
-
-* Layer type: `HDF5Output`
-* Parameters
-    - Required
-        - `file_name`: name of file to write to
-
-The HDF5 output layer performs the opposite function of the other layers in this section: it writes its input blobs to disk.
-
-#### Images
-
-* Layer type: `ImageData`
-* Parameters
-    - Required
-        - `source`: name of a text file, with each line giving an image filename and label
-        - `batch_size`: number of images to batch together
-    - Optional
-        - `rand_skip`
-        - `shuffle` [default false]
-        - `new_height`, `new_width`: if provided, resize all images to this size
-
-#### Windows
-
-`WindowData`
-
-#### Dummy
-
-`DummyData` is for development and debugging. See `DummyDataParameter`.
-
-### Common Layers
-
-#### Inner Product
-
-* Layer type: `InnerProduct`
-* CPU implementation: `./src/caffe/layers/inner_product_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/inner_product_layer.cu`
-* Parameters (`InnerProductParameter inner_product_param`)
-    - Required
-        - `num_output` (`c_o`): the number of filters
-    - Strongly recommended
-        - `weight_filler` [default `type: 'constant' value: 0`]
-    - Optional
-        - `bias_filler` [default `type: 'constant' value: 0`]
-        - `bias_term` [default `true`]: specifies whether to learn and apply a set of additive biases to the filter outputs
-* Input
-    - `n * c_i * h_i * w_i`
-* Output
-    - `n * c_o * 1 * 1`
-* Sample
-
-      layer {
-        name: "fc8"
-        type: "InnerProduct"
-        # learning rate and decay multipliers for the weights
-        param { lr_mult: 1 decay_mult: 1 }
-        # learning rate and decay multipliers for the biases
-        param { lr_mult: 2 decay_mult: 0 }
-        inner_product_param {
-          num_output: 1000
-          weight_filler {
-            type: "gaussian"
-            std: 0.01
-          }
-          bias_filler {
-            type: "constant"
-            value: 0
-          }
-        }
-        bottom: "fc7"
-        top: "fc8"
-      }
-
-The `InnerProduct` layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob's height and width set to 1).
-
-#### Splitting
-
-The `Split` layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers.
-
-#### Flattening
-
-The `Flatten` layer is a utility layer that flattens an input of shape `n * c * h * w` to a simple vector output of shape `n * (c*h*w)`
-
-#### Reshape
-
-* Layer type: `Reshape`
-* Implementation: `./src/caffe/layers/reshape_layer.cpp`
-* Parameters (`ReshapeParameter reshape_param`)
-    - Optional: (also see detailed description below)
-        - `shape`
-
-* Input
-    - a single blob with arbitrary dimensions
-* Output
-    - the same blob, with modified dimensions, as specified by `reshape_param`
-
-* Sample
-
-        layer {
-          name: "reshape"
-          type: "Reshape"
-          bottom: "input"
-          top: "output"
-          reshape_param {
-            shape {
-              dim: 0  # copy the dimension from below
-              dim: 2
-              dim: 3
-              dim: -1 # infer it from the other dimensions
-            }
-          }
-        }
-
-The `Reshape` layer can be used to change the dimensions of its input, without changing its data. Just like the `Flatten` layer, only the dimensions are changed; no data is copied in the process.
-
-Output dimensions are specified by the `ReshapeParam` proto. Positive numbers are used directly, setting the corresponding dimension of the output blob. In addition, two special values are accepted for any of the target dimension values:
-
-* **0** means "copy the respective dimension of the bottom layer". That is, if the bottom has 2 as its 1st dimension, the top will have 2 as its 1st dimension as well, given `dim: 0` as the 1st target dimension.
-* **-1** stands for "infer this from the other dimensions". This behavior is similar to that of -1 in *numpy*'s or `[]` for *MATLAB*'s reshape: this dimension is calculated to keep the overall element count the same as in the bottom layer. At most one -1 can be used in a reshape operation.
-
-As another example, specifying `reshape_param { shape { dim: 0 dim: -1 } }` makes the layer behave in exactly the same way as the `Flatten` layer.
-
-#### Concatenation
-
-* Layer type: `Concat`
-* CPU implementation: `./src/caffe/layers/concat_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/concat_layer.cu`
-* Parameters (`ConcatParameter concat_param`)
-    - Optional
-        - `axis` [default 1]: 0 for concatenation along num and 1 for channels.
-* Input
-    - `n_i * c_i * h * w` for each input blob i from 1 to K.
-* Output
-    - if `axis = 0`: `(n_1 + n_2 + ... + n_K) * c_1 * h * w`, and all input `c_i` should be the same.
-    - if `axis = 1`: `n_1 * (c_1 + c_2 + ... + c_K) * h * w`, and all input `n_i` should be the same.
-* Sample
-
-      layer {
-        name: "concat"
-        bottom: "in1"
-        bottom: "in2"
-        top: "out"
-        type: "Concat"
-        concat_param {
-          axis: 1
-        }
-      }
-
-The `Concat` layer is a utility layer that concatenates its multiple input blobs to one single output blob.
-
-#### Slicing
-
-The `Slice` layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.
-
-* Sample
-
-      layer {
-        name: "slicer_label"
-        type: "Slice"
-        bottom: "label"
-        ## Example of label with a shape N x 3 x 1 x 1
-        top: "label1"
-        top: "label2"
-        top: "label3"
-        slice_param {
-          axis: 1
-          slice_point: 1
-          slice_point: 2
-        }
-      }
-
-`axis` indicates the target axis; `slice_point` indicates indexes in the selected dimension (the number of indices must be equal to the number of top blobs minus one).
-
-
-#### Elementwise Operations
-
-`Eltwise`
-
-#### Argmax
-
-`ArgMax`
-
-#### Softmax
+Loss drives learning by comparing an output to a target and assigning cost to minimize. The loss itself is computed by the forward pass and the gradient w.r.t. to the loss is computed by the backward pass.
 
-`Softmax`
+Layers:
 
-#### Mean-Variance Normalization
+* [Multinomial Logistic Loss](layers/multinomiallogisticloss.html)
+* [Infogain Loss](layers/infogainloss.html) - a generalization of MultinomialLogisticLossLayer.
+* [Softmax with Loss](layers/softmaxwithloss.html) - computes the multinomial logistic loss of the softmax of its inputs. It's conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.
+* [Sum-of-Squares / Euclidean](layers/euclideanloss.html) - computes the sum of squares of differences of its two inputs, $$\frac 1 {2N} \sum_{i=1}^N \| x^1_i - x^2_i \|_2^2$$.
+* [Hinge / Margin](layers/hingeloss.html) - The hinge loss layer computes a one-vs-all hinge (L1) or squared hinge loss (L2).
+* [Sigmoid Cross-Entropy Loss](layers/sigmoidcrossentropyloss.html) - computes the cross-entropy (logistic) loss, often used for predicting targets interpreted as probabilities.
+* [Accuracy / Top-k layer](layers/accuracy.html) - scores the output as an accuracy with respect to target -- it is not actually a loss and has no backward step.
+* [Contrastive Loss](layers/contrastiveloss.html)
 
-`MVN`
diff --git a/docs/tutorial/layers/absval.md b/docs/tutorial/layers/absval.md
new file mode 100644
index 00000000..220c4118
--- /dev/null
+++ b/docs/tutorial/layers/absval.md
@@ -0,0 +1,22 @@
+---
+title: Absolute Value Layer
+---
+
+# Absolute Value Layer
+
+* Layer type: `AbsVal`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1AbsValLayer.html)
+* Header: [`./include/caffe/layers/absval_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/absval_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/absval_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/absval_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/absval_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/absval_layer.cu)
+
+* Sample
+
+      layer {
+        name: "layer"
+        bottom: "in"
+        top: "out"
+        type: "AbsVal"
+      }
+
+The `AbsVal` layer computes the output as abs(x) for each input element x.
diff --git a/docs/tutorial/layers/accuracy.md b/docs/tutorial/layers/accuracy.md
new file mode 100644
index 00000000..80293b1c
--- /dev/null
+++ b/docs/tutorial/layers/accuracy.md
@@ -0,0 +1,20 @@
+---
+title: Accuracy and Top-k
+---
+
+# Accuracy and Top-k
+
+`Accuracy` scores the output as the accuracy of output with respect to target -- it is not actually a loss and has no backward step.
+
+* Layer type: `Accuracy`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1AccuracyLayer.html)
+* Header: [`./include/caffe/layers/accuracy_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/accuracy_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/accuracy_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/accuracy_layer.cpp)
+
+## Parameters
+* Parameters (`AccuracyParameter accuracy_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/AccuracyParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/argmax.md b/docs/tutorial/layers/argmax.md
new file mode 100644
index 00000000..9eb8b773
--- /dev/null
+++ b/docs/tutorial/layers/argmax.md
@@ -0,0 +1,18 @@
+---
+title: ArgMax Layer
+---
+
+# ArgMax Layer
+
+* Layer type: `ArgMax`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ArgMaxLayer.html)
+* Header: [`./include/caffe/layers/argmax_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/argmax_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/argmax_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/argmax_layer.cpp)
+
+## Parameters
+* Parameters (`ArgMaxParameter argmax_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/ArgMaxParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/batchnorm.md b/docs/tutorial/layers/batchnorm.md
new file mode 100644
index 00000000..a5be5ce0
--- /dev/null
+++ b/docs/tutorial/layers/batchnorm.md
@@ -0,0 +1,20 @@
+---
+title: Batch Norm Layer
+---
+
+# Batch Norm Layer
+
+* Layer type: `BatchNorm`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1BatchNormLayer.html)
+* Header: [`./include/caffe/layers/batch_norm_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/batch_norm_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/batch_norm_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/batch_norm_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/batch_norm_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/batch_norm_layer.cu)
+
+## Parameters
+
+* Parameters (`BatchNormParameter batch_norm_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/BatchNormParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/batchreindex.md b/docs/tutorial/layers/batchreindex.md
new file mode 100644
index 00000000..21b36c39
--- /dev/null
+++ b/docs/tutorial/layers/batchreindex.md
@@ -0,0 +1,16 @@
+---
+title: Batch Reindex Layer
+---
+
+# Batch Reindex Layer
+
+* Layer type: `BatchReindex`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1BatchReindexLayer.html)
+* Header: [`./include/caffe/layers/batch_reindex_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/batch_reindex_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/batch_reindex_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/batch_reindex_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/batch_reindex_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/batch_reindex_layer.cu)
+
+
+## Parameters
+
+No parameters.
diff --git a/docs/tutorial/layers/bias.md b/docs/tutorial/layers/bias.md
new file mode 100644
index 00000000..d3a00c2f
--- /dev/null
+++ b/docs/tutorial/layers/bias.md
@@ -0,0 +1,19 @@
+---
+title: Bias Layer
+---
+
+# Bias Layer
+
+* Layer type: `Bias`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1BiasLayer.html)
+* Header: [`./include/caffe/layers/bias_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/bias_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/bias_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/bias_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/bias_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/bias_layer.cu)
+
+## Parameters
+* Parameters (`BiasParameter bias_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/BiasParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/bnll.md b/docs/tutorial/layers/bnll.md
new file mode 100644
index 00000000..2b68b79f
--- /dev/null
+++ b/docs/tutorial/layers/bnll.md
@@ -0,0 +1,25 @@
+---
+title: BNLL Layer
+---
+
+# BNLL Layer
+
+* Layer type: `BNLL`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1BNLLLayer.html)
+* Header: [`./include/caffe/layers/bnll_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/bnll_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/bnll_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/bnll_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/bnll_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/bnll_layer.cu)
+
+The `BNLL` (binomial normal log likelihood) layer computes the output as log(1 + exp(x)) for each input element x.
+
+## Parameters
+No parameters.
+
+## Sample
+
+      layer {
+        name: "layer"
+        bottom: "in"
+        top: "out"
+        type: BNLL
+      }
diff --git a/docs/tutorial/layers/concat.md b/docs/tutorial/layers/concat.md
new file mode 100644
index 00000000..c7b25395
--- /dev/null
+++ b/docs/tutorial/layers/concat.md
@@ -0,0 +1,40 @@
+---
+title: Concat Layer
+---
+
+# Concat Layer
+
+* Layer type: `Concat`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ConcatLayer.html)
+* Header: [`./include/caffe/layers/concat_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/concat_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/concat_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/concat_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/concat_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/concat_layer.cu)
+* Input
+    - `n_i * c_i * h * w` for each input blob i from 1 to K.
+* Output
+    - if `axis = 0`: `(n_1 + n_2 + ... + n_K) * c_1 * h * w`, and all input `c_i` should be the same.
+    - if `axis = 1`: `n_1 * (c_1 + c_2 + ... + c_K) * h * w`, and all input `n_i` should be the same.
+* Sample
+
+      layer {
+        name: "concat"
+        bottom: "in1"
+        bottom: "in2"
+        top: "out"
+        type: "Concat"
+        concat_param {
+          axis: 1
+        }
+      }
+
+The `Concat` layer is a utility layer that concatenates its multiple input blobs to one single output blob.
+
+## Parameters
+* Parameters (`ConcatParameter concat_param`)
+    - Optional
+        - `axis` [default 1]: 0 for concatenation along num and 1 for channels.
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/ConcatParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/contrastiveloss.md b/docs/tutorial/layers/contrastiveloss.md
new file mode 100644
index 00000000..bb1859d9
--- /dev/null
+++ b/docs/tutorial/layers/contrastiveloss.md
@@ -0,0 +1,20 @@
+---
+title: Contrastive Loss Layer
+---
+
+# Contrastive Loss Layer
+
+* Layer type: `ContrastiveLoss`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ContrastiveLossLayer.html)
+* Header: [`./include/caffe/layers/contrastive_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/contrastive_loss_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/contrastive_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/contrastive_loss_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/contrastive_loss_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/contrastive_loss_layer.cu)
+
+## Parameters
+
+* Parameters (`ContrastiveLossParameter contrastive_loss_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/ContrastiveLossParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/convolution.md b/docs/tutorial/layers/convolution.md
new file mode 100644
index 00000000..cc9f4fd0
--- /dev/null
+++ b/docs/tutorial/layers/convolution.md
@@ -0,0 +1,63 @@
+---
+title: Convolution Layer
+---
+
+# Convolution Layer
+
+* Layer type: `Convolution`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ConvolutionLayer.html)
+* Header: [`./include/caffe/layers/conv_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/conv_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/conv_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/conv_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/conv_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/conv_layer.cu)
+* Input
+    - `n * c_i * h_i * w_i`
+* Output
+    - `n * c_o * h_o * w_o`, where `h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1` and `w_o` likewise.
+
+The `Convolution` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.
+
+## Sample
+
+Sample (as seen in [`./models/bvlc_reference_caffenet/train_val.prototxt`](https://github.com/BVLC/caffe/blob/master/models/bvlc_reference_caffenet/train_val.prototxt)):
+
+      layer {
+        name: "conv1"
+        type: "Convolution"
+        bottom: "data"
+        top: "conv1"
+        # learning rate and decay multipliers for the filters
+        param { lr_mult: 1 decay_mult: 1 }
+        # learning rate and decay multipliers for the biases
+        param { lr_mult: 2 decay_mult: 0 }
+        convolution_param {
+          num_output: 96     # learn 96 filters
+          kernel_size: 11    # each filter is 11x11
+          stride: 4          # step 4 pixels between each filter application
+          weight_filler {
+            type: "gaussian" # initialize the filters from a Gaussian
+            std: 0.01        # distribution with stdev 0.01 (default mean: 0)
+          }
+          bias_filler {
+            type: "constant" # initialize the biases to zero (0)
+            value: 0
+          }
+        }
+      }
+
+## Parameters
+* Parameters (`ConvolutionParameter convolution_param`)
+    - Required
+        - `num_output` (`c_o`): the number of filters
+        - `kernel_size` (or `kernel_h` and `kernel_w`): specifies height and width of each filter
+    - Strongly Recommended
+        - `weight_filler` [default `type: 'constant' value: 0`]
+    - Optional
+        - `bias_term` [default `true`]: specifies whether to learn and apply a set of additive biases to the filter outputs
+        - `pad` (or `pad_h` and `pad_w`) [default 0]: specifies the number of pixels to (implicitly) add to each side of the input
+        - `stride` (or `stride_h` and `stride_w`) [default 1]: specifies the intervals at which to apply the filters to the input
+        - `group` (g) [default 1]: If g > 1, we restrict the connectivity of each filter to a subset of the input. Specifically, the input and output channels are separated into g groups, and the $$i$$th output group channels will be only connected to the $$i$$th input group channels.
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/ConvolutionParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/crop.md b/docs/tutorial/layers/crop.md
new file mode 100644
index 00000000..28f91241
--- /dev/null
+++ b/docs/tutorial/layers/crop.md
@@ -0,0 +1,20 @@
+---
+title: Crop Layer
+---
+
+# Crop Layer
+
+* Layer type: `Crop`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1CropLayer.html)
+* Header: [`./include/caffe/layers/crop_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/crop_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/crop_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/crop_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/crop_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/crop_layer.cu)
+
+## Parameters
+
+* Parameters (`CropParameter crop_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/CropParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/data.md b/docs/tutorial/layers/data.md
new file mode 100644
index 00000000..58e0dcaa
--- /dev/null
+++ b/docs/tutorial/layers/data.md
@@ -0,0 +1,29 @@
+---
+title: Database Layer
+---
+
+# Database Layer
+
+* Layer type: `Data`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1DataLayer.html)
+* Header: [`./include/caffe/layers/data_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/data_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/data_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/data_layer.cpp)
+
+
+## Parameters
+
+* Parameters (`DataParameter data_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/DataParameter.txt %}
+{% endhighlight %}
+
+* Parameters
+    - Required
+        - `source`: the name of the directory containing the database
+        - `batch_size`: the number of inputs to process at one time
+    - Optional
+        - `rand_skip`: skip up to this number of inputs at the beginning; useful for asynchronous sgd
+        - `backend` [default `LEVELDB`]: choose whether to use a `LEVELDB` or `LMDB`
+
diff --git a/docs/tutorial/layers/deconvolution.md b/docs/tutorial/layers/deconvolution.md
new file mode 100644
index 00000000..2eff967d
--- /dev/null
+++ b/docs/tutorial/layers/deconvolution.md
@@ -0,0 +1,22 @@
+---
+title: Deconvolution Layer
+---
+
+# Deconvolution Layer
+
+* Layer type: `Deconvolution`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1DeconvolutionLayer.html)
+* Header: [`./include/caffe/layers/deconv_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/deconv_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/deconv_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/deconv_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/deconv_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/deconv_layer.cu)
+
+## Parameters
+
+Uses the same parameters as the Convolution layer.
+
+* Parameters (`ConvolutionParameter convolution_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/ConvolutionParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/dropout.md b/docs/tutorial/layers/dropout.md
new file mode 100644
index 00000000..d8c6f955
--- /dev/null
+++ b/docs/tutorial/layers/dropout.md
@@ -0,0 +1,20 @@
+---
+title: Dropout Layer
+---
+
+# Dropout Layer
+
+* Layer type: `Dropout`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1DropoutLayer.html)
+* Header: [`./include/caffe/layers/dropout_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/dropout_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/dropout_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/dropout_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/dropout_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/dropout_layer.cu)
+
+## Parameters
+
+* Parameters (`DropoutParameter dropout_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/DropoutParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/dummydata.md b/docs/tutorial/layers/dummydata.md
new file mode 100644
index 00000000..d069f9c5
--- /dev/null
+++ b/docs/tutorial/layers/dummydata.md
@@ -0,0 +1,20 @@
+---
+title: Dummy Data Layer
+---
+
+# Dummy Data Layer
+
+* Layer type: `DummyData`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1DummyDataLayer.html)
+* Header: [`./include/caffe/layers/dummy_data_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/dummy_data_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/dummy_data_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/dummy_data_layer.cpp)
+
+
+## Parameters
+
+* Parameters (`DummyDataParameter dummy_data_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/DummyDataParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/eltwise.md b/docs/tutorial/layers/eltwise.md
new file mode 100644
index 00000000..70fe7910
--- /dev/null
+++ b/docs/tutorial/layers/eltwise.md
@@ -0,0 +1,20 @@
+---
+title: Eltwise Layer
+---
+
+# Eltwise Layer
+
+* Layer type: `Eltwise`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1EltwiseLayer.html)
+* Header: [`./include/caffe/layers/eltwise_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/eltwise_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/eltwise_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/eltwise_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/eltwise_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/eltwise_layer.cu)
+
+## Parameters
+
+* Parameters (`EltwiseParameter eltwise_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/EltwiseParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/elu.md b/docs/tutorial/layers/elu.md
new file mode 100644
index 00000000..11db0f0e
--- /dev/null
+++ b/docs/tutorial/layers/elu.md
@@ -0,0 +1,25 @@
+---
+title: ELU Layer
+---
+
+# ELU Layer
+
+* Layer type: `ELU`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ELULayer.html)
+* Header: [`./include/caffe/layers/elu_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/elu_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/elu_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/elu_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/elu_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/elu_layer.cu)
+
+## References
+
+* Clevert, Djork-Arne, Thomas Unterthiner, and Sepp Hochreiter.
+  "Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)" [arXiv:1511.07289](https://arxiv.org/abs/1511.07289). (2015).
+
+## Parameters
+
+* Parameters (`ELUParameter elu_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ELUParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/embed.md b/docs/tutorial/layers/embed.md
new file mode 100644
index 00000000..271636d8
--- /dev/null
+++ b/docs/tutorial/layers/embed.md
@@ -0,0 +1,20 @@
+---
+title: Embed Layer
+---
+
+# Embed Layer
+
+* Layer type: `Embed`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1EmbedLayer.html)
+* Header: [`./include/caffe/layers/embed_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/embed_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/embed_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/embed_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/embed_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/embed_layer.cu)
+
+## Parameters
+
+* Parameters (`EmbedParameter embed_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/EmbedParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/euclideanloss.md b/docs/tutorial/layers/euclideanloss.md
new file mode 100644
index 00000000..c1b72084
--- /dev/null
+++ b/docs/tutorial/layers/euclideanloss.md
@@ -0,0 +1,16 @@
+---
+title: Euclidean Loss Layer
+---
+# Sum-of-Squares / Euclidean Loss Layer
+
+* Layer type: `EuclideanLoss`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1EuclideanLossLayer.html)
+* Header: [`./include/caffe/layers/euclidean_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/euclidean_loss_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/euclidean_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/euclidean_loss_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/euclidean_loss_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/euclidean_loss_layer.cu)
+
+The Euclidean loss layer computes the sum of squares of differences of its two inputs, $$\frac 1 {2N} \sum_{i=1}^N \| x^1_i - x^2_i \|_2^2$$.
+
+## Parameters
+
+Does not take any parameters.
diff --git a/docs/tutorial/layers/exp.md b/docs/tutorial/layers/exp.md
new file mode 100644
index 00000000..ef2500ec
--- /dev/null
+++ b/docs/tutorial/layers/exp.md
@@ -0,0 +1,24 @@
+---
+title: Exponential Layer
+---
+
+# Exponential Layer
+
+* Layer type: `Exp`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ExpLayer.html)
+* Header: [`./include/caffe/layers/exp_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/exp_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/exp_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/exp_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/exp_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/exp_layer.cu)
+
+## Parameters
+
+* Parameters (`Parameter exp_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ExpParameter.txt %}
+{% endhighlight %}
+
+## See also
+
+* [Power layer](power.html)
diff --git a/docs/tutorial/layers/filter.md b/docs/tutorial/layers/filter.md
new file mode 100644
index 00000000..aeda9ee6
--- /dev/null
+++ b/docs/tutorial/layers/filter.md
@@ -0,0 +1,15 @@
+---
+title: Filter Layer
+---
+
+# Filter Layer
+
+* Layer type: `Filter`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1FilterLayer.html)
+* Header: [`./include/caffe/layers/filter_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/filter_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/filter_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/filter_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/filter_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/filter_layer.cu)
+
+## Parameters
+
+Does not take any parameters.
diff --git a/docs/tutorial/layers/flatten.md b/docs/tutorial/layers/flatten.md
new file mode 100644
index 00000000..ecf08262
--- /dev/null
+++ b/docs/tutorial/layers/flatten.md
@@ -0,0 +1,21 @@
+---
+title: Flatten Layer
+---
+
+# Flatten Layer
+
+* Layer type: `Flatten`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1FlattenLayer.html)
+* Header: [`./include/caffe/layers/flatten_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/flatten_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/flatten_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/flatten_layer.cpp)
+
+The `Flatten` layer is a utility layer that flattens an input of shape `n * c * h * w` to a simple vector output of shape `n * (c*h*w)`.
+
+## Parameters
+
+* Parameters (`FlattenParameter flatten_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/FlattenParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/hdf5data.md b/docs/tutorial/layers/hdf5data.md
new file mode 100644
index 00000000..d6b7ea24
--- /dev/null
+++ b/docs/tutorial/layers/hdf5data.md
@@ -0,0 +1,20 @@
+---
+title: HDF5 Data Layer
+---
+
+# HDF5 Data Layer
+
+* Layer type: `HDF5Data`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1HDF5DataLayer.html)
+* Header: [`./include/caffe/layers/hdf5_data_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/hdf5_data_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/hdf5_data_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/hdf5_data_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/hdf5_data_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/hdf5_data_layer.cu)
+
+## Parameters
+
+* Parameters (`HDF5DataParameter hdf5_data_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/HDF5DataParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/hdf5output.md b/docs/tutorial/layers/hdf5output.md
new file mode 100644
index 00000000..cfbe4ddb
--- /dev/null
+++ b/docs/tutorial/layers/hdf5output.md
@@ -0,0 +1,25 @@
+---
+title: HDF5 Output Layer
+---
+
+# HDF5 Output Layer
+
+* Layer type: `HDF5Output`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1HDF5OutputLayer.html)
+* Header: [`./include/caffe/layers/hdf5_output_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/hdf5_output_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/hdf5_output_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/hdf5_output_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/hdf5_output_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/hdf5_output_layer.cu)
+
+The HDF5 output layer performs the opposite function of the other layers in this section: it writes its input blobs to disk.
+
+## Parameters
+
+* Parameters (`HDF5OutputParameter hdf5_output_param`)
+    - Required
+        - `file_name`: name of file to write to
+
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/HDF5OutputParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/hingeloss.md b/docs/tutorial/layers/hingeloss.md
new file mode 100644
index 00000000..ef4fd95e
--- /dev/null
+++ b/docs/tutorial/layers/hingeloss.md
@@ -0,0 +1,19 @@
+---
+title: Hinge Loss Layer
+---
+
+# Hinge (L1, L2) Loss Layer
+
+* Layer type: `HingeLoss`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1HingeLossLayer.html)
+* Header: [`./include/caffe/layers/hinge_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/hinge_loss_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/hinge_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/hinge_loss_layer.cpp)
+
+## Parameters
+
+* Parameters (`HingeLossParameter hinge_loss_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/HingeLossParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/im2col.md b/docs/tutorial/layers/im2col.md
new file mode 100644
index 00000000..0badc1cd
--- /dev/null
+++ b/docs/tutorial/layers/im2col.md
@@ -0,0 +1,16 @@
+---
+title: Im2col Layer
+---
+
+# im2col
+
+* File type: `Im2col`
+* Header: [`./include/caffe/layers/im2col_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/im2col_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/im2col_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/im2col_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/im2col_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/im2col_layer.cu)
+
+`Im2col` is a helper for doing the image-to-column transformation that you most
+likely do not need to know about. This is used in Caffe's original convolution
+to do matrix multiplication by laying out all patches into a matrix.
+
+
diff --git a/docs/tutorial/layers/imagedata.md b/docs/tutorial/layers/imagedata.md
new file mode 100644
index 00000000..82c8a600
--- /dev/null
+++ b/docs/tutorial/layers/imagedata.md
@@ -0,0 +1,27 @@
+---
+title: ImageData Layer
+---
+
+# ImageData Layer
+
+* Layer type: `ImageData`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ImageDataLayer.html)
+* Header: [`./include/caffe/layers/image_data_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/image_data_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/image_data_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/image_data_layer.cpp)
+
+## Parameters
+
+* Parameters (`ImageDataParameter image_data_parameter`)
+    - Required
+        - `source`: name of a text file, with each line giving an image filename and label
+        - `batch_size`: number of images to batch together
+    - Optional
+        - `rand_skip`
+        - `shuffle` [default false]
+        - `new_height`, `new_width`: if provided, resize all images to this size
+
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ImageDataParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/infogainloss.md b/docs/tutorial/layers/infogainloss.md
new file mode 100644
index 00000000..b3b690d2
--- /dev/null
+++ b/docs/tutorial/layers/infogainloss.md
@@ -0,0 +1,23 @@
+---
+title: Infogain Loss Layer
+---
+
+# Infogain Loss Layer
+
+* Layer type: `InfogainLoss`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1InfogainLossLayer.html)
+* Header: [`./include/caffe/layers/infogain_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/infogain_loss_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/infogain_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/infogain_loss_layer.cpp)
+
+A generalization of [MultinomialLogisticLossLayer](multinomiallogisticloss.html) that takes an "information gain" (infogain) matrix specifying the "value" of all label pairs.
+
+Equivalent to the [MultinomialLogisticLossLayer](multinomiallogisticloss.html) if the infogain matrix is the identity.
+
+## Parameters
+
+* Parameters (`Parameter infogain_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/InfogainLossParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/innerproduct.md b/docs/tutorial/layers/innerproduct.md
new file mode 100644
index 00000000..98b9bea8
--- /dev/null
+++ b/docs/tutorial/layers/innerproduct.md
@@ -0,0 +1,59 @@
+---
+title: Inner Product / Fully Connected Layer
+---
+
+# Inner Product / Fully Connected Layer
+
+* Layer type: `InnerProduct`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1InnerProductLayer.html)
+* Header: [`./include/caffe/layers/inner_product_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/inner_product_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/inner_product_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/inner_product_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/inner_product_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/inner_product_layer.cu)
+
+* Input
+    - `n * c_i * h_i * w_i`
+* Output
+    - `n * c_o * 1 * 1`
+* Sample
+
+      layer {
+        name: "fc8"
+        type: "InnerProduct"
+        # learning rate and decay multipliers for the weights
+        param { lr_mult: 1 decay_mult: 1 }
+        # learning rate and decay multipliers for the biases
+        param { lr_mult: 2 decay_mult: 0 }
+        inner_product_param {
+          num_output: 1000
+          weight_filler {
+            type: "gaussian"
+            std: 0.01
+          }
+          bias_filler {
+            type: "constant"
+            value: 0
+          }
+        }
+        bottom: "fc7"
+        top: "fc8"
+      }
+
+The `InnerProduct` layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob's height and width set to 1).
+
+
+## Parameters
+
+* Parameters (`InnerProductParameter inner_product_param`)
+    - Required
+        - `num_output` (`c_o`): the number of filters
+    - Strongly recommended
+        - `weight_filler` [default `type: 'constant' value: 0`]
+    - Optional
+        - `bias_filler` [default `type: 'constant' value: 0`]
+        - `bias_term` [default `true`]: specifies whether to learn and apply a set of additive biases to the filter outputs
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/InnerProductParameter.txt %}
+{% endhighlight %}
+ 
diff --git a/docs/tutorial/layers/input.md b/docs/tutorial/layers/input.md
new file mode 100644
index 00000000..b74c35d2
--- /dev/null
+++ b/docs/tutorial/layers/input.md
@@ -0,0 +1,19 @@
+---
+title: Input Layer
+---
+
+# Input Layer
+
+* Layer type: `Input`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1InputLayer.html)
+* Header: [`./include/caffe/layers/input_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/input_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/input_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/input_layer.cpp)
+
+## Parameters
+
+* Parameters (`InputParameter input_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/InputParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/log.md b/docs/tutorial/layers/log.md
new file mode 100644
index 00000000..df520374
--- /dev/null
+++ b/docs/tutorial/layers/log.md
@@ -0,0 +1,20 @@
+---
+title: Log Layer
+---
+
+# Log Layer
+
+* Layer type: `Log`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1LogLayer.html)
+* Header: [`./include/caffe/layers/log_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/log_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/log_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/log_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/log_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/log_layer.cu)
+
+## Parameters
+
+* Parameters (`Parameter log_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/LogParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/lrn.md b/docs/tutorial/layers/lrn.md
new file mode 100644
index 00000000..f5e48292
--- /dev/null
+++ b/docs/tutorial/layers/lrn.md
@@ -0,0 +1,28 @@
+---
+title: Local Response Normalization (LRN)
+---
+
+# Local Response Normalization (LRN)
+
+* Layer type: `LRN`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1LRNLayer.html)
+* Header: [`./include/caffe/layers/lrn_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/lrn_layer.hpp)
+* CPU Implementation: [`./src/caffe/layers/lrn_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/lrn_layer.cpp)
+* CUDA GPU Implementation: [`./src/caffe/layers/lrn_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/lrn_layer.cu)
+* Parameters (`LRNParameter lrn_param`)
+    - Optional
+        - `local_size` [default 5]: the number of channels to sum over (for cross channel LRN) or the side length of the square region to sum over (for within channel LRN)
+        - `alpha` [default 1]: the scaling parameter (see below)
+        - `beta` [default 5]: the exponent (see below)
+        - `norm_region` [default `ACROSS_CHANNELS`]: whether to sum over adjacent channels (`ACROSS_CHANNELS`) or nearby spatial locations (`WITHIN_CHANNEL`)
+
+The local response normalization layer performs a kind of "lateral inhibition" by normalizing over local input regions. In `ACROSS_CHANNELS` mode, the local regions extend across nearby channels, but have no spatial extent (i.e., they have shape `local_size x 1 x 1`). In `WITHIN_CHANNEL` mode, the local regions extend spatially, but are in separate channels (i.e., they have shape `1 x local_size x local_size`). Each input value is divided by $$(1 + (\alpha/n) \sum_i x_i^2)^\beta$$, where $$n$$ is the size of each local region, and the sum is taken over the region centered at that value (zero padding is added where necessary).
+
+## Parameters
+
+* Parameters (`LRNParameter lrn_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/LRNParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/lstm.md b/docs/tutorial/layers/lstm.md
new file mode 100644
index 00000000..8e4095e9
--- /dev/null
+++ b/docs/tutorial/layers/lstm.md
@@ -0,0 +1,21 @@
+---
+title: LSTM Layer
+---
+
+# LSTM Layer
+
+* Layer type: `LSTM`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1LSTMLayer.html)
+* Header: [`./include/caffe/layers/lstm_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/lstm_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/lstm_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/lstm_layer.cpp)
+* CPU implementation (helper): [`./src/caffe/layers/lstm_unit_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/lstm_unit_layer.cpp)
+* CUDA GPU implementation (helper): [`./src/caffe/layers/lstm_unit_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/lstm_unit_layer.cu)
+
+## Parameters
+
+* Parameters (`Parameter recurrent_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/RecurrentParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/memorydata.md b/docs/tutorial/layers/memorydata.md
new file mode 100644
index 00000000..afce4a24
--- /dev/null
+++ b/docs/tutorial/layers/memorydata.md
@@ -0,0 +1,25 @@
+---
+title: Memory Data Layer
+---
+
+# Memory Data Layer
+
+* Layer type: `MemoryData`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1MemoryDataLayer.html)
+* Header: [`./include/caffe/layers/memory_data_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/memory_data_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/memory_data_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/memory_data_layer.cpp)
+
+The memory data layer reads data directly from memory, without copying it. In order to use it, one must call `MemoryDataLayer::Reset` (from C++) or `Net.set_input_arrays` (from Python) in order to specify a source of contiguous data (as 4D row major array), which is read one batch-sized chunk at a time.
+
+# Parameters
+
+* Parameters (`MemoryDataParameter memory_data_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/MemoryDataParameter.txt %}
+{% endhighlight %}
+
+* Parameters
+    - Required
+        - `batch_size`, `channels`, `height`, `width`: specify the size of input chunks to read from memory
diff --git a/docs/tutorial/layers/multinomiallogisticloss.md b/docs/tutorial/layers/multinomiallogisticloss.md
new file mode 100644
index 00000000..5eab74a8
--- /dev/null
+++ b/docs/tutorial/layers/multinomiallogisticloss.md
@@ -0,0 +1,19 @@
+---
+title: Multinomial Logistic Loss Layer
+---
+
+# Multinomial Logistic Loss Layer
+
+* Layer type: `MultinomialLogisticLoss`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1MultinomialLogisticLossLayer.html)
+* Header: [`./include/caffe/layers/multinomial_logistic_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/multinomial_logistic_loss_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/multinomial_logistic_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/multinomial_logistic_loss_layer.cpp)
+
+## Parameters
+
+* Parameters (`LossParameter loss_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/LossParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/mvn.md b/docs/tutorial/layers/mvn.md
new file mode 100644
index 00000000..08e44887
--- /dev/null
+++ b/docs/tutorial/layers/mvn.md
@@ -0,0 +1,20 @@
+---
+title: Mean-Variance Normalization (MVN) Layer
+---
+
+# Mean-Variance Normalization (MVN) Layer
+
+* Layer type: `MVN`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1MVNLayer.html)
+* Header: [`./include/caffe/layers/mvn_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/mvn_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/mvn_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/mvn_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/mvn_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/mvn_layer.cu)
+
+## Parameters
+
+* Parameters (`MVNParameter mvn_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/MVNParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/parameter.md b/docs/tutorial/layers/parameter.md
new file mode 100644
index 00000000..b7e85ec5
--- /dev/null
+++ b/docs/tutorial/layers/parameter.md
@@ -0,0 +1,21 @@
+---
+title: Parameter Layer
+---
+
+# Parameter Layer
+
+* Layer type: `Parameter`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ParameterLayer.html)
+* Header: [`./include/caffe/layers/parameter_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/parameter_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/parameter_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/parameter_layer.cpp)
+
+See [https://github.com/BVLC/caffe/pull/2079](https://github.com/BVLC/caffe/pull/2079).
+
+## Parameters
+
+* Parameters (`ParameterParameter parameter_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ParameterParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/pooling.md b/docs/tutorial/layers/pooling.md
new file mode 100644
index 00000000..12669ee8
--- /dev/null
+++ b/docs/tutorial/layers/pooling.md
@@ -0,0 +1,47 @@
+---
+title: Pooling Layer
+---
+# Pooling
+
+* Layer type: `Pooling`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1PoolingLayer.html)
+* Header: [`./include/caffe/layers/pooling_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/pooling_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/pooling_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/pooling_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/pooling_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/pooling_layer.cu)
+
+* Input
+    - `n * c * h_i * w_i`
+* Output
+    - `n * c * h_o * w_o`, where h_o and w_o are computed in the same way as convolution.
+
+## Parameters
+
+* Parameters (`PoolingParameter pooling_param`)
+    - Required
+        - `kernel_size` (or `kernel_h` and `kernel_w`): specifies height and width of each filter
+    - Optional
+        - `pool` [default MAX]: the pooling method. Currently MAX, AVE, or STOCHASTIC
+        - `pad` (or `pad_h` and `pad_w`) [default 0]: specifies the number of pixels to (implicitly) add to each side of the input
+        - `stride` (or `stride_h` and `stride_w`) [default 1]: specifies the intervals at which to apply the filters to the input
+
+
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/PoolingParameter.txt %}
+{% endhighlight %}
+
+## Sample
+* Sample (as seen in [`./models/bvlc_reference_caffenet/train_val.prototxt`](https://github.com/BVLC/caffe/blob/master/models/bvlc_reference_caffenet/train_val.prototxt))
+
+      layer {
+        name: "pool1"
+        type: "Pooling"
+        bottom: "conv1"
+        top: "pool1"
+        pooling_param {
+          pool: MAX
+          kernel_size: 3 # pool over a 3x3 region
+          stride: 2      # step two pixels (in the bottom blob) between pooling regions
+        }
+      }
diff --git a/docs/tutorial/layers/power.md b/docs/tutorial/layers/power.md
new file mode 100644
index 00000000..d6617529
--- /dev/null
+++ b/docs/tutorial/layers/power.md
@@ -0,0 +1,46 @@
+---
+title: Power Layer
+---
+
+# Power Layer
+
+* Layer type: `Power`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1PowerLayer.html)
+* Header: [`./include/caffe/layers/power_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/power_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/power_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/power_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/power_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/power_layer.cu)
+
+The `Power` layer computes the output as (shift + scale * x) ^ power for each input element x.
+
+## Parameters
+* Parameters (`PowerParameter power_param`)
+    - Optional
+        - `power` [default 1]
+        - `scale` [default 1]
+        - `shift` [default 0]
+
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/PowerParameter.txt %}
+{% endhighlight %}
+ 
+ 
+ 
+## Sample
+
+      layer {
+        name: "layer"
+        bottom: "in"
+        top: "out"
+        type: "Power"
+        power_param {
+          power: 1
+          scale: 1
+          shift: 0
+        }
+      }
+
+## See also
+
+* [Exponential layer](exp.html)
diff --git a/docs/tutorial/layers/prelu.md b/docs/tutorial/layers/prelu.md
new file mode 100644
index 00000000..e7b7b44a
--- /dev/null
+++ b/docs/tutorial/layers/prelu.md
@@ -0,0 +1,20 @@
+---
+title: PReLU Layer
+---
+
+# PReLU Layer
+
+* Layer type: `PReLU`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1PReLULayer.html)
+* Header: [`./include/caffe/layers/prelu_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/prelu_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/prelu_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/prelu_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/prelu_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/prelu_layer.cu)
+
+## Parameters
+
+* Parameters (`PReLUParameter prelu_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/PReLUParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/python.md b/docs/tutorial/layers/python.md
new file mode 100644
index 00000000..2e30b3a7
--- /dev/null
+++ b/docs/tutorial/layers/python.md
@@ -0,0 +1,27 @@
+---
+title: Python Layer
+---
+
+# Python Layer
+
+* Layer type: `Python`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1PythonLayer.html)
+* Header: [`./include/caffe/layers/python_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/python_layer.hpp)
+
+The Python layer allows users to add customized layers without modifying the Caffe core code.
+
+## Parameters
+
+* Parameters (`PythonParameter python_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/PythonParameter.txt %}
+{% endhighlight %}
+
+## Examples and tutorials
+
+* Simple Euclidean loss example
+** [Python code](https://github.com/BVLC/caffe/blob/master/examples/pycaffe/layers/pyloss.py)
+** [Prototxt](https://github.com/BVLC/caffe/blob/master/examples/pycaffe/linreg.prototxt)
+* [Tutorial for writing Python layers with DIGITS](https://github.com/NVIDIA/DIGITS/tree/master/examples/python-layer)
diff --git a/docs/tutorial/layers/recurrent.md b/docs/tutorial/layers/recurrent.md
new file mode 100644
index 00000000..a882b722
--- /dev/null
+++ b/docs/tutorial/layers/recurrent.md
@@ -0,0 +1,20 @@
+---
+title: Recurrent Layer
+---
+
+# Recurrent Layer
+
+* Layer type: `Recurrent`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1RecurrentLayer.html)
+* Header: [`./include/caffe/layers/recurrent_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/recurrent_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/recurrent_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/recurrent_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/recurrent_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/recurrent_layer.cu)
+
+## Parameters
+
+* Parameters (`RecurrentParameter recurrent_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/RecurrentParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/reduction.md b/docs/tutorial/layers/reduction.md
new file mode 100644
index 00000000..db55414b
--- /dev/null
+++ b/docs/tutorial/layers/reduction.md
@@ -0,0 +1,20 @@
+---
+title: Reduction Layer
+---
+
+# Reduction Layer
+
+* Layer type: `Reduction`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ReductionLayer.html)
+* Header: [`./include/caffe/layers/reduction_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/reduction_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/reduction_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/reduction_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/reduction_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/reduction_layer.cu)
+
+## Parameters
+
+* Parameters (`ReductionParameter reduction_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ReductionParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/relu.md b/docs/tutorial/layers/relu.md
new file mode 100644
index 00000000..01aab0af
--- /dev/null
+++ b/docs/tutorial/layers/relu.md
@@ -0,0 +1,32 @@
+---
+title: ReLU / Rectified-Linear and Leaky-ReLU Layer
+---
+
+# ReLU / Rectified-Linear and Leaky-ReLU Layer
+
+* Layer type: `ReLU`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ReLULayer.html)
+* Header: [`./include/caffe/layers/relu_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/relu_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/relu_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/relu_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/relu_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/relu_layer.cu)
+* Sample (as seen in [`./models/bvlc_reference_caffenet/train_val.prototxt`](https://github.com/BVLC/caffe/blob/master/models/bvlc_reference_caffenet/train_val.prototxt))
+
+      layer {
+        name: "relu1"
+        type: "ReLU"
+        bottom: "conv1"
+        top: "conv1"
+      }
+
+Given an input value x, The `ReLU` layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.
+
+## Parameters
+
+* Parameters (`ReLUParameter relu_param`)
+    - Optional
+        - `negative_slope` [default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ReLUParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/reshape.md b/docs/tutorial/layers/reshape.md
new file mode 100644
index 00000000..92d23f2c
--- /dev/null
+++ b/docs/tutorial/layers/reshape.md
@@ -0,0 +1,51 @@
+---
+title: Reshape Layer
+---
+
+# Reshape Layer
+* Layer type: `Reshape`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ReshapeLayer.html)
+* Header: [`./include/caffe/layers/reshape_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/reshape_layer.hpp)
+* Implementation: [`./src/caffe/layers/reshape_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/reshape_layer.cpp)
+
+* Input
+    - a single blob with arbitrary dimensions
+* Output
+    - the same blob, with modified dimensions, as specified by `reshape_param`
+
+* Sample
+
+        layer {
+          name: "reshape"
+          type: "Reshape"
+          bottom: "input"
+          top: "output"
+          reshape_param {
+            shape {
+              dim: 0  # copy the dimension from below
+              dim: 2
+              dim: 3
+              dim: -1 # infer it from the other dimensions
+            }
+          }
+        }
+
+The `Reshape` layer can be used to change the dimensions of its input, without changing its data. Just like the `Flatten` layer, only the dimensions are changed; no data is copied in the process.
+
+Output dimensions are specified by the `ReshapeParam` proto. Positive numbers are used directly, setting the corresponding dimension of the output blob. In addition, two special values are accepted for any of the target dimension values:
+
+* **0** means "copy the respective dimension of the bottom layer". That is, if the bottom has 2 as its 1st dimension, the top will have 2 as its 1st dimension as well, given `dim: 0` as the 1st target dimension.
+* **-1** stands for "infer this from the other dimensions". This behavior is similar to that of -1 in *numpy*'s or `[]` for *MATLAB*'s reshape: this dimension is calculated to keep the overall element count the same as in the bottom layer. At most one -1 can be used in a reshape operation.
+
+As another example, specifying `reshape_param { shape { dim: 0 dim: -1 } }` makes the layer behave in exactly the same way as the `Flatten` layer.
+ 
+## Parameters
+
+* Parameters (`ReshapeParameter reshape_param`)
+    - Optional: (also see detailed description below)
+        - `shape`
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ReshapeParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/rnn.md b/docs/tutorial/layers/rnn.md
new file mode 100644
index 00000000..b6fcf471
--- /dev/null
+++ b/docs/tutorial/layers/rnn.md
@@ -0,0 +1,19 @@
+---
+title: RNN Layer
+---
+
+# RNN Layer
+
+* Layer type: `RNN`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1RNNLayer.html)
+* Header: [`./include/caffe/layers/rnn_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/rnn_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/rnn_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/rnn_layer.cpp)
+
+## Parameters
+
+* Parameters (`RecurrentParameter recurrent_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/RecurrentParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/scale.md b/docs/tutorial/layers/scale.md
new file mode 100644
index 00000000..0e27549a
--- /dev/null
+++ b/docs/tutorial/layers/scale.md
@@ -0,0 +1,20 @@
+---
+title: Scale Layer
+---
+
+# Scale Layer
+
+* Layer type: `Scale`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ScaleLayer.html)
+* Header: [`./include/caffe/layers/scale_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/scale_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/scale_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/scale_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/scale_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/scale_layer.cu)
+
+## Parameters
+
+* Parameters (`ScaleParameter scale_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ScaleParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/sigmoid.md b/docs/tutorial/layers/sigmoid.md
new file mode 100644
index 00000000..f18ac4b8
--- /dev/null
+++ b/docs/tutorial/layers/sigmoid.md
@@ -0,0 +1,30 @@
+---
+title: Sigmoid Layer
+---
+
+# Sigmoid Layer
+
+* Layer type: `Sigmoid`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SigmoidLayer.html)
+* Header: [`./include/caffe/layers/sigmoid_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/sigmoid_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/sigmoid_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/sigmoid_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/sigmoid_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/sigmoid_layer.cu)
+* Example (from [`./examples/mnist/mnist_autoencoder.prototxt`](https://github.com/BVLC/caffe/blob/master/examples/mnist/mnist_autoencoder.prototxt)):
+
+      layer {
+        name: "encode1neuron"
+        bottom: "encode1"
+        top: "encode1neuron"
+        type: "Sigmoid"
+      }
+
+The `Sigmoid` layer computes `sigmoid(x)` for each element `x` in the bottom blob.
+
+## Parameters
+
+* Parameters (`SigmoidParameter sigmoid_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/SigmoidParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/sigmoidcrossentropyloss.md b/docs/tutorial/layers/sigmoidcrossentropyloss.md
new file mode 100644
index 00000000..a6e42cad
--- /dev/null
+++ b/docs/tutorial/layers/sigmoidcrossentropyloss.md
@@ -0,0 +1,13 @@
+---
+title: Sigmoid Cross-Entropy Loss Layer
+---
+
+# Sigmoid Cross-Entropy Loss Layer
+
+* Layer type: `SigmoidCrossEntropyLoss`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SigmoidCrossEntropyLossLayer.html)
+* Header: [`./include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu)
+
+To-do.
diff --git a/docs/tutorial/layers/silence.md b/docs/tutorial/layers/silence.md
new file mode 100644
index 00000000..8b4579a9
--- /dev/null
+++ b/docs/tutorial/layers/silence.md
@@ -0,0 +1,17 @@
+---
+title: Silence Layer
+---
+
+# Silence Layer
+
+* Layer type: `Silence`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SilenceLayer.html)
+* Header: [`./include/caffe/layers/silence_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/silence_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/silence_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/silence_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/silence_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/silence_layer.cu)
+
+Silences a blob, so that it is not printed.
+
+## Parameters
+
+No parameters.
diff --git a/docs/tutorial/layers/slice.md b/docs/tutorial/layers/slice.md
new file mode 100644
index 00000000..a492f1e8
--- /dev/null
+++ b/docs/tutorial/layers/slice.md
@@ -0,0 +1,42 @@
+---
+title: Slice Layer
+---
+
+# Slice Layer
+
+* Layer type: `Slice`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SliceLayer.html)
+* Header: [`./include/caffe/layers/slice_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/slice_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/slice_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/slice_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/slice_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/slice_layer.cu)
+
+The `Slice` layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.
+
+* Sample
+
+      layer {
+        name: "slicer_label"
+        type: "Slice"
+        bottom: "label"
+        ## Example of label with a shape N x 3 x 1 x 1
+        top: "label1"
+        top: "label2"
+        top: "label3"
+        slice_param {
+          axis: 1
+          slice_point: 1
+          slice_point: 2
+        }
+      }
+
+`axis` indicates the target axis; `slice_point` indicates indexes in the selected dimension (the number of indices must be equal to the number of top blobs minus one).
+
+## Parameters
+
+* Parameters (`SliceParameter slice_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/SliceParameter.txt %}
+{% endhighlight %}
+
diff --git a/docs/tutorial/layers/softmax.md b/docs/tutorial/layers/softmax.md
new file mode 100644
index 00000000..e5d53425
--- /dev/null
+++ b/docs/tutorial/layers/softmax.md
@@ -0,0 +1,24 @@
+---
+title: Softmax Layer
+---
+
+# Softmax Layer
+
+* Layer type: `Softmax`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SoftmaxLayer.html)
+* Header: [`./include/caffe/layers/softmax_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/softmax_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/softmax_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/softmax_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/softmax_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/softmax_layer.cu)
+
+## Parameters
+
+* Parameters (`SoftmaxParameter softmax_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/SoftmaxParameter.txt %}
+{% endhighlight %}
+
+## See also
+
+* [Softmax loss layer](softmaxwithloss.html)
diff --git a/docs/tutorial/layers/softmaxwithloss.md b/docs/tutorial/layers/softmaxwithloss.md
new file mode 100644
index 00000000..d9a6774a
--- /dev/null
+++ b/docs/tutorial/layers/softmaxwithloss.md
@@ -0,0 +1,33 @@
+---
+title: Softmax with Loss Layer
+---
+
+# Softmax with Loss Layer
+
+* Layer type: `SoftmaxWithLoss`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SoftmaxWithLossLayer.html)
+* Header: [`./include/caffe/layers/softmax_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/softmax_loss_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/softmax_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/softmax_loss_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/softmax_loss_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/softmax_loss_layer.cu)
+
+The softmax loss layer computes the multinomial logistic loss of the softmax of its inputs. It's conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.
+
+## Parameters
+
+* Parameters (`SoftmaxParameter softmax_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/SoftmaxParameter.txt %}
+{% endhighlight %}
+
+* Parameters (`LossParameter loss_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/LossParameter.txt %}
+{% endhighlight %}
+
+## See also
+
+* [Softmax layer](softmax.html)
diff --git a/docs/tutorial/layers/split.md b/docs/tutorial/layers/split.md
new file mode 100644
index 00000000..4fb71d1f
--- /dev/null
+++ b/docs/tutorial/layers/split.md
@@ -0,0 +1,17 @@
+---
+title: Split Layer
+---
+
+# Split Layer
+
+* Layer type: `Split`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SplitLayer.html)
+* Header: [`./include/caffe/layers/split_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/split_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/split_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/split_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/split_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/split_layer.cu)
+
+The `Split` layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers.
+
+## Parameters
+
+Does not take any parameters.
diff --git a/docs/tutorial/layers/spp.md b/docs/tutorial/layers/spp.md
new file mode 100644
index 00000000..26e58620
--- /dev/null
+++ b/docs/tutorial/layers/spp.md
@@ -0,0 +1,20 @@
+---
+title: Spatial Pyramid Pooling Layer
+---
+
+# Spatial Pyramid Pooling Layer
+
+* Layer type: `SPP`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SPPLayer.html)
+* Header: [`./include/caffe/layers/spp_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/spp_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/spp_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/spp_layer.cpp)
+
+
+## Parameters
+
+* Parameters (`SPPParameter spp_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/SPPParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/tanh.md b/docs/tutorial/layers/tanh.md
new file mode 100644
index 00000000..36063459
--- /dev/null
+++ b/docs/tutorial/layers/tanh.md
@@ -0,0 +1,18 @@
+---
+title: TanH Layer
+---
+
+# TanH Layer
+
+* Header: [`./include/caffe/layers/tanh_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/tanh_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/tanh_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/tanh_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/tanh_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/tanh_layer.cu)
+
+## Parameters
+
+* Parameters (`TanHParameter tanh_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/TanHParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/threshold.md b/docs/tutorial/layers/threshold.md
new file mode 100644
index 00000000..819e9e6f
--- /dev/null
+++ b/docs/tutorial/layers/threshold.md
@@ -0,0 +1,18 @@
+---
+title: Threshold Layer
+---
+
+# Threshold Layer
+
+* Header: [`./include/caffe/layers/threshold_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/threshold_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/threshold_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/threshold_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/threshold_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/threshold_layer.cu)
+
+## Parameters
+
+* Parameters (`ThresholdParameter threshold_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ThresholdParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/tile.md b/docs/tutorial/layers/tile.md
new file mode 100644
index 00000000..ea03aaa4
--- /dev/null
+++ b/docs/tutorial/layers/tile.md
@@ -0,0 +1,20 @@
+---
+title: Tile Layer
+---
+
+# Tile Layer
+
+* Layer type: `Tile`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1TileLayer.html)
+* Header: [`./include/caffe/layers/tile_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/tile_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/tile_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/tile_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/tile_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/tile_layer.cu)
+
+## Parameters
+
+* Parameters (`TileParameter tile_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/TileParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/windowdata.md b/docs/tutorial/layers/windowdata.md
new file mode 100644
index 00000000..0cb4a8df
--- /dev/null
+++ b/docs/tutorial/layers/windowdata.md
@@ -0,0 +1,19 @@
+---
+title: WindowData Layer
+---
+
+# WindowData Layer
+
+* Layer type: `WindowData`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1WindowDataLayer.html)
+* Header: [`./include/caffe/layers/window_data_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/window_data_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/window_data_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/window_data_layer.cpp)
+
+## Parameters
+
+* Parameters (`WindowDataParameter`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/WindowDataParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/solver.md b/docs/tutorial/solver.md
index b719f715..81c62638 100644
--- a/docs/tutorial/solver.md
+++ b/docs/tutorial/solver.md
@@ -209,18 +209,11 @@ What distinguishes the method from SGD is the weight setting $$ W $$ on which we
 The **RMSprop** (`type: "RMSProp"`), suggested by Tieleman in a Coursera course lecture, is a gradient-based optimization method (like SGD). The update formulas are
 
 $$
-(v_t)_i =
-\begin{cases}
-(v_{t-1})_i + \delta, &(\nabla L(W_t))_i(\nabla L(W_{t-1}))_i > 0\\
-(v_{t-1})_i \cdot (1-\delta), & \text{else}
-\end{cases}
+\operatorname{MS}((W_t)_i)= \delta\operatorname{MS}((W_{t-1})_i)+ (1-\delta)(\nabla L(W_t))_i^2 \\
+(W_{t+1})_i= (W_{t})_i -\alpha\frac{(\nabla L(W_t))_i}{\sqrt{\operatorname{MS}((W_t)_i)}}
 $$
 
-$$
-(W_{t+1})_i =(W_t)_i - \alpha (v_t)_i,
-$$
-
-If the gradient updates results in oscillations the gradient is reduced by times $$1-\delta$$. Otherwise it will be increased by $$\delta$$. The default value of $$\delta$$ (`rms_decay`) is set to $$\delta = 0.02$$.
+The default value of $$\delta$$ (`rms_decay`) is set to $$\delta=0.99$$.
 
 [1] T. Tieleman, and G. Hinton.
     [RMSProp: Divide the gradient by a running average of its recent magnitude](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf).
diff --git a/examples/00-classification.ipynb b/examples/00-classification.ipynb
index 89b7dd34..1950f08f 100644
--- a/examples/00-classification.ipynb
+++ b/examples/00-classification.ipynb
@@ -4,54 +4,72 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Instant Recognition with Caffe\n",
+    "# Classification: Instant Recognition with Caffe\n",
     "\n",
-    "In this example we'll classify an image with the bundled CaffeNet model based on the network architecture of Krizhevsky et al. for ImageNet. We'll compare CPU and GPU operation then reach into the model to inspect features and the output.\n",
+    "In this example we'll classify an image with the bundled CaffeNet model (which is based on the network architecture of Krizhevsky et al. for ImageNet).\n",
     "\n",
-    "(These feature visualizations follow the DeCAF visualizations originally by Yangqing Jia.)"
+    "We'll compare CPU and GPU modes and then dig into the model to inspect features and the output."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First, import required modules, set plotting parameters, and run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model if it hasn't already been fetched."
+    "### 1. Setup\n",
+    "\n",
+    "* First, set up Python, `numpy`, and `matplotlib`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
+    "# set up Python environment: numpy for numerical routines, and matplotlib for plotting\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "# display plots in this notebook\n",
     "%matplotlib inline\n",
     "\n",
-    "# Make sure that caffe is on the python path:\n",
-    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+    "# set display defaults\n",
+    "plt.rcParams['figure.figsize'] = (10, 10)        # large images\n",
+    "plt.rcParams['image.interpolation'] = 'nearest'  # don't interpolate: show square pixels\n",
+    "plt.rcParams['image.cmap'] = 'gray'  # use grayscale output rather than a (potentially misleading) color heatmap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Load `caffe`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# The caffe module needs to be on the Python path;\n",
+    "#  we'll add it here explicitly.\n",
     "import sys\n",
+    "caffe_root = '../'  # this file should be run from {caffe_root}/examples (otherwise change this line)\n",
     "sys.path.insert(0, caffe_root + 'python')\n",
     "\n",
     "import caffe\n",
-    "\n",
-    "plt.rcParams['figure.figsize'] = (10, 10)\n",
-    "plt.rcParams['image.interpolation'] = 'nearest'\n",
-    "plt.rcParams['image.cmap'] = 'gray'\n",
-    "\n",
-    "import os\n",
-    "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
-    "    print(\"Downloading pre-trained CaffeNet model...\")\n",
-    "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
+    "# If you get \"No module named _caffe\", either you have not built pycaffe or you have the wrong path."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing."
+    "* If needed, download the reference model (\"CaffeNet\", a variant of AlexNet)."
    ]
   },
   {
@@ -60,50 +78,65 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CaffeNet found.\n"
+     ]
+    }
+   ],
    "source": [
-    "caffe.set_mode_cpu()\n",
-    "net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
-    "                caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
-    "                caffe.TEST)\n",
-    "\n",
-    "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
-    "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
-    "transformer.set_transpose('data', (2,0,1))\n",
-    "transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel\n",
-    "transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
-    "transformer.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB"
+    "import os\n",
+    "if os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
+    "    print 'CaffeNet found.'\n",
+    "else:\n",
+    "    print 'Downloading pre-trained CaffeNet model...'\n",
+    "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's start with a simple classification. We'll set a batch of 50 to demonstrate batch processing, even though we'll only be classifying one image. (Note that the batch size can also be changed on-the-fly.)"
+    "### 2. Load net and set up input preprocessing\n",
+    "\n",
+    "* Set Caffe to CPU mode and load the net from disk."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "# set net to batch size of 50\n",
-    "net.blobs['data'].reshape(50,3,227,227)"
+    "caffe.set_mode_cpu()\n",
+    "\n",
+    "model_def = caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt'\n",
+    "model_weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n",
+    "\n",
+    "net = caffe.Net(model_def,      # defines the structure of the model\n",
+    "                model_weights,  # contains the trained weights\n",
+    "                caffe.TEST)     # use test mode (e.g., don't perform dropout)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Feed in the image (with some preprocessing) and classify with a forward pass."
+    "* Set up input preprocessing. (We'll use Caffe's `caffe.io.Transformer` to do this, but this step is independent of other parts of Caffe, so any custom preprocessing code may be used).\n",
+    "\n",
+    "    Our default CaffeNet is configured to take images in BGR format. Values are expected to start in the range [0, 255] and then have the mean ImageNet pixel value subtracted from them. In addition, the channel dimension is expected as the first (_outermost_) dimension.\n",
+    "    \n",
+    "    As matplotlib will load images with values in the range [0, 1] in RGB format with the channel as the _innermost_ dimension, we are arranging for the needed transformations here."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -112,21 +145,54 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Predicted class is #281.\n"
+      "mean-subtracted values: [('B', 104.0069879317889), ('G', 116.66876761696767), ('R', 122.6789143406786)]\n"
      ]
     }
    ],
    "source": [
-    "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n",
-    "out = net.forward()\n",
-    "print(\"Predicted class is #{}.\".format(out['prob'][0].argmax()))"
+    "# load the mean ImageNet image (as distributed with Caffe) for subtraction\n",
+    "mu = np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')\n",
+    "mu = mu.mean(1).mean(1)  # average over pixels to obtain the mean (BGR) pixel values\n",
+    "print 'mean-subtracted values:', zip('BGR', mu)\n",
+    "\n",
+    "# create transformer for the input called 'data'\n",
+    "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
+    "\n",
+    "transformer.set_transpose('data', (2,0,1))  # move image channels to outermost dimension\n",
+    "transformer.set_mean('data', mu)            # subtract the dataset-mean value in each channel\n",
+    "transformer.set_raw_scale('data', 255)      # rescale from [0, 1] to [0, 255]\n",
+    "transformer.set_channel_swap('data', (2,1,0))  # swap channels from RGB to BGR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. CPU classification\n",
+    "\n",
+    "* Now we're ready to perform classification. Even though we'll only classify one image, we'll set a batch size of 50 to demonstrate batching."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# set the size of the input (we can skip this if we're happy\n",
+    "#  with the default; we can also change it later, e.g., for different batch sizes)\n",
+    "net.blobs['data'].reshape(50,        # batch size\n",
+    "                          3,         # 3-channel (BGR) images\n",
+    "                          227, 227)  # image size is 227x227"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "What did the input look like?"
+    "* Load an image (that comes with Caffe) and perform the preprocessing we've set up."
    ]
   },
   {
@@ -139,7 +205,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f665b02ae90>"
+       "<matplotlib.image.AxesImage at 0x7f09693a8c90>"
       ]
      },
      "execution_count": 7,
@@ -148,2843 +214,9 @@
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     {
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vAqVQbxvPBEJwHEd8Z1W22xNju7F/2Pn4crC/TAKKBefM4juKyeIIujtFkh+lMaFlclzN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w6H4LmXKSzzup33Bz5+8wlhecbNmlhypNnYsffcQww19/4aGX0xC9tmSOwIiMA6PXHcbHOgTy5Z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tOZ2GOkEfd/eMIXJ5/VRdu52rHkQSIIUR61t9XmNahA8ixFnJxmQfsrLSb3DM2bPJk4068z08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oxBRRmbs00dNMLX3vo6437HNKpj8/n9Gp9DwfQbQjhi24YwjEt763B9y7t/8SOeP7vGyj0C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h6kLU2FmzlEsB8fNYTiQhRjENYmNFVkwA1AxYSvG6UBvZmxdENBNq6mK5MI8hjHGUMmj3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\nmqAYfsg3sKhv50hcYl4QBQuCKPZGII9KmRWZQdvzQqRSHcb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P2RhGixNUQEtBnCfPplcM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6nVoYasNuXKPTAT83XmLl2g4sxfaMNRiD45o7Fyjrz942Zr4G1iVhJSqMmfOe6xnm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\nuVwu3OdlH5m9PP/E8/MLzjn+7d/+jZTrTjAOIfDy8sLnz59JqfD6+mdaH4e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caPfQS7zDQ01LG6qKyqDo+sa6G0xJdvfsKLu0wqAqIFQfAJ2qxso9ZoJj53D5hBHE4malsx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WLbkfnv6erUbMyx+5vW7kMlCo7Jd4zZUpZEaj3UxrLJPXQmPJEapFWiOnim35wB+kIogN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\n3gAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f66a46f8a10>"
+       "<matplotlib.figure.Figure at 0x7f09b97eab50>"
       ]
      },
      "metadata": {},
@@ -2992,14 +224,16 @@
     }
    ],
    "source": [
-    "plt.imshow(transformer.deprocess('data', net.blobs['data'].data[0]))"
+    "image = caffe.io.load_image(caffe_root + 'examples/images/cat.jpg')\n",
+    "transformed_image = transformer.preprocess('data', image)\n",
+    "plt.imshow(image)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Adorable, but was our classification correct?"
+    "* Adorable! Let's classify it!"
    ]
   },
   {
@@ -3013,31 +247,27 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
-      " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
-      " 'n02127052 lynx, catamount']\n"
+      "predicted class is: 281\n"
      ]
     }
    ],
    "source": [
-    "# load labels\n",
-    "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
-    "try:\n",
-    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
-    "except:\n",
-    "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
-    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+    "# copy the image data into the memory allocated for the net\n",
+    "net.blobs['data'].data[...] = transformed_image\n",
+    "\n",
+    "### perform classification\n",
+    "output = net.forward()\n",
+    "\n",
+    "output_prob = output['prob'][0]  # the output probability vector for the first image in the batch\n",
     "\n",
-    "# sort top k predictions from softmax output\n",
-    "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n",
-    "print labels[top_k]"
+    "print 'predicted class is:', output_prob.argmax()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Indeed! But how long did it take?"
+    "* The net gives us a vector of probabilities; the most probable class was the 281st one. But is that correct? Let's check the ImageNet labels..."
    ]
   },
   {
@@ -3051,26 +281,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1 loops, best of 3: 7.14 s per loop\n"
+      "output label: n02123045 tabby, tabby cat\n"
      ]
     }
    ],
    "source": [
-    "# CPU mode\n",
-    "net.forward()  # call once for allocation\n",
-    "%timeit net.forward()"
+    "# load ImageNet labels\n",
+    "labels_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
+    "if not os.path.exists(labels_file):\n",
+    "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
+    "    \n",
+    "labels = np.loadtxt(labels_file, str, delimiter='\\t')\n",
+    "\n",
+    "print 'output label:', labels[output_prob.argmax()]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "That's a while, even for a batch size of 50 images. Let's switch to GPU mode."
+    "* \"Tabby cat\" is correct! But let's also look at other top (but less confident predictions)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -3079,8706 +314,244 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10 loops, best of 3: 90.9 ms per loop\n"
+      "probabilities and labels:\n"
      ]
-    }
-   ],
-   "source": [
-    "# GPU mode\n",
-    "caffe.set_device(0)\n",
-    "caffe.set_mode_gpu()\n",
-    "net.forward()  # call once for allocation\n",
-    "%timeit net.forward()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Much better. Now let's look at the net in more detail.\n",
-    "\n",
-    "First, the layer features and their shapes (1 is the batch size, corresponding to the single input image in this example)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('data', (50, 3, 227, 227)),\n",
-       " ('conv1', (50, 96, 55, 55)),\n",
-       " ('pool1', (50, 96, 27, 27)),\n",
-       " ('norm1', (50, 96, 27, 27)),\n",
-       " ('conv2', (50, 256, 27, 27)),\n",
-       " ('pool2', (50, 256, 13, 13)),\n",
-       " ('norm2', (50, 256, 13, 13)),\n",
-       " ('conv3', (50, 384, 13, 13)),\n",
-       " ('conv4', (50, 384, 13, 13)),\n",
-       " ('conv5', (50, 256, 13, 13)),\n",
-       " ('pool5', (50, 256, 6, 6)),\n",
-       " ('fc6', (50, 4096)),\n",
-       " ('fc7', (50, 4096)),\n",
-       " ('fc8', (50, 1000)),\n",
-       " ('prob', (50, 1000))]"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[(k, v.data.shape) for k, v in net.blobs.items()]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The parameters and their shapes. The parameters are `net.params['name'][0]` while biases are `net.params['name'][1]`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
+    },
     {
      "data": {
       "text/plain": [
-       "[('conv1', (96, 3, 11, 11)),\n",
-       " ('conv2', (256, 48, 5, 5)),\n",
-       " ('conv3', (384, 256, 3, 3)),\n",
-       " ('conv4', (384, 192, 3, 3)),\n",
-       " ('conv5', (256, 192, 3, 3)),\n",
-       " ('fc6', (4096, 9216)),\n",
-       " ('fc7', (4096, 4096)),\n",
-       " ('fc8', (1000, 4096))]"
+       "[(0.31243637, 'n02123045 tabby, tabby cat'),\n",
+       " (0.2379719, 'n02123159 tiger cat'),\n",
+       " (0.12387239, 'n02124075 Egyptian cat'),\n",
+       " (0.10075711, 'n02119022 red fox, Vulpes vulpes'),\n",
+       " (0.070957087, 'n02127052 lynx, catamount')]"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "[(k, v[0].data.shape) for k, v in net.params.items()]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Helper functions for visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# take an array of shape (n, height, width) or (n, height, width, channels)\n",
-    "# and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n",
-    "def vis_square(data, padsize=1, padval=0):\n",
-    "    data -= data.min()\n",
-    "    data /= data.max()\n",
-    "    \n",
-    "    # force the number of filters to be square\n",
-    "    n = int(np.ceil(np.sqrt(data.shape[0])))\n",
-    "    padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)\n",
-    "    data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))\n",
-    "    \n",
-    "    # tile the filters into an image\n",
-    "    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
-    "    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
-    "    \n",
-    "    plt.imshow(data)"
+    "# sort top five predictions from softmax output\n",
+    "top_inds = output_prob.argsort()[::-1][:5]  # reverse sort and take five largest items\n",
+    "\n",
+    "print 'probabilities and labels:'\n",
+    "zip(output_prob[top_inds], labels[top_inds])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The input image"
+    "* We see that less confident predictions are sensible."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The first layer filters, `conv1`"
+    "### 4. Switching to GPU mode\n",
+    "\n",
+    "* Let's see how long classification took, and compare it to GPU mode."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
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-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffaf3877710>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loop, best of 3: 1.42 s per loop\n"
+     ]
     }
    ],
    "source": [
-    "# the parameters are a list of [weights, biases]\n",
-    "filters = net.params['conv1'][0].data\n",
-    "vis_square(filters.transpose(0, 2, 3, 1))"
+    "%timeit net.forward()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The first layer output, `conv1` (rectified responses of the filters above, first 36 only)"
+    "* That's a while, even for a batch of 50 images. Let's switch to GPU mode."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
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-       "IiIiIvaI/w/CAMCOMj3yxQAAAABJRU5ErkJggg==\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb01f952d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 loops, best of 3: 70.2 ms per loop\n"
+     ]
     }
    ],
    "source": [
-    "feat = net.blobs['conv1'].data[0, :36]\n",
-    "vis_square(feat, padval=1)"
+    "caffe.set_device(0)  # if we have multiple GPUs, pick the first one\n",
+    "caffe.set_mode_gpu()\n",
+    "net.forward()  # run once before timing to set up memory\n",
+    "%timeit net.forward()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The second layer filters, `conv2`\n",
-    "\n",
-    "There are 256 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "p1oHxwnZGNQ6OHbYcjxv1vf9atd1PxsR/yci5iPijsrYOy54bkT84aH1FFsi4n/0ff/hrus+HRG/\n",
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-       "GIMrI+I/dl33ijj0uuKBiHhzRI3BM4Tviogfj4jPdl135+HPLo9aB8cTs8bglyPidbUOjg2KIqZQ\n",
-       "KBQKhUJhg6jK5oVCoVAoFAobRB2kCoVCoVAoFDaIOkgVCoVCoVAobBB1kCoUCoVCoVDYIOogVSgU\n",
-       "CoVCobBB1EGqUCgUCoVCYYOog1ShUCgUCoXCBlEHqUKhUCgUCoUN4v8BaJ+sNGqpC6EAAAAASUVO\n",
-       "RK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb01f42290>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "filters = net.params['conv2'][0].data\n",
-    "vis_square(filters[:48].reshape(48**2, 5, 5))"
+    "* That should be much faster!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The second layer output, `conv2` (rectified, only the first 36 of 256 channels)"
+    "### 5. Examining intermediate output\n",
+    "\n",
+    "* A net is not just a black box; let's take a look at some of the parameters and intermediate activations.\n",
+    "\n",
+    "First we'll see how to read out the structure of the net in terms of activation and parameter shapes.\n",
+    "\n",
+    "* For each layer, let's look at the activation shapes, which typically have the form `(batch_size, channel_dim, height, width)`.\n",
+    "\n",
+    "    The activations are exposed as an `OrderedDict`, `net.blobs`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "data": {
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-       "MQ8HvQAAAABJRU5ErkJggg==\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb01f42310>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data\t(50, 3, 227, 227)\n",
+      "conv1\t(50, 96, 55, 55)\n",
+      "pool1\t(50, 96, 27, 27)\n",
+      "norm1\t(50, 96, 27, 27)\n",
+      "conv2\t(50, 256, 27, 27)\n",
+      "pool2\t(50, 256, 13, 13)\n",
+      "norm2\t(50, 256, 13, 13)\n",
+      "conv3\t(50, 384, 13, 13)\n",
+      "conv4\t(50, 384, 13, 13)\n",
+      "conv5\t(50, 256, 13, 13)\n",
+      "pool5\t(50, 256, 6, 6)\n",
+      "fc6\t(50, 4096)\n",
+      "fc7\t(50, 4096)\n",
+      "fc8\t(50, 1000)\n",
+      "prob\t(50, 1000)\n"
+     ]
     }
    ],
    "source": [
-    "feat = net.blobs['conv2'].data[0, :36]\n",
-    "vis_square(feat, padval=1)"
+    "# for each layer, show the output shape\n",
+    "for layer_name, blob in net.blobs.iteritems():\n",
+    "    print layer_name + '\\t' + str(blob.data.shape)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The third layer output, `conv3` (rectified, all 384 channels)"
+    "* Now look at the parameter shapes. The parameters are exposed as another `OrderedDict`, `net.params`. We need to index the resulting values with either `[0]` for weights or `[1]` for biases.\n",
+    "\n",
+    "    The param shapes typically have the form `(output_channels, input_channels, filter_height, filter_width)` (for the weights) and the 1-dimensional shape `(output_channels,)` (for the biases)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
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-       "iZQxxhhjTCOeSBljjDHGNOKJlDHGGGNMI55IGWOMMcY08v8BGKNoaFbqcjsAAAAASUVORK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb01dfb3d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "conv1\t(96, 3, 11, 11) (96,)\n",
+      "conv2\t(256, 48, 5, 5) (256,)\n",
+      "conv3\t(384, 256, 3, 3) (384,)\n",
+      "conv4\t(384, 192, 3, 3) (384,)\n",
+      "conv5\t(256, 192, 3, 3) (256,)\n",
+      "fc6\t(4096, 9216) (4096,)\n",
+      "fc7\t(4096, 4096) (4096,)\n",
+      "fc8\t(1000, 4096) (1000,)\n"
+     ]
     }
    ],
    "source": [
-    "feat = net.blobs['conv3'].data[0]\n",
-    "vis_square(feat, padval=0.5)"
+    "for layer_name, param in net.params.iteritems():\n",
+    "    print layer_name + '\\t' + str(param[0].data.shape), str(param[1].data.shape)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The fourth layer output, `conv4` (rectified, all 384 channels)"
+    "* Since we're dealing with four-dimensional data here, we'll define a helper function for visualizing sets of rectangular heatmaps."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
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-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb01cf9110>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "feat = net.blobs['conv4'].data[0]\n",
-    "vis_square(feat, padval=0.5)"
+    "def vis_square(data):\n",
+    "    \"\"\"Take an array of shape (n, height, width) or (n, height, width, 3)\n",
+    "       and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\"\"\"\n",
+    "    \n",
+    "    # normalize data for display\n",
+    "    data = (data - data.min()) / (data.max() - data.min())\n",
+    "    \n",
+    "    # force the number of filters to be square\n",
+    "    n = int(np.ceil(np.sqrt(data.shape[0])))\n",
+    "    padding = (((0, n ** 2 - data.shape[0]),\n",
+    "               (0, 1), (0, 1))                 # add some space between filters\n",
+    "               + ((0, 0),) * (data.ndim - 3))  # don't pad the last dimension (if there is one)\n",
+    "    data = np.pad(data, padding, mode='constant', constant_values=1)  # pad with ones (white)\n",
+    "    \n",
+    "    # tile the filters into an image\n",
+    "    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
+    "    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
+    "    \n",
+    "    plt.imshow(data); plt.axis('off')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The fifth layer output, `conv5` (rectified, all 256 channels)"
+    "* First we'll look at the first layer filters, `conv1`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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hhBAN0EuUEEIIIUQDjt6JokGafol49vvy4e971FhiHpOvYm1oSOehuxD6Pfy9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\nOZnk8c7L5yWCwGxSs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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb01c2f0d0>"
+       "<matplotlib.figure.Figure at 0x7f09692b9ed0>"
       ]
      },
      "metadata": {},
@@ -11786,334 +559,30 @@
     }
    ],
    "source": [
-    "feat = net.blobs['conv5'].data[0]\n",
-    "vis_square(feat, padval=0.5)"
+    "# the parameters are a list of [weights, biases]\n",
+    "filters = net.params['conv1'][0].data\n",
+    "vis_square(filters.transpose(0, 2, 3, 1))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The fifth layer after pooling, `pool5`"
+    "* The first layer output, `conv1` (rectified responses of the filters above, first 36 only)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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VFrjP0X4XL170YlpZLvEiA0aI2wiqOlbjgtlAG5w9ezZRyIu5uTk1+kWZpqamTJWyayCf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KfAolWVToe9N9VCm8XyZqv0slo0qQGED99oNGr0xfl8vjIBmJ2lTMH7slcUoExdwGg0arSrioNj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GFX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PwW3wODycw/2KBOpxOMWaFQ0PXI8Bguj5nGfoE102g0tC+xvuv1uvab1y82TlXkSFqGa5D+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\nw7nvMCeWaRSkgshg7rAWO52OtmGvSFSGm/YcDofD4XA49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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb01bbe590>"
+       "<matplotlib.figure.Figure at 0x7f09692c7350>"
       ]
      },
      "metadata": {},
@@ -12121,416 +590,29 @@
     }
    ],
    "source": [
-    "feat = net.blobs['pool5'].data[0]\n",
-    "vis_square(feat, padval=1)"
+    "feat = net.blobs['conv1'].data[0, :36]\n",
+    "vis_square(feat)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The first fully connected layer, `fc6` (rectified)\n",
-    "\n",
-    "We show the output values and the histogram of the positive values"
+    "* The fifth layer after pooling, `pool5`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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5OZnE5ufns2IP8sCdO1EAAAUUUQAABRRRAAAFFFEAAAUabyxvk83NzSRWd3Nuv5r0mvCV\nr3wla90//MM/JLHo3EeaaJaemJjo+TEGWZUm8si9TeQ0I2roj+zZk/7bO2osb0KUS5X3c/R5/eUv\nfzlr29deey2Jzc3NFedCLHrNZ2ZmsraNHhyryp0oAIACiigAgAKKKACAAoooAIACrW0sjyb5Rk1h\nVRr3oibCvXvTUxJNLM8VNcE9bIaGhvqdwsBpouF+UE1NTSWxsbGxJLa8vJzE6m56X1hYqHV/Vfzb\nv/1b1roqn4fRZ240fT8695FoKvWBAwcePLGfy/2FiX6Znp5OYktLS33IpF1yH4rIva4ehG94AIAC\niigAgAKKKACAAoooAIAC3aiRrqcH7HazDhg1DEax69evZx03+jsPHz6cxKKmyWgic25zZdTwFk3r\nzp1sXnfje3Re+jVlXS4xudSbR92N5W05J53O7swlemAo9xcNItHrGz2I0fbzEqm7sXw3Xi+R3Cn1\na2trxbl0Op0wGXeiAAAKKKIAAAooogAACiiiAAAKtLaxvG7R33no0KEkdvfu3SRWpbE8N5fd2MxX\nN7nE5NL7PEZHR5PYnTt3+pJLFXKJySUml5jGcgCAHlNEAQAUUEQBABRQRAEAFEjHXz9EFhYW+p0C\n0LCPfvSjWeuuXbuWxKIp5lXs2ZP+Ozb6lYOo0TV3qnc05To6bjT5emdnJyu/mZmZrFyiRuEqDzeN\nj48nsdOnTxfvr4qhoaEkFv0qRuTEiRPFx71y5UrxtlTnThQAQAFFFABAAUUUAEABRRQAQIHGJ5Z3\nOp2+TCwHAChkYjkAQF0UUQAABRRRAAAFFFEAAAUan1geTaxtQtRA3/Zcogm4ufvLPcbdu3drzSXa\nX67d+Bo1IcplcnIya936+nrxcUdHR5PYxsZGEuvHeany+jzxxBNZ695+++2e51K3KJdogvfVq1eT\n2Pb2dvFxo8+HaH9tOi+f//znk1h0fb/55ptJLJraPjExkcSOHDmSxP7zP/8zibXpvIyNjSWxO3fu\n1HrcaNL82tpaEmvTebkfd6IAAAooogAACiiiAAAKKKIAAAo03lhOvipN2pGomS9X3bnULWpAnJqa\nytp2ZWWl7nR6LmrCjJw5cyZr3fnz55NY3c2k9Mfc3FwSq/v9XPf+nn/++ax1P/zhD5NYblPwD37w\ngySWe81HTeSR119/PWtdm0TfE7mfpQsLC3Wn03ruRAEAFFBEAQAUUEQBABRQRAEAFNBYnmHv3rzT\nVGXiL/XLbf7cjY3luaLpww+73EnkTdizJ/137M7OTq3HyG36fuyxx7LWvf/++1XSabXo9RgZGUli\nUQN6tO2g2NzcTGJRA3o0oX1paSmJPchE8LYb3FcdAKCHFFEAAAUUUQAABRRRAAAFNJZnGJSG8dwG\n+d0oalRcXl7uQyZxM/fGxkYfMul0VldX+3LcSDT1uE35tUUTzeZt9+qrr/b8GFWaw3Ob9XMfbmmT\nW7duJbHo+jt69GjW/q5fv145pzZzJwoAoIAiCgCggCIKAKCAIgoAoMDgdhrz0OtXM3e/jhu5dOlS\nv1NonajB/cCBA0lscXExia2vr9eaS5saxi9fvtzvFPouej3a9H5uQu6DDSdPnsza33vvvVc5pzZz\nJwoAoIAiCgCggCIKAKCAIgoAoIDG8odINImWakZGRpLY+Ph4Eoum3t++fTuJRU3PbTc7O5vEFhYW\nktja2loSi85fZHNz88ETu48zZ84ksf379yex6DWru7E80o9z0unEE7xzc8k1PDycxKJfG4gmgkfr\nco/RhEGZvh9NWY+ujdyp7YPOnSgAgAKKKACAAoooAIACiigAgALd3Ga9GjV+QACACrpR0J0oAIAC\niigAgAKKKACAAoooAIACjU8sj6Y5b2xs1HqMaPrw8vJyEut20z6xKFal+T76e6PJzdFxc42Ojiax\naMJsJPrbnn322SR2/PjxJLayspLEHn300ST2ne98J4k9/vjjSeyll15KYkePHk1iuZPXc6+rI0eO\nJLFr164lsdzXaHJyMolF08lzRa9RleulirbkEuVx8uTJJBad9+izINf09HQSu3nzZhLLPSfR9R2J\nrsdIldfnwIEDWetyz1/duVR53arksndv+jUZTbOvksuePen9jCYe+qr7/Rydq0h0/try2dLpPNi5\ndycKAKCAIgoAoIAiCgCggCIKAKBA443ldTeRR3IbjyNRg9/s7GzWttevX09i6+vrxbnkym0iz/XO\nO+8ksUuXLiWx+fn5JPbyyy8nsagZ9+LFi1m5ROe0bnUfo0oTOeXGxsaS2MjISBKr0qA8NTVVvO2g\nqLvR+sknn0xiv/7rv5617Te/+c0klvvZkiv626LvicjOzk7Wuj78ckhP5D6csLCw0ONMmuNOFABA\nAUUUAEABRRQAQAFFFABAgcYby9tueHg4ieU2k964cSOJ7caGwWhKbJXm9dxp8XWLGo0jTTzsQO9F\nzb51N/lHvzZQRe4k8iZEv/QwNDSUta7uZu5c0S8f1J1L9HDC4cOHs7a9fPlyrbm03cP4WepOFABA\nAUUUAEABRRQAQAFFFABAAY3l94gaqB+2CdRR03fUcD86OprEtra2kljUnHry5MnC7NitoknXExMT\nWdtW+RWCOtX96wBRM3wUqzIRPFfUMB6972/evFnrcX/84x8nsaiZ+9ixY7UeN1d03RJ72L4rOx13\nogAAiiiiAAAKKKIAAAooogAACuiYu0c0YTx3CmubppMfOHAgieVO2Y3cvXs3iUVNp9E5iBozJycn\ni3PJ1fbpuVEDf9RUPMii6yV6OCFHtF10jeaKXovx8fHi/eXa2dnp+TEi0S8LzMzMJLEmmtyjh1ai\npv6FhYWe5xKpu7me3evh+sQGAKiJIgoAoIAiCgCggCIKAKBAaxvLcxtsm2jCjBouc+U2tk5NTSWx\n1dXV4uNWETX7VpnUvLm5mcTOnz9fvL9+OXv2bBKLmuZXVlaS2OLiYhL75Cc/mcSiSc2DImpIjs5V\nW0SfLbmfN9FDHNF7KHcS+2OPPZbEqjTNR6LXIrq+Sxv/H0T0KwdRs3kT1tbW+nJcdgd3ogAACiii\nAAAKKKIAAAooogAACnT7MGW7PWO9AQB+sfQnJjruRAEAFFFEAQAUUEQBABRQRAEAFGh8Ynm3G/Zm\nJZ544okkFjXBz8/PJ7FoOnS0bW4udcvNJZraHk1MjrbNfWCgynn5tV/7tax1r7/+ehK7fv16rbnU\nLTeXycnJJBa9Ruvr61nHzZ0QPT4+nsT27duXxKKJ2NG2S0tLSWxmZiaJLSwsJLHc1yiasB1NaL99\n+/Yv3Ff0+nzkIx9JYtHfdfPmzSQWnfexsbGs40a/aND26zb6bImu5SoT5aNcPvWpT2VtOzExkbXu\nwoULSezo0aNJ7Hvf+14Sa/tr1IQquZw+fTqJRb9KEH0fR9P7235e7sedKACAAoooAIACiigAgAKK\nKACAAo03lueKGsqiWNQQWreoYTdSpQkzEjUoR/owdZ6fy2mCfhBRY2YkasyM3h/RuigWiRpCq4ge\nFhkdHU1iFy9eTGLRgwj3unr1ahJbXV3Nyu3u3btJLPc8tV3UMB5dt3V/fkWiY9y4cSOJ5V57UfP/\n9PT0gyfGQyt6fzwId6IAAAooogAACiiiAAAKKKIAAAq0trE8mjQciRpC63b48OGsdU00ZrbJiy++\nmMQ0dTYjepggdyp6JJocXve04A8++CCJRdPTo4niQKdz/PjxrHVzc3M9ziT+DBoaGkpi0S8V5D60\nEe0v+szIfYCkF9yJAgAooIgCACigiAIAKKCIAgAo0NrG8qjxbM+etOZrorF8eXm558eI5E5SrXtq\ndhW5091zHxzIdejQoax18/PztR43t/m67qny0aT+3Gnnkc3NzSrpZImu0zqv3Sb+hqrTje8VvV+i\nWHTd5v69bfp8iD7Xo/yiSfbRtlGTcd2/YtGv9zixra2tWvdX9f3hThQAQAFFFABAAUUUAEABRRQA\nQIFd1VgeNRE2YWFhoS/HjZorI21qHL106VK/U3gozMzMJLGdnZ2sbXOb66MHOdrs2LFjSWxtbS1r\n2+icRNP3625qPXDgQBIbGxur9Rhtshv/ttxfYYgeQMp9T+ZqYhJ5rtwG/irTxKNm/dxp503ZXZ+S\nAAAtoYgCACigiAIAKKCIAgAo0HhjedQsnTt1PJrQG+0varqt4uDBg1nrVlZWkliVKdKLi4vF29Yt\nd4Jw7tTeEydOVM7pf+vXVPk2TSmue2J3labY2dnZrHXRe+bw4cNJLGci/Y0bN5LY+vp6EsttiI2u\nqbpf7+g9HjVfNzGNvYrh4eGsddEvFUSfI1Ezd/Rg0c2bN5PYq6++mpVL3aIHMepuLG9CNDE/eqDi\n6tWrSazupu+TJ08mseg6iN5H0cMiTz/9dBI7fvx4YXb/zZ0oAIACiigAgAKKKACAAoooAIAC3T40\nxranExcA4BfrRkF3ogAACiiiAAAKKKIAAAooogAACjQ+sbzbDXuzei5qoN+NuUQTXK9fv561bTR1\nNjeXI0eOZB0jd925c+eKc4mcPXs2iV26dCmJRdN45+bmas2lboOSy6lTp7LWRa9bnXnULTeX6NqL\nRFPc685lZGQka3/RZ0Y0mTv61Ykqr1G0rspDULm5nD59Omt/uX/HhQsXinNpQpRL9Po28QBa7nmJ\nfu0i+pWDaHp69N66fPlyVi73404UAEABRRQAQAFFFABAAUUUAECBxhvLc83MzGStW1xcrPW4jz32\nWBL71re+lbXt1772tST2wgsvVM7pf4ua4IaGhpJY3Y2At27dSmJRA2JuY3ndcpvrqzTtkm9sbCyJ\nff7zn8/a9hvf+Ebd6fwfzz33XNa6H/3oR7UeN7r2jh49msQmJiaS2LVr12rNZXNzs3jbfjUZN+Hi\nxYtZ6x555JEeZ1J/c32ufp37XMPDw1nr9u5tprxxJwoAoIAiCgCggCIKAKBAa3uiJicns9ZtbGwk\nsbW1tbrTaY1o2GYk6p2qIjrPkWiIZhPq7o1jcOUO/ay7J2pQ7Ozs9PwY0WDSaKBnvz7royGadYsG\nokYDJOl0tre3s9b1ok/KnSgAgAKKKACAAoooAIACiigAgALdpgdrdbvdrANGA+eioZK3b99OYlHj\nY9t/OTs3l7obywflvNRNLrG25FIlj4MHD2atu3nzZs9zqdug5BINa819uKXuXOqWm0uVYZu5DdRb\nW1tZx21C7nnJHbYZDYOORM369znP4YlxJwoAoIAiCgCggCIKAKCAIgoAoEBrG8vrthsbCyPRJPfZ\n2dmsbaMpu4NyXuoml1iVXB599NEkdubMmaxtv/Od79SWR93kEquSy8zMTNa6paWlJFb3g0XHjh1L\nYtHn8E9/+tMkFk3Szs0ltzE6+ntzz9/CwkJWLk2o+9qNGtCjRvrcXDoaywEA6qOIAgAooIgCACig\niAIAKJA31rQlcpvlFhcXe5xJ/4yOjiaxaLr7wyZqwhwZGUliR44cSWJRwz31+/SnP53ETpw4kcSi\nCcL3NpYz2B555JGsddEU87W1tVpzOXToUK37i0S/xnH48OGsbXMnuUcN6LtR9H2Xe66iX/KImv8f\nhDtRAAAFFFEAAAUUUQAABRRRAAAFGp9Y3ul0+jKxHACgkInlAAB1UUQBABRQRAEAFFBEAQAUaHxi\nebeb9mb98R//cRKLphu//vrrWcf4+te/nsSiBvoolz/90z9NYs8991wS++lPf5rE/u7v/i6JnT9/\nvjiXSO508typvVVyqZtcYrsxl2iyfu7+ognRV65cKcqjCYOSy9mzZ7PWvfXWWz3PpW5VchkeHs7a\ndnNzM4nt27cvid26das4l7q1/TWKvu/+5E/+JIl94QtfSGLRhPZ///d/T2J//dd/ncRWV1fvm+e9\n3IkCACigiAIAKKCIAgAooIgCACjQeGN55KWXXkpib7/9dhJ75plnmkgnS9T8undv709nbsN43U6c\nOJG17t4GYJpz7NixJPalL30pa9u///u/rzWXPXvy/n129+7dWo9LntOnTyexN998M2vbP/zDP0xi\nVa6f6FqJYtvb28XHqCL3YZ6osXxlZaXudB4q6+vrPT/G7du3K23vThQAQAFFFABAAUUUAEABRRQA\nQIFuNCXotdrEAAAXHElEQVS0pwfsdps94M/lTmbdv39/1v6iqbPRZNutra3iXJqQm8vU1FTW/h5k\n0mtpLk3Yjbk00Viem0v0XohE749oyvO9Dbq78fXJNTY2lsQ2NjZqzSVqLL9w4ULWMXIbywf5NapC\nLrEquUxOTiaxoaGhJBY1+kfHvU9dFCbjThQAQAFFFABAAUUUAEABRRQAQAGN5feYnp7O2t/S0lLP\nc2lCbi5Rs2sktwG2Si5NkEusiVwee+yxJHbvVOGrV6/2PI9c/Wosj46xs7NTnEvugzG5HrbrNpdc\nYm3PpaOxHACgPoooAIACiigAgAKKKACAAnv7nUDbRA3je/aoNaEXZmdnk9iRI0eS2NzcXBPptEKV\n6eRVRE3kJ06cyNr2ypUrteayd2/61RRdK5Fr167Vmgv8/6gOAAAKKKIAAAooogAACiiiAAAKtLax\n/PTp01nr1tfXk9j8/HzWtrmTgaMpwJGJiYkktra2lrVt242OjmatqzKxvO2GhoaSWNQA+9RTTxUf\n47XXXivetu2icxVNJI4apm/cuNGTnHazuh94eeKJJ5JY7udw3Y3lzzzzTBKLPl8juY3lU1NTWesO\nHjyYte7WrVtJLJoC33bRa37vLwZ0Op3O4uJiEovO6alTp5LYu+++W5hd+7gTBQBQQBEFAFBAEQUA\nUEARBQBQoFv31NsMjR8QAKCC9CmYjjtRAABFFFEAAAUUUQAABRRRAAAFGp9YHk0ojuROk42mIC8t\nLSWxqIE+ymXfvn1Zx40muOZONs/NJZp2G/290dT26enpJFblvDRBLrHcXEZGRrL2t7m52fNceq0t\neXQ6+bl84QtfyNrf+++/n7Xu4sWLxbk0oUouMzMzWeuiqdlVcvnKV76Stb8f/ehHWeveeOON4lwi\nhw8fzlqXO+F/N14vs7OzSex3f/d3k1g0Ff273/1ucS73404UAEABRRQAQAFFFABAAUUUAECBxieW\nd7vd5IBPPfVUsu7JJ59MYtvb20ksahRbWFhIYrlNaxMTE0kssrGxkcTqbixvwm7M5fHHH09if/EX\nf5F1jK997WtJrMr1Ejlx4kTWuitXrmStq5LL888/n7Xu1Vdf7XkudWpLHp1Ofi6HDh3K2t/a2lrx\nurafl+iBl+Xl5b7kknteonVVvjerfM4999xzSSx6sOif//mfa82lCbm5RA9Xff7zn09id+/eTWIV\nG8tNLAcAqIsiCgCggCIKAKCAIgoAoEDjE8vrFjUFV5Hb1NmEaHr66OhoEoump0fNhnX77d/+7ax1\n3/72t5NYE/n1y/79+7PW5TaWN2FsbCyJRQ9PUG5lZSVr3dDQUI8z4UFETcZnz57N2vatt94qPm70\n/ouuodzralBED5i99NJLfcjkv7kTBQBQQBEFAFBAEQUAUEARBQBQoBUTy5uwGyezRk3kUbP5zZs3\nk1g0rbVKLpG6G8t342sUmZqaylq3urra81yiCfybm5tJLGrWrDuXOrUlj05HLvczKLlED1188Ytf\nzNr2hRdeKM4liv3BH/xB1nHffffdJBb9KsGgvEZ1M7EcAKDHFFEAAAUUUQAABRRRAAAFWjuxPGoo\nixr8ogbqqHF2N7pz505WrF9eeeWVJDYzM5PEZmdnk9ilS5d6klMb5DaMN6FNE/hJRZ9p0cTyqNF1\nN76209PTSWxpaakPmeSLzn3ugxhNiK4hmuNOFABAAUUUAEABRRQAQAFFFABAgdY2ln/sYx9LYsPD\nw0ksmnz9zjvvZB0janjObXLMnQg+yD760Y8msd/6rd/K2vYv//Iv606nNQ4ePJjEoqnyD5vo/RZZ\nXFxMYk3/skIvRNfFM888k7XtlStXktjbb79dOac22LMn/bf8yMhI1rYbGxtJrO5G6+hhnmgieN2i\naz66DqLz9/rrr9eaS/Tdu7W1Vesxdit3ogAACiiiAAAKKKIAAAooogAACnT70LC5+ztEAYCHSfoz\nKh13ogAAiiiiAAAKKKIAAAooogAACjQ+sbzbDXuzEkNDQ0ksmmIbTSyPRA30ubk8/vjjSWx6ejqJ\nvf/++0ksd/pybi5VjI6OJrFo4m8TuUT6dV4ig5LLJz7xiax10ZT/6L3VlvPSljw6nWq5jI+PJ7Fo\nEvT29natueRO9Y4+hyO3b98uziXyK7/yK1nrFhYWkthbb71VnEv0905NTSWxvXvTr87l5eUkFr1u\nu/HaPXPmTNb+Hnnkkax13/3ud7NyiaaxR+c+es9E39G5v0jyIA/cuRMFAFBAEQUAUEARBQBQQBEF\nAFCg8cbyug0PDyexqMmsiqeeeiqJnTp1KolFTdpRY3m/3Llzp98pwEMrejDmsccey9r2jTfeqDWX\n3IbnJh48OX78eBJ78skns7b9wQ9+UGsuExMTSWxnZyeJRQ3tTch9ICB63ao4f/581rqo6buK6Jo8\ncuRI1rbRAwH79+9PYhcuXHjwxP4Xd6IAAAooogAACiiiAAAKKKIAAAq0trH87t27SSx3Onm/5E4V\n7peosfVhs2/fviS2srLSh0ya8V//9V9J7MMf/nASO3HiRBL7yU9+0pOcHlabm5tJbH5+vg+ZxJOg\nowbqyINMcy71wQcfJLGoUZhmRJ8PUZN29B343nvv1ZpL9DBZdD1H11CkajO8O1EAAAUUUQAABRRR\nAAAFFFEAAAW6TTQJ/p8DdrvNHvDnor+z7sm7ufqVS9RYHk0xf9jOS2SQc/n4xz+ete7111/veS6l\n2pJHpyOX+6k7l6effjpr3blz53qeSxWDksv4+HgSq/LwV24u0cNBkSoPDN2nLgpPjDtRAAAFFFEA\nAAUUUQAABRRRAAAFWjuxvO2iprpIm6asRxOT63bmzJmsdefPn6/1uNPT00lsaWmp1mMMiitXrvQ7\nBfj/Onv2bBKL3uP0T7++2/rVhH8/7kQBABRQRAEAFFBEAQAUUEQBABTQWM6uc+rUqSR24MCBJHbr\n1q0ktrOz05Ocmnb8+PEkdvDgwSS2traWxKKm/uiBgGPHjhXldj979+Z93Gxvbxftf3JyMondvn27\naF9V7d+/P4lFD6NEr1k0LXlxcTGJzc/PF2bX6QwNDSWx6BcN+tU8/KEPfSiJ7dmT/pt/eXm5iXRo\nkbGxsax10ed/pGqjujtRAAAFFFEAAAUUUQAABRRRAAAFulETY481fkAAgArCDnR3ogAACiiiAAAK\nKKIAAAooogAACjQ+sbzKdNDp6ekktrGxkcQ2NzeT2N27d2vNpYqomV8u+blE05afeeaZJLayspLE\n3nnnneJcoonJVR7MiCZER9du7nmZmprKOm70d0Si8xdNfM+9Xn7nd34na93LL7+cxBYWFv7Pf+/G\n63Z4eDhrf1tbW7XmEk3zjyaR5x43us6ia/ne16zTaf9r1IQquUS/LBCJfpWg7lzqlptL9IsVhw4d\nSmLRRP9o8n/0qw4P8rnuThQAQAFFFABAAUUUAEABRRQAQIHGG8sjuY2uS0tLPc6k0xkbG8tat729\nnRUbFFGD3/79+7O2XV5erjWXqIn1Qx/6UNa2uY3lkbqn+0dN5FXMzMwksa9+9atZ2+aua0Lue7DX\nos+lqLE+V9S4/fTTT2dte+7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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb01ae5a90>"
+       "<matplotlib.figure.Figure at 0x7f09691fce50>"
       ]
      },
      "metadata": {},
@@ -12538,361 +620,31 @@
     }
    ],
    "source": [
-    "feat = net.blobs['fc6'].data[0]\n",
-    "plt.subplot(2, 1, 1)\n",
-    "plt.plot(feat.flat)\n",
-    "plt.subplot(2, 1, 2)\n",
-    "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
+    "feat = net.blobs['pool5'].data[0]\n",
+    "vis_square(feat)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The second fully connected layer, `fc7` (rectified)"
+    "* The first fully connected layer, `fc6` (rectified)\n",
+    "\n",
+    "    We show the output values and the histogram of the positive values"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": [
-       "iVBORw0KGgoAAAANSUhEUgAAAlcAAAJPCAYAAABRvvFyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
-       "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xv0LGdd5/vPJ3dygRADOzGJJsgdkWRxNyAb5BJw5OaI\n",
-       "okBE5CAiIJ6jgs6Y7XgDR5DjcMRZQ4KRYXB00JyIoyYoP424JKIJCTcjZ5JlgskOs4gKXkGe80dX\n",
-       "79TuXdVdl29VPdX9fq211+5fd9VT37p/+3mefsopJQEAACDGUVMHAAAAsE1IrgAAAAKRXAEAAAQi\n",
-       "uQIAAAhEcgUAABCI5AoAACDQ2uTK9jm2P2D7Y7Y/avs1xfun2b7a9k22r7J96jjhAgAA5M3rxrmy\n",
-       "fYakM1JK19s+WdKfSXqupJdK+t8ppZ+x/UOS7p1Sev0oEQMAAGRsbc1VSumOlNL1xevPS/qEpLMk\n",
-       "PVvS5cVkl2uRcAEAAOy8xn2ubJ8r6QJJH5K0L6V0sPjooKR94ZEBAADMUKPkqmgSfK+k16aUPlf+\n",
-       "LC3aFXmGDgAAgKRjNk1g+1gtEqt3pZSuKN4+aPuMlNIdts+UdGfFfCRcAABgNlJKjihnbXJl25Iu\n",
-       "lfTxlNJbSx9dKeliSW8q/r+iYvawIOfE9oGU0oGp4xgb671bWO/dwnrvlh1e77BKoU3NghdKepGk\n",
-       "J9u+rvh3kaQ3Snqa7ZskPaX4GwCAWbGVbH3z1HFgu6ytuUop/ZHqE7CnxocDAMDovlrSr00dBLYH\n",
-       "I7TH25s6gInsTR3ARPamDmAie1MHMJG9qQOYyN7UAUxkb+oAJrI3dQBzt3YQ0V4F22kX+1wBAObD\n",
-       "VpL0H1LSJVPHgmlF5i3UXAEAAAQiuQIAAAhEcgUAABCI5AoAACAQyRUAAEAgkisAAIBAJFcAAACB\n",
-       "SK4AAAACkVwBAAAEIrkCAAAIRHIFAAAQiOQKAAAgEMkVAABAIJIrAACAQCRXAAAAgUiuAAAAApFc\n",
-       "AQAABCK5AgAACERyBQAAEIjkCgAAIBDJFQAAQCCSKwAAgEAkVwAAAIFIrgAAAAKRXAEAAAQiuQIA\n",
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MViiLF298EU1brscek/78Z/9lSmv16s7+2YZ9tO5i2Nebhv3ev8rvIox1o8ZaLl98PEy0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\nFLIAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb01953f90>"
+       "<matplotlib.figure.Figure at 0x7f096914f6d0>"
       ]
      },
      "metadata": {},
@@ -12900,7 +652,7 @@
     }
    ],
    "source": [
-    "feat = net.blobs['fc7'].data[0]\n",
+    "feat = net.blobs['fc6'].data[0]\n",
     "plt.subplot(2, 1, 1)\n",
     "plt.plot(feat.flat)\n",
     "plt.subplot(2, 1, 2)\n",
@@ -12911,12 +663,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The final probability output, `prob`"
+    "* The final probability output, `prob`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
@@ -12924,191 +676,18 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7ffb01f12a50>]"
+       "[<matplotlib.lines.Line2D at 0x7f09587dfb50>]"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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boqmI2CRpU8O2dZnlvRo/FbNlWwDl0KqCx0VWAAAAiin3RucATkyN5+DVA96xY2MhDwAA\nAMVCwAPQ1MjI2BTNWm1siuZIesdLAh4AAEDxEPAANDVZBe/o0WQbUzQBAACKh4AHoKnsRVao4AEA\nAJQDAQ9AU9mLrFDBAwAAKAcCHoCmslM0sxW8esCjggcAAFA8BDwATdUvshLRvIJHwAMAACgeAh6A\npkZHk2B35Mj4G53Xz8FjiiYAAEDxEPAANFUPcocOjb/Reb2Cd+iQdMMNvesfAAAAJiLgAWiqPgXz\n4MHxFbx6wNu7V/qLv+hd/wAAADARAQ9AU/UK3sGD4yt42e31sAcAAIBiIOABaKpewatfaKWxgkfA\nAwAAKB4CHoCmJqvgZc/BGx1N9gEAAKAYCHgAmhodld75zokVvHrwe+ut5JEqHgAAQHEQ8AA0NTIi\nnXrq5BW8+k3QCXgAAADFkRvwbK+0PWR7u+01kxzz7XT/Y7Y/ntm+w/bjth+1vaWTHQfQXaOjYwGv\n2Tl4hw4lj0eO9K6PAAAAGK+/1U7bfZJuknSppN2SttreGBGDmWMul/ThiFhq+yJJN0take4OSZWI\neLUrvQfQNVTwAAAAyievgrdc0nBE7IiIEUl3SFrVcMxnJd0iSRHxsKTTbJ+R2e9OdRbAzKlX8A4f\nTip4jbdJqF9lk4AHAABQHHkBb6GknZn1Xem2do8JSffZ3mb7uul0FMDMGh2V5s9PAl6tNnGKZh1T\nNAEAAIqj5RRNJQGtHZNV6T4RES/aPl3SvbaHIuKBxoMGBgbeXq5UKqpUKm3+WADdMjIiLVgwdhXN\n7BTNOXPGgh4VPAAAgKmpVquqVqtdee68gLdb0uLM+mIlFbpWxyxKtykiXkwf99u+U8mUz5YBD0Ax\njI4mAa+xgjcyIr3jHQQ8AACA49VY1Fq7dm3HnjtviuY2SUttL7E9R9KVkjY2HLNR0jWSZHuFpNcj\nYp/tubYXpNvnSbpM0hMd6zmArqpX8BrPwTt6NAl4dQQ8AACA4mhZwYuIUdurJW2W1CdpfUQM2r4+\n3b8uIu62fbntYUlvSfpy2vxMSRts13/O7RFxT7deCIDOqp+D99prE2+TkA14nIMHAABQHHlTNBUR\nmyRtati2rmF9dZN2z0n62HQ7CKA36hW8PXvG3yZhZER65zvHjqOCBwAAUBy5NzoHcGLKnoPXqoJH\nwAMAACgOAh6ApkZGxt8mYbJz8JiiCQAAUBwEPAAT1GrJ47x5Y7dJyFbwmKIJAABQTAQ8ABOMjEj9\n/UmQa6zg1W+TUEfAAwAAKA4CHoAJRkelk0+WTjkl/xw8pmgCAAAUBwEPwATZCt6hQ5Ofg9fXRwUP\nAACgSAh4ACaoV/DqUzSzFbzsbRIWLCDgAQAAFAkBD8AE9QpefYrmZBW8BQuYogkAAFAkBDwAE4yO\njr/ISraCd+zY+IBHBQ8AAKA4CHgAJshO0Ww8B08am6I5fz4BDwAAoEgIeAAmaLxNQsT4gFev4BHw\nAAAAioWAB2CCxtsk1GpjUzQlzsEDAAAoKgIegAnqFbz+/mT96NGkgvf+9yfrXEUTAIByOXy41z3A\nTCHgAZigXsGTkjB38GBSvVu6NNk2Z07ySMADAKD4XnxROvvs5JQLzH4EPAAT1Ct4UjJN89ixpIJX\nD3h9fcn++fOZogkAQNE99VQS8l56qdc9wUzIDXi2V9oesr3d9ppJjvl2uv8x2x+fSlugyKrVaq+7\nMC2bN0s/+9nU22UrePXz7U46SXrXu5Ll/fuTkEcFr3fKPjYxezE2UWQn6vgcGkoen3mmO89/9CjV\nwSJpGfBs90m6SdJKScskXWX7/IZjLpf04YhYKuk/S7q53bZA0ZX9g+CGG6Rvf7v940dGpC99SXr1\n1bEK3u7dyeOpp44d9+tfJ/uLFPAOH5b+7u+S6aTbtiWPs1nZx2ajK66QtmzpdS/QCbNtbGL6Dh+W\n/u3fet2LxGwZny+/PLXP32eeSb6YrQe9Tvv856Xvfa87z42py6vgLZc0HBE7ImJE0h2SVjUc81lJ\nt0hSRDws6TTbZ7bZFiegCOlf/mXmT/Z9440T6wTjt96SHnxQuv/+5EPghRekb30rmW45mY0bpdtu\nk269dSzgSdKKFckUTSl5PPXU4k3RvPVW6etfl973PmnVKunaa6W//MvkQ3A6Hn5YuvFGvpmUkqup\ndsPgoLRhg/T3f99+mxdekNatO/H+XWq13nx5ESEdODDzPxfdEyE98kj3fq+zvvMd6Xd+R3r++e7/\nrBPB6Kj0qU9Ja6YwN25oSPrkJ6dfwYuY+L770kvST38q/eAH03vuotm/X/rzPy/n/x37c/YvlLQz\ns75L0kVtHLNQ0m+00VaS9Pu/305XMVvs3y8ND0tnnCGdddbM/MyIJOyccoq0bFkSTvr6ku179kjv\nfrc0d+7Eds88k3wAdkOtJj35pLRo0Vh1zB4fpLKPU3XggHThhckb02WXjVXdfvSjJAT19SXTLutv\n1i++KO3aJX3xi9I//VPyYVz3mc+MLb/xRnKRlZ/8JPl7e+SRYvwOb92afHFw7rnSe96TnEz+4IPS\n978vnXfe8T/vli3Jv89tt0mnn578vU0mL2x0cv/wsPTQQzP3s19+Wdq+XbrkktZ/B8fj+eeTyvGP\nf5y8P9TVasl/HN773rEpw3W//GXyeNttSSW5Vktew3Qfj6fNSScl465+ddk8r7+e/B4tXjz1v6vn\nnkvC7Sc+Mfm/w2TvGcf7XiIl7w9PPpn8B7E+bXsyzz6bVNFPFBHJv+eRI8mVhpv9Pff6i4hjx5Lf\n4blzk9+lo0elQ4eS//QvXSotXJj/HAcOJOPgvPOmPpZ+8Qvpd383+Sz60IfGf4E407r5uT5TXn9d\nmjcvCVTDw+21eeih5Eveb30r+R09Hvv2Je+9H/ygdP7547d//vPJKSG/93vTe69px9Gjyes+55yJ\nnw2dNDSUvJY770xec+O4bfZ7fbzbPvCB4+9nM44W7zq2r5C0MiKuS9evlnRRRHwlc8xPJP1VRPw8\nXb9P0hpJS/LapttPsO9fAQAAAGC8iOhIPM77DmW3pOx3jIuVVOJaHbMoPebkNtp27IUAAAAAwIku\n7xy8bZKW2l5ie46kKyVtbDhmo6RrJMn2CkmvR8S+NtsCAAAAADqkZQUvIkZtr5a0WVKfpPURMWj7\n+nT/uoi42/bltoclvSXpy63advPFAAAAAMCJrOU5eAAAAACA8si90Xk3cSN09JLtxbbvt/2U7Sdt\nfzXd/h7b99p+1vY9tk/LtPlGOl6HbF/Wu97jRGC7z/aj6cWsGJsoDNun2f6R7UHbT9u+iPGJIkjH\n2lO2n7D9f22/g7GJXrD9D7b32X4is23KY9H2b6Xjebvtv23nZ/cs4HEjdBTAiKT/FhEfkbRC0n9J\nx+CfSbo3Is6R9K/pumwvU3Iu6TIl4/Y7tnv6JQlmva9JelpSfaoFYxNF8beS7o6I8yVdIGlIjE/0\nmO0lkq6TdGFEfFTJKUJfEGMTvfGPSsZV1lTGYv1ClDdLujYiliq5vknjc07Qy0HMjdDRUxGxNyJ+\nmS6/KWlQyT0cPyvplvSwWyR9Ll1eJemHETESETskDSsZx0DH2V4k6XJJ/0dS/U2esYmes/0uSZdE\nxD9IyTn3EXFAjE/03htKvryda7tf0lxJL4qxiR6IiAckvdaweSpj8SLbH5C0ICK2pMf9INNmUr0M\neJPdIB2Ycem3fh+X9LCkM9IrwUrSPklnpMu/ofG3+mDMopv+t6SvS6pltjE2UQRnSdpv+x9t/7vt\n79meJ8YneiwiXpV0g6QXlAS71yPiXjE2URxTHYuN23erjTHay4DH1V1QCLbnS/qxpK9FxP/L7ovk\nKkStxirjGB1n+zOSXoqIRzVWvRuHsYke6pd0oaTvRMSFSq6g/WfZAxif6AXbZ0v6r5KWKPmP8Xzb\nV2ePYWyiKNoYi8etlwGvnZuoA11l+2Ql4e7WiLgr3bzP9pnp/g9Ieind3jhmF6XbgE77T5I+a/vX\nkn4o6dO2bxVjE8WwS9KuiNiarv9ISeDby/hEj/0HSb+IiFciYlTSBkn/UYxNFMdUPsd3pdsXNWzP\nHaO9DHjcCB09lZ68ul7S0xFxY2bXRkl/mC7/oaS7Mtu/YHuO7bMkLZW0RUCHRcR/j4jFEXGWkgsE\n/CwiviTGJgogIvZK2mn7nHTTpZKekvQTMT7RW0OSVtg+Jf2Mv1TJhaoYmyiKKX2Op++3b6RXKrak\nL2XaTKrljc67iRuhowAulnS1pMdtP5pu+4akv5L0z7avlbRD0h9IUkQ8bfuflXxYjEr6k+BGkpgZ\n9XHG2ERRfEXS7ekXtL+S9GUln+WMT/RMRDxm+wdKigg1Sf8u6buSFoixiRlm+4eSPiXpfbZ3Svqm\nju9z/E8kfV/SKUquXvzT3J/NOAYAAACA2YF7fQAAAADALEHAAwAAAIBZgoAHAAAAALMEAQ8AAAAA\nZgkCHgAAAADMEgQ8AAAAAJglCHgAAAAAMEv8f6u7ZzVYZbnsAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb019b9dd0>"
+       "<matplotlib.figure.Figure at 0x7f0969270290>"
       ]
      },
      "metadata": {},
@@ -13117,6 +696,7 @@
    ],
    "source": [
     "feat = net.blobs['prob'].data[0]\n",
+    "plt.figure(figsize=(15, 3))\n",
     "plt.plot(feat.flat)"
    ]
   },
@@ -13124,38 +704,51 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's see the top 5 predicted labels."
+    "Note the cluster of strong predictions; the labels are sorted semantically. The top peaks correspond to the top predicted labels, as shown above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. Try your own image\n",
+    "\n",
+    "Now we'll grab an image from the web and classify it using the steps above.\n",
+    "\n",
+    "* Try setting `my_image_url` to any JPEG image URL."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
-      " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
-      " 'n02127052 lynx, catamount']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# load labels\n",
-    "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
-    "try:\n",
-    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
-    "except:\n",
-    "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
-    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+    "# download an image\n",
+    "my_image_url = \"...\"  # paste your URL here\n",
+    "# for example:\n",
+    "# my_image_url = \"https://upload.wikimedia.org/wikipedia/commons/b/be/Orang_Utan%2C_Semenggok_Forest_Reserve%2C_Sarawak%2C_Borneo%2C_Malaysia.JPG\"\n",
+    "!wget -O image.jpg $my_image_url\n",
+    "\n",
+    "# transform it and copy it into the net\n",
+    "image = caffe.io.load_image('image.jpg')\n",
+    "net.blobs['data'].data[...] = transformer.preprocess('data', image)\n",
+    "\n",
+    "# perform classification\n",
+    "net.forward()\n",
+    "\n",
+    "# obtain the output probabilities\n",
+    "output_prob = net.blobs['prob'].data[0]\n",
+    "\n",
+    "# sort top five predictions from softmax output\n",
+    "top_inds = output_prob.argsort()[::-1][:5]\n",
+    "\n",
+    "plt.imshow(image)\n",
     "\n",
-    "# sort top k predictions from softmax output\n",
-    "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n",
-    "print labels[top_k]"
+    "print 'probabilities and labels:'\n",
+    "zip(output_prob[top_inds], labels[top_inds])"
    ]
   }
  ],
@@ -13178,7 +771,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.10"
   },
   "priority": 1
  },
diff --git a/examples/01-learning-lenet.ipynb b/examples/01-learning-lenet.ipynb
index 3562c7ad..1c328260 100644
--- a/examples/01-learning-lenet.ipynb
+++ b/examples/01-learning-lenet.ipynb
@@ -4,11 +4,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Python solving with LeNet\n",
+    "# Solving in Python with LeNet\n",
     "\n",
     "In this example, we'll explore learning with Caffe in Python, using the fully-exposed `Solver` interface."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Set up the Python environment: we'll use the `pylab` import for numpy and plot inline."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -17,8 +31,15 @@
    },
    "outputs": [],
    "source": [
-    "import os\n",
-    "os.chdir('..')"
+    "from pylab import *\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Import `caffe`, adding it to `sys.path` if needed. Make sure you've built pycaffe."
    ]
   },
   {
@@ -29,19 +50,18 @@
    },
    "outputs": [],
    "source": [
-    "import sys\n",
-    "sys.path.insert(0, './python')\n",
-    "import caffe\n",
+    "caffe_root = '../'  # this file should be run from {caffe_root}/examples (otherwise change this line)\n",
     "\n",
-    "from pylab import *\n",
-    "%matplotlib inline"
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "import caffe"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll be running the provided LeNet example (make sure you've downloaded the data and created the databases, as below)."
+    "* We'll be using the provided LeNet example data and networks (make sure you've downloaded the data and created the databases, as below)."
    ]
   },
   {
@@ -56,72 +76,36 @@
      "output_type": "stream",
      "text": [
       "Downloading...\n",
-      "--2015-06-30 14:41:56--  http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
-      "Resolving yann.lecun.com... 128.122.47.89\n",
-      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 9912422 (9.5M) [application/x-gzip]\n",
-      "Saving to: 'train-images-idx3-ubyte.gz'\n",
-      "\n",
-      "train-images-idx3-u 100%[=====================>]   9.45M   146KB/s   in 57s    \n",
-      "\n",
-      "2015-06-30 14:42:53 (171 KB/s) - 'train-images-idx3-ubyte.gz' saved [9912422/9912422]\n",
-      "\n",
-      "--2015-06-30 14:42:53--  http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
-      "Resolving yann.lecun.com... 128.122.47.89\n",
-      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 28881 (28K) [application/x-gzip]\n",
-      "Saving to: 'train-labels-idx1-ubyte.gz'\n",
-      "\n",
-      "train-labels-idx1-u 100%[=====================>]  28.20K   107KB/s   in 0.3s   \n",
-      "\n",
-      "2015-06-30 14:42:53 (107 KB/s) - 'train-labels-idx1-ubyte.gz' saved [28881/28881]\n",
-      "\n",
-      "--2015-06-30 14:42:53--  http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
-      "Resolving yann.lecun.com... 128.122.47.89\n",
-      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 1648877 (1.6M) [application/x-gzip]\n",
-      "Saving to: 't10k-images-idx3-ubyte.gz'\n",
-      "\n",
-      "t10k-images-idx3-ub 100%[=====================>]   1.57M   205KB/s   in 8.2s   \n",
-      "\n",
-      "2015-06-30 14:43:02 (197 KB/s) - 't10k-images-idx3-ubyte.gz' saved [1648877/1648877]\n",
-      "\n",
-      "--2015-06-30 14:43:02--  http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
-      "Resolving yann.lecun.com... 128.122.47.89\n",
-      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 4542 (4.4K) [application/x-gzip]\n",
-      "Saving to: 't10k-labels-idx1-ubyte.gz'\n",
-      "\n",
-      "t10k-labels-idx1-ub 100%[=====================>]   4.44K  26.9KB/s   in 0.2s   \n",
-      "\n",
-      "2015-06-30 14:43:02 (26.9 KB/s) - 't10k-labels-idx1-ubyte.gz' saved [4542/4542]\n",
-      "\n",
-      "Unzipping...\n",
-      "Done.\n",
       "Creating lmdb...\n",
       "Done.\n"
      ]
     }
    ],
    "source": [
-    "# Download and prepare data\n",
+    "# run scripts from caffe root\n",
+    "import os\n",
+    "os.chdir(caffe_root)\n",
+    "# Download data\n",
     "!data/mnist/get_mnist.sh\n",
-    "!examples/mnist/create_mnist.sh"
+    "# Prepare data\n",
+    "!examples/mnist/create_mnist.sh\n",
+    "# back to examples\n",
+    "os.chdir('examples')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We need two external files to help out:\n",
-    "* the net prototxt, defining the architecture and pointing to the train/test data\n",
-    "* the solver prototxt, defining the learning parameters\n",
+    "### 2. Creating the net \n",
+    "\n",
+    "Now let's make a variant of LeNet, the classic 1989 convnet architecture.\n",
     "\n",
-    "We start with the net. We'll write the net in a succinct and natural way as Python code that serializes to Caffe's protobuf model format.\n",
+    "We'll need two external files to help out:\n",
+    "* the net `prototxt`, defining the architecture and pointing to the train/test data\n",
+    "* the solver `prototxt`, defining the learning parameters\n",
+    "\n",
+    "We start by creating the net. We'll write the net in a succinct and natural way as Python code that serializes to Caffe's protobuf model format.\n",
     "\n",
     "This network expects to read from pregenerated LMDBs, but reading directly from `ndarray`s is also possible using `MemoryDataLayer`."
    ]
@@ -134,36 +118,38 @@
    },
    "outputs": [],
    "source": [
-    "from caffe import layers as L\n",
-    "from caffe import params as P\n",
+    "from caffe import layers as L, params as P\n",
     "\n",
     "def lenet(lmdb, batch_size):\n",
     "    # our version of LeNet: a series of linear and simple nonlinear transformations\n",
     "    n = caffe.NetSpec()\n",
+    "    \n",
     "    n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n",
     "                             transform_param=dict(scale=1./255), ntop=2)\n",
+    "    \n",
     "    n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n",
     "    n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
     "    n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n",
     "    n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
-    "    n.ip1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n",
-    "    n.relu1 = L.ReLU(n.ip1, in_place=True)\n",
-    "    n.ip2 = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\n",
-    "    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n",
+    "    n.fc1 =   L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n",
+    "    n.relu1 = L.ReLU(n.fc1, in_place=True)\n",
+    "    n.score = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\n",
+    "    n.loss =  L.SoftmaxWithLoss(n.score, n.label)\n",
+    "    \n",
     "    return n.to_proto()\n",
     "    \n",
-    "with open('examples/mnist/lenet_auto_train.prototxt', 'w') as f:\n",
-    "    f.write(str(lenet('examples/mnist/mnist_train_lmdb', 64)))\n",
+    "with open('mnist/lenet_auto_train.prototxt', 'w') as f:\n",
+    "    f.write(str(lenet('mnist/mnist_train_lmdb', 64)))\n",
     "    \n",
-    "with open('examples/mnist/lenet_auto_test.prototxt', 'w') as f:\n",
-    "    f.write(str(lenet('examples/mnist/mnist_test_lmdb', 100)))"
+    "with open('mnist/lenet_auto_test.prototxt', 'w') as f:\n",
+    "    f.write(str(lenet('mnist/mnist_test_lmdb', 100)))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The net has been written to disk in more verbose but human-readable serialization format using Google's protobuf library. You can read, write, and modify this description directly. Let's take a look at the train net."
+    "The net has been written to disk in a more verbose but human-readable serialization format using Google's protobuf library. You can read, write, and modify this description directly. Let's take a look at the train net."
    ]
   },
   {
@@ -186,7 +172,7 @@
       "    scale: 0.00392156862745\r\n",
       "  }\r\n",
       "  data_param {\r\n",
-      "    source: \"examples/mnist/mnist_train_lmdb\"\r\n",
+      "    source: \"mnist/mnist_train_lmdb\"\r\n",
       "    batch_size: 64\r\n",
       "    backend: LMDB\r\n",
       "  }\r\n",
@@ -240,10 +226,10 @@
       "  }\r\n",
       "}\r\n",
       "layer {\r\n",
-      "  name: \"ip1\"\r\n",
+      "  name: \"fc1\"\r\n",
       "  type: \"InnerProduct\"\r\n",
       "  bottom: \"pool2\"\r\n",
-      "  top: \"ip1\"\r\n",
+      "  top: \"fc1\"\r\n",
       "  inner_product_param {\r\n",
       "    num_output: 500\r\n",
       "    weight_filler {\r\n",
@@ -254,14 +240,14 @@
       "layer {\r\n",
       "  name: \"relu1\"\r\n",
       "  type: \"ReLU\"\r\n",
-      "  bottom: \"ip1\"\r\n",
-      "  top: \"ip1\"\r\n",
+      "  bottom: \"fc1\"\r\n",
+      "  top: \"fc1\"\r\n",
       "}\r\n",
       "layer {\r\n",
-      "  name: \"ip2\"\r\n",
+      "  name: \"score\"\r\n",
       "  type: \"InnerProduct\"\r\n",
-      "  bottom: \"ip1\"\r\n",
-      "  top: \"ip2\"\r\n",
+      "  bottom: \"fc1\"\r\n",
+      "  top: \"score\"\r\n",
       "  inner_product_param {\r\n",
       "    num_output: 10\r\n",
       "    weight_filler {\r\n",
@@ -272,7 +258,7 @@
       "layer {\r\n",
       "  name: \"loss\"\r\n",
       "  type: \"SoftmaxWithLoss\"\r\n",
-      "  bottom: \"ip2\"\r\n",
+      "  bottom: \"score\"\r\n",
       "  bottom: \"label\"\r\n",
       "  top: \"loss\"\r\n",
       "}\r\n"
@@ -280,14 +266,14 @@
     }
    ],
    "source": [
-    "!cat examples/mnist/lenet_auto_train.prototxt"
+    "!cat mnist/lenet_auto_train.prototxt"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now let's see the learning parameters, which are also written as a `prototxt` file. We're using SGD with momentum, weight decay, and a specific learning rate schedule."
+    "Now let's see the learning parameters, which are also written as a `prototxt` file (already provided on disk). We're using SGD with momentum, weight decay, and a specific learning rate schedule."
    ]
   },
   {
@@ -302,8 +288,8 @@
      "output_type": "stream",
      "text": [
       "# The train/test net protocol buffer definition\r\n",
-      "train_net: \"examples/mnist/lenet_auto_train.prototxt\"\r\n",
-      "test_net: \"examples/mnist/lenet_auto_test.prototxt\"\r\n",
+      "train_net: \"mnist/lenet_auto_train.prototxt\"\r\n",
+      "test_net: \"mnist/lenet_auto_test.prototxt\"\r\n",
       "# test_iter specifies how many forward passes the test should carry out.\r\n",
       "# In the case of MNIST, we have test batch size 100 and 100 test iterations,\r\n",
       "# covering the full 10,000 testing images.\r\n",
@@ -324,39 +310,44 @@
       "max_iter: 10000\r\n",
       "# snapshot intermediate results\r\n",
       "snapshot: 5000\r\n",
-      "snapshot_prefix: \"examples/mnist/lenet\"\r\n"
+      "snapshot_prefix: \"mnist/lenet\"\r\n"
      ]
     }
    ],
    "source": [
-    "!cat examples/mnist/lenet_auto_solver.prototxt"
+    "!cat mnist/lenet_auto_solver.prototxt"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's pick a device and load the solver. We'll use SGD (with momentum), but Adagrad and Nesterov's accelerated gradient are also available."
+    "### 3. Loading and checking the solver\n",
+    "\n",
+    "* Let's pick a device and load the solver. We'll use SGD (with momentum), but other methods (such as Adagrad and Nesterov's accelerated gradient) are also available."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
     "caffe.set_device(0)\n",
     "caffe.set_mode_gpu()\n",
-    "solver = caffe.SGDSolver('examples/mnist/lenet_auto_solver.prototxt')"
+    "\n",
+    "### load the solver and create train and test nets\n",
+    "solver = None  # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)\n",
+    "solver = caffe.SGDSolver('mnist/lenet_auto_solver.prototxt')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To get an idea of the architecture of our net, we can check the dimensions of the intermediate features (blobs) and parameters (these will also be useful to refer to when manipulating data later)."
+    "* To get an idea of the architecture of our net, we can check the dimensions of the intermediate features (blobs) and parameters (these will also be useful to refer to when manipulating data later)."
    ]
   },
   {
@@ -376,8 +367,8 @@
        " ('pool1', (64, 20, 12, 12)),\n",
        " ('conv2', (64, 50, 8, 8)),\n",
        " ('pool2', (64, 50, 4, 4)),\n",
-       " ('ip1', (64, 500)),\n",
-       " ('ip2', (64, 10)),\n",
+       " ('fc1', (64, 500)),\n",
+       " ('score', (64, 10)),\n",
        " ('loss', ())]"
       ]
      },
@@ -403,8 +394,8 @@
       "text/plain": [
        "[('conv1', (20, 1, 5, 5)),\n",
        " ('conv2', (50, 20, 5, 5)),\n",
-       " ('ip1', (500, 800)),\n",
-       " ('ip2', (10, 500))]"
+       " ('fc1', (500, 800)),\n",
+       " ('score', (10, 500))]"
       ]
      },
      "execution_count": 9,
@@ -413,7 +404,7 @@
     }
    ],
    "source": [
-    "# just print the weight sizes (not biases)\n",
+    "# just print the weight sizes (we'll omit the biases)\n",
     "[(k, v[0].data.shape) for k, v in solver.net.params.items()]"
    ]
   },
@@ -421,7 +412,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Before taking off, let's check that everything is loaded as we expect. We'll run a forward pass on the train and test nets and check that they contain our data."
+    "* Before taking off, let's check that everything is loaded as we expect. We'll run a forward pass on the train and test nets and check that they contain our data."
    ]
   },
   {
@@ -434,7 +425,7 @@
     {
      "data": {
       "text/plain": [
-       "{'loss': array(2.301163673400879, dtype=float32)}"
+       "{'loss': array(2.365971088409424, dtype=float32)}"
       ]
      },
      "execution_count": 10,
@@ -458,216 +449,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 5.  0.  4.  1.  9.  2.  1.  3.]\n"
+      "train labels: [ 5.  0.  4.  1.  9.  2.  1.  3.]\n"
      ]
     },
     {
      "data": {
-      "image/png": [
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-       "mVlZW1dWd0XH9ERMzxIzEzE1M3/M1HvzIt68iZjpF9HRPdFvpqeqZ6ajJyozy5VbpZ1e0k4n6R0w\n",
-       "JBizrwIJxCYJgQTc9wfcW+D0KiOwK+8ngkBcJN2jo3N/95zf+f5+PyFJEioqKioqzz6arW6AioqK\n",
-       "isrjoRpsFRUVlecE1WCrqKioPCeoBltFRUXlOUE12CoqKirPCarBVlFRUXlOiNpgCyFeEUJ0CCG6\n",
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-       "OxvXLBUVFRWVe4nWYLuAoTV/D68eU1FRUVGJEXFRvu6RfhQhhBrzrqKiohIFkiSJ+x2PdoY9AmSt\n",
-       "+TuLlVm2ioqKikqMiNZg3wQKhRA5Qgg98H3gw41rloqKiorKvUTlEpEkaVEI8Y+AM4AW+GtVIaKi\n",
-       "oqISW6KS9SkvFqIfmAWWgIgkSbvX/O9b7cPWaDRotVr0ej06nQ6tVkskEiEcDhOJRFheXkZNbaui\n",
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-       "UF1djcvlQqvVkpqaSnp6Oqmpqbjd7k1ry72s7Ter1UpqaiparZbs7Gzq6ur46U9/Slpa2gOvZb/f\n",
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-       "OrKyVtSbQ0NDRCKRDXvvpyE7O5v6+npOnDjB9u3bkSRJ3RN4DEwmE+np6ezevZv8/HxcLhdpaWlY\n",
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QPKRCoKCggObmZgYHB5PeBEroW/1z4/fC4qy2tpacnJykjWtfC7ZSqcRsNlNT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\nSUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7939901710>"
+       "<matplotlib.figure.Figure at 0x7f51a65ee690>"
       ]
      },
      "metadata": {},
@@ -676,8 +465,8 @@
    ],
    "source": [
     "# we use a little trick to tile the first eight images\n",
-    "imshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray')\n",
-    "print solver.net.blobs['label'].data[:8]"
+    "imshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray'); axis('off')\n",
+    "print 'train labels:', solver.net.blobs['label'].data[:8]"
    ]
   },
   {
@@ -691,204 +480,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 7.  2.  1.  0.  4.  1.  4.  9.]\n"
+      "test labels: [ 7.  2.  1.  0.  4.  1.  4.  9.]\n"
      ]
     },
     {
      "data": {
-      "image/png": [
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7939901490>"
+       "<matplotlib.figure.Figure at 0x7f51a4030a50>"
       ]
      },
      "metadata": {},
@@ -896,17 +495,19 @@
     }
    ],
    "source": [
-    "imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray')\n",
-    "print solver.test_nets[0].blobs['label'].data[:8]"
+    "imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray'); axis('off')\n",
+    "print 'test labels:', solver.test_nets[0].blobs['label'].data[:8]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "### 4. Stepping the solver\n",
+    "\n",
     "Both train and test nets seem to be loading data, and to have correct labels.\n",
     "\n",
-    "Let's take one step of (minibatch) SGD and see what happens."
+    "* Let's take one step of (minibatch) SGD and see what happens."
    ]
   },
   {
@@ -937,7 +538,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f79383819d0>"
+       "(-0.5, 24.5, 19.5, -0.5)"
       ]
      },
      "execution_count": 14,
@@ -946,439 +547,9 @@
     },
     {
      "data": {
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jFTwwn93j7J28IyBzl56sTpaurot9FallO3DApo6nS85qMkS9O7qfyl2WnLeCHYvQYu8o\nrBP2HIhdg8dqwzo7cE2XnQi119fXd28lM8xiQqiA5gCmAtxEnAmpc3y2V+dUXgegI5vbFhalVfcy\nmKlz7FmXAtpk2YkAmy49bwG1u0Rok+cqFSAw4lDQYobdSQczBTdV/mTJ6URnWRBq+V6mw7z8XGu9\nMdZssMwBsD6EGFsmueKAg13L9qpcVg/edwRgzImZ/qr2ZL2pfY7OXJhhm9yNQayaANVk6MDsrwRa\nSGXwMbCd5AHL9TqGrtqUy9tdclbR2c6SUy01s9Gq+1hb8rWTmZc5B7apA1k15t19FRhYHeoaBbWj\n8HKd2NlXdh3pSWT28PAgx9iBmQMxF2Sdvh17YHL3JSczfhdm2THZ4FdpdtzJZMmJZbN2HllyRvuZ\nk2F9FeTWWnQ5UTks1s0gtwM11xHy/Wx8Owiw89W9O2DrdNe1Ge2ku5fBS4EO2zmJzHa3CfTVmDty\ntyXnTmMRYBi1sHon9TDYKLg6wK0g1gEnHzNhDhcww3urutbSz9CywVdAU1CrZAdoVdkVxFidHdS6\n/u46adferMuq3Wv9s+zsgJXBhhBjUENbcN92Tvr+UVD79AiNzTrObM6cW+V3dU/EhVnkqT6p5d4E\naqz/CLNYVlRloM7cN5wVvFj+NEqbGDTChu3Vueq+CmK32FS7VF4FN9ZGtQTNS8587WTJ+RkR2ZcF\nmmqIAtk0aguHuZWwtmCeug+Pq0iPRX0d1FT9bPAzzBCECnJhuN1HtQgr1gYWNWMa+8D6w/p5VBzQ\nTTcEhMpjIK3SKi/GF/9axoEZggzbvAMsx1aq/lVj9aWA9vPnT5rvRg476ZBOCa+vr39C9c4o8zJO\nlaWOMVrKaQQebs6va+R/UqL+YYmj206yYXX14rErCJYujQ5UQeTnz5/r8fFxXS6XNyB+fn5e1+uV\nLsk6x490tzyLY+XcyuEnNhF9u1wuf+qJvuLEiGXnbzlzfrQf/aR6JOH4ExsfNY5/FdA6Z6ucj51j\nUkUFlZKVUisjxH0GGIKsA5rKr2BS/QemTq8qMmD5Cl6qDSFsnDBP6dKFGnOMtda6XC7r8fFxPT4+\nvvlk5eXlZV2v1z/lPT8/S7Ax550s2bq+YbqbzLI9vLy8/OkjTj5xjbI3BbRc9o6/dMtOdf7LAu0/\n/uM/5DnXgSM/71lePocKUMc7AxH3sWOWjn2GmgKaSsex85to3TVK90pXOT+314nOMEKrxk+1oZtA\n3C3ac7lc3vyhfgAs0vm7LndfLcnUcoz1jU2ILtCen5/fBBAKSOp8jFVezh55DqbGB/OrKPvLAa2K\n0GLvOF6+p0pnUbNeTsdA7g5aNfPkj31jkAJiDGjuvovUIt91BqXDyojc6Ez9tJPqF6u3Op44EwN4\ndqDn5+c/5xm4qrwqast5Hcx2gZZtLCTbSCxDUfJEiXYZk4Bqn9N+5/oOaNgvR+665KwiBxZBdMcM\nXiqPDVgHKnXMDJYZSVybgZb7UYG7isKOQE1NCkovbnSWI7Rusura0LWtOlbpGAt2vgMZ5jvpKRAm\nPsLuCZihbaJddTqsxmJyjOnOj1hk6ciXANoOzJRUMwXuHYdgRp2hlJ+TOVEfGlHVPzS+DCpnuYcG\nrnSMbaykis5Yu9wJTNXljnEFB/d5l/uMqAJe9YC8ayeCp9NX6DufD93jy4nKrhjA2IRbSbYlR5yJ\n46+N0JTzTcWZ+dwZPKerpUXALLeBQZFtE1Fw6mBSgSU/EGa6ZJv7UmAaJe5MYJUj5v31ev3zWUrO\nj7x424kvBRTEJtGFen5WTaS5/06UlvPz80L2hrWbONU5d8/KY+W7E8NfB7TO8NfyqO/O1h3k2LXs\nzZVyRgUzlcf6wPJxOeks99xIbSJdfTttyFFGrgfrVbqp0qG7p6entdY/39zFuF6v1/X09PRnr+DV\nwc2BXrTHAVr02QVbBlm88cyTMApOZkz3FUS7tlX76Oe3Appj6EwqEExnwWpmz2n8W8eIygJsR9qi\n6szpKsJyI6TqONfXbZOXAgxqDMyVM6g8pnM1DnlijDGLdIDs9+/f6/fv34fe7k03Zg+RZsBgadR9\nfJ6SI1K0t6q8zt6mwYjKd5fquFzu5C5AqxTDHG0tbyaOfWU0qpzqOJ5JBMgeHv7/RxFRFDQVSKt9\nGHWGmguIatmJ6Vzf5XIhI/a2f9OXAgxsVQQXfXVmeUdCfzgBRfQSQPv169f69etXCbSsqx1gOeci\n3wHOWv98zhNR2fV6/bN8xgitgg/Tf7UawLQDuGxzDFwKahP5dKC5jpZn1RAFgEhPDaeTDLTY4sty\ndm0FL9V+N+0YmYrQqqioAgTqLL4+d6OzKp8dr7X3RrTrQ8Dser2+gXjk/f79e/369Wv97//+7xhg\nauwdaFVldkDPQMvLTIzQ1NLNCSi6FYBrb5i31l8ItGg4y58AbS0voolopoIXGkulLDynYJU3fDlQ\n7bsyu/btSNbTWm/14EK/A2EFnZzvRAhVOpfTSQfukA5eU6DtQC3yo82Rxv7kfAYrpbuJ/zkQw0nO\ngVq0B1+q5fSuD3w60JwOZ5JnJ+z2IZ0S2Hl1jzJkfKDZfTzJ8ibXsHZWfc7Qqu7pAHZr6eCC0GP3\nOXCqyt69fy3vkYbKn+TlNuMExPpVbWwpOH184UTkLjAzzHL7WXoqn/6PhqcR2hRmWPcUbioyY/BS\naSzHAViX7sCE7WWzN5aRv6dTTrkjLKpA2YnQ4vizZCeyqnTpHqOwPjO9sfzsU+rRw250ppacHczY\neP/4wf/Bz1S+VYSWQ1e8l8kEECpCYx9mYjkdyNjxpH1deq33MMN6GMg+K2rLUkVo7n1Z1GTXlefY\ngLOx+3MeS0/6l887Wwe1Cj4Ix+r5WXUc7c0Qy33A/Il8iQhNKSTKcSM0NNYqiqvAg3kMYmrPyuvq\nmogLsOo+dq5yxhAcUzbGTnSW71czNqtjZ+becYpucsvpI4DL5XfRGdp4Pq90WEVf7r6LxFyQ5bzc\nD9ZmTE/kLhHa5OGhG6HFnx3lZVTUp2DmQM2J0HKkNil/ItgPx+kcqSD2EREaOh47n6/LeZie1Lkj\nO/ByPvvIZWIetluBLOdVURRLOxCrgDYBHYPbWksuMb8s0LoIrXt+lpecsa+ghgqKrYrMME8ZnTJY\ntuzEOiq4dfpSOpxEaQ6UusjsVmBTfXQitF0oTdsTsmsT1bmu3NizqCzn57Y7EZsCTAe6Wzw/Yxu2\ntYPbRL58hIbgqiI0R4khncHmdAc1B2isruhf5OFgo0GrPqj2Y/ns/qxXhJq6b0eU4+F5B2qOobNo\nx5FuwnNgNonSVB0/frz9f7POZNBBbBK1udGXCzHcFMC+LNBUYyqAdS8FolzMi/wKYDnPhVm+nhkt\nvgzoIhzMq4w1t5vN2i50Oiiy/jOwHREGM3Y8gZpb55E+OJNbBbLKLhz7yBO4018GBwab7rmaev61\nu+RkvukeT+WuEVoHtbi+i9BwRuvgtpYXmeW0Y8zdZxssjX2JNjPgVdGDSrPJAOve+SKbieN8CKkO\nZgxqE9m5H3XBoITActKsfGYvWCdu+U1hBTIFpS7tAmwHZthW9qgon5/K3SO0CmprcafHCO3h4Z+X\nAhkKnUxhhlEZ26aGiwBTenRm9NxmBTKlB7Wp9hwVBpojULtVNJnLYzroIjP2axFswlB2gvUoYROD\ngpjyMwdyLswY1FTbcj5ew56nTeTTgaZmZdbhEHRQlC7aYuc6gOVjtqxEkMXfzuW6unQ3+KyfyqEy\n7JnzVMZRgawDxW4ExXRRtXMS+bFJw2lD1dYO+szmunJV2x27mEREFeim53aux/bmYzyXJ+FuImby\n6UDD8+4szByNzZKTn3/BctgxewGQf8kg/0jgEaA5RpNnrvx3cHmfwYaQUOBQeunGjh0ro1U6UBBm\nNlCBXt3bOYRrK1Oosbbn9nQTPlvO7S7xHBAdLRNf5LlAy/pSYJvIp//ndOwU7nOaGUdlVBOosbJU\n+Wp5mX8nDX8RNcpi6TiezHShEwdqWX95XwGOwQz3ClzqWIkaE3R6VQ5zBKXnqg2YvgXUJm2t+nf0\nOVW2p+6B/065VduiD+pYTeYMbu54hnzpCM2FGlsSdmBj5XRQy3UxmO0CLQwi8tTbXhaNMaiF7hTA\nWFpBrRPmoO6sqoBQ3c/shd3rzPDdmN8CaNGGrG/Vz9y3I+C5VQSnQKbORdsRYjnNxqeD20Q+PUJb\nawaz3DlmgN2r8gm0unPquVk+7iCW02EIr6+vbwwl+p+XmZGXn5epCC33gwEM24QO2Tkny1Ngq+pE\noFb1oG5yO/E48pT+WX9vAbEKbpWts3QGyCRa+4hvxjqQMdtlPs7g1oGL2U8ld3kpwPYqj0kFnC49\nMVQGMbX07CI0PA6I4Z7pJ84fidCyThFunZOyMazAps7nupmuc78VgJm9KLBVUGP9nmz4iY6rN3ff\nwav6tuwWEHNBx/JVnxi0u60DHsqXiNBYHhpnCIukcMm5s9R0DLgCWfz6aW4ntpv1IwYtQy33P6K0\nyFPPy3JeLpuBDEHhRiBsHCt4sbHMkGVAQxhVNjIBmSM7dsHarvQQaebkmKc+oUCgHAXZ7rM51hY2\nRh3glO3syl1eCuQ9y2OddKG0E425szF7IYBgy+3F9rM+BcgyzHJ6rSUjNBWxZR1miDGQZbA4EFOi\nZmolue6sH4RY7kuuJ1/HQJavcQFenVM2lsvM+0pHTF+Ypx7kuwD6qIitg2oeL+XjqKOuzonc5aXA\nFGYoVQR1a8Dhm9MuSsttxDYzPSDIEGZ4fQe1fK0yCHR05pyqvROpIBppzM+6Z3aCxwjGKs3k6GSH\nZTEdsL648Og+jJ3C6JbfmLGXV1nXnZ8fAZeSu0ZoCmZVB12Q3QJmDGrqpYD6Di23m+ki16NAFtd2\nMMOXArnuyngwQuna7G5KlH7Q8au6c78QkkwHXTumtqCgz/pXOXEFCrWfwuejv1tjz35VmumCje8u\n4L58hJaPHQPMwLkFzFi5eblZvRTIUgGt02PU70It7nONI8MMweaAYVe68VSSIaYitE632Gfsuxud\nTfTjgojBYgq3W4LMBZuCGR5XIGP6mshdXgowUYbZwQW3W0LMBRzWr/qHEgMWEHL67cAsl+1s2L4d\n+HblM13gZIXlK7A5oJpK5Yy3qItFJhUcHKg547oDrHwfK0OVq3RX2cCuPpXcBWgIrZBwzHizx6Ii\ntszL34BVoMnH0Y5q9lVtr+DW9TlLhhcrvzuOuqMsLHsXaEom0ML7quMsFdhiy3bDQMz2WKYCR5Sf\nn2sigHKe64RMZxW0qnRVzs64sH0Fqm78Oul8rhrvTu4aoalG5oiDvU1kD+MZtPIxpitosLYppePn\nIkycGb8bWDzGTzSw7snMjW2sAOTAcaqDMFqlD6WXfMz2mGZtCiDElstQYMvXuVJFN1UE5ixHWdks\nnduCaXaPOp6KM0lVwcRE13cDmmpkNhwGMgYzBrQMG3YutwGh0bVbRWlTI1eD6BpAFRFWAKtm4A5s\nHcAmEcJUOpCxPDYmHVxeX1/fgQwjs3z/TpTWPRvrPpx1lp3YV2wLpjswVhNdFma7eOw+IprK3Zac\noQw0xjCQDLQKZAxobI/piTOwCInBDJ9hKWHnugit2hjYJiBjBpvHobuuA5yjk0ofVaSGaZWHzsFA\nxmCGy9AMNbcfrD4Hbrtgy/Wx/mKeSneTVtVfZ5yYT1Zj7MinAy1DS+XHnj0Lq4CGyqmOc70szdqN\n6SpCyzDIkvveRWgd4AJk+cNbrMsFWXaQfD8ri92P12E7HMl9q6CPeXmPeQpkrA+45MQ/TVPXVv2p\n9K2eoTFITV8EsP6qPBeEHci7gCCPySTQmMiXeoaGsGMQU2BzQli23HTalfMZjBAwjgPjNQpiqt6X\nl5d3IIsy0fkmIItyc3ls34GsM37W/wpieL4DGdMd1q/AwmDG4FYJXtPpH2F2q88yKpmAzRUGM5Wn\nIrMjULvrW8613j+DyOkqQmOgqxRVAWOn/Qxm+UF93mNfM3g6eKJD48AHgPLehVnkBcDwuVGWrky8\nLh+zvatj1JUDMMxjomDSRWZupMLgW0HsFm803U3pQulmCjcGLjxmkdktoHb379BYg3OEhlFZteyM\ne5WC1GwvhCxfAAAgAElEQVSPos5VIGPgUcaTDVyV20VpoZ8MsSizgxk7DgknVnpSMFMg2xEGMoR5\n1kNOs+gM71POm/vOnp3tPkNzQcPgdgtgoahrXfA59XQ+yGCWr4n0VL7MW052XH2Hhr9ywb4ty8cs\nvRZ/aM3OVY6SB6mCGaunGjQGybXeLgtzRBZlT2CG/Wdt6pwH09jP6eye9ZPblHWQr8/7Kl31KS/1\nEGoIper5GWs/q68C2hGYTQFXXduVgf6xs1Urqx2565Kzm2Gdj2pzXtyPZbE9GzwGMryPzTY4MK4D\nI9i6LUv+CBnzHJhhOh93UMttV3lVf5UomFVQ69KqL7ntCDOVRrBNpALYZMnZATKfw7rZOaedzn2V\nTGDG7pnIXd5yuvvuA1ncqvJYGh3GdUQFtUr5u8bA2p5FPTPrgJb7rEDmRmq5fw7cOj2rsdmFGquj\nAgZbWqr+KgjHNaoNDDiVftn97DyWW51ztq5MJhW03Gfdu/JhQFONcoCj7t2ZdSqjcvfTwa+Mkenj\n6AAjmDpjV5Eo09G0P1VZbJ/rYWkU1AWDrts/1j4lKoLANnRAzfdmJ8/AZtEgG+MoCyf2ym5uYc9K\nVyoCY9+E3sLumXwY0Kqv2NfylgpKHHiwmZIZe7fP9ailQL6GtXUKA2dg0cAnchRMla6ceqtJQ0kX\n9WA9+RrXIXNdLoywfV1abRlq+SVPHmcWNa61JCiYXpS+HLAxXWRddzDDD9BVm49A7dMjNHXeMVK1\nnzg1gqrbV9GPGvQKbpXkmTkfM11hWaqd+Rmb0w42MTAdT/rX6dyZoLCfnXRtccqYRhBTm8Y68ENp\nBrLc/ww0jM6wfqVjNnF316h+sP5gBKmAVh1P5MsAjeWra1wHyLNHhgSWwfaYN/nbuXw/A4TqN4IM\n03hdPsYZnX0oq3SCbe2g4hh4dZ2CWSdO1K7O7UwwrlQg6yI0ls8iNAYyBJoqm/W3st8qr+q7ijzV\nkjjfk8tS7XfkbkBzr8niDggOYjWomGb7yav03DZsK6sXBQ0YB5fBLKezkecPZbMuXMjiNV0fnTK6\n8iqnwb7iNV2/jgCsi9Cc6Gy6xOom37hm5xlaTjs2vaufqn25jypvKncF2s71O4pHqEUeplW04A48\nK8eNPBBikZ/31T2Yzn89gEBkzq/0xvpwBBSsTKU/JqgjdW01cU1ArMBQ5au8DOTY8qQVzp/HTPU1\np51naGoymtq26lsFMQY11BWz9b8aaEeEGakCGDozc6KclyFROXjlKLuOqoDGwMTy8vdTcQ27h4nT\nR9WnCiCqzG5SwT7GNV1kVk1SnTC9d5FZla8gls+ttd4tOXNfWfudZ2h43xRqeI/qswIbQg3bdxRk\nIZ/+lrMzJOd85Qj5umpAc9rJm8xcTrmVw0Y651fRlQIcm/UxOmBt75y/m7WZTCKAIzDDOvM1k/Yy\n/eMxA15VVgYZlpvHCcuqbBmhwUCZr1eAqgAXeaxfrE/qDWd8EF9N1Ep3rtzlO7QKAhWpp46V81VZ\nypEms1c1+BMnYu2s8rLDZqfBOtmbTkeUkXf3TMufQp7BW0GY7SeAU2BjQGP3sL4oEKh7K6dmP1ev\ngBtSwWs60WA7VYTGlpxdWTty9yUnM9bqukgzA3XqqcpU105fCqjypnAL6ZaczLmzw7CZm4mj+5xX\n9V+Vs+MoKB3McrlOhIb5FVRYtOa0N7cHy8v7vNx0ZfIMbTpRx7VYFvaNgawC20fJ3YGWBSMNdU3s\nXUeq6qvS2SEmP7DHymH1VMIMkjlGdb9aeuAyBIUZ8xF9M4BNy1NLMKbzajKp4DbpVzc5sHYzEFeR\nmVPmWrNnaGvVunACBWWbbnTWAW3Xn9f6QKBdr1ea7ypzrfevpKtnA5iH4oTyTJGvr69v/vg94Pb4\n+Lh+/vz5p12qb13ezixZzZi5jdWWr7lcLutyuazHx8d1uVz+9C/yI2/6SxDqD67VjxhOpNJnTr++\nvv5pf97nPucN9ZfTz8/P63K5rOv1ui6Xy3jJuTsxVpLHKspe6x/IXa/Xdb1e1+/fv9fv37/fjDna\nBOZ1QQX2ufqPbPhDrE6ZO/JhQIvfJ2PiOr562NnNPNWSYPogMgYqQys7Q7QvjDX3pwPUZGP9VKLA\noX6zHh0dnX7yO114TVd33k9ETYwI/tfX13f9QZjlNJsM8JddLpeL/KfS1fPObuzzOdZXJgpoEQw8\nPz+vp6en9fT0tH79+rUul0s74akxqdqRg48MMsxDoE2g6chdIrQOaDgobE2OZcb1ShjknH0eqKgr\nHARn9DCWzsl3tolMy2YzNEtXfWH5GOF0gMPx7PqorsdjjEIU2AJo6PABMIw4ppOlsneV5/YtAy3q\nzRFTRGiPj4/2mOCLJDYmOa9bYuLWlVflVXK3CE1BLJ9DkOXlHXueFOnYd6DK6eo4jDfPtJfL5U+d\n2ehV9DH5vasKas6zM+Ysas8M2Z29VX+wr8qJptHAxLizvjqIIdB+/Pjno+QY18vl8i76qADG0hWw\nFMS6812EFkvOiNAmY6j0rsZBfUzL0l2ZU5CF3C1CYwBjgHPf4KzlRWisrG7DZ2gIs+fn5z/LT+W4\nzJHReCrQYT/c46wfJS5wHDgzmHW6cIDmGLha7mF09vj4+Oc5GMItYBZAe3l5+QMz/J4q19nBjInq\nUzcR5XQADfWY2/n09PQGeJMJ1B0XDDpUGp+fVeXvQO3TI7QOaHlTkdPuMzQGK/Z6WZ3L7Q0DeX19\nXY+Pj28iOCfS2YnSqmWzSjuO5sBJgUzBTYGri/jYeGI6SwWPGJO8bMZnhAG3AFqGVgBNLacqHbNz\nXfux7xXYEGjVM7SI0JTfsfomY5F91d2qMo9A7a4RmjNDVMvGXCbmZVFKVeFwNaOsxT9UzQaGm3qj\n1PUfHb3SAYJf9ZvlT8HKgKZgN4F71qNKu3YR+4iwFMRin6PsyjbwvNMGN52PO9gwoKGt5GdoAR0F\nyaPpqLOzwzh2x3kqd43QOmdZy1tSTYUZbLX+72CDUYmKCHDvlpsHFw2D5TkRZ047kUB2oApoOzCL\nLdej2oN2oGCdr8kwy+mAWzh+RNqOziZAU2NW7VUEhXldhBYwy2Vn6cAxAZsTsUa6AuoRqH16hFYt\nteIcEjwL5rPrGARVhMaejSDcMKpigGLLGXQePHZhxoDWwUwBG/NQV51+O4CxPPebpwqs2JYqCkUd\nYb0Istjyd4XKXlTEy8ZCtdM5rib8fB7tLZeVn/XlN/VdkJCPu+hpCh0VnXWRoxvIfHqElh+cozPn\naCgv6xy4qU7jzKCgVn0zE/CJetAx8rdM7I2a2hyQZV0oh1X9wn6o40oY8NjzsS46cz4JmUSKCjC4\nPTw82OORn4U6es55Ttrdcp+7TS3d85tEhFsH3ZzXgQbzJseTCcyVu0Vo8dA1BgZhhmEpU0q+J9J5\nn0VFMczB8avmKC/KDkf8+fPn+vnz55+/Gvj58+c7B2HpOO6Axp6huVv1pTamUT/VXgFNAa6LZNmS\nswKZAppKh81VEMv6uFXkxc5VL6ByXu67M+nhuag7+oR+5sAWfQ3Tk/3uOazbkbv8G7us9LXeRlcI\nIyfcRIPBfTcbMoNl4s6S6qG3imgc43X6z46dc6zPeXLIovqiQIf96ZbVTjq3JQMgtx37gukOnJU+\ncNLMoMjHeC63l9XFJnFnqyT8oJoYVd5RYb7L9Kfa7fg+yt7vyhyUriPsWAHJgVj1LZtqy45hVQ7K\njE/V3emn6lv3dq4DPeqA9YM9LmBgw/yJIyKEWDvYOLF7Md2dV9d3dXYAVvewc1Wec46NpxprJyLD\nfuM5dX8nKvJl7XLkLv85HQUjATUTqntYSO+CLd+Poupmzqmct+tznsnxPOZXcMe+O58d5AfFrA0s\nv+pvBzBWzsTBMcrINsLy8tgpuKj+YoSFdeX68tio43wPqwPPTUDG6sAyWRvcyLyqK+dV9l61EfOY\njlz5EkBbq4ZaCB7jwCnnVtEIm8VQKudkTqwcGvvK+h/1VddmY6y+nas+Q0GdoG5VpML0US2rq0jN\n0bmqM7epghlzNOV87JhBLdeFaXXM6nFgyHTS6U4BHvtU2b2yBUePrKxOlC7+qgitInQ+DlEdzHnO\nQ1cV2nZw68CGTt7NohO9sHaqCI19isJghg/AUc8sanLhriA2fTZYXcvgVUEtj0EHbAX5atJhdsv6\nN43QHD0xUdGqmiBRKttl7VETzaS9+b5Kh5V8mQgtxI1U2H0VyFhUoiBWRQOVg06fF3V9VXCvoFbB\nS0VyuX9VZKOgVS01u4iVgVMJu7eCGd6rymR5zMFwPDqIdfBizutEJZ1tdbqoIFbpbWeCrkQFNbn8\naZR2V6B10Uh3Lx530YvaOnGiFBV9OEbggq2D2CQyqyI01ndnU5Bz3mxO6gtdYBnowBVAVV8VXCr4\nOGBz7q/a302OKKws19a7fKa36t6unezeKchC7h6hOaFlN8NgxOVADCMeJlNHY87Lyoo6nTzV3w7g\n3fM0BXQVsdwSZqqeTiqAxTlMq7459bMoQY1P5ZAILJbGPu3oh7Url8ukq0fBq7p/Z4x3AYZyd6CF\nKKNh4FF5uxuWGW1A2OY9g9juJwpMD8xBsL0qUmNQY6CPa/JHreicqs8IsQ7uDPRKN50Oc14ViSiA\nVU7IRMFK2Wl1/SQ9kUlAgO129LEDtnvIlwFalioC6/ZHIMbEjcyqreunO+urPjJIdYBTzxRZlKBA\n1kVo7iccqOtuDDCPRWWTaKeCHbORHFmpMWLlVREapnftq7NpBnw1iTF9sLwjk0WWW0RpXxJoWRBW\nVV4Fru4cExWpxF5FIyqi6PrYRQIVwCuIOR/YhqEz43ZA1uV1DsmcpNIjOn7WVT5mZbrjwtqFUkVm\n7PxHRWhYV3cuAx/z8x7zUab5Xbvcc0ru8m/s3Fkol3MEbCpfGb0K0dVgY38RDAwarO544xhLwJyP\nEEJYVVGb0gMeu0ZYRUsqryrbcR52DUIsO2lVvwOyrj1Mqog7zrOoaCdC69qpbDin1SSt+uCOS5ee\ntHkKtQ8DWv4CHcWZrVlHnb3rvG5kxvKqejEvnlGptNId6uX19ZX+tBFCDvvl9DfrfOJMyrkq471l\neXEOwTbtzw50K6nsikXdLN3Vh7Yxaa8Dst1JZnIcUq1QJnpf64sATeVlQYiwdBWxYRrv65xdCYuM\nAlpdGxi4lF4wKuveXHZ9yn1zjaYbo0lZzn3TtuUoeAdarEx1HPXkce8E4ZXLVOec9k91PgWaW+cU\naBXIVB2dfKn/KcCOGYCqtIIIy9uFWL7/VtEZOiHqogKa84mKCzjUgQOwXZBFGaysKXhYdFNBrapL\nOXPl5PhLGiFZ5wxe7Ji1XZ2rRJ3vQHYLmHV5rM9HQBby6RGaclqWRmEO2UGt2qsyw0FUO5hU0ZkC\nW5zP0HKB5gKsmhCYOE7kAscFyREYVpHOFGIdvLr2IqSwXXhdZWOsPR18O6ng5fqgm9e1jcFMAW4i\ndwNapFne1MArqLE8do1qq2pTBxEGLkzjMoNFGvn8NErDtjJ9sf5i3g5wpo52C6hhvfijkV00gmU4\n+2znDIIMWs5Si0E5l30EIrtAm8LUGdNKFzs2cRegxd41HFe6CMxJ53aiUll7OphVaWX0bB/3VJFa\n9xxtMuNVEYKzdeVWjjoF3E59biRSOb2yDzY5KejmcwwYFcic9qs+sb5VoJ/CzLnmI5addwWas6/K\nYFI58M7yi9XHIj0EiLPszGVkB1CDqv42012CVn1W4O4M3ZEOUurcrlG7IM7XsrFmfcfr2K/m4r5b\nTqnoROndSVfHU6A5MHPg1d1zi2Xn3YCW08qYWLrKy8IU4Sgnl4vLP1aWG53FtficDWdwZTwYhXV7\nbKOrCzU+DiS68jC/i0Cq+6vzOG7d35MqR2fHVRvzHqM01Lfbrx3odOmun1UZznEWJ+K65bLzbp9t\n5P3RdBx3wJo8O4r8ylmrKEhBjJ3PbaiWv7is7D7ZcJec1XgokFX6wjKqa5z7XKOu6qucuKpDwTvG\nJD86yABjUOsk24KCD7Z1J+1AUulld2yYVCDbLfuuQMO0OufkOdGXCvOPrtujjEl0lh0B26bEfWbG\n4OqALaSbyVUe3t/lu1FG1U62ZGd1TKIRd2PlYFpFaK5+VFura7u8Sfud9impQF4tL4/44V2WnGiE\n3Tk2A+JybaoEvMed0eLeapm5Fn+2gnnTNlcA2/kWrVqSKlGG3k1MCo7TiCGXyWCGAHEAxtITQTtG\nfU70q4T1T0E82oR7N6107uR1QUN17gjIQj79bzmnIHJmOXWuKtuZ0aqBRZi9vPz//7jMz8cinV8A\nYN5kEF1wTcEX+mPQy7rpQF/BqsvvohHMY3ZUgaOzj+k4xF7pdPfD5w7SzqSu9DeZOCa6meod5RYg\nC7nLH6d34sCrmo2xDQ401Z4ZQy4/jDfOK4AhzBjMHcPZich2ozcHbKgnlacg5gKN1Y3Ovyus3SgY\neXQAm/xpGsvHPndQU5Ovq2dnEnfkVuOwK3f9tY0szCgZtJTgNWj4bMZRecwJsf0MZmutUYSmBlDl\n3xJe+DZUga3StwO3XahVe0wz2LBz2E4nL8rIkVDOd3Xd6ZtBLtu/0gfqhE3icR2uCiZAU3o8AjBW\nz3SSR7nrZxtT6WaqXD6DpipTORzmZckwy33didCcQXWdZwd0uY0KaEw33TlXrztAY46b813pIhIF\nsziuADZZcmJ5na3nfbY3Z2wmumZ+tatrFDVBdWNSyZdbcuIMu3svzm442Mz5nI0Zb5YjEVo1wEeB\n5m4hOa2imQngOpBNgcbauSusfAVKBAoDWZenIIb5LtTWemt3ClwTHU8mit1JBI+d8e7kbi8F2GyS\nZTJDsTIn9Tib6gtCTQEMYYaGl/cs7TpDtam/98SPfdXYVfqo4DTZOn3cEmoVwLB8FaEwaLHILKfz\nvWpMp3bvjBt7EVXBpKsP9bEjCr6Y58qXi9COiAOw6l7HyUIQBmGw1RtNzHMdNzvTrbdcbu4Xph19\nsWtwPwGaKgPbxvKy4LXdxNFBk00wDFxs2RllVZNTHE+hlsvB/k4nDBYYKGHnJj6I7aj8rpO7P0Or\nALQzoOycEuaMlaPlNmNf47owKIzGWF5lZFOgdeerrfpBykpXlT4VxCodu46G44pjPxUFOCVK3+qv\nOCZLzsib2H7Yn+r7w8PbP//CvlaAz+3Jfce00pELJMfvHPkybzmz7Bimgpqqz4FYGEK+hvUvl7/z\nDK3as9n3FhDrnKrTdQcl1Fl3jwu0nGYgQ9upnJz1iV2vykCIMZipa+J+NZE4MMN24/gpW1b6ZHpx\ndMAmP7xP6du1CVe+/JLTnanytZGOdnTwjD2m1bIw18vqmzxDU/VjXpTnzO5HgXZ07BigqrQDNDUG\nOY3t7vrhOEtVpgMxBrV8L5aD/VF2nvfO2Cn94jGe27EFBjE2XlU7vxzQlKiOqU46MHPhtZYGRz7n\nKFQZOgObyuscHfvTzew7YMvXVv3bnUUdOE1hFsfM2XKf2DkEUiXsWga0CmJ5wzZU41DZObYx/80w\n6ihPzGysqmNWp9IZg9hEVNu+DNDUfzRay5ux4zjvnTxUgutwFdzWqmfqfBwvCKo/Uq+MgkUdOBOz\ndNW2qu9R/mTWzvXHFmXhNvmxS4xCqsmq+l04BZTcpu65lrNXdeY93leNYT52JvGsEzUpB8x+/Pix\nLpeLZReVqIkkt8ERNVnnclw7zvIlgYbpfF+Vru53ylLlZANaSxv4WstyYCcyYPVWdbvRRzUDqwmh\nEwU1F2bsw1DsO+rDgVneVxvri5NWdTv1dcc4Zgxi7DoWleX/ZK/Grxtf1h7M60RBzI0CHfl0oHXw\n6JYe1fGRcqp72QAqQ8+/qKHeHjoDxgy4Apsj2I9q8sjn3XbmNk1ghv1iURkCjkHEWfo5wHGhU0WA\nR8pFiOe0iljzODKQMaCxenJ+LtfJ25VblbPWnYHmggjvPVpuFemx0F0pnBkr+0jV+XC1moHV9V0a\n9YMzIjNSFqXl86yt6IgMbAh5tuTM7eygxoBVQU3BJsp14MPGGoHZvdHEMtVxN26O3SLULpfLmzrU\nRO0CrPINZzL8CPmrgFYJK6sqRxlE1Q5nZsffRVtrScBFmQxgzJDzeSbOTMfKVQBj+lB1YvSEzxLj\n2gy2nM7ldZFJ1IGA6iK0buyi/Ml266hPQQ31o8Ypj1d+fpYjtFxnBTXVji6vEmXvt5IvBzQ8F+I6\nq7PP13dgw+vZ4KOBr/UeYtUsnMtSkckRYdEUpquNCRomtpEtLZ2XAkoP2G4FrGqZ6TzrijpdkGF0\n5kRpDtBU9OPCDCO0y+VCgaZsq2pHFZlVbazqUOem8mFAq96ouOCZRikMRg4sFdjQybB+ZuAVyBB2\nTBTUOplEtBj17EpndC7MqpcCLL1WH6EpsHVvNzuAVUCrrndhlnW6s0phS01cclb9U+W6UJvArPLp\nDppK7vaW09lj2ulwVUYVfUUegxuDGe4RWkycPwCP8tCBu5nZHXwGMyciq0TV7cIsR2gYGSi4dwDD\n8xVcKtioh/1d3VN45ryJVOPI4KZsNT8CYON6q6iMCdo42sBEvjzQ1pp3sINZPqccOeehs0Wb2CxX\nRWjMiKOsqs/sGPXRGZRabqprnaWnamOkXZjtfLZRRWiT8x1oVHkVNKdLWsxj49EJW3IGxHIabTVD\nrANKd+yKmqhYvVNQfnmgVUaer8H7nDRrF9uqQWOGH2W5QMN+dn1j7T5ynRuhqXNKP9lZu2dq+R5l\n7Ln9qHPnedoEMh28piBzYYbAQP0646Ois8vl8mZpiVDDSSPn5TpuAbXoVzeBTeVLAY3lqVmrK1sd\n30KU8aPTRv0O0KJc1eY84CradKSK+CZlqckln0Nw4RtOdKbYo1Ezo2egmr4AYGCJPaZ3YJbLzGV1\nefmcMx5sNcE+rI10jopjuR+63QHJEait9d62j5T1YUBTg9BFIY5xsfS0DSwcz1vO7yKADkTZ6J32\nqfPZ4BSIEABu2XH9y8v///eqh4eH9fz8/O4cOk4uC/dZj0znmIdlqDwFjdz/7Ljs4fjj4+ObB/rP\nz8/r8fGxhZcLNHYu2trZeO4rggmjr4eHh/Xz58/1+Pi4Hh8f/+RFmc/Pz+t6vb6bCCZ7taH9Z1tx\npQtApgHJXSI01WGlHAYyB2hK1Myl0s7g5rJZ21n0xu5hbcV2u8vKiWBbGcwmQEOn6/TMNiwLozhm\nL1k3YYOXy+XN9vLy8i4vw2zX4btjBrK8z2kXZg8PD+vx8fFNX/KE9vLysq7X61qLvxnuwOxu2A+0\nLVd2bDfLp0doWdhAqtmrUiIz7E6Yo6m9Y4iqz7ltuLzq7sVzleM75VRlZ4PG9ucoNbcD24TnphHa\nZGPRDfY/ro3/l8qAliM1F15VBKNgEW3N+yrtwkytLtb6x96iHfjZxnQ53p1Du3GPnbQrdwPa6+v7\nP2aOfAdmSrm5/KpeFUGw9E4fox5sCz6zcMrMsGDtwajQjfhQFMxii6WoA7RphFY5LTtGvUbdeXxz\nHy6Xy3p+fn6zD5jFvgOYG82o/KxXJpGvdNhFbfneKC9H1tfrVfpWl4fn2Tnsm/JJtq/yJvLpS86u\nwxMDUQNQlR8ycaK43o1MqvrZfSgqLyIlBBv2S0kH4LXezuoPD/882FeRYZU3jdDYg+s4DjAxPWNU\nxvSW3/LFPsMMn58xu3MiMnVO6VuN0wRmWB6OZc5zYeKcw2uq66pyXX925K4RWk5XRuIaUi63Axxz\n0Grp04X9qt+5Hdn4OifE4zx745uqfC6nO8HILgSXlnjsgI1BbQK2gFj0SUEtR2W5T7lvEZUh2DLU\nprbmTrJ5yVnpH/MnQHOe7TG4TtIdoKb7bgJAv3blLkDLhprz2KBMH2BGWdU+t9HZ2DOKcK7seF2E\nlvMciLHjDDOE5S408/1d+yo94fkpwNhx9DP0jXWotubzDGJsm4Bqei1KF2UyHarJ9fn5+c+LnOfn\n5zdti7zYOqlslh0zH875Fczc55MT+fQlZ36GhMJmlKqzFdGr9Fr6wTbLjz/sjT1GZJ3SK6C6xzmd\nnTz3z4nKWLlMP9j2fP1km0Zo1d8b5v6zPnUTEkKNlV9BKut6F2hVJIl2xyDGVgbX6/XPm8xoV/Ql\nPtvI1zA7mEgFPQU1TGcfRn/H9ETu8h1a3rpnENVbJ8xbSz+4ZA7L4MXS7O1YiFpuKshNoFZFWLl+\n1JuK0qoymb5YnooSJjCrIrTX11cKN5Ts0Cy6QcfH52cMSqz/LtyOAI3lOTCL9O/fv9+MeURi0b/r\n9bqenp7W09NTaQeOvSnpggkGNHebyN2AxkR1VpEb046xxTk1U7J9fsaS+4HPslSf8h7TqKsqnaMp\n5TDYPyXsvLO8Z8BSIHNhlqETQItJQ+kqL/Wxz9gOhFnU49oNy1fXVuPDJk0FtgpiqPc8fvGsMNoX\nS82np6f1+/fvd/bE0sw+HMBN9BXL5NjyMZ6byF3ecjKooeNUUFPH09lyrX52Qojk/uGzrLg29yn3\nLacROl0adZbbhXVMBPvX6b8CmjqevAR4fX19E5nlaJg5WdZlFSEizJRNVE5YjWU3geIYsvZmWCuI\nsSVnhkT8VcBab5ecT09P69evX239yt6U/rO4k0EGbQaY2k/k0/9zen54iZ1i+VVUtvMgtwMai4pi\n1nPqXMt7exTH1cynZtAKcmrfncsbA0RuewUPBTPnGL/ez88uWT5uKj8mnoAauwahegRo7BzTW5U3\nidBUxKZskIGI2R1e29krExf2zHd25MOApt6oMHCprXtudhRolZPH/uHhoQXoWv7nIt2AuVBiDsHO\nVWXg/dlBWDSWr+8iB4RVBbIKaApqFZSculikiJNDBlGOBBFSzMYw3x23bozVZIY2xO6pbM4BFZs8\nUVdTce3clU8HmorGcLter+ULAYTaUaApCFSRGXugzPaYRnHAytrZGXzVtwpomI+AU0tIB2Zq6yIy\nPEPF/3AAAA/CSURBVOfC6uHhoY3McjkKShXI8FhFd9Nx7EBWjX9na45UAMN0zuvgps5XgHfl05ec\nCl4O0Dq4reU/13Bh8fr69m8ZGcxuGULfytB3YZjhFVFZPmbQqvJYNKXypstNVh9rE74QUHDLOmGw\nwnMIserYsbmdsVY21J2f5KtrUF+Y79o+1jsBNMpdlpw5Sssww3QFMpW3Vg+1tXpw5GP2Krkqe2cg\nq7QDI/faqozqfH4W5YBEAYx909fBqwNatX94qCM0vEdFXU4abS8fo82pMe+AVo0XAkBdj2OsbNLJ\nU1BT0kVnVZsd+TLP0AJmee98RsCipbX6h5EuGDLQHFCyZaYLuinI1HXT6xFaVdoBWOQhvKr9dMmZ\n29KluxcBVYR2JJ3tkI1vlVeNU1UGO3Ztrru/AnIcY6Q2XaG4bVFy1yUng1j+splBpIKcC7W1eogh\n0KIuBbeoL6SCWDXI7kzrQiofqzTmVWDLoGAwc56NORDrwIZtYLqJJXNuC4Nbvp5FaLtQyzYY5eFY\nq7SzVXZTXcds7ojk/lYQcyb0qo+OfMkIrQJaBzIHamv5zyry87PJC4EqnaUauF2IOQBj90Qe9l2B\nzYXZJPJyl5wTp2cwY/3IfWdwYmkFL5aX9Yvj3I27O8aOjTmgYPbQ7R2orcV9Ae//a4CGbznxuVne\npgBzYRaG6DrE5BORkGmo7QL21sc5H/Nin5+fTWCm4Ja3+NlodV5FdBOHj/GrIrPYdiKxCm7MuTtH\nrWwAr0FYVjCooLYDD4RZB7Is2G42UXwpoE3fcrJtAjAXapGXjdkFWgW1XC4O6hRuTHYh1R1j+Rle\nbJ/L6UDG4NXlOdFaHjPsC9OF8+wM71ORmAs1hNtkjHG8Krh1AKugMAUG6hXzKqg5umA6nMqXWXIy\nyN0qGmN5YcgsWkOHUW84qw9sMV3lZXEM+cixAloAK86x/SQqq8Cl0pPPNqp2opNNIssKWrlMBiy1\nd8Z9agcMVh30sm5UnW4+ls+iUCdqUxDbgdlad/rTJ7bh5xy3XmJWUMsgizbGcfeJhpIdmDGpINSV\nq2Y5ZuBdtJHrqWDJNvXMigHQWabGOOX2V8eTLe45EpW5zqxk0t4MbKX3bkJDOCldqrxKOj/IaTZx\nTOUuz9DUd107X/3vQi4PZgZZdpbdsBcHohpUd8bs2lH10SmfneuckrXPdUSEXM53tiPC2qIiNKYL\nF1ZsUmC669rXAVg9t0T9TiLbibiT9w6gpvJlIjTnE41dgLHr2DOiuH+t9cZ4JxsaQwc3B5rV+Wn0\nyBzraFRROYYTNUxeNITTOnpl+Q54q6gs6wzTOzpjx7tAq54RsomgitCmkGP6VmPgyo5u7w60IwA7\nErmtVT8I3x2M6no22N0MXhlUvq8CWr5X1XXE6Lp9FaWp5agCWY6koj+oE6Yn1WZsiwJZB7OcxnHB\nMWYAcXXGgIa6qcBW1auOmVTRp+sDjkyjxbsCLSDW/Y1kt+1Ecmu9fRCOTqJgEceOMIBN7kdRYELH\nQ2hnp+vKd6O0blbvQKaOq+dscazGJeuGpbsoKC85me4U5DqJe6pJCvPU8y83Qst6xSUnE2YbU5hM\nQbbrB5XcPUKr/kbzKMCqvyIICSNGsKl7EHpKXJhVM50T7uP90Ze8hFb3h1MoiHVgq6KLnK6iM/Ws\nR0Vq+KzLGSvWZwXX3O8uQsNr2bio+iudreUBLfTgLtkngJpcW8FK2c9HwGytOwItA8d9KaDOT+9j\n4oBsZ5bpwOZETeo6BBGWzxxTOZlyWNUeduzCTEGlAhhbcuaJKPcZdVEJ1tuBTB0zYWPMdIT6WqsG\nWj4X106foU2AomyU6dvRR2dfR2B3lw9rqwht8nJg91qUbJzOrM9m406qyAFBU0FDlYuGkB2T1ZHL\nPjpbZueqjicw66K0GM+13v4f0dxXNYl1gD0KMpR8L4sGWZ4CGosoGfBV5IuQ7SbpSaSG9zsAu7Xc\n9bONAM30sw3nZUJ1TZaHh7f/SBedIO8rKGWpBjKfq6ImJggKVm4+zpsDSRWpIfRUG5Sz5rTjdApi\n2WnXevuZTXw72DmPAgm+bJiATU3elX7YhnrqYOZGaFm/lY7UeFfCorTuekdPWLYrd3+GhlBDAFVg\n6sCmjkMCZjGAOc2gFmmVr0TNWNWAMUNX5ao6VYSS68jpiUFj+6rznZNOn6PlOvHPsdBpWT8qqKHO\nnAgNn8M6Ous2B2hRb/WWE3WZJ9IsCKYJSLqJW13TyQSsIXcH2u6ycZpWQIu9ApmK0EI6he/ADEXB\nLOczA8Vj1odu1nbbt7NlB3WWmhihoS5QT1WfsB0BxjjXgY1JlFO14Qi82HHU60Rm7BkhSnWukiPg\nuqV8GNCqsLbbWBlOeiIs8sE8jJA6AFXwUnsnkmL1IMzY7FrpdafeXWF6VFESy6u2uB6F6dhtI5ts\nMthUXXFeHaOt7fZ5917VJhdik6jtXvJhQOtkZxBC8kyI4T7OQjlKy4OGz2XYLzrg3w/mGS8Li+yq\nfaSVc1QO6MBMAauaRKpPXG4JvKqsKsp09TvZunay67oJuDvOY7WWfl7kHrsTVmejIRlwU7mVnRwB\n56cDrYt0HKCt9T68x+cfCLHstBF+s1916ICG4X+I43CY1zlY53gI6coYpyC7Jcw6UFXXKCjsQqxz\nfNX+bhxuAbIdyLF2uDBTsrPkxHZMREXD7Fwnd4vQsiiAVUBbiz+zyNcruGWgMXAxyLFnEiEZDlkq\noOX78JgZKsKKgYvN2gxgTIed4R8FWweE7lo1YVRpB2Sqnu5cVW7VZgUy5bgd5BxRbWX1hEygNrEN\ntgRnde+UvdadgaYghs9GqkFUkRlz7ihrrVVGZ+q3uBh4QyYGwwbUcep8ji0zGWQRZqivLkLr2sJ0\nUIG5K5Ndg3l5XFW93YZtdvrTXeOW2UGN2TueX+u9/eyOo8p3wMLGqBMGMlXXXxOhqWjMWW525TJj\nj3RIFZ05z9ByG5XjZVGOqtrK7qkiMsfY8zF7Btk5xEeLA7Pch5yewA3LwHqq9nXArvImIMN78LzT\nZtXvHTm6DK3ux3NfMkLrnpNV6YAFG9Suzuzka713jDg/AVoFs5DOaDrjd9K5/QpmLOqr0mq56Up3\nnwP27p6cx/rowIydZ+2Z6oJdWwFvGp11x6otlU5c34z787lbT3DVEnQana31BZacmEaoTcpiMxxz\noPwMrYrI1EsB9QxtArTuvh2woHHkPIRYPDtjS3bWNlXnR0o1KVUQccDG9qoNXXl4rcrrJqBdqHX1\nVnbW+VgFMdfOnbIV2P6aJWcWBrO8V/eomYcNOEr32/XdSwGUAEUl2I4qksQ8FpWpPd7Dyo99/tgY\nHaDS30Sq8lzYKPhUsHE+RXGg5vTNhdtHQY21RaXX4tDAAEPBZldXTplH67rLZxtqj5u6nxmLs0TN\n5ydAy3msjcqoqzbk9BHnYlBj+mAgi0h1px1HIedIB/tbbKwv0/vZfZjHJpouEnGgxtKoM2wX1o+S\nfXIClx2bUKuKKO+vitDUzIB50/Kc65z/LMRgxj7bWIvPyJ04EUPeq2hMlc3S+Q+641hdXzmM0y/l\nZKq8ql62dzb8UQKVZsdH+4plO1BzIFY956qAzfqmbIlNkpUOXFEriIltV3IXoCmAdREaU26O2Bwl\nRNn4N4LO8zOEWWVYEz3swqzbs3a5z0R2ZttcBxvDiZF2gGXXTze872h5rKxsmwpGu87LdND1wV3y\nHq17IreC2Vpf6BlaBTS2zMQ8BklWT+zVLxRk2HV/7FvV4fZb3dM5XNaBawjTtlVGqsbs6KZEgUj9\nwEE+x34Iwf2fFpMfT3ABh7aaN7YS6PzCGVc2OeI4szF3JttKcNLOeawP2M6pfMm/5YwlEf7GVYZY\nNQiRr44daLG3mhjduQ5Z5VdRnnIKppMdmEX9nS47OQIvB2TsWAFJ/YqLc44By4Gegi2zVUdvFcyY\n/nJ5lVQRmrIBBi4GNRdsuc7qut2I79N/baMSNrBK0RluyqnVYEfZ3X/LQfBNDUz1sRMVbbCZDct0\n4VaV5QKOlbMDNXWvAgTLuyXcMF3l5baotBPNoK25+lSwDGETgpoQXaixc6y/VV/xGO9x/ITJ3Zec\nODjK2FmUhuk4n+9TaTdCyyDrlgEVUFk+62uWzkDwPgUzZxbv4NWVyfo0iTg6qYCmlpJsydktTxm0\n1J7BqotemOO6E2U1ETi6y8cd1PB+TFd9UhLtZBDeBRjK3d9y5s6xwWPwYvdiuZif0xlWzrO07JTq\n0w0FtSpPtU/12dHnRNgsXxk21lM5HNOl67woyqGq6KmDWvVsrSor52F7WBtVP5ges94q/bKxc8QZ\n53yu69+0rw7I3KBAyV2foeWILPJir5abHeTynuVFfZN/zuo+qJ0OVOfE3VLFGWzVJkxPHAPLv9XG\n+lQt31iUxkA03RgMFdywnepYncsgU/pQNrwr2Xe6SawDWU5Xdspgin3K12OeK58ONNZp1cl8TqVx\nIJSi0BimS0619MxlYttZX1jfcz6bHfHbMdSFKlsJ06+7V2UcBVnVB7Z862A2ic6qt6BVtMfaWEkV\nbaNNZn24Nq1Ah5PBdKwdcHdQw3q7vuS8idx9ybnW+2c/8XeGFcBwALDMTlGTTzaysaiZNNe1k87C\njCJDDfXCdIiiYHpEWP+VjiqnU46YhUVo1ScVLrjY/ep6lqf04uqu01Fl250ogDqRGbtHHTv3K1+t\nAoMvBbTJEoY1nC05K6jlctQMEOlbLTM7sHUgq2ZVpkOcBZmRKH12bXONW4ERN2d57gIPpQKRSk/e\ndLqQU7pBHUaf1OoE9YYRGtO9MzEyvcX1zHc6G6igxo5zuSzt2sVEvsRbzu68mlEq6lfp7oXA5FON\nHah1My0zgJyf90w/E2EG3IHN1cXOZMB0gVvOV6CqzqnIbhKdIdBQZ9gntiRTToyPF1SZ7jms14GZ\ngvMkr2rnjk85cvcfeKyMoJo11CB0QMOZ0PkGrZtFVN8qgHVwq4zGWXo77cD2TMGG1zpQY9fmPOxv\nthMGtGrp6T4jm0RleJ/SKY6HWv65Tjx1bNRj3jOYYV1O9OVGcqoOdxKcCJ8KTjnllC8l7sTyb5cT\naKec8hfIkQjt3yQn0E75VNl1zH+7Q58Rmicn0E75VNl1zH+7Q//bge7KCbRTTvkL5N8OdFceTkWd\ncsop30XOCO2UU075NnIC7ZRTTvk2cgLtlFNO+TZyAu2UU075NnIC7ZRTTvk2cgLtlFNO+TZyAu2U\nU075NnIC7ZRTTvk2cgLtlFNO+TZyAu2UU075NnIC7ZRTTvk2cgLtlFNO+TZyAu2UU075NnIC7ZRT\nTvk2cgLtlFNO+TZyAu2UU075NnIC7ZRTTvk2cgLtlFNO+TZyAu2UU075NnIC7ZRTTvk28n9AwRVK\nLtEpzAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7939901850>"
+       "<matplotlib.figure.Figure at 0x7f519c0eab90>"
       ]
      },
      "metadata": {},
@@ -1387,13 +558,15 @@
    ],
    "source": [
     "imshow(solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5)\n",
-    "       .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray')"
+    "       .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray'); axis('off')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "### 5. Writing a custom training loop\n",
+    "\n",
     "Something is happening. Let's run the net for a while, keeping track of a few things as it goes.\n",
     "Note that this process will be the same as if training through the `caffe` binary. In particular:\n",
     "* logging will continue to happen as normal\n",
@@ -1424,8 +597,8 @@
       "Iteration 125 testing...\n",
       "Iteration 150 testing...\n",
       "Iteration 175 testing...\n",
-      "CPU times: user 12.3 s, sys: 3.96 s, total: 16.2 s\n",
-      "Wall time: 15.7 s\n"
+      "CPU times: user 12.6 s, sys: 2.4 s, total: 15 s\n",
+      "Wall time: 14.4 s\n"
      ]
     }
    ],
@@ -1448,7 +621,7 @@
     "    # store the output on the first test batch\n",
     "    # (start the forward pass at conv1 to avoid loading new data)\n",
     "    solver.test_nets[0].forward(start='conv1')\n",
-    "    output[it] = solver.test_nets[0].blobs['ip2'].data[:8]\n",
+    "    output[it] = solver.test_nets[0].blobs['score'].data[:8]\n",
     "    \n",
     "    # run a full test every so often\n",
     "    # (Caffe can also do this for us and write to a log, but we show here\n",
@@ -1458,7 +631,7 @@
     "        correct = 0\n",
     "        for test_it in range(100):\n",
     "            solver.test_nets[0].forward()\n",
-    "            correct += sum(solver.test_nets[0].blobs['ip2'].data.argmax(1)\n",
+    "            correct += sum(solver.test_nets[0].blobs['score'].data.argmax(1)\n",
     "                           == solver.test_nets[0].blobs['label'].data)\n",
     "        test_acc[it // test_interval] = correct / 1e4"
    ]
@@ -1467,7 +640,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's plot the train loss and test accuracy."
+    "* Let's plot the train loss and test accuracy."
    ]
   },
   {
@@ -1480,7 +653,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7f793878f490>"
+       "<matplotlib.text.Text at 0x7f5199b33610>"
       ]
      },
      "execution_count": 16,
@@ -1489,331 +662,9 @@
     },
     {
      "data": {
-      "image/png": [
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Hww8vdMscx3FqRT4toD3Adap6BHAC8DURqdRbisiZQD9V7Q/8L3B3TQ9y8skwaVIumttA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\nJGkQBpAkaRA3ABGGQ9Z+SfjXAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f79387025d0>"
+       "<matplotlib.figure.Figure at 0x7f519bf16690>"
       ]
      },
      "metadata": {},
@@ -1827,7 +678,8 @@
     "ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n",
     "ax1.set_xlabel('iteration')\n",
     "ax1.set_ylabel('train loss')\n",
-    "ax2.set_ylabel('test accuracy')"
+    "ax2.set_ylabel('test accuracy')\n",
+    "ax2.set_title('Test Accuracy: {:.2f}'.format(test_acc[-1]))"
    ]
   },
   {
@@ -1836,7 +688,7 @@
    "source": [
     "The loss seems to have dropped quickly and coverged (except for stochasticity), while the accuracy rose correspondingly. Hooray!\n",
     "\n",
-    "Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence."
+    "* Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence."
    ]
   },
   {
@@ -1849,109 +701,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": [
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-       "AAALEgAACxIB0t1+/AAAFZtJREFUeJztnVtsY8d5x//f4Z2H94skaiXvemUbsAsD9otbwA2ahyCw\n",
-       "USBpXxoYKFD0EvShN7QPddyHJo9pgAZF+1CgiB30hqRFCxfpQ1vbRQukD724sGOnaydZY8XVihJF\n",
-       "iXfykDwipw/kNzuHklbiRRRJzQ8Y8OgsdXYk/vXNN9988w0JIaDRjIJx1R3QLB5aNJqR0aLRjIwW\n",
-       "jWZktGg0I6NFoxmZsUVDRC8R0cdE9CMienWandLMNzROnIaIXAB+AOAzAHYB/A+AV4QQH023e5p5\n",
-       "ZFxL8wKAu0KIbSGEDeDbAD4/vW5p5hn3mN93A8CO8vUDAD+uvoGIdKh5wRFC0Gn3x7U0WhDXmHFF\n",
-       "swtgU/l6E31ro7kGjCuadwE8SUS3iMgL4AsAvjO9bmnmmbF8GiHEMRH9OoB/AeAC8LqeOV0fxppy\n",
-       "X+jB2hFeeKbtCGuuMVo0mpHRotGMjBaNZmS0aDQjo0WjGRktGs3IaNFoRkaLRjMyWjSakdGi0YzM\n",
-       "uElYAAAi2gZQBdAFYAshXphGpzTzzUSiQT8Z69NCiOI0OqNZDKYxPJ26EqpZXiYVjQDwDhG9S0Rf\n",
-       "nEaHNPPPpMPTi0KIPSJKA3ibiD4WQnx3Gh3TzC8TWRohxN7gtQDgTfS3tmiWnEl2WAaJKDy4NgF8\n",
-       "FsCH0+qYZn6ZZHhaBfAmEfFz/loI8dZUeqWZaxYyR9gwDBARiEheq/eY0342IQSEEOj1evJafR9f\n",
-       "D79eR87KEZ7UEZ45hmHA5/PB6/XC6/XC5/PB7/fD7/fL+71ez9FUAfR6PbTbbdk6nY58D7+/2+2i\n",
-       "2+3Ka42ThRSN1+tFKBSSLRKJyBYIBNDtdnF8fOz48NmidLtd1Go11Ot11Go1NBqNE++3bVs2LZqT\n",
-       "LJxoiAg+nw+hUAjxeBzxeBzpdBqpVAqpVAqRSASdTkd+6MfHxw6LY9s2isUiisUifD4f3G63QyS2\n",
-       "bYOIpMA0J1k40RiGAb/fj0gkgmQyidXVVWQyGaytrWFtbQ2xWAydTkc227YdQ5Vt2wiHwwgGg3I4\n",
-       "U9/f6XRgWZZsbvfV/IrUIVX1wYaH26vwuRZONC6XC6ZpIplMYmNjAxsbG9LKsKVhC8Ov6i/7+PgY\n",
-       "wWAQ8XgcKysrqFarDstk2zaazSYsy0Kz2USr1Zr5zzjsk7VaLViWJV/55+I2a+EspGhCoRBSqRQ2\n",
-       "Nzdx69YtxGIxxGIxRKNRmKZ5wpFVZ0m9Xg/xeBzNZhONRgOWZTkExqLhZlnWzH9G9rG41Wo1lMtl\n",
-       "2ZrNJtrttnyvFs05GIbhsDSPP/44QqEQTNNEKBSC3+8/MZ0eNucsDvUvlj8oVTQsqlnDfWMLeHh4\n",
-       "iHw+D4/Hg16vJ8MK3W4XnU5n5v1bSNH4fD6Ew2HpBPOU2+/3w+v1noi78C/5rFe2Sr1eD8fHxw4r\n",
-       "02w2Hc9Sv28aqP1jbNuW4YB2u41IJCIF0263Hf7ZNPtyURZONDxlzufzyGazMAwDgUBANq/XK4cn\n",
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7938797710>"
+       "<matplotlib.figure.Figure at 0x7f5199aaab50>"
       ]
      },
      "metadata": {},
@@ -1959,94 +711,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7938323190>"
+       "<matplotlib.figure.Figure at 0x7f5199a4ba10>"
       ]
      },
      "metadata": {},
@@ -2054,124 +721,9 @@
     },
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7938233050>"
+       "<matplotlib.figure.Figure at 0x7f51999dd210>"
       ]
      },
      "metadata": {},
@@ -2179,98 +731,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f793821a310>"
+       "<matplotlib.figure.Figure at 0x7f519994c650>"
       ]
      },
      "metadata": {},
@@ -2278,84 +741,9 @@
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      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f793811c290>"
+       "<matplotlib.figure.Figure at 0x7f519984f250>"
       ]
      },
      "metadata": {},
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+      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f79380a2550>"
+       "<matplotlib.figure.Figure at 0x7f51997d3ad0>"
       ]
      },
      "metadata": {},
@@ -2591,96 +771,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792bd10b10>"
+       "<matplotlib.figure.Figure at 0x7f519bee6f50>"
       ]
      },
      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792bca2290>"
+       "<matplotlib.figure.Figure at 0x7f519c0b0c10>"
       ]
      },
      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792bc15650>"
+       "<matplotlib.figure.Figure at 0x7f519c0d5bd0>"
       ]
      },
      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792bb98fd0>"
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      },
      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792bb3b910>"
+       "<matplotlib.figure.Figure at 0x7f519969d110>"
       ]
      },
      "metadata": {},
@@ -3082,124 +821,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792ba9ddd0>"
+       "<matplotlib.figure.Figure at 0x7f5199603550>"
       ]
      },
      "metadata": {},
@@ -3207,97 +831,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b9b9550>"
+       "<matplotlib.figure.Figure at 0x7f5199591d10>"
       ]
      },
      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b97ec50>"
+       "<matplotlib.figure.Figure at 0x7f51994fb990>"
       ]
      },
      "metadata": {},
@@ -3429,99 +851,9 @@
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+      "image/png": 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FCgAAoCESKgAAgIZIqAAAABoioQIAAGiIhAoAAKAhEioAAICGujKhevjhhzvdBDTwk5/8\npNNNwElavXp1p5uABlatOq5cKnrE7bff3ukmoKGuTKgeeeSRTjcBDTz44IOdbgJO0po1azrdBDRA\nQtW7SKh6X1cmVAAAAL2EhAoAAKAhZ4bAP+Urtzu3cgAAgKSIGLU+WkcTKgAAgNMBt/wAAAAaIqEC\nAABoqOsSKttX2N5o+0e239fp9mBstv/V9ibb946YN9f2rbZ/aPubtud0so0Ym+0h27fZvs/2etvv\nas+nD7uc7em2V9peY3uD7Q+059N3PcT2ZNurbX+1PU3/9bCuSqhsT5Z0vaQrJF0g6Srbz+5sq3AC\nN6rVVyNdI+nWiHiWpP9tT6M7HZC0LCIulHSppHe0jzf6sMtFxD5Jl0fE8yQ9V9Lltl8k+q7XXC1p\ng6QjDzPTfz2sqxIqSS+U9OOIeCgiDkj6gqTXdLhNGENE3CFp2zGzr5T0qfbrT0l67YQ2CsUi4vGI\nWNN+PSzpfknniD7sCRGxp/1yqqTJah2L9F2PsD0o6ZWSPiHpyK/G6L8e1m0J1TmSHh0x/Vh7HnrH\ngojY1H69SdKCTjYGZWwvknSRpJWiD3uC7Um216jVR7dFxH2i73rJRyS9V9LhEfPovx7WbQkVYzic\nRqI1Jgd92uVsD0j6kqSrI2LXyPfow+4VEYfbt/wGJb3E9uXHvE/fdSnbr5K0OSJW6+dXp56G/us9\n3ZZQ/VTS0IjpIbWuUqF3bLL9DEmy/UuSNne4PTgB231qJVM3RcTN7dn0YQ+JiB2S/kvSb4i+6xVL\nJV1p+0FJn5f0m7ZvEv3X07otobpb0jNtL7I9VdIbJN3S4TYh5xZJb2m/foukm08Qiw6ybUk3SNoQ\nEdeNeIs+7HK25x35BZjtfkm/JWm16LueEBHXRsRQRJwn6Y2SvhURbxL919O6bqR0278j6Tq1HrK8\nISI+0OEmYQy2Py/ppZLmqXW//28lfUXSv0s6V9JDkl4fEds71UaMrf2rsO9IWqef31p4v6RVog+7\nmu3nqPXQ8qT2n5si4sO254q+6ym2Xyrp3RFxJf3X27ouoQIAAOg13XbLDwAAoOeQUAEAADREQgUA\nANAQCRUAAEBDJFQAAAANkVABAAA0REIFoONs39n++5dtX3WKl33taOsCgFOJcagAdA3bL1NrkMNX\nJz4zJSIOnuD9XRFxxqloHwCMhStUADrO9nD75Qclvdj2attX255k+8O2V9lea/vP2/Evs32H7a9I\nWt+ed7Ptu22vt/229rwPSupvL++mketyy4dt32t7ne3Xj1j27bb/w/b9tj8zsVsDQC+a0ukGAIB+\nXvrmfZLec+QKVTuB2h4RL7Q9TdJ3bX+zHXuRpAsj4uH29FsjYlu7tt0q21+MiGtsvyMiLhplXb8n\n6dclPVfSfEl32f5O+73nSbpA0v9JutP2ZRHBrUIAY+IKFYBu4mOmf1vSm22vlvQ9SXMl/Wr7vVUj\nkilJutr2GkkrJA1JeuY463qRpM9Fy2ZJ35Z0sVoJ16qI+Fm0nolYI2lRg+8E4BcAV6gAdLt3RsSt\nI2e0n7Xafcz0yyVdGhH7bN8mafo4yw0dn8AduXq1f8S8Q+JcCWAcXKEC0E12SRr5APk3JL3d9hRJ\nsv0s2zNG+dwsSdvaydRiSZeOeO/Akc8f4w5Jb2g/pzVf0kskrdLxSRYAjIv/dQHoBkeuDK2VdKh9\n6+5GSR9T63bb921b0mZJv9uOH/kT5a9L+gvbGyT9QK3bfkd8XNI62/dExJuOfC4i/tP2kvY6Q9J7\nI2Kz7Wcfs2yNMg0AT8OwCQAAAA1xyw8AAKAhEioAAICGSKgAAAAaIqECAABoiIQKAACgIRIqAACA\nhkioAAAAGiKhAgAAaOj/AYSDQCwV4p2TAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b91c3d0>"
+       "<matplotlib.figure.Figure at 0x7f519948af90>"
       ]
      },
      "metadata": {},
@@ -3544,7 +876,7 @@
    "source": [
     "We started with little idea about any of these digits, and ended up with correct classifications for each. If you've been following along, you'll see the last digit is the most difficult, a slanted \"9\" that's (understandably) most confused with \"4\".\n",
     "\n",
-    "Note that these are the \"raw\" output scores rather than the softmax-computed probability vectors. The latter, shown below, make it easier to see the confidence of our net (but harder to see the scores for less likely digits)."
+    "* Note that these are the \"raw\" output scores rather than the softmax-computed probability vectors. The latter, shown below, make it easier to see the confidence of our net (but harder to see the scores for less likely digits)."
    ]
   },
   {
@@ -3557,109 +889,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": [
-       "iVBORw0KGgoAAAANSUhEUgAAAI0AAACPCAYAAADHlliuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
-       "AAALEgAACxIB0t1+/AAAFZtJREFUeJztnVtsY8d5x//f4Z2H94skaiXvemUbsAsD9otbwA2ahyCw\n",
-       "USBpXxoYKFD0EvShN7QPddyHJo9pgAZF+1CgiB30hqRFCxfpQ1vbRQukD724sGOnaydZY8XVihJF\n",
-       "iXfykDwipw/kNzuHklbiRRRJzQ8Y8OgsdXYk/vXNN9988w0JIaDRjIJx1R3QLB5aNJqR0aLRjIwW\n",
-       "jWZktGg0I6NFoxmZsUVDRC8R0cdE9CMienWandLMNzROnIaIXAB+AOAzAHYB/A+AV4QQH023e5p5\n",
-       "ZFxL8wKAu0KIbSGEDeDbAD4/vW5p5hn3mN93A8CO8vUDAD+uvoGIdKh5wRFC0Gn3x7U0WhDXmHFF\n",
-       "swtgU/l6E31ro7kGjCuadwE8SUS3iMgL4AsAvjO9bmnmmbF8GiHEMRH9OoB/AeAC8LqeOV0fxppy\n",
-       "X+jB2hFeeKbtCGuuMVo0mpHRotGMjBaNZmS0aDQjo0WjGRktGs3IaNFoRkaLRjMyWjSakdGi0YzM\n",
-       "uElYAAAi2gZQBdAFYAshXphGpzTzzUSiQT8Z69NCiOI0OqNZDKYxPJ26EqpZXiYVjQDwDhG9S0Rf\n",
-       "nEaHNPPPpMPTi0KIPSJKA3ibiD4WQnx3Gh3TzC8TWRohxN7gtQDgTfS3tmiWnEl2WAaJKDy4NgF8\n",
-       "FsCH0+qYZn6ZZHhaBfAmEfFz/loI8dZUeqWZaxYyR9gwDBARiEheq/eY0342IQSEEOj1evJafR9f\n",
-       "D79eR87KEZ7UEZ45hmHA5/PB6/XC6/XC5/PB7/fD7/fL+71ez9FUAfR6PbTbbdk6nY58D7+/2+2i\n",
-       "2+3Ka42ThRSN1+tFKBSSLRKJyBYIBNDtdnF8fOz48NmidLtd1Go11Ot11Go1NBqNE++3bVs2LZqT\n",
-       "LJxoiAg+nw+hUAjxeBzxeBzpdBqpVAqpVAqRSASdTkd+6MfHxw6LY9s2isUiisUifD4f3G63QyS2\n",
-       "bYOIpMA0J1k40RiGAb/fj0gkgmQyidXVVWQyGaytrWFtbQ2xWAydTkc227YdQ5Vt2wiHwwgGg3I4\n",
-       "U9/f6XRgWZZsbvfV/IrUIVX1wYaH26vwuRZONC6XC6ZpIplMYmNjAxsbG9LKsKVhC8Ov6i/7+PgY\n",
-       "wWAQ8XgcKysrqFarDstk2zaazSYsy0Kz2USr1Zr5zzjsk7VaLViWJV/55+I2a+EspGhCoRBSqRQ2\n",
-       "Nzdx69YtxGIxxGIxRKNRmKZ5wpFVZ0m9Xg/xeBzNZhONRgOWZTkExqLhZlnWzH9G9rG41Wo1lMtl\n",
-       "2ZrNJtrttnyvFs05GIbhsDSPP/44QqEQTNNEKBSC3+8/MZ0eNucsDvUvlj8oVTQsqlnDfWMLeHh4\n",
-       "iHw+D4/Hg16vJ8MK3W4XnU5n5v1bSNH4fD6Ew2HpBPOU2+/3w+v1noi78C/5rFe2Sr1eD8fHxw4r\n",
-       "02w2Hc9Sv28aqP1jbNuW4YB2u41IJCIF0263Hf7ZNPtyURZONDxlzufzyGazMAwDgUBANq/XK4cn\n",
-       "FoPL5XI0t9sNt9strw3DgGEYcLlcMlDo9XpBRHC73Sd8DH4/t1E4zbFVA5SGYZwYLrvdLlqtFlqt\n",
-       "lhyWhBBot9taNBeBRVMoFLC9vQ3bthEMBmXj2RCb9263C6/XC4/HI199Pp+cOfG1eo+I4PV64Xa7\n",
-       "4ff7Hf5Ft9t1CO6is6vhAKMq7GFBs8VT36OK5vj4GO12G7VabWTRToOFFE29Xkc+n4dhGGg0GggG\n",
-       "gzBNE6Zpwuv1yl9wq9WCbduO4cvv9ztEFgwGZZBQCOH48PhaFWGn05HRaBbheZzmU6nN7XbD4/HI\n",
-       "V36vao3UCHar1UK9XkexWJxP0RDRGwB+GsCBEOLZwb0EgL8BcBPANoCfE0KUL7GfEhbN4eEhjo+P\n",
-       "Ua1WpWBYNOrsx7ZtBAKBU0XCjWM77GSy1fF4PPB4PNJJBh4KgIezi4pmuPH/xT4NWzW/339iDc22\n",
-       "bTQaDdTrdTQaDZTLZWlV53V4+iaAPwHwF8q9LwF4WwjxtUHh6S8N2qXDUV3LsmAYBrrdLprNJur1\n",
-       "Ovx+P9xuN9rttsOU8/oUi0H1gQKBAMLhMEKhkHzlD499JI6PcKxk+PvPY3g2xw4uW65oNCrjTMlk\n",
-       "Eh6Px7Egy8MVW5l2uy19nbkM7gkhvktEt4Zufw7ATw2u/xzAv2NGoun1euh0Omg2m/Ja9VdcLpdj\n",
-       "Ot3tdh2mn9+r+jfBYFBao+FXv9/vsFzNZhOmaTos13l/7cPRXFXU7XYbmUwGt27dgmEYCIfDICK4\n",
-       "XC4YhiG/7yzRXAXj+jSrQoj84DqPfm7NTGBLw3+xPPvhXzJbH3W9SZ3p8PtU53PYGWZRmKaJQCDg\n",
-       "GBoajYZjaDNN80KiUfujRnhbrRa2trZgGAZCoRAymYx0rvm57DgPi0ZdUpglEzvCQggxy/p6LBrb\n",
-       "tqfyPPYnuHk8HhkoZPHU63XHqng4HHYMaeehCkZdFmDhCCEQj8dx48YNdDod+Hw+EJGcjrNg2u22\n",
-       "jB91Op0rWUIAxhdNnojWhBD7RJQBcDDNTs0S1TFlc99ut6Uvoa5F8QfVbrfhcrkuvBI+PDzxB81T\n",
-       "fHUBlf0Znmp3Oh3UajVUKhUUi0UUCgWUSiXU63V0Op2FEs13APwCgD8YvP7D1Hp0BfR6PQAPBdRq\n",
-       "taRgWq2WdFjb7Ta63S7a7bacOnOw7VEMz5zUBDKObpumCb/fL0WjLm3w2tPR0REKhQKKxSLq9bqM\n",
-       "Ds+ai0y5v4W+05sioh0Avw/gqwD+loh+GYMp92V28rIZjomw49lqteByuRxBNp6xqBZnlP8HAEKh\n",
-       "kHTCI5GIw9K43W45NLGjzKJRLY1lWVK8s+Yis6dXzvinz0y5L1fGcF4Kx2TOYtJZi8fjQTQaRSAQ\n",
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b65d950>"
+       "<matplotlib.figure.Figure at 0x7f51991d5790>"
       ]
      },
      "metadata": {},
@@ -3667,81 +899,9 @@
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      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b44f290>"
+       "<matplotlib.figure.Figure at 0x7f5198fc5750>"
       ]
      },
      "metadata": {},
@@ -3874,92 +919,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b3b3a50>"
+       "<matplotlib.figure.Figure at 0x7f5198f2af90>"
       ]
      },
      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b33bd10>"
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       ]
      },
      "metadata": {},
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      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b23ec90>"
+       "<matplotlib.figure.Figure at 0x7f5198dc0d90>"
       ]
      },
      "metadata": {},
@@ -4267,87 +959,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b1bd290>"
+       "<matplotlib.figure.Figure at 0x7f51994b6550>"
       ]
      },
      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b1419d0>"
+       "<matplotlib.figure.Figure at 0x7f51994cc7d0>"
       ]
      },
      "metadata": {},
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      },
      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b044750>"
+       "<matplotlib.figure.Figure at 0x7f5199196050>"
       ]
      },
      "metadata": {},
@@ -4643,82 +999,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b06c950>"
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       ]
      },
      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792af4a550>"
+       "<matplotlib.figure.Figure at 0x7f519c085f10>"
       ]
      },
      "metadata": {},
@@ -4851,89 +1019,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792aed8c90>"
+       "<matplotlib.figure.Figure at 0x7f519998c390>"
       ]
      },
      "metadata": {},
@@ -4941,123 +1029,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b570150>"
+       "<matplotlib.figure.Figure at 0x7f519c0f7b50>"
       ]
      },
      "metadata": {},
@@ -5065,92 +1039,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792baffb50>"
+       "<matplotlib.figure.Figure at 0x7f5199b2b090>"
       ]
      },
      "metadata": {},
@@ -5166,6 +1057,207 @@
     "    xlabel('iteration')\n",
     "    ylabel('label')"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. Experiment with architecture and optimization\n",
+    "\n",
+    "Now that we've defined, trained, and tested LeNet there are many possible next steps:\n",
+    "\n",
+    "- Define new architectures for comparison\n",
+    "- Tune optimization by setting `base_lr` and the like or simply training longer\n",
+    "- Switching the solver type from `SGD` to an adaptive method like `AdaDelta` or `Adam`\n",
+    "\n",
+    "Feel free to explore these directions by editing the all-in-one example that follows.\n",
+    "Look for \"`EDIT HERE`\" comments for suggested choice points.\n",
+    "\n",
+    "By default this defines a simple linear classifier as a baseline.\n",
+    "\n",
+    "In case your coffee hasn't kicked in and you'd like inspiration, try out\n",
+    "\n",
+    "1. Switch the nonlinearity from `ReLU` to `ELU` or a saturing nonlinearity like `Sigmoid`\n",
+    "2. Stack more fully connected and nonlinear layers\n",
+    "3. Search over learning rate 10x at a time (trying `0.1` and `0.001`)\n",
+    "4. Switch the solver type to `Adam` (this adaptive solver type should be less sensitive to hyperparameters, but no guarantees...)\n",
+    "5. Solve for longer by setting `niter` higher (to 500 or 1,000 for instance) to better show training differences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration 0 testing...\n",
+      "Iteration 25 testing...\n",
+      "Iteration 50 testing...\n",
+      "Iteration 75 testing...\n",
+      "Iteration 100 testing...\n",
+      "Iteration 125 testing...\n",
+      "Iteration 150 testing...\n",
+      "Iteration 175 testing...\n",
+      "Iteration 200 testing...\n",
+      "Iteration 225 testing...\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7f5199af9f50>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ObSIiqKpElpsBC4ETgFXAVOA8VZ0f2Se1Qs3fVXVgMdoXxwV3LzAXKzInWALC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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5198f46a10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train_net_path = 'mnist/custom_auto_train.prototxt'\n",
+    "test_net_path = 'mnist/custom_auto_test.prototxt'\n",
+    "solver_config_path = 'mnist/custom_auto_solver.prototxt'\n",
+    "\n",
+    "### define net\n",
+    "def custom_net(lmdb, batch_size):\n",
+    "    # define your own net!\n",
+    "    n = caffe.NetSpec()\n",
+    "    \n",
+    "    # keep this data layer for all networks\n",
+    "    n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n",
+    "                             transform_param=dict(scale=1./255), ntop=2)\n",
+    "    \n",
+    "    # EDIT HERE to try different networks\n",
+    "    # this single layer defines a simple linear classifier\n",
+    "    # (in particular this defines a multiway logistic regression)\n",
+    "    n.score =   L.InnerProduct(n.data, num_output=10, weight_filler=dict(type='xavier'))\n",
+    "    \n",
+    "    # EDIT HERE this is the LeNet variant we have already tried\n",
+    "    # n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n",
+    "    # n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
+    "    # n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n",
+    "    # n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
+    "    # n.fc1 =   L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n",
+    "    # EDIT HERE consider L.ELU or L.Sigmoid for the nonlinearity\n",
+    "    # n.relu1 = L.ReLU(n.fc1, in_place=True)\n",
+    "    # n.score =   L.InnerProduct(n.fc1, num_output=10, weight_filler=dict(type='xavier'))\n",
+    "    \n",
+    "    # keep this loss layer for all networks\n",
+    "    n.loss =  L.SoftmaxWithLoss(n.score, n.label)\n",
+    "    \n",
+    "    return n.to_proto()\n",
+    "\n",
+    "with open(train_net_path, 'w') as f:\n",
+    "    f.write(str(custom_net('mnist/mnist_train_lmdb', 64)))    \n",
+    "with open(test_net_path, 'w') as f:\n",
+    "    f.write(str(custom_net('mnist/mnist_test_lmdb', 100)))\n",
+    "\n",
+    "### define solver\n",
+    "from caffe.proto import caffe_pb2\n",
+    "s = caffe_pb2.SolverParameter()\n",
+    "\n",
+    "# Set a seed for reproducible experiments:\n",
+    "# this controls for randomization in training.\n",
+    "s.random_seed = 0xCAFFE\n",
+    "\n",
+    "# Specify locations of the train and (maybe) test networks.\n",
+    "s.train_net = train_net_path\n",
+    "s.test_net.append(test_net_path)\n",
+    "s.test_interval = 500  # Test after every 500 training iterations.\n",
+    "s.test_iter.append(100) # Test on 100 batches each time we test.\n",
+    "\n",
+    "s.max_iter = 10000     # no. of times to update the net (training iterations)\n",
+    " \n",
+    "# EDIT HERE to try different solvers\n",
+    "# solver types include \"SGD\", \"Adam\", and \"Nesterov\" among others.\n",
+    "s.type = \"SGD\"\n",
+    "\n",
+    "# Set the initial learning rate for SGD.\n",
+    "s.base_lr = 0.01  # EDIT HERE to try different learning rates\n",
+    "# Set momentum to accelerate learning by\n",
+    "# taking weighted average of current and previous updates.\n",
+    "s.momentum = 0.9\n",
+    "# Set weight decay to regularize and prevent overfitting\n",
+    "s.weight_decay = 5e-4\n",
+    "\n",
+    "# Set `lr_policy` to define how the learning rate changes during training.\n",
+    "# This is the same policy as our default LeNet.\n",
+    "s.lr_policy = 'inv'\n",
+    "s.gamma = 0.0001\n",
+    "s.power = 0.75\n",
+    "# EDIT HERE to try the fixed rate (and compare with adaptive solvers)\n",
+    "# `fixed` is the simplest policy that keeps the learning rate constant.\n",
+    "# s.lr_policy = 'fixed'\n",
+    "\n",
+    "# Display the current training loss and accuracy every 1000 iterations.\n",
+    "s.display = 1000\n",
+    "\n",
+    "# Snapshots are files used to store networks we've trained.\n",
+    "# We'll snapshot every 5K iterations -- twice during training.\n",
+    "s.snapshot = 5000\n",
+    "s.snapshot_prefix = 'mnist/custom_net'\n",
+    "\n",
+    "# Train on the GPU\n",
+    "s.solver_mode = caffe_pb2.SolverParameter.GPU\n",
+    "\n",
+    "# Write the solver to a temporary file and return its filename.\n",
+    "with open(solver_config_path, 'w') as f:\n",
+    "    f.write(str(s))\n",
+    "\n",
+    "### load the solver and create train and test nets\n",
+    "solver = None  # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)\n",
+    "solver = caffe.get_solver(solver_config_path)\n",
+    "\n",
+    "### solve\n",
+    "niter = 250  # EDIT HERE increase to train for longer\n",
+    "test_interval = niter / 10\n",
+    "# losses will also be stored in the log\n",
+    "train_loss = zeros(niter)\n",
+    "test_acc = zeros(int(np.ceil(niter / test_interval)))\n",
+    "\n",
+    "# the main solver loop\n",
+    "for it in range(niter):\n",
+    "    solver.step(1)  # SGD by Caffe\n",
+    "    \n",
+    "    # store the train loss\n",
+    "    train_loss[it] = solver.net.blobs['loss'].data\n",
+    "    \n",
+    "    # run a full test every so often\n",
+    "    # (Caffe can also do this for us and write to a log, but we show here\n",
+    "    #  how to do it directly in Python, where more complicated things are easier.)\n",
+    "    if it % test_interval == 0:\n",
+    "        print 'Iteration', it, 'testing...'\n",
+    "        correct = 0\n",
+    "        for test_it in range(100):\n",
+    "            solver.test_nets[0].forward()\n",
+    "            correct += sum(solver.test_nets[0].blobs['score'].data.argmax(1)\n",
+    "                           == solver.test_nets[0].blobs['label'].data)\n",
+    "        test_acc[it // test_interval] = correct / 1e4\n",
+    "\n",
+    "_, ax1 = subplots()\n",
+    "ax2 = ax1.twinx()\n",
+    "ax1.plot(arange(niter), train_loss)\n",
+    "ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n",
+    "ax1.set_xlabel('iteration')\n",
+    "ax1.set_ylabel('train loss')\n",
+    "ax2.set_ylabel('test accuracy')\n",
+    "ax2.set_title('Custom Test Accuracy: {:.2f}'.format(test_acc[-1]))"
+   ]
   }
  ],
  "metadata": {
@@ -5187,7 +1279,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.10"
   },
   "priority": 2
  },
diff --git a/examples/02-brewing-logreg.ipynb b/examples/02-brewing-logreg.ipynb
deleted file mode 100644
index d36871fc..00000000
--- a/examples/02-brewing-logreg.ipynb
+++ /dev/null
@@ -1,5771 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Brewing Logistic Regression then Going Deeper\n",
-    "\n",
-    "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "import os\n",
-    "os.chdir('..')\n",
-    "\n",
-    "import sys\n",
-    "sys.path.insert(0, './python')\n",
-    "import caffe\n",
-    "\n",
-    "\n",
-    "import os\n",
-    "import h5py\n",
-    "import shutil\n",
-    "import tempfile\n",
-    "\n",
-    "import sklearn\n",
-    "import sklearn.datasets\n",
-    "import sklearn.linear_model\n",
-    "\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "e2tCtteAbN732b7Xs3l/X88cyco8yKM1IJ/u//y896/HgsnXL+AhoGXWsQbgH7LQ9tuZ2yLdmaW+\n",
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-       "jsX8yPb1zmZ71lxYPLJxra01IL9Z0saIhYWFhYWFRf5jbdNYWFhYWFhY5BTLGLGwsLCwsLDIKZYx\n",
-       "YmFhYWFhYZFTLGMkjxCRt4nIKRE5KyJ/lOv+WCw8IvIVERkWkWO57ovF4iAiTSLyrIicEJHjIvLb\n",
-       "ue6TxcIjIh4ReVVEDovISRH5TK77lE9YAax5gojYgdPAfUA/8DpaK6A9px2zWFBE5E4gBHzdGLM5\n",
-       "1/2xWHhEpBaoNcYcFhEvcBB4j3WvL31EpNAYMy0iDuBF4PeNMS/mul/5gOUZyR/2AOeMMV3GmATw\n",
-       "HeBnc9wniwXGGLMPmMh1PywWD2PMkDHmcOb7ENAO1Oe2VxaLgTFmOvOtC616O57D7uQVljGSPzQA\n",
-       "vTN+7sscs7CwWKKISCuwHXg1tz2xWAxExCYih4Fh4FljzMlc9ylfsIyR/MHaL7OwWEZktmi+D/xO\n",
-       "xkNiscQxxqSNMduARuAuEbk7x13KGyxjJH/oB5pm/NyEekcsLCyWGCLiBB4BvmGMeTTX/bFYXIwx\n",
-       "QeA/gF257ku+YBkj+cMBYLWItIqIC3g/8MMc98nCwiLLiIgA/wScNMb8ba77Y7E4iEiliPgy3xcA\n",
-       "9wNv5LZX+YNljOQJxpgk8FvAE8BJ4LtWdP3SR0S+DbwMrBGRXhH5aK77ZLHg3A58CLhHRN7IfL0t\n",
-       "152yWHDqgGcyMSOvAv9ujHk6x33KG6zUXgsLCwsLC4ucYnlGLCwsLCwsLHKKZYxYWFhYWFhY5BTL\n",
-       "GLGwsLCwsLDIKZYxYmFhYWFhYZFTLGPEwsLCwsLCIqdYxoiFhYWFhYVFTrGMEQsLCwsLC4ucYhkj\n",
-       "FhYWFhYWFjnl/wPBByFp6Gp27QAAAABJRU5ErkJggg==\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11dbd0090>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "X, y = sklearn.datasets.make_classification(\n",
-    "    n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n",
-    "    n_clusters_per_class=2, hypercube=False, random_state=0\n",
-    ")\n",
-    "\n",
-    "# Split into train and test\n",
-    "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n",
-    "\n",
-    "# Visualize sample of the data\n",
-    "ind = np.random.permutation(X.shape[0])[:1000]\n",
-    "df = pd.DataFrame(X[ind])\n",
-    "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.783\n",
-      "Accuracy: 0.783\n",
-      "Accuracy: 0.783\n",
-      "Accuracy: 0.783\n",
-      "1 loops, best of 3: 508 ms per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "# Train and test the scikit-learn SGD logistic regression.\n",
-    "clf = sklearn.linear_model.SGDClassifier(\n",
-    "    loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')\n",
-    "\n",
-    "clf.fit(X, y)\n",
-    "yt_pred = clf.predict(Xt)\n",
-    "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Save the dataset to HDF5 for loading in Caffe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Write out the data to HDF5 files in a temp directory.\n",
-    "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n",
-    "dirname = os.path.abspath('./examples/hdf5_classification/data')\n",
-    "if not os.path.exists(dirname):\n",
-    "    os.makedirs(dirname)\n",
-    "\n",
-    "train_filename = os.path.join(dirname, 'train.h5')\n",
-    "test_filename = os.path.join(dirname, 'test.h5')\n",
-    "\n",
-    "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n",
-    "# To show this off, we'll list the same data file twice.\n",
-    "with h5py.File(train_filename, 'w') as f:\n",
-    "    f['data'] = X\n",
-    "    f['label'] = y.astype(np.float32)\n",
-    "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n",
-    "    f.write(train_filename + '\\n')\n",
-    "    f.write(train_filename + '\\n')\n",
-    "    \n",
-    "# HDF5 is pretty efficient, but can be further compressed.\n",
-    "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n",
-    "with h5py.File(test_filename, 'w') as f:\n",
-    "    f.create_dataset('data', data=Xt, **comp_kwargs)\n",
-    "    f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n",
-    "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n",
-    "    f.write(test_filename + '\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's define logistic regression in Caffe through Python net specification. This is a quick and natural way to define nets that sidesteps manually editing the protobuf model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from caffe import layers as L\n",
-    "from caffe import params as P\n",
-    "\n",
-    "def logreg(hdf5, batch_size):\n",
-    "    # logistic regression: data, matrix multiplication, and 2-class softmax loss\n",
-    "    n = caffe.NetSpec()\n",
-    "    n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
-    "    n.ip1 = L.InnerProduct(n.data, num_output=2, weight_filler=dict(type='xavier'))\n",
-    "    n.accuracy = L.Accuracy(n.ip1, n.label)\n",
-    "    n.loss = L.SoftmaxWithLoss(n.ip1, n.label)\n",
-    "    return n.to_proto()\n",
-    "    \n",
-    "with open('examples/hdf5_classification/logreg_auto_train.prototxt', 'w') as f:\n",
-    "    f.write(str(logreg('examples/hdf5_classification/data/train.txt', 10)))\n",
-    "    \n",
-    "with open('examples/hdf5_classification/logreg_auto_test.prototxt', 'w') as f:\n",
-    "    f.write(str(logreg('examples/hdf5_classification/data/test.txt', 10)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Time to learn and evaluate our Caffeinated logistic regression in Python."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.782\n",
-      "Accuracy: 0.782\n",
-      "Accuracy: 0.782\n",
-      "Accuracy: 0.782\n",
-      "1 loops, best of 3: 287 ms per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "caffe.set_mode_cpu()\n",
-    "solver = caffe.get_solver('examples/hdf5_classification/solver.prototxt')\n",
-    "solver.solve()\n",
-    "\n",
-    "accuracy = 0\n",
-    "batch_size = solver.test_nets[0].blobs['data'].num\n",
-    "test_iters = int(len(Xt) / batch_size)\n",
-    "for i in range(test_iters):\n",
-    "    solver.test_nets[0].forward()\n",
-    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
-    "accuracy /= test_iters\n",
-    "\n",
-    "print(\"Accuracy: {:.3f}\".format(accuracy))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Do the same through the command line interface for detailed output on the model and solving."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "I0318 00:58:32.322571 2013098752 caffe.cpp:117] Use CPU.\n",
-      "I0318 00:58:32.643163 2013098752 caffe.cpp:121] Starting Optimization\n",
-      "I0318 00:58:32.643229 2013098752 solver.cpp:32] Initializing solver from parameters: \n",
-      "train_net: \"examples/hdf5_classification/logreg_auto_train.prototxt\"\n",
-      "test_net: \"examples/hdf5_classification/logreg_auto_test.prototxt\"\n",
-      "test_iter: 250\n",
-      "test_interval: 1000\n",
-      "base_lr: 0.01\n",
-      "display: 1000\n",
-      "max_iter: 10000\n",
-      "lr_policy: \"step\"\n",
-      "gamma: 0.1\n",
-      "momentum: 0.9\n",
-      "weight_decay: 0.0005\n",
-      "stepsize: 5000\n",
-      "snapshot: 10000\n",
-      "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
-      "solver_mode: CPU\n",
-      "I0318 00:58:32.643333 2013098752 solver.cpp:61] Creating training net from train_net file: examples/hdf5_classification/logreg_auto_train.prototxt\n",
-      "I0318 00:58:32.643465 2013098752 net.cpp:42] Initializing net from parameters: \n",
-      "state {\n",
-      "  phase: TRAIN\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"data\"\n",
-      "  type: \"HDF5Data\"\n",
-      "  top: \"data\"\n",
-      "  top: \"label\"\n",
-      "  hdf5_data_param {\n",
-      "    source: \"examples/hdf5_classification/data/train.txt\"\n",
-      "    batch_size: 10\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"ip1\"\n",
-      "  type: \"InnerProduct\"\n",
-      "  bottom: \"data\"\n",
-      "  top: \"ip1\"\n",
-      "  inner_product_param {\n",
-      "    num_output: 2\n",
-      "    weight_filler {\n",
-      "      type: \"xavier\"\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"accuracy\"\n",
-      "  type: \"Accuracy\"\n",
-      "  bottom: \"ip1\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"accuracy\"\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"loss\"\n",
-      "  type: \"SoftmaxWithLoss\"\n",
-      "  bottom: \"ip1\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"loss\"\n",
-      "}\n",
-      "I0318 00:58:32.644197 2013098752 layer_factory.hpp:74] Creating layer data\n",
-      "I0318 00:58:32.644219 2013098752 net.cpp:84] Creating Layer data\n",
-      "I0318 00:58:32.644230 2013098752 net.cpp:338] data -> data\n",
-      "I0318 00:58:32.644256 2013098752 net.cpp:338] data -> label\n",
-      "I0318 00:58:32.644269 2013098752 net.cpp:113] Setting up data\n",
-      "I0318 00:58:32.644278 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
-      "I0318 00:58:32.644327 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n",
-      "I0318 00:58:32.646458 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
-      "I0318 00:58:32.646502 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:32.646518 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
-      "I0318 00:58:32.646538 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
-      "I0318 00:58:32.646546 2013098752 net.cpp:380] label_data_1_split <- label\n",
-      "I0318 00:58:32.646556 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
-      "I0318 00:58:32.646569 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
-      "I0318 00:58:32.646579 2013098752 net.cpp:113] Setting up label_data_1_split\n",
-      "I0318 00:58:32.646586 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:32.646595 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:32.646601 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
-      "I0318 00:58:32.646615 2013098752 net.cpp:84] Creating Layer ip1\n",
-      "I0318 00:58:32.646622 2013098752 net.cpp:380] ip1 <- data\n",
-      "I0318 00:58:32.646664 2013098752 net.cpp:338] ip1 -> ip1\n",
-      "I0318 00:58:32.646689 2013098752 net.cpp:113] Setting up ip1\n",
-      "I0318 00:58:32.652330 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:32.652371 2013098752 layer_factory.hpp:74] Creating layer ip1_ip1_0_split\n",
-      "I0318 00:58:32.652393 2013098752 net.cpp:84] Creating Layer ip1_ip1_0_split\n",
-      "I0318 00:58:32.652407 2013098752 net.cpp:380] ip1_ip1_0_split <- ip1\n",
-      "I0318 00:58:32.652421 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
-      "I0318 00:58:32.652467 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
-      "I0318 00:58:32.652480 2013098752 net.cpp:113] Setting up ip1_ip1_0_split\n",
-      "I0318 00:58:32.652489 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:32.652498 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:32.652505 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
-      "I0318 00:58:32.652521 2013098752 net.cpp:84] Creating Layer accuracy\n",
-      "I0318 00:58:32.652534 2013098752 net.cpp:380] accuracy <- ip1_ip1_0_split_0\n",
-      "I0318 00:58:32.652545 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
-      "I0318 00:58:32.652562 2013098752 net.cpp:338] accuracy -> accuracy\n",
-      "I0318 00:58:32.652577 2013098752 net.cpp:113] Setting up accuracy\n",
-      "I0318 00:58:32.652590 2013098752 net.cpp:120] Top shape: (1)\n",
-      "I0318 00:58:32.652642 2013098752 layer_factory.hpp:74] Creating layer loss\n",
-      "I0318 00:58:32.652655 2013098752 net.cpp:84] Creating Layer loss\n",
-      "I0318 00:58:32.652663 2013098752 net.cpp:380] loss <- ip1_ip1_0_split_1\n",
-      "I0318 00:58:32.652672 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
-      "I0318 00:58:32.652679 2013098752 net.cpp:338] loss -> loss\n",
-      "I0318 00:58:32.652689 2013098752 net.cpp:113] Setting up loss\n",
-      "I0318 00:58:32.652701 2013098752 layer_factory.hpp:74] Creating layer loss\n",
-      "I0318 00:58:32.652716 2013098752 net.cpp:120] Top shape: (1)\n",
-      "I0318 00:58:32.652724 2013098752 net.cpp:122]     with loss weight 1\n",
-      "I0318 00:58:32.652740 2013098752 net.cpp:167] loss needs backward computation.\n",
-      "I0318 00:58:32.652746 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
-      "I0318 00:58:32.652753 2013098752 net.cpp:167] ip1_ip1_0_split needs backward computation.\n",
-      "I0318 00:58:32.652760 2013098752 net.cpp:167] ip1 needs backward computation.\n",
-      "I0318 00:58:32.652786 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
-      "I0318 00:58:32.652801 2013098752 net.cpp:169] data does not need backward computation.\n",
-      "I0318 00:58:32.652808 2013098752 net.cpp:205] This network produces output accuracy\n",
-      "I0318 00:58:32.652815 2013098752 net.cpp:205] This network produces output loss\n",
-      "I0318 00:58:32.652825 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
-      "I0318 00:58:32.652833 2013098752 net.cpp:217] Network initialization done.\n",
-      "I0318 00:58:32.652839 2013098752 net.cpp:218] Memory required for data: 528\n",
-      "I0318 00:58:32.652964 2013098752 solver.cpp:154] Creating test net (#0) specified by test_net file: examples/hdf5_classification/logreg_auto_test.prototxt\n",
-      "I0318 00:58:32.652986 2013098752 net.cpp:42] Initializing net from parameters: \n",
-      "state {\n",
-      "  phase: TEST\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"data\"\n",
-      "  type: \"HDF5Data\"\n",
-      "  top: \"data\"\n",
-      "  top: \"label\"\n",
-      "  hdf5_data_param {\n",
-      "    source: \"examples/hdf5_classification/data/test.txt\"\n",
-      "    batch_size: 10\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"ip1\"\n",
-      "  type: \"InnerProduct\"\n",
-      "  bottom: \"data\"\n",
-      "  top: \"ip1\"\n",
-      "  inner_product_param {\n",
-      "    num_output: 2\n",
-      "    weight_filler {\n",
-      "      type: \"xavier\"\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"accuracy\"\n",
-      "  type: \"Accuracy\"\n",
-      "  bottom: \"ip1\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"accuracy\"\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"loss\"\n",
-      "  type: \"SoftmaxWithLoss\"\n",
-      "  bottom: \"ip1\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"loss\"\n",
-      "}\n",
-      "I0318 00:58:32.653069 2013098752 layer_factory.hpp:74] Creating layer data\n",
-      "I0318 00:58:32.653080 2013098752 net.cpp:84] Creating Layer data\n",
-      "I0318 00:58:32.653090 2013098752 net.cpp:338] data -> data\n",
-      "I0318 00:58:32.653128 2013098752 net.cpp:338] data -> label\n",
-      "I0318 00:58:32.653146 2013098752 net.cpp:113] Setting up data\n",
-      "I0318 00:58:32.653154 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
-      "I0318 00:58:32.653192 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n",
-      "I0318 00:58:32.654850 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
-      "I0318 00:58:32.654897 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:32.654914 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
-      "I0318 00:58:32.654933 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
-      "I0318 00:58:32.654943 2013098752 net.cpp:380] label_data_1_split <- label\n",
-      "I0318 00:58:32.654953 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
-      "I0318 00:58:32.654966 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
-      "I0318 00:58:32.654976 2013098752 net.cpp:113] Setting up label_data_1_split\n",
-      "I0318 00:58:32.654985 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:32.654992 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:32.655000 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
-      "I0318 00:58:32.655010 2013098752 net.cpp:84] Creating Layer ip1\n",
-      "I0318 00:58:32.655017 2013098752 net.cpp:380] ip1 <- data\n",
-      "I0318 00:58:32.655030 2013098752 net.cpp:338] ip1 -> ip1\n",
-      "I0318 00:58:32.655041 2013098752 net.cpp:113] Setting up ip1\n",
-      "I0318 00:58:32.655061 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:32.655072 2013098752 layer_factory.hpp:74] Creating layer ip1_ip1_0_split\n",
-      "I0318 00:58:32.655148 2013098752 net.cpp:84] Creating Layer ip1_ip1_0_split\n",
-      "I0318 00:58:32.655159 2013098752 net.cpp:380] ip1_ip1_0_split <- ip1\n",
-      "I0318 00:58:32.655170 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
-      "I0318 00:58:32.655180 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
-      "I0318 00:58:32.655190 2013098752 net.cpp:113] Setting up ip1_ip1_0_split\n",
-      "I0318 00:58:32.655199 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:32.655206 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:32.655213 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
-      "I0318 00:58:32.655223 2013098752 net.cpp:84] Creating Layer accuracy\n",
-      "I0318 00:58:32.655230 2013098752 net.cpp:380] accuracy <- ip1_ip1_0_split_0\n",
-      "I0318 00:58:32.655237 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
-      "I0318 00:58:32.655251 2013098752 net.cpp:338] accuracy -> accuracy\n",
-      "I0318 00:58:32.655259 2013098752 net.cpp:113] Setting up accuracy\n",
-      "I0318 00:58:32.655267 2013098752 net.cpp:120] Top shape: (1)\n",
-      "I0318 00:58:32.655340 2013098752 layer_factory.hpp:74] Creating layer loss\n",
-      "I0318 00:58:32.655354 2013098752 net.cpp:84] Creating Layer loss\n",
-      "I0318 00:58:32.655361 2013098752 net.cpp:380] loss <- ip1_ip1_0_split_1\n",
-      "I0318 00:58:32.655369 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
-      "I0318 00:58:32.655378 2013098752 net.cpp:338] loss -> loss\n",
-      "I0318 00:58:32.655388 2013098752 net.cpp:113] Setting up loss\n",
-      "I0318 00:58:32.655397 2013098752 layer_factory.hpp:74] Creating layer loss\n",
-      "I0318 00:58:32.655414 2013098752 net.cpp:120] Top shape: (1)\n",
-      "I0318 00:58:32.655422 2013098752 net.cpp:122]     with loss weight 1\n",
-      "I0318 00:58:32.655438 2013098752 net.cpp:167] loss needs backward computation.\n",
-      "I0318 00:58:32.655446 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
-      "I0318 00:58:32.655455 2013098752 net.cpp:167] ip1_ip1_0_split needs backward computation.\n",
-      "I0318 00:58:32.655462 2013098752 net.cpp:167] ip1 needs backward computation.\n",
-      "I0318 00:58:32.655469 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
-      "I0318 00:58:32.655477 2013098752 net.cpp:169] data does not need backward computation.\n",
-      "I0318 00:58:32.655483 2013098752 net.cpp:205] This network produces output accuracy\n",
-      "I0318 00:58:32.655489 2013098752 net.cpp:205] This network produces output loss\n",
-      "I0318 00:58:32.655503 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
-      "I0318 00:58:32.655511 2013098752 net.cpp:217] Network initialization done.\n",
-      "I0318 00:58:32.655517 2013098752 net.cpp:218] Memory required for data: 528\n",
-      "I0318 00:58:32.655547 2013098752 solver.cpp:42] Solver scaffolding done.\n",
-      "I0318 00:58:32.655567 2013098752 solver.cpp:222] Solving \n",
-      "I0318 00:58:32.655575 2013098752 solver.cpp:223] Learning Rate Policy: step\n",
-      "I0318 00:58:32.655583 2013098752 solver.cpp:266] Iteration 0, Testing net (#0)\n",
-      "I0318 00:58:32.683643 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.3736\n",
-      "I0318 00:58:32.683686 2013098752 solver.cpp:315]     Test net output #1: loss = 1.00555 (* 1 = 1.00555 loss)\n",
-      "I0318 00:58:32.683846 2013098752 solver.cpp:189] Iteration 0, loss = 0.869394\n",
-      "I0318 00:58:32.683861 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.3\n",
-      "I0318 00:58:32.683871 2013098752 solver.cpp:204]     Train net output #1: loss = 0.869394 (* 1 = 0.869394 loss)\n",
-      "I0318 00:58:32.683883 2013098752 solver.cpp:464] Iteration 0, lr = 0.01\n",
-      "I0318 00:58:32.698721 2013098752 solver.cpp:266] Iteration 1000, Testing net (#0)\n",
-      "I0318 00:58:32.701917 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7848\n",
-      "I0318 00:58:32.701961 2013098752 solver.cpp:315]     Test net output #1: loss = 0.590972 (* 1 = 0.590972 loss)\n",
-      "I0318 00:58:32.702014 2013098752 solver.cpp:189] Iteration 1000, loss = 0.54742\n",
-      "I0318 00:58:32.702029 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
-      "I0318 00:58:32.702041 2013098752 solver.cpp:204]     Train net output #1: loss = 0.54742 (* 1 = 0.54742 loss)\n",
-      "I0318 00:58:32.702051 2013098752 solver.cpp:464] Iteration 1000, lr = 0.01\n",
-      "I0318 00:58:32.718360 2013098752 solver.cpp:266] Iteration 2000, Testing net (#0)\n",
-      "I0318 00:58:32.721529 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7696\n",
-      "I0318 00:58:32.721562 2013098752 solver.cpp:315]     Test net output #1: loss = 0.593946 (* 1 = 0.593946 loss)\n",
-      "I0318 00:58:32.721593 2013098752 solver.cpp:189] Iteration 2000, loss = 0.729569\n",
-      "I0318 00:58:32.721603 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.5\n",
-      "I0318 00:58:32.721613 2013098752 solver.cpp:204]     Train net output #1: loss = 0.729569 (* 1 = 0.729569 loss)\n",
-      "I0318 00:58:32.721622 2013098752 solver.cpp:464] Iteration 2000, lr = 0.01\n",
-      "I0318 00:58:32.740182 2013098752 solver.cpp:266] Iteration 3000, Testing net (#0)\n",
-      "I0318 00:58:32.743494 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.77\n",
-      "I0318 00:58:32.743544 2013098752 solver.cpp:315]     Test net output #1: loss = 0.591229 (* 1 = 0.591229 loss)\n",
-      "I0318 00:58:32.744209 2013098752 solver.cpp:189] Iteration 3000, loss = 0.406097\n",
-      "I0318 00:58:32.744231 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.8\n",
-      "I0318 00:58:32.744249 2013098752 solver.cpp:204]     Train net output #1: loss = 0.406096 (* 1 = 0.406096 loss)\n",
-      "I0318 00:58:32.744266 2013098752 solver.cpp:464] Iteration 3000, lr = 0.01\n",
-      "I0318 00:58:32.764135 2013098752 solver.cpp:266] Iteration 4000, Testing net (#0)\n",
-      "I0318 00:58:32.769110 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7848\n",
-      "I0318 00:58:32.769170 2013098752 solver.cpp:315]     Test net output #1: loss = 0.590972 (* 1 = 0.590972 loss)\n",
-      "I0318 00:58:32.769223 2013098752 solver.cpp:189] Iteration 4000, loss = 0.54742\n",
-      "I0318 00:58:32.769242 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
-      "I0318 00:58:32.769255 2013098752 solver.cpp:204]     Train net output #1: loss = 0.54742 (* 1 = 0.54742 loss)\n",
-      "I0318 00:58:32.769265 2013098752 solver.cpp:464] Iteration 4000, lr = 0.01\n",
-      "I0318 00:58:32.785846 2013098752 solver.cpp:266] Iteration 5000, Testing net (#0)\n",
-      "I0318 00:58:32.788722 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7696\n",
-      "I0318 00:58:32.788751 2013098752 solver.cpp:315]     Test net output #1: loss = 0.593946 (* 1 = 0.593946 loss)\n",
-      "I0318 00:58:32.788811 2013098752 solver.cpp:189] Iteration 5000, loss = 0.72957\n",
-      "I0318 00:58:32.788833 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.5\n",
-      "I0318 00:58:32.788846 2013098752 solver.cpp:204]     Train net output #1: loss = 0.729569 (* 1 = 0.729569 loss)\n",
-      "I0318 00:58:32.788856 2013098752 solver.cpp:464] Iteration 5000, lr = 0.001\n",
-      "I0318 00:58:32.804762 2013098752 solver.cpp:266] Iteration 6000, Testing net (#0)\n",
-      "I0318 00:58:32.808061 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7856\n",
-      "I0318 00:58:32.808112 2013098752 solver.cpp:315]     Test net output #1: loss = 0.59028 (* 1 = 0.59028 loss)\n",
-      "I0318 00:58:32.808732 2013098752 solver.cpp:189] Iteration 6000, loss = 0.415444\n",
-      "I0318 00:58:32.808753 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
-      "I0318 00:58:32.808773 2013098752 solver.cpp:204]     Train net output #1: loss = 0.415444 (* 1 = 0.415444 loss)\n",
-      "I0318 00:58:32.808786 2013098752 solver.cpp:464] Iteration 6000, lr = 0.001\n",
-      "I0318 00:58:32.827118 2013098752 solver.cpp:266] Iteration 7000, Testing net (#0)\n",
-      "I0318 00:58:32.831614 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7848\n",
-      "I0318 00:58:32.831657 2013098752 solver.cpp:315]     Test net output #1: loss = 0.589454 (* 1 = 0.589454 loss)\n",
-      "I0318 00:58:32.831707 2013098752 solver.cpp:189] Iteration 7000, loss = 0.538038\n",
-      "I0318 00:58:32.831728 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.8\n",
-      "I0318 00:58:32.831745 2013098752 solver.cpp:204]     Train net output #1: loss = 0.538037 (* 1 = 0.538037 loss)\n",
-      "I0318 00:58:32.831759 2013098752 solver.cpp:464] Iteration 7000, lr = 0.001\n",
-      "I0318 00:58:32.849634 2013098752 solver.cpp:266] Iteration 8000, Testing net (#0)\n",
-      "I0318 00:58:32.852712 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7796\n",
-      "I0318 00:58:32.852748 2013098752 solver.cpp:315]     Test net output #1: loss = 0.589365 (* 1 = 0.589365 loss)\n",
-      "I0318 00:58:32.852792 2013098752 solver.cpp:189] Iteration 8000, loss = 0.684219\n",
-      "I0318 00:58:32.852840 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.5\n",
-      "I0318 00:58:32.852852 2013098752 solver.cpp:204]     Train net output #1: loss = 0.684219 (* 1 = 0.684219 loss)\n",
-      "I0318 00:58:32.852861 2013098752 solver.cpp:464] Iteration 8000, lr = 0.001\n",
-      "I0318 00:58:32.868440 2013098752 solver.cpp:266] Iteration 9000, Testing net (#0)\n",
-      "I0318 00:58:32.871438 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7816\n",
-      "I0318 00:58:32.871461 2013098752 solver.cpp:315]     Test net output #1: loss = 0.589656 (* 1 = 0.589656 loss)\n",
-      "I0318 00:58:32.872109 2013098752 solver.cpp:189] Iteration 9000, loss = 0.421879\n",
-      "I0318 00:58:32.872131 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
-      "I0318 00:58:32.872143 2013098752 solver.cpp:204]     Train net output #1: loss = 0.421879 (* 1 = 0.421879 loss)\n",
-      "I0318 00:58:32.872153 2013098752 solver.cpp:464] Iteration 9000, lr = 0.001\n",
-      "I0318 00:58:32.889981 2013098752 solver.cpp:334] Snapshotting to examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
-      "I0318 00:58:32.890224 2013098752 solver.cpp:342] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\n",
-      "I0318 00:58:32.890362 2013098752 solver.cpp:248] Iteration 10000, loss = 0.538933\n",
-      "I0318 00:58:32.890380 2013098752 solver.cpp:266] Iteration 10000, Testing net (#0)\n",
-      "I0318 00:58:32.893728 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.782\n",
-      "I0318 00:58:32.893757 2013098752 solver.cpp:315]     Test net output #1: loss = 0.589366 (* 1 = 0.589366 loss)\n",
-      "I0318 00:58:32.893775 2013098752 solver.cpp:253] Optimization Done.\n",
-      "I0318 00:58:32.893786 2013098752 caffe.cpp:134] Optimization Done.\n"
-     ]
-    }
-   ],
-   "source": [
-    "!./build/tools/caffe train -solver examples/hdf5_classification/solver.prototxt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you look at output or the `logreg_auto_train.prototxt`, you'll see that the model is simple logistic regression.\n",
-    "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n",
-    "That network is given in `nonlinear_auto_train.prototxt`, and that's the only change made in `nonlinear_solver.prototxt` which we will now use.\n",
-    "\n",
-    "The final accuracy of the new network should be higher than logistic regression!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from caffe import layers as L\n",
-    "from caffe import params as P\n",
-    "\n",
-    "def nonlinear_net(hdf5, batch_size):\n",
-    "    # one small nonlinearity, one leap for model kind\n",
-    "    n = caffe.NetSpec()\n",
-    "    n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
-    "    # define a hidden layer of dimension 40\n",
-    "    n.ip1 = L.InnerProduct(n.data, num_output=40, weight_filler=dict(type='xavier'))\n",
-    "    # transform the output through the ReLU (rectified linear) non-linearity\n",
-    "    n.relu1 = L.ReLU(n.ip1, in_place=True)\n",
-    "    # score the (now non-linear) features\n",
-    "    n.ip2 = L.InnerProduct(n.ip1, num_output=2, weight_filler=dict(type='xavier'))\n",
-    "    # same accuracy and loss as before\n",
-    "    n.accuracy = L.Accuracy(n.ip2, n.label)\n",
-    "    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n",
-    "    return n.to_proto()\n",
-    "    \n",
-    "with open('examples/hdf5_classification/nonlinear_auto_train.prototxt', 'w') as f:\n",
-    "    f.write(str(nonlinear_net('examples/hdf5_classification/data/train.txt', 10)))\n",
-    "    \n",
-    "with open('examples/hdf5_classification/nonlinear_auto_test.prototxt', 'w') as f:\n",
-    "    f.write(str(nonlinear_net('examples/hdf5_classification/data/test.txt', 10)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.832\n",
-      "Accuracy: 0.832\n",
-      "Accuracy: 0.832\n",
-      "Accuracy: 0.831\n",
-      "1 loops, best of 3: 386 ms per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "caffe.set_mode_cpu()\n",
-    "solver = caffe.get_solver('examples/hdf5_classification/nonlinear_solver.prototxt')\n",
-    "solver.solve()\n",
-    "\n",
-    "accuracy = 0\n",
-    "batch_size = solver.test_nets[0].blobs['data'].num\n",
-    "test_iters = int(len(Xt) / batch_size)\n",
-    "for i in range(test_iters):\n",
-    "    solver.test_nets[0].forward()\n",
-    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
-    "accuracy /= test_iters\n",
-    "\n",
-    "print(\"Accuracy: {:.3f}\".format(accuracy))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Do the same through the command line interface for detailed output on the model and solving."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "I0318 00:58:43.336922 2013098752 caffe.cpp:117] Use CPU.\n",
-      "I0318 00:58:43.654698 2013098752 caffe.cpp:121] Starting Optimization\n",
-      "I0318 00:58:43.654747 2013098752 solver.cpp:32] Initializing solver from parameters: \n",
-      "train_net: \"examples/hdf5_classification/nonlinear_auto_train.prototxt\"\n",
-      "test_net: \"examples/hdf5_classification/nonlinear_auto_test.prototxt\"\n",
-      "test_iter: 250\n",
-      "test_interval: 1000\n",
-      "base_lr: 0.01\n",
-      "display: 1000\n",
-      "max_iter: 10000\n",
-      "lr_policy: \"step\"\n",
-      "gamma: 0.1\n",
-      "momentum: 0.9\n",
-      "weight_decay: 0.0005\n",
-      "stepsize: 5000\n",
-      "snapshot: 10000\n",
-      "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
-      "solver_mode: CPU\n",
-      "I0318 00:58:43.654855 2013098752 solver.cpp:61] Creating training net from train_net file: examples/hdf5_classification/nonlinear_auto_train.prototxt\n",
-      "I0318 00:58:43.655004 2013098752 net.cpp:42] Initializing net from parameters: \n",
-      "state {\n",
-      "  phase: TRAIN\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"data\"\n",
-      "  type: \"HDF5Data\"\n",
-      "  top: \"data\"\n",
-      "  top: \"label\"\n",
-      "  hdf5_data_param {\n",
-      "    source: \"examples/hdf5_classification/data/train.txt\"\n",
-      "    batch_size: 10\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"ip1\"\n",
-      "  type: \"InnerProduct\"\n",
-      "  bottom: \"data\"\n",
-      "  top: \"ip1\"\n",
-      "  inner_product_param {\n",
-      "    num_output: 40\n",
-      "    weight_filler {\n",
-      "      type: \"xavier\"\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"relu1\"\n",
-      "  type: \"ReLU\"\n",
-      "  bottom: \"ip1\"\n",
-      "  top: \"ip1\"\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"ip2\"\n",
-      "  type: \"InnerProduct\"\n",
-      "  bottom: \"ip1\"\n",
-      "  top: \"ip2\"\n",
-      "  inner_product_param {\n",
-      "    num_output: 2\n",
-      "    weight_filler {\n",
-      "      type: \"xavier\"\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"accuracy\"\n",
-      "  type: \"Accuracy\"\n",
-      "  bottom: \"ip2\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"accuracy\"\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"loss\"\n",
-      "  type: \"SoftmaxWithLoss\"\n",
-      "  bottom: \"ip2\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"loss\"\n",
-      "}\n",
-      "I0318 00:58:43.655120 2013098752 layer_factory.hpp:74] Creating layer data\n",
-      "I0318 00:58:43.655139 2013098752 net.cpp:84] Creating Layer data\n",
-      "I0318 00:58:43.655264 2013098752 net.cpp:338] data -> data\n",
-      "I0318 00:58:43.655297 2013098752 net.cpp:338] data -> label\n",
-      "I0318 00:58:43.655310 2013098752 net.cpp:113] Setting up data\n",
-      "I0318 00:58:43.655318 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
-      "I0318 00:58:43.655365 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n",
-      "I0318 00:58:43.657317 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
-      "I0318 00:58:43.657342 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:43.657356 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
-      "I0318 00:58:43.657373 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
-      "I0318 00:58:43.657384 2013098752 net.cpp:380] label_data_1_split <- label\n",
-      "I0318 00:58:43.657395 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
-      "I0318 00:58:43.657407 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
-      "I0318 00:58:43.657418 2013098752 net.cpp:113] Setting up label_data_1_split\n",
-      "I0318 00:58:43.657426 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:43.657433 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:43.657441 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
-      "I0318 00:58:43.657451 2013098752 net.cpp:84] Creating Layer ip1\n",
-      "I0318 00:58:43.657459 2013098752 net.cpp:380] ip1 <- data\n",
-      "I0318 00:58:43.657467 2013098752 net.cpp:338] ip1 -> ip1\n",
-      "I0318 00:58:43.657479 2013098752 net.cpp:113] Setting up ip1\n",
-      "I0318 00:58:43.662454 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
-      "I0318 00:58:43.662477 2013098752 layer_factory.hpp:74] Creating layer relu1\n",
-      "I0318 00:58:43.662497 2013098752 net.cpp:84] Creating Layer relu1\n",
-      "I0318 00:58:43.662508 2013098752 net.cpp:380] relu1 <- ip1\n",
-      "I0318 00:58:43.662520 2013098752 net.cpp:327] relu1 -> ip1 (in-place)\n",
-      "I0318 00:58:43.662530 2013098752 net.cpp:113] Setting up relu1\n",
-      "I0318 00:58:43.662539 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
-      "I0318 00:58:43.662546 2013098752 layer_factory.hpp:74] Creating layer ip2\n",
-      "I0318 00:58:43.662555 2013098752 net.cpp:84] Creating Layer ip2\n",
-      "I0318 00:58:43.662562 2013098752 net.cpp:380] ip2 <- ip1\n",
-      "I0318 00:58:43.662571 2013098752 net.cpp:338] ip2 -> ip2\n",
-      "I0318 00:58:43.662580 2013098752 net.cpp:113] Setting up ip2\n",
-      "I0318 00:58:43.662595 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:43.662606 2013098752 layer_factory.hpp:74] Creating layer ip2_ip2_0_split\n",
-      "I0318 00:58:43.662654 2013098752 net.cpp:84] Creating Layer ip2_ip2_0_split\n",
-      "I0318 00:58:43.662665 2013098752 net.cpp:380] ip2_ip2_0_split <- ip2\n",
-      "I0318 00:58:43.662678 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
-      "I0318 00:58:43.662689 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
-      "I0318 00:58:43.662698 2013098752 net.cpp:113] Setting up ip2_ip2_0_split\n",
-      "I0318 00:58:43.662706 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:43.662714 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:43.662722 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
-      "I0318 00:58:43.662734 2013098752 net.cpp:84] Creating Layer accuracy\n",
-      "I0318 00:58:43.662740 2013098752 net.cpp:380] accuracy <- ip2_ip2_0_split_0\n",
-      "I0318 00:58:43.662749 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
-      "I0318 00:58:43.662756 2013098752 net.cpp:338] accuracy -> accuracy\n",
-      "I0318 00:58:43.662766 2013098752 net.cpp:113] Setting up accuracy\n",
-      "I0318 00:58:43.662818 2013098752 net.cpp:120] Top shape: (1)\n",
-      "I0318 00:58:43.662827 2013098752 layer_factory.hpp:74] Creating layer loss\n",
-      "I0318 00:58:43.662839 2013098752 net.cpp:84] Creating Layer loss\n",
-      "I0318 00:58:43.662847 2013098752 net.cpp:380] loss <- ip2_ip2_0_split_1\n",
-      "I0318 00:58:43.662854 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
-      "I0318 00:58:43.662863 2013098752 net.cpp:338] loss -> loss\n",
-      "I0318 00:58:43.662873 2013098752 net.cpp:113] Setting up loss\n",
-      "I0318 00:58:43.662883 2013098752 layer_factory.hpp:74] Creating layer loss\n",
-      "I0318 00:58:43.662901 2013098752 net.cpp:120] Top shape: (1)\n",
-      "I0318 00:58:43.662909 2013098752 net.cpp:122]     with loss weight 1\n",
-      "I0318 00:58:43.662922 2013098752 net.cpp:167] loss needs backward computation.\n",
-      "I0318 00:58:43.662930 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
-      "I0318 00:58:43.662936 2013098752 net.cpp:167] ip2_ip2_0_split needs backward computation.\n",
-      "I0318 00:58:43.662942 2013098752 net.cpp:167] ip2 needs backward computation.\n",
-      "I0318 00:58:43.662976 2013098752 net.cpp:167] relu1 needs backward computation.\n",
-      "I0318 00:58:43.662988 2013098752 net.cpp:167] ip1 needs backward computation.\n",
-      "I0318 00:58:43.662997 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
-      "I0318 00:58:43.663003 2013098752 net.cpp:169] data does not need backward computation.\n",
-      "I0318 00:58:43.663009 2013098752 net.cpp:205] This network produces output accuracy\n",
-      "I0318 00:58:43.663017 2013098752 net.cpp:205] This network produces output loss\n",
-      "I0318 00:58:43.663028 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
-      "I0318 00:58:43.663035 2013098752 net.cpp:217] Network initialization done.\n",
-      "I0318 00:58:43.663041 2013098752 net.cpp:218] Memory required for data: 3728\n",
-      "I0318 00:58:43.663158 2013098752 solver.cpp:154] Creating test net (#0) specified by test_net file: examples/hdf5_classification/nonlinear_auto_test.prototxt\n",
-      "I0318 00:58:43.663179 2013098752 net.cpp:42] Initializing net from parameters: \n",
-      "state {\n",
-      "  phase: TEST\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"data\"\n",
-      "  type: \"HDF5Data\"\n",
-      "  top: \"data\"\n",
-      "  top: \"label\"\n",
-      "  hdf5_data_param {\n",
-      "    source: \"examples/hdf5_classification/data/test.txt\"\n",
-      "    batch_size: 10\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"ip1\"\n",
-      "  type: \"InnerProduct\"\n",
-      "  bottom: \"data\"\n",
-      "  top: \"ip1\"\n",
-      "  inner_product_param {\n",
-      "    num_output: 40\n",
-      "    weight_filler {\n",
-      "      type: \"xavier\"\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"relu1\"\n",
-      "  type: \"ReLU\"\n",
-      "  bottom: \"ip1\"\n",
-      "  top: \"ip1\"\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"ip2\"\n",
-      "  type: \"InnerProduct\"\n",
-      "  bottom: \"ip1\"\n",
-      "  top: \"ip2\"\n",
-      "  inner_product_param {\n",
-      "    num_output: 2\n",
-      "    weight_filler {\n",
-      "      type: \"xavier\"\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"accuracy\"\n",
-      "  type: \"Accuracy\"\n",
-      "  bottom: \"ip2\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"accuracy\"\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"loss\"\n",
-      "  type: \"SoftmaxWithLoss\"\n",
-      "  bottom: \"ip2\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"loss\"\n",
-      "}\n",
-      "I0318 00:58:43.663349 2013098752 layer_factory.hpp:74] Creating layer data\n",
-      "I0318 00:58:43.663365 2013098752 net.cpp:84] Creating Layer data\n",
-      "I0318 00:58:43.663373 2013098752 net.cpp:338] data -> data\n",
-      "I0318 00:58:43.663385 2013098752 net.cpp:338] data -> label\n",
-      "I0318 00:58:43.663396 2013098752 net.cpp:113] Setting up data\n",
-      "I0318 00:58:43.663422 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
-      "I0318 00:58:43.663457 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n",
-      "I0318 00:58:43.664719 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
-      "I0318 00:58:43.664739 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:43.664754 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
-      "I0318 00:58:43.664772 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
-      "I0318 00:58:43.664783 2013098752 net.cpp:380] label_data_1_split <- label\n",
-      "I0318 00:58:43.664791 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
-      "I0318 00:58:43.664803 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
-      "I0318 00:58:43.664813 2013098752 net.cpp:113] Setting up label_data_1_split\n",
-      "I0318 00:58:43.664822 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:43.664829 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:43.664837 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
-      "I0318 00:58:43.664846 2013098752 net.cpp:84] Creating Layer ip1\n",
-      "I0318 00:58:43.664854 2013098752 net.cpp:380] ip1 <- data\n",
-      "I0318 00:58:43.664862 2013098752 net.cpp:338] ip1 -> ip1\n",
-      "I0318 00:58:43.664875 2013098752 net.cpp:113] Setting up ip1\n",
-      "I0318 00:58:43.664901 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
-      "I0318 00:58:43.664924 2013098752 layer_factory.hpp:74] Creating layer relu1\n",
-      "I0318 00:58:43.664945 2013098752 net.cpp:84] Creating Layer relu1\n",
-      "I0318 00:58:43.664958 2013098752 net.cpp:380] relu1 <- ip1\n",
-      "I0318 00:58:43.664966 2013098752 net.cpp:327] relu1 -> ip1 (in-place)\n",
-      "I0318 00:58:43.664975 2013098752 net.cpp:113] Setting up relu1\n",
-      "I0318 00:58:43.664983 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
-      "I0318 00:58:43.664990 2013098752 layer_factory.hpp:74] Creating layer ip2\n",
-      "I0318 00:58:43.665000 2013098752 net.cpp:84] Creating Layer ip2\n",
-      "I0318 00:58:43.665006 2013098752 net.cpp:380] ip2 <- ip1\n",
-      "I0318 00:58:43.665015 2013098752 net.cpp:338] ip2 -> ip2\n",
-      "I0318 00:58:43.665030 2013098752 net.cpp:113] Setting up ip2\n",
-      "I0318 00:58:43.665052 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:43.665066 2013098752 layer_factory.hpp:74] Creating layer ip2_ip2_0_split\n",
-      "I0318 00:58:43.665077 2013098752 net.cpp:84] Creating Layer ip2_ip2_0_split\n",
-      "I0318 00:58:43.665086 2013098752 net.cpp:380] ip2_ip2_0_split <- ip2\n",
-      "I0318 00:58:43.665093 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
-      "I0318 00:58:43.665103 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
-      "I0318 00:58:43.665113 2013098752 net.cpp:113] Setting up ip2_ip2_0_split\n",
-      "I0318 00:58:43.665122 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:43.665128 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:43.665137 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
-      "I0318 00:58:43.665144 2013098752 net.cpp:84] Creating Layer accuracy\n",
-      "I0318 00:58:43.665153 2013098752 net.cpp:380] accuracy <- ip2_ip2_0_split_0\n",
-      "I0318 00:58:43.665168 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
-      "I0318 00:58:43.665180 2013098752 net.cpp:338] accuracy -> accuracy\n",
-      "I0318 00:58:43.665192 2013098752 net.cpp:113] Setting up accuracy\n",
-      "I0318 00:58:43.665200 2013098752 net.cpp:120] Top shape: (1)\n",
-      "I0318 00:58:43.665207 2013098752 layer_factory.hpp:74] Creating layer loss\n",
-      "I0318 00:58:43.665216 2013098752 net.cpp:84] Creating Layer loss\n",
-      "I0318 00:58:43.665223 2013098752 net.cpp:380] loss <- ip2_ip2_0_split_1\n",
-      "I0318 00:58:43.665230 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
-      "I0318 00:58:43.665241 2013098752 net.cpp:338] loss -> loss\n",
-      "I0318 00:58:43.665251 2013098752 net.cpp:113] Setting up loss\n",
-      "I0318 00:58:43.665259 2013098752 layer_factory.hpp:74] Creating layer loss\n",
-      "I0318 00:58:43.665273 2013098752 net.cpp:120] Top shape: (1)\n",
-      "I0318 00:58:43.665282 2013098752 net.cpp:122]     with loss weight 1\n",
-      "I0318 00:58:43.665290 2013098752 net.cpp:167] loss needs backward computation.\n",
-      "I0318 00:58:43.665338 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
-      "I0318 00:58:43.665351 2013098752 net.cpp:167] ip2_ip2_0_split needs backward computation.\n",
-      "I0318 00:58:43.665380 2013098752 net.cpp:167] ip2 needs backward computation.\n",
-      "I0318 00:58:43.665387 2013098752 net.cpp:167] relu1 needs backward computation.\n",
-      "I0318 00:58:43.665393 2013098752 net.cpp:167] ip1 needs backward computation.\n",
-      "I0318 00:58:43.665400 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
-      "I0318 00:58:43.665407 2013098752 net.cpp:169] data does not need backward computation.\n",
-      "I0318 00:58:43.665415 2013098752 net.cpp:205] This network produces output accuracy\n",
-      "I0318 00:58:43.665421 2013098752 net.cpp:205] This network produces output loss\n",
-      "I0318 00:58:43.665431 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
-      "I0318 00:58:43.665441 2013098752 net.cpp:217] Network initialization done.\n",
-      "I0318 00:58:43.665446 2013098752 net.cpp:218] Memory required for data: 3728\n",
-      "I0318 00:58:43.665534 2013098752 solver.cpp:42] Solver scaffolding done.\n",
-      "I0318 00:58:43.665568 2013098752 solver.cpp:222] Solving \n",
-      "I0318 00:58:43.665577 2013098752 solver.cpp:223] Learning Rate Policy: step\n",
-      "I0318 00:58:43.665586 2013098752 solver.cpp:266] Iteration 0, Testing net (#0)\n",
-      "I0318 00:58:43.683938 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.5184\n",
-      "I0318 00:58:43.683981 2013098752 solver.cpp:315]     Test net output #1: loss = 0.716141 (* 1 = 0.716141 loss)\n",
-      "I0318 00:58:43.684236 2013098752 solver.cpp:189] Iteration 0, loss = 0.764954\n",
-      "I0318 00:58:43.684267 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.5\n",
-      "I0318 00:58:43.684285 2013098752 solver.cpp:204]     Train net output #1: loss = 0.764954 (* 1 = 0.764954 loss)\n",
-      "I0318 00:58:43.684305 2013098752 solver.cpp:464] Iteration 0, lr = 0.01\n",
-      "I0318 00:58:43.714700 2013098752 solver.cpp:266] Iteration 1000, Testing net (#0)\n",
-      "I0318 00:58:43.721762 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8168\n",
-      "I0318 00:58:43.721818 2013098752 solver.cpp:315]     Test net output #1: loss = 0.434918 (* 1 = 0.434918 loss)\n",
-      "I0318 00:58:43.721899 2013098752 solver.cpp:189] Iteration 1000, loss = 0.282425\n",
-      "I0318 00:58:43.721917 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
-      "I0318 00:58:43.721932 2013098752 solver.cpp:204]     Train net output #1: loss = 0.282426 (* 1 = 0.282426 loss)\n",
-      "I0318 00:58:43.721942 2013098752 solver.cpp:464] Iteration 1000, lr = 0.01\n",
-      "I0318 00:58:43.750509 2013098752 solver.cpp:266] Iteration 2000, Testing net (#0)\n",
-      "I0318 00:58:43.754590 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8224\n",
-      "I0318 00:58:43.754621 2013098752 solver.cpp:315]     Test net output #1: loss = 0.416874 (* 1 = 0.416874 loss)\n",
-      "I0318 00:58:43.754660 2013098752 solver.cpp:189] Iteration 2000, loss = 0.51988\n",
-      "I0318 00:58:43.754672 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
-      "I0318 00:58:43.754683 2013098752 solver.cpp:204]     Train net output #1: loss = 0.51988 (* 1 = 0.51988 loss)\n",
-      "I0318 00:58:43.754690 2013098752 solver.cpp:464] Iteration 2000, lr = 0.01\n",
-      "I0318 00:58:43.782609 2013098752 solver.cpp:266] Iteration 3000, Testing net (#0)\n",
-      "I0318 00:58:43.789728 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8176\n",
-      "I0318 00:58:43.789777 2013098752 solver.cpp:315]     Test net output #1: loss = 0.415907 (* 1 = 0.415907 loss)\n",
-      "I0318 00:58:43.790487 2013098752 solver.cpp:189] Iteration 3000, loss = 0.5093\n",
-      "I0318 00:58:43.790510 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
-      "I0318 00:58:43.790530 2013098752 solver.cpp:204]     Train net output #1: loss = 0.509301 (* 1 = 0.509301 loss)\n",
-      "I0318 00:58:43.790544 2013098752 solver.cpp:464] Iteration 3000, lr = 0.01\n",
-      "I0318 00:58:43.817451 2013098752 solver.cpp:266] Iteration 4000, Testing net (#0)\n",
-      "I0318 00:58:43.821740 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8252\n",
-      "I0318 00:58:43.821770 2013098752 solver.cpp:315]     Test net output #1: loss = 0.409124 (* 1 = 0.409124 loss)\n",
-      "I0318 00:58:43.821822 2013098752 solver.cpp:189] Iteration 4000, loss = 0.284815\n",
-      "I0318 00:58:43.821835 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
-      "I0318 00:58:43.821846 2013098752 solver.cpp:204]     Train net output #1: loss = 0.284815 (* 1 = 0.284815 loss)\n",
-      "I0318 00:58:43.821890 2013098752 solver.cpp:464] Iteration 4000, lr = 0.01\n",
-      "I0318 00:58:43.847015 2013098752 solver.cpp:266] Iteration 5000, Testing net (#0)\n",
-      "I0318 00:58:43.852102 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8256\n",
-      "I0318 00:58:43.852145 2013098752 solver.cpp:315]     Test net output #1: loss = 0.404445 (* 1 = 0.404445 loss)\n",
-      "I0318 00:58:43.852188 2013098752 solver.cpp:189] Iteration 5000, loss = 0.511566\n",
-      "I0318 00:58:43.852200 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
-      "I0318 00:58:43.852210 2013098752 solver.cpp:204]     Train net output #1: loss = 0.511566 (* 1 = 0.511566 loss)\n",
-      "I0318 00:58:43.852219 2013098752 solver.cpp:464] Iteration 5000, lr = 0.001\n",
-      "I0318 00:58:43.876060 2013098752 solver.cpp:266] Iteration 6000, Testing net (#0)\n",
-      "I0318 00:58:43.880080 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8328\n",
-      "I0318 00:58:43.880105 2013098752 solver.cpp:315]     Test net output #1: loss = 0.396847 (* 1 = 0.396847 loss)\n",
-      "I0318 00:58:43.880700 2013098752 solver.cpp:189] Iteration 6000, loss = 0.397858\n",
-      "I0318 00:58:43.880718 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
-      "I0318 00:58:43.880729 2013098752 solver.cpp:204]     Train net output #1: loss = 0.397858 (* 1 = 0.397858 loss)\n",
-      "I0318 00:58:43.880738 2013098752 solver.cpp:464] Iteration 6000, lr = 0.001\n",
-      "I0318 00:58:43.913795 2013098752 solver.cpp:266] Iteration 7000, Testing net (#0)\n",
-      "I0318 00:58:43.917851 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8316\n",
-      "I0318 00:58:43.917876 2013098752 solver.cpp:315]     Test net output #1: loss = 0.398135 (* 1 = 0.398135 loss)\n",
-      "I0318 00:58:43.917956 2013098752 solver.cpp:189] Iteration 7000, loss = 0.243849\n",
-      "I0318 00:58:43.917971 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
-      "I0318 00:58:43.917989 2013098752 solver.cpp:204]     Train net output #1: loss = 0.243849 (* 1 = 0.243849 loss)\n",
-      "I0318 00:58:43.918002 2013098752 solver.cpp:464] Iteration 7000, lr = 0.001\n",
-      "I0318 00:58:43.943681 2013098752 solver.cpp:266] Iteration 8000, Testing net (#0)\n",
-      "I0318 00:58:43.947589 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8312\n",
-      "I0318 00:58:43.947615 2013098752 solver.cpp:315]     Test net output #1: loss = 0.394763 (* 1 = 0.394763 loss)\n",
-      "I0318 00:58:43.947651 2013098752 solver.cpp:189] Iteration 8000, loss = 0.513399\n",
-      "I0318 00:58:43.947664 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
-      "I0318 00:58:43.947674 2013098752 solver.cpp:204]     Train net output #1: loss = 0.513399 (* 1 = 0.513399 loss)\n",
-      "I0318 00:58:43.947682 2013098752 solver.cpp:464] Iteration 8000, lr = 0.001\n",
-      "I0318 00:58:43.973080 2013098752 solver.cpp:266] Iteration 9000, Testing net (#0)\n",
-      "I0318 00:58:43.977033 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.834\n",
-      "I0318 00:58:43.977056 2013098752 solver.cpp:315]     Test net output #1: loss = 0.395663 (* 1 = 0.395663 loss)\n",
-      "I0318 00:58:43.977710 2013098752 solver.cpp:189] Iteration 9000, loss = 0.399341\n",
-      "I0318 00:58:43.977735 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
-      "I0318 00:58:43.977746 2013098752 solver.cpp:204]     Train net output #1: loss = 0.399342 (* 1 = 0.399342 loss)\n",
-      "I0318 00:58:43.977756 2013098752 solver.cpp:464] Iteration 9000, lr = 0.001\n",
-      "I0318 00:58:44.003437 2013098752 solver.cpp:334] Snapshotting to examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
-      "I0318 00:58:44.003702 2013098752 solver.cpp:342] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\n",
-      "I0318 00:58:44.003850 2013098752 solver.cpp:248] Iteration 10000, loss = 0.244639\n",
-      "I0318 00:58:44.003871 2013098752 solver.cpp:266] Iteration 10000, Testing net (#0)\n",
-      "I0318 00:58:44.008216 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8308\n",
-      "I0318 00:58:44.008252 2013098752 solver.cpp:315]     Test net output #1: loss = 0.397291 (* 1 = 0.397291 loss)\n",
-      "I0318 00:58:44.008262 2013098752 solver.cpp:253] Optimization Done.\n",
-      "I0318 00:58:44.008270 2013098752 caffe.cpp:134] Optimization Done.\n"
-     ]
-    }
-   ],
-   "source": [
-    "!./build/tools/caffe train -solver examples/hdf5_classification/nonlinear_solver.prototxt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n",
-    "shutil.rmtree(dirname)"
-   ]
-  }
- ],
- "metadata": {
-  "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
-  "example_name": "Off-the-shelf SGD for classification",
-  "include_in_docs": true,
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
-  },
-  "priority": 3
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/02-fine-tuning.ipynb b/examples/02-fine-tuning.ipynb
new file mode 100644
index 00000000..422259de
--- /dev/null
+++ b/examples/02-fine-tuning.ipynb
@@ -0,0 +1,1175 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fine-tuning a Pretrained Network for Style Recognition\n",
+    "\n",
+    "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\n",
+    "\n",
+    "The advantage of this approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful generic visual feature that you can treat as a black box. On top of that, only a relatively small amount of data is needed for good performance on the target task."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we will need to prepare the data. This involves the following parts:\n",
+    "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n",
+    "(2) Download a subset of the overall Flickr style dataset for this demo.\n",
+    "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "caffe_root = '../'  # this file should be run from {caffe_root}/examples (otherwise change this line)\n",
+    "\n",
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "import caffe\n",
+    "\n",
+    "caffe.set_device(0)\n",
+    "caffe.set_mode_gpu()\n",
+    "\n",
+    "import numpy as np\n",
+    "from pylab import *\n",
+    "%matplotlib inline\n",
+    "import tempfile\n",
+    "\n",
+    "# Helper function for deprocessing preprocessed images, e.g., for display.\n",
+    "def deprocess_net_image(image):\n",
+    "    image = image.copy()              # don't modify destructively\n",
+    "    image = image[::-1]               # BGR -> RGB\n",
+    "    image = image.transpose(1, 2, 0)  # CHW -> HWC\n",
+    "    image += [123, 117, 104]          # (approximately) undo mean subtraction\n",
+    "\n",
+    "    # clamp values in [0, 255]\n",
+    "    image[image < 0], image[image > 255] = 0, 255\n",
+    "\n",
+    "    # round and cast from float32 to uint8\n",
+    "    image = np.round(image)\n",
+    "    image = np.require(image, dtype=np.uint8)\n",
+    "\n",
+    "    return image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Setup and dataset download\n",
+    "\n",
+    "Download data required for this exercise.\n",
+    "\n",
+    "- `get_ilsvrc_aux.sh` to download the ImageNet data mean, labels, etc.\n",
+    "- `download_model_binary.py` to download the pretrained reference model\n",
+    "- `finetune_flickr_style/assemble_data.py` downloads the style training and testing data\n",
+    "\n",
+    "We'll download just a small subset of the full dataset for this exercise: just 2000 of the 80K images, from 5 of the 20 style categories.  (To download the full dataset, set `full_dataset = True` in the cell below.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading...\n",
+      "--2016-02-24 00:28:36--  http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz\n",
+      "Resolving dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)... 169.229.222.251\n",
+      "Connecting to dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)|169.229.222.251|:80... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 17858008 (17M) [application/octet-stream]\n",
+      "Saving to: ‘caffe_ilsvrc12.tar.gz’\n",
+      "\n",
+      "100%[======================================>] 17,858,008   112MB/s   in 0.2s   \n",
+      "\n",
+      "2016-02-24 00:28:36 (112 MB/s) - ‘caffe_ilsvrc12.tar.gz’ saved [17858008/17858008]\n",
+      "\n",
+      "Unzipping...\n",
+      "Done.\n",
+      "Model already exists.\n",
+      "Downloading 2000 images with 7 workers...\n",
+      "Writing train/val for 1996 successfully downloaded images.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Download just a small subset of the data for this exercise.\n",
+    "# (2000 of 80K images, 5 of 20 labels.)\n",
+    "# To download the entire dataset, set `full_dataset = True`.\n",
+    "full_dataset = False\n",
+    "if full_dataset:\n",
+    "    NUM_STYLE_IMAGES = NUM_STYLE_LABELS = -1\n",
+    "else:\n",
+    "    NUM_STYLE_IMAGES = 2000\n",
+    "    NUM_STYLE_LABELS = 5\n",
+    "\n",
+    "# This downloads the ilsvrc auxiliary data (mean file, etc),\n",
+    "# and a subset of 2000 images for the style recognition task.\n",
+    "import os\n",
+    "os.chdir(caffe_root)  # run scripts from caffe root\n",
+    "!data/ilsvrc12/get_ilsvrc_aux.sh\n",
+    "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n",
+    "!python examples/finetune_flickr_style/assemble_data.py \\\n",
+    "    --workers=-1  --seed=1701 \\\n",
+    "    --images=$NUM_STYLE_IMAGES  --label=$NUM_STYLE_LABELS\n",
+    "# back to examples\n",
+    "os.chdir('examples')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define `weights`, the path to the ImageNet pretrained weights we just downloaded, and make sure it exists."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "weights = os.path.join(caffe_root, 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+    "assert os.path.exists(weights)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the 1000 ImageNet labels from `ilsvrc12/synset_words.txt`, and the 5 style labels from `finetune_flickr_style/style_names.txt`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded ImageNet labels:\n",
+      "n01440764 tench, Tinca tinca\n",
+      "n01443537 goldfish, Carassius auratus\n",
+      "n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias\n",
+      "n01491361 tiger shark, Galeocerdo cuvieri\n",
+      "n01494475 hammerhead, hammerhead shark\n",
+      "n01496331 electric ray, crampfish, numbfish, torpedo\n",
+      "n01498041 stingray\n",
+      "n01514668 cock\n",
+      "n01514859 hen\n",
+      "n01518878 ostrich, Struthio camelus\n",
+      "...\n",
+      "\n",
+      "Loaded style labels:\n",
+      "Detailed, Pastel, Melancholy, Noir, HDR\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load ImageNet labels to imagenet_labels\n",
+    "imagenet_label_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
+    "imagenet_labels = list(np.loadtxt(imagenet_label_file, str, delimiter='\\t'))\n",
+    "assert len(imagenet_labels) == 1000\n",
+    "print 'Loaded ImageNet labels:\\n', '\\n'.join(imagenet_labels[:10] + ['...'])\n",
+    "\n",
+    "# Load style labels to style_labels\n",
+    "style_label_file = caffe_root + 'examples/finetune_flickr_style/style_names.txt'\n",
+    "style_labels = list(np.loadtxt(style_label_file, str, delimiter='\\n'))\n",
+    "if NUM_STYLE_LABELS > 0:\n",
+    "    style_labels = style_labels[:NUM_STYLE_LABELS]\n",
+    "print '\\nLoaded style labels:\\n', ', '.join(style_labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.  Defining and running the nets\n",
+    "\n",
+    "We'll start by defining `caffenet`, a function which initializes the *CaffeNet* architecture (a minor variant on *AlexNet*), taking arguments specifying the data and number of output classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from caffe import layers as L\n",
+    "from caffe import params as P\n",
+    "\n",
+    "weight_param = dict(lr_mult=1, decay_mult=1)\n",
+    "bias_param   = dict(lr_mult=2, decay_mult=0)\n",
+    "learned_param = [weight_param, bias_param]\n",
+    "\n",
+    "frozen_param = [dict(lr_mult=0)] * 2\n",
+    "\n",
+    "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1,\n",
+    "              param=learned_param,\n",
+    "              weight_filler=dict(type='gaussian', std=0.01),\n",
+    "              bias_filler=dict(type='constant', value=0.1)):\n",
+    "    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n",
+    "                         num_output=nout, pad=pad, group=group,\n",
+    "                         param=param, weight_filler=weight_filler,\n",
+    "                         bias_filler=bias_filler)\n",
+    "    return conv, L.ReLU(conv, in_place=True)\n",
+    "\n",
+    "def fc_relu(bottom, nout, param=learned_param,\n",
+    "            weight_filler=dict(type='gaussian', std=0.005),\n",
+    "            bias_filler=dict(type='constant', value=0.1)):\n",
+    "    fc = L.InnerProduct(bottom, num_output=nout, param=param,\n",
+    "                        weight_filler=weight_filler,\n",
+    "                        bias_filler=bias_filler)\n",
+    "    return fc, L.ReLU(fc, in_place=True)\n",
+    "\n",
+    "def max_pool(bottom, ks, stride=1):\n",
+    "    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n",
+    "\n",
+    "def caffenet(data, label=None, train=True, num_classes=1000,\n",
+    "             classifier_name='fc8', learn_all=False):\n",
+    "    \"\"\"Returns a NetSpec specifying CaffeNet, following the original proto text\n",
+    "       specification (./models/bvlc_reference_caffenet/train_val.prototxt).\"\"\"\n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data = data\n",
+    "    param = learned_param if learn_all else frozen_param\n",
+    "    n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4, param=param)\n",
+    "    n.pool1 = max_pool(n.relu1, 3, stride=2)\n",
+    "    n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n",
+    "    n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2, param=param)\n",
+    "    n.pool2 = max_pool(n.relu2, 3, stride=2)\n",
+    "    n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n",
+    "    n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1, param=param)\n",
+    "    n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2, param=param)\n",
+    "    n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2, param=param)\n",
+    "    n.pool5 = max_pool(n.relu5, 3, stride=2)\n",
+    "    n.fc6, n.relu6 = fc_relu(n.pool5, 4096, param=param)\n",
+    "    if train:\n",
+    "        n.drop6 = fc7input = L.Dropout(n.relu6, in_place=True)\n",
+    "    else:\n",
+    "        fc7input = n.relu6\n",
+    "    n.fc7, n.relu7 = fc_relu(fc7input, 4096, param=param)\n",
+    "    if train:\n",
+    "        n.drop7 = fc8input = L.Dropout(n.relu7, in_place=True)\n",
+    "    else:\n",
+    "        fc8input = n.relu7\n",
+    "    # always learn fc8 (param=learned_param)\n",
+    "    fc8 = L.InnerProduct(fc8input, num_output=num_classes, param=learned_param)\n",
+    "    # give fc8 the name specified by argument `classifier_name`\n",
+    "    n.__setattr__(classifier_name, fc8)\n",
+    "    if not train:\n",
+    "        n.probs = L.Softmax(fc8)\n",
+    "    if label is not None:\n",
+    "        n.label = label\n",
+    "        n.loss = L.SoftmaxWithLoss(fc8, n.label)\n",
+    "        n.acc = L.Accuracy(fc8, n.label)\n",
+    "    # write the net to a temporary file and return its filename\n",
+    "    with tempfile.NamedTemporaryFile(delete=False) as f:\n",
+    "        f.write(str(n.to_proto()))\n",
+    "        return f.name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's create a *CaffeNet* that takes unlabeled \"dummy data\" as input, allowing us to set its input images externally and see what ImageNet classes it predicts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dummy_data = L.DummyData(shape=dict(dim=[1, 3, 227, 227]))\n",
+    "imagenet_net_filename = caffenet(data=dummy_data, train=False)\n",
+    "imagenet_net = caffe.Net(imagenet_net_filename, weights, caffe.TEST)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define a function `style_net` which calls `caffenet` on data from the Flickr style dataset.\n",
+    "\n",
+    "The new network will also have the *CaffeNet* architecture, with differences in the input and output:\n",
+    "\n",
+    "- the input is the Flickr style data we downloaded, provided by an `ImageData` layer\n",
+    "- the output is a distribution over 20 classes rather than the original 1000 ImageNet classes\n",
+    "- the classification layer is renamed from `fc8` to `fc8_flickr` to tell Caffe not to load the original classifier (`fc8`) weights from the ImageNet-pretrained model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def style_net(train=True, learn_all=False, subset=None):\n",
+    "    if subset is None:\n",
+    "        subset = 'train' if train else 'test'\n",
+    "    source = caffe_root + 'data/flickr_style/%s.txt' % subset\n",
+    "    transform_param = dict(mirror=train, crop_size=227,\n",
+    "        mean_file=caffe_root + 'data/ilsvrc12/imagenet_mean.binaryproto')\n",
+    "    style_data, style_label = L.ImageData(\n",
+    "        transform_param=transform_param, source=source,\n",
+    "        batch_size=50, new_height=256, new_width=256, ntop=2)\n",
+    "    return caffenet(data=style_data, label=style_label, train=train,\n",
+    "                    num_classes=NUM_STYLE_LABELS,\n",
+    "                    classifier_name='fc8_flickr',\n",
+    "                    learn_all=learn_all)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the `style_net` function defined above to initialize `untrained_style_net`, a *CaffeNet* with input images from the style dataset and weights from the pretrained ImageNet model.\n",
+    "\n",
+    "\n",
+    "Call `forward` on `untrained_style_net` to get a batch of style training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "untrained_style_net = caffe.Net(style_net(train=False, subset='train'),\n",
+    "                                weights, caffe.TEST)\n",
+    "untrained_style_net.forward()\n",
+    "style_data_batch = untrained_style_net.blobs['data'].data.copy()\n",
+    "style_label_batch = np.array(untrained_style_net.blobs['label'].data, dtype=np.int32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pick one of the style net training images from the batch of 50 (we'll arbitrarily choose #8 here).  Display it, then run it through `imagenet_net`, the ImageNet-pretrained network to view its top 5 predicted classes from the 1000 ImageNet classes.\n",
+    "\n",
+    "Below we chose an image where the network's predictions happen to be reasonable, as the image is of a beach, and \"sandbar\" and \"seashore\" both happen to be ImageNet-1000 categories.  For other images, the predictions won't be this good, sometimes due to the network actually failing to recognize the object(s) present in the image, but perhaps even more often due to the fact that not all images contain an object from the (somewhat arbitrarily chosen) 1000 ImageNet categories. Modify the `batch_index` variable by changing its default setting of 8 to another value from 0-49 (since the batch size is 50) to see predictions for other images in the batch.  (To go beyond this batch of 50 images, first rerun the *above* cell to load a fresh batch of data into `style_net`.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def disp_preds(net, image, labels, k=5, name='ImageNet'):\n",
+    "    input_blob = net.blobs['data']\n",
+    "    net.blobs['data'].data[0, ...] = image\n",
+    "    probs = net.forward(start='conv1')['probs'][0]\n",
+    "    top_k = (-probs).argsort()[:k]\n",
+    "    print 'top %d predicted %s labels =' % (k, name)\n",
+    "    print '\\n'.join('\\t(%d) %5.2f%% %s' % (i+1, 100*probs[p], labels[p])\n",
+    "                    for i, p in enumerate(top_k))\n",
+    "\n",
+    "def disp_imagenet_preds(net, image):\n",
+    "    disp_preds(net, image, imagenet_labels, name='ImageNet')\n",
+    "\n",
+    "def disp_style_preds(net, image):\n",
+    "    disp_preds(net, image, style_labels, name='style')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "actual label = Melancholy\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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yvUJS3DfmLUsURECnW6oGgUxGBSVh7180Q52jz0UjoWnYDZxRmdmA1bqxNeau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6EmVpSTd7osozItUDOtN5ksBa2bdacbpJGyRKLhrAxEttNG0O8AWK1TShCtYaElalB19S3dVz6K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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f75dc02e4d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_index = 8\n",
+    "image = style_data_batch[batch_index]\n",
+    "plt.imshow(deprocess_net_image(image))\n",
+    "print 'actual label =', style_labels[style_label_batch[batch_index]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top 5 predicted ImageNet labels =\n",
+      "\t(1) 69.89% n09421951 sandbar, sand bar\n",
+      "\t(2) 21.76% n09428293 seashore, coast, seacoast, sea-coast\n",
+      "\t(3)  3.22% n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty\n",
+      "\t(4)  1.89% n04592741 wing\n",
+      "\t(5)  1.23% n09332890 lakeside, lakeshore\n"
+     ]
+    }
+   ],
+   "source": [
+    "disp_imagenet_preds(imagenet_net, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also look at `untrained_style_net`'s predictions, but we won't see anything interesting as its classifier hasn't been trained yet.\n",
+    "\n",
+    "In fact, since we zero-initialized the classifier (see `caffenet` definition -- no `weight_filler` is passed to the final `InnerProduct` layer), the softmax inputs should be all zero and we should therefore see a predicted probability of 1/N for each label (for N labels).  Since we set N = 5, we get a predicted probability of 20% for each class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top 5 predicted style labels =\n",
+      "\t(1) 20.00% Detailed\n",
+      "\t(2) 20.00% Pastel\n",
+      "\t(3) 20.00% Melancholy\n",
+      "\t(4) 20.00% Noir\n",
+      "\t(5) 20.00% HDR\n"
+     ]
+    }
+   ],
+   "source": [
+    "disp_style_preds(untrained_style_net, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also verify that the activations in layer `fc7` immediately before the classification layer are the same as (or very close to) those in the ImageNet-pretrained model, since both models are using the same pretrained weights in the `conv1` through `fc7` layers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "diff = untrained_style_net.blobs['fc7'].data[0] - imagenet_net.blobs['fc7'].data[0]\n",
+    "error = (diff ** 2).sum()\n",
+    "assert error < 1e-8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Delete `untrained_style_net` to save memory.  (Hang on to `imagenet_net` as we'll use it again later.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "del untrained_style_net"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Training the style classifier\n",
+    "\n",
+    "Now, we'll define a function `solver` to create our Caffe solvers, which are used to train the network (learn its weights).  In this function we'll set values for various parameters used for learning, display, and \"snapshotting\" -- see the inline comments for explanations of what they mean.  You may want to play with some of the learning parameters to see if you can improve on the results here!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from caffe.proto import caffe_pb2\n",
+    "\n",
+    "def solver(train_net_path, test_net_path=None, base_lr=0.001):\n",
+    "    s = caffe_pb2.SolverParameter()\n",
+    "\n",
+    "    # Specify locations of the train and (maybe) test networks.\n",
+    "    s.train_net = train_net_path\n",
+    "    if test_net_path is not None:\n",
+    "        s.test_net.append(test_net_path)\n",
+    "        s.test_interval = 1000  # Test after every 1000 training iterations.\n",
+    "        s.test_iter.append(100) # Test on 100 batches each time we test.\n",
+    "\n",
+    "    # The number of iterations over which to average the gradient.\n",
+    "    # Effectively boosts the training batch size by the given factor, without\n",
+    "    # affecting memory utilization.\n",
+    "    s.iter_size = 1\n",
+    "    \n",
+    "    s.max_iter = 100000     # # of times to update the net (training iterations)\n",
+    "    \n",
+    "    # Solve using the stochastic gradient descent (SGD) algorithm.\n",
+    "    # Other choices include 'Adam' and 'RMSProp'.\n",
+    "    s.type = 'SGD'\n",
+    "\n",
+    "    # Set the initial learning rate for SGD.\n",
+    "    s.base_lr = base_lr\n",
+    "\n",
+    "    # Set `lr_policy` to define how the learning rate changes during training.\n",
+    "    # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\n",
+    "    # every `stepsize` iterations.\n",
+    "    s.lr_policy = 'step'\n",
+    "    s.gamma = 0.1\n",
+    "    s.stepsize = 20000\n",
+    "\n",
+    "    # Set other SGD hyperparameters. Setting a non-zero `momentum` takes a\n",
+    "    # weighted average of the current gradient and previous gradients to make\n",
+    "    # learning more stable. L2 weight decay regularizes learning, to help prevent\n",
+    "    # the model from overfitting.\n",
+    "    s.momentum = 0.9\n",
+    "    s.weight_decay = 5e-4\n",
+    "\n",
+    "    # Display the current training loss and accuracy every 1000 iterations.\n",
+    "    s.display = 1000\n",
+    "\n",
+    "    # Snapshots are files used to store networks we've trained.  Here, we'll\n",
+    "    # snapshot every 10K iterations -- ten times during training.\n",
+    "    s.snapshot = 10000\n",
+    "    s.snapshot_prefix = caffe_root + 'models/finetune_flickr_style/finetune_flickr_style'\n",
+    "    \n",
+    "    # Train on the GPU.  Using the CPU to train large networks is very slow.\n",
+    "    s.solver_mode = caffe_pb2.SolverParameter.GPU\n",
+    "    \n",
+    "    # Write the solver to a temporary file and return its filename.\n",
+    "    with tempfile.NamedTemporaryFile(delete=False) as f:\n",
+    "        f.write(str(s))\n",
+    "        return f.name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we'll invoke the solver to train the style net's classification layer.\n",
+    "\n",
+    "For the record, if you want to train the network using only the command line tool, this is the command:\n",
+    "\n",
+    "<code>\n",
+    "build/tools/caffe train \\\n",
+    "    -solver models/finetune_flickr_style/solver.prototxt \\\n",
+    "    -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n",
+    "    -gpu 0\n",
+    "</code>\n",
+    "\n",
+    "However, we will train using Python in this example.\n",
+    "\n",
+    "We'll first define `run_solvers`, a function that takes a list of solvers and steps each one in a round robin manner, recording the accuracy and loss values each iteration.  At the end, the learned weights are saved to a file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def run_solvers(niter, solvers, disp_interval=10):\n",
+    "    \"\"\"Run solvers for niter iterations,\n",
+    "       returning the loss and accuracy recorded each iteration.\n",
+    "       `solvers` is a list of (name, solver) tuples.\"\"\"\n",
+    "    blobs = ('loss', 'acc')\n",
+    "    loss, acc = ({name: np.zeros(niter) for name, _ in solvers}\n",
+    "                 for _ in blobs)\n",
+    "    for it in range(niter):\n",
+    "        for name, s in solvers:\n",
+    "            s.step(1)  # run a single SGD step in Caffe\n",
+    "            loss[name][it], acc[name][it] = (s.net.blobs[b].data.copy()\n",
+    "                                             for b in blobs)\n",
+    "        if it % disp_interval == 0 or it + 1 == niter:\n",
+    "            loss_disp = '; '.join('%s: loss=%.3f, acc=%2d%%' %\n",
+    "                                  (n, loss[n][it], np.round(100*acc[n][it]))\n",
+    "                                  for n, _ in solvers)\n",
+    "            print '%3d) %s' % (it, loss_disp)     \n",
+    "    # Save the learned weights from both nets.\n",
+    "    weight_dir = tempfile.mkdtemp()\n",
+    "    weights = {}\n",
+    "    for name, s in solvers:\n",
+    "        filename = 'weights.%s.caffemodel' % name\n",
+    "        weights[name] = os.path.join(weight_dir, filename)\n",
+    "        s.net.save(weights[name])\n",
+    "    return loss, acc, weights"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's create and run solvers to train nets for the style recognition task.  We'll create two solvers -- one (`style_solver`) will have its train net initialized to the ImageNet-pretrained weights (this is done by the call to the `copy_from` method), and the other (`scratch_style_solver`) will start from a *randomly* initialized net.\n",
+    "\n",
+    "During training, we should see that the ImageNet pretrained net is learning faster and attaining better accuracies than the scratch net."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running solvers for 200 iterations...\n",
+      "  0) pretrained: loss=1.609, acc=28%; scratch: loss=1.609, acc=28%\n",
+      " 10) pretrained: loss=1.293, acc=52%; scratch: loss=1.626, acc=14%\n",
+      " 20) pretrained: loss=1.110, acc=56%; scratch: loss=1.646, acc=10%\n",
+      " 30) pretrained: loss=1.084, acc=60%; scratch: loss=1.616, acc=20%\n",
+      " 40) pretrained: loss=0.898, acc=64%; scratch: loss=1.588, acc=26%\n",
+      " 50) pretrained: loss=1.024, acc=54%; scratch: loss=1.607, acc=32%\n",
+      " 60) pretrained: loss=0.925, acc=66%; scratch: loss=1.616, acc=20%\n",
+      " 70) pretrained: loss=0.861, acc=74%; scratch: loss=1.598, acc=24%\n",
+      " 80) pretrained: loss=0.967, acc=60%; scratch: loss=1.588, acc=30%\n",
+      " 90) pretrained: loss=1.274, acc=52%; scratch: loss=1.608, acc=20%\n",
+      "100) pretrained: loss=1.113, acc=62%; scratch: loss=1.588, acc=30%\n",
+      "110) pretrained: loss=0.922, acc=62%; scratch: loss=1.578, acc=36%\n",
+      "120) pretrained: loss=0.918, acc=62%; scratch: loss=1.599, acc=20%\n",
+      "130) pretrained: loss=0.959, acc=58%; scratch: loss=1.594, acc=22%\n",
+      "140) pretrained: loss=1.228, acc=50%; scratch: loss=1.608, acc=14%\n",
+      "150) pretrained: loss=0.727, acc=76%; scratch: loss=1.623, acc=16%\n",
+      "160) pretrained: loss=1.074, acc=66%; scratch: loss=1.607, acc=20%\n",
+      "170) pretrained: loss=0.887, acc=60%; scratch: loss=1.614, acc=20%\n",
+      "180) pretrained: loss=0.961, acc=62%; scratch: loss=1.614, acc=18%\n",
+      "190) pretrained: loss=0.737, acc=76%; scratch: loss=1.613, acc=18%\n",
+      "199) pretrained: loss=0.836, acc=70%; scratch: loss=1.614, acc=16%\n",
+      "Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "niter = 200  # number of iterations to train\n",
+    "\n",
+    "# Reset style_solver as before.\n",
+    "style_solver_filename = solver(style_net(train=True))\n",
+    "style_solver = caffe.get_solver(style_solver_filename)\n",
+    "style_solver.net.copy_from(weights)\n",
+    "\n",
+    "# For reference, we also create a solver that isn't initialized from\n",
+    "# the pretrained ImageNet weights.\n",
+    "scratch_style_solver_filename = solver(style_net(train=True))\n",
+    "scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\n",
+    "\n",
+    "print 'Running solvers for %d iterations...' % niter\n",
+    "solvers = [('pretrained', style_solver),\n",
+    "           ('scratch', scratch_style_solver)]\n",
+    "loss, acc, weights = run_solvers(niter, solvers)\n",
+    "print 'Done.'\n",
+    "\n",
+    "train_loss, scratch_train_loss = loss['pretrained'], loss['scratch']\n",
+    "train_acc, scratch_train_acc = acc['pretrained'], acc['scratch']\n",
+    "style_weights, scratch_style_weights = weights['pretrained'], weights['scratch']\n",
+    "\n",
+    "# Delete solvers to save memory.\n",
+    "del style_solver, scratch_style_solver, solvers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the training loss and accuracy produced by the two training procedures.  Notice how quickly the ImageNet pretrained model's loss value (blue) drops, and that the randomly initialized model's loss value (green) barely (if at all) improves from training only the classifier layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7f75d49e1090>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ROQeFwjsdShwuuADYvx84etT1dX2eYOtWYP16632UlgI9e/LPRnFISFCzsnYE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\ncoiIiIb/AxSD6Sq0YLMCAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f75d496e890>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(np.vstack([train_loss, scratch_train_loss]).T)\n",
+    "xlabel('Iteration #')\n",
+    "ylabel('Loss')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7f75d49e1a90>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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rQuOqcvDiVuL2qW1z41YqRg4clLQiB7U2kAoeIaZShjGxcyu5UQ1WKEdAmg1O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\nFwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f75d47cad90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(np.vstack([train_acc, scratch_train_acc]).T)\n",
+    "xlabel('Iteration #')\n",
+    "ylabel('Accuracy')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a look at the testing accuracy after running 200 iterations of training. Note that we're classifying among 5 classes, giving chance accuracy of 20%. We expect both results to be better than chance accuracy (20%), and we further expect the result from training using the ImageNet pretraining initialization to be much better than the one from training from scratch. Let's see."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def eval_style_net(weights, test_iters=10):\n",
+    "    test_net = caffe.Net(style_net(train=False), weights, caffe.TEST)\n",
+    "    accuracy = 0\n",
+    "    for it in xrange(test_iters):\n",
+    "        accuracy += test_net.forward()['acc']\n",
+    "    accuracy /= test_iters\n",
+    "    return test_net, accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy, trained from ImageNet initialization: 50.0%\n",
+      "Accuracy, trained from   random initialization: 23.6%\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_net, accuracy = eval_style_net(style_weights)\n",
+    "print 'Accuracy, trained from ImageNet initialization: %3.1f%%' % (100*accuracy, )\n",
+    "scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights)\n",
+    "print 'Accuracy, trained from   random initialization: %3.1f%%' % (100*scratch_accuracy, )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. End-to-end finetuning for style\n",
+    "\n",
+    "Finally, we'll train both nets again, starting from the weights we just learned.  The only difference this time is that we'll be learning the weights \"end-to-end\" by turning on learning in *all* layers of the network, starting from the RGB `conv1` filters directly applied to the input image.  We pass the argument `learn_all=True` to the `style_net` function defined earlier in this notebook, which tells the function to apply a positive (non-zero) `lr_mult` value for all parameters.  Under the default, `learn_all=False`, all parameters in the pretrained layers (`conv1` through `fc7`) are frozen (`lr_mult = 0`), and we learn only the classifier layer `fc8_flickr`.\n",
+    "\n",
+    "Note that both networks start at roughly the accuracy achieved at the end of the previous training session, and improve significantly with end-to-end training.  To be more scientific, we'd also want to follow the same additional training procedure *without* the end-to-end training, to ensure that our results aren't better simply because we trained for twice as long.  Feel free to try this yourself!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running solvers for 200 iterations...\n",
+      "  0) pretrained, end-to-end: loss=0.781, acc=64%; scratch, end-to-end: loss=1.585, acc=28%\n",
+      " 10) pretrained, end-to-end: loss=1.178, acc=62%; scratch, end-to-end: loss=1.638, acc=14%\n",
+      " 20) pretrained, end-to-end: loss=1.084, acc=60%; scratch, end-to-end: loss=1.637, acc= 8%\n",
+      " 30) pretrained, end-to-end: loss=0.902, acc=76%; scratch, end-to-end: loss=1.600, acc=20%\n",
+      " 40) pretrained, end-to-end: loss=0.865, acc=64%; scratch, end-to-end: loss=1.574, acc=26%\n",
+      " 50) pretrained, end-to-end: loss=0.888, acc=60%; scratch, end-to-end: loss=1.604, acc=26%\n",
+      " 60) pretrained, end-to-end: loss=0.538, acc=78%; scratch, end-to-end: loss=1.555, acc=34%\n",
+      " 70) pretrained, end-to-end: loss=0.717, acc=72%; scratch, end-to-end: loss=1.563, acc=30%\n",
+      " 80) pretrained, end-to-end: loss=0.695, acc=74%; scratch, end-to-end: loss=1.502, acc=42%\n",
+      " 90) pretrained, end-to-end: loss=0.708, acc=68%; scratch, end-to-end: loss=1.523, acc=26%\n",
+      "100) pretrained, end-to-end: loss=0.432, acc=78%; scratch, end-to-end: loss=1.500, acc=38%\n",
+      "110) pretrained, end-to-end: loss=0.611, acc=78%; scratch, end-to-end: loss=1.618, acc=18%\n",
+      "120) pretrained, end-to-end: loss=0.610, acc=76%; scratch, end-to-end: loss=1.473, acc=30%\n",
+      "130) pretrained, end-to-end: loss=0.471, acc=78%; scratch, end-to-end: loss=1.488, acc=26%\n",
+      "140) pretrained, end-to-end: loss=0.500, acc=76%; scratch, end-to-end: loss=1.514, acc=38%\n",
+      "150) pretrained, end-to-end: loss=0.476, acc=80%; scratch, end-to-end: loss=1.452, acc=46%\n",
+      "160) pretrained, end-to-end: loss=0.368, acc=82%; scratch, end-to-end: loss=1.419, acc=34%\n",
+      "170) pretrained, end-to-end: loss=0.556, acc=76%; scratch, end-to-end: loss=1.583, acc=36%\n",
+      "180) pretrained, end-to-end: loss=0.574, acc=72%; scratch, end-to-end: loss=1.556, acc=22%\n",
+      "190) pretrained, end-to-end: loss=0.360, acc=88%; scratch, end-to-end: loss=1.429, acc=44%\n",
+      "199) pretrained, end-to-end: loss=0.458, acc=78%; scratch, end-to-end: loss=1.370, acc=44%\n",
+      "Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "end_to_end_net = style_net(train=True, learn_all=True)\n",
+    "\n",
+    "# Set base_lr to 1e-3, the same as last time when learning only the classifier.\n",
+    "# You may want to play around with different values of this or other\n",
+    "# optimization parameters when fine-tuning.  For example, if learning diverges\n",
+    "# (e.g., the loss gets very large or goes to infinity/NaN), you should try\n",
+    "# decreasing base_lr (e.g., to 1e-4, then 1e-5, etc., until you find a value\n",
+    "# for which learning does not diverge).\n",
+    "base_lr = 0.001\n",
+    "\n",
+    "style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\n",
+    "style_solver = caffe.get_solver(style_solver_filename)\n",
+    "style_solver.net.copy_from(style_weights)\n",
+    "\n",
+    "scratch_style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\n",
+    "scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\n",
+    "scratch_style_solver.net.copy_from(scratch_style_weights)\n",
+    "\n",
+    "print 'Running solvers for %d iterations...' % niter\n",
+    "solvers = [('pretrained, end-to-end', style_solver),\n",
+    "           ('scratch, end-to-end', scratch_style_solver)]\n",
+    "_, _, finetuned_weights = run_solvers(niter, solvers)\n",
+    "print 'Done.'\n",
+    "\n",
+    "style_weights_ft = finetuned_weights['pretrained, end-to-end']\n",
+    "scratch_style_weights_ft = finetuned_weights['scratch, end-to-end']\n",
+    "\n",
+    "# Delete solvers to save memory.\n",
+    "del style_solver, scratch_style_solver, solvers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's now test the end-to-end finetuned models.  Since all layers have been optimized for the style recognition task at hand, we expect both nets to get better results than the ones above, which were achieved by nets with only their classifier layers trained for the style task (on top of either ImageNet pretrained or randomly initialized weights)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy, finetuned from ImageNet initialization: 53.6%\n",
+      "Accuracy, finetuned from   random initialization: 39.2%\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_net, accuracy = eval_style_net(style_weights_ft)\n",
+    "print 'Accuracy, finetuned from ImageNet initialization: %3.1f%%' % (100*accuracy, )\n",
+    "scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights_ft)\n",
+    "print 'Accuracy, finetuned from   random initialization: %3.1f%%' % (100*scratch_accuracy, )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll first look back at the image we started with and check our end-to-end trained model's predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top 5 predicted style labels =\n",
+      "\t(1) 55.67% Melancholy\n",
+      "\t(2) 27.21% HDR\n",
+      "\t(3) 16.46% Pastel\n",
+      "\t(4)  0.63% Detailed\n",
+      "\t(5)  0.03% Noir\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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yvUJS3DfmLUsURECnW6oGgUxGBSVh7180Q52jz0UjoWnYDZxRmdmA1bqxNeau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6EmVpSTd7osozItUDOtN5ksBa2bdacbpJGyRKLhrAxEttNG0O8AWK1TShCtYaElalB19S3dVz6K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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f75d0683e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(deprocess_net_image(image))\n",
+    "disp_style_preds(test_net, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whew, that looks a lot better than before!  But note that this image was from the training set, so the net got to see its label at training time.\n",
+    "\n",
+    "Finally, we'll pick an image from the test set (an image the model hasn't seen) and look at our end-to-end finetuned style model's predictions for it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "actual label = Pastel\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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WKs9hFZYitTN2cR87zMqTyhlYiOUvdRVeXdJREPOPBUQLuaLGrEgo3mWYO8JX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9p9Ybt4Of19W7OXfbsAgKNmoNTwI0vwBGcQKvGY5rN9HPF6L//O5aJjNCQtQWBMX/OJhfm4G1Ge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\nKJVy+/ZPkPUZu9dfpDdYJ8gc51qO7ryOOHqPk7d+SLY8oaEk6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EIa92mO1lg/t8jkyCSB\nGFIMYnJPMsxizFATp1eZmR7Fnx3h0rkFGmN1Blmbtcs9/uR3P8fkZIX2Wo+lxS1mdjWZ33+QTrfN\nnsZhKo1d+OGA175ziaRnOLp3nuXTF/EPldJtva2cZmOa73zrIkuri3hRhfHxXbie4OrVl9i9dx9j\nsxNcXVsh6Q9wHZe4PyDutwldD7wKcS9mrDqGSKDWGiOxkMYpNkx44L7bCKsh3fUVXn/xVcgz3vWB\n97FZaFqzh6hM7uWhRz9KtdkkKwRJbqih/rPd/792P2vt14UQ89+nX721fQL4jLU2BxaEEG8C9wHP\nvXXHTG+S6xahchF+yGDYx6+khFHEcLCFTWMK0SfRz5Lkq6xfnSdeWePYcYeYJYxJ0Pl+ROVx3Onb\nWH7jD9CbJxHDmCiSNKoOoWdQkSRTDaqhhvyz6NUWI65hTTksLeaYKUM6ALoBzYqPO15jojWJFG6p\n+oxkfPbHMMVeBuLTpPIF/JFjyPAA0hqS7AqplWURlOOQDzWBu5dKaw5aPdJiWPIsOG2GSZvxyUeY\n3ftJEA554dNrv8Lm6r+lGHwdrx9jMsgM9JMCP7f0+wmVlkuzDlvtiGjsOFPjv0Bj1z3kokZhEiIZ\nvfX2vmMT2wKcjgpwlE+mM1A5rdGArTUfYRzyoEpqUpQb4soarhuC9BgZn6bRnMTi4UURjudeL1IC\nTGHQaUI67OM5lu+eWOS3f+cEL58e0I8Fx+8bZ2RS852vbnLk0Dzvf+9djDQq5EKX2YzSwdmmRDFZ\nhut5FFlBEFUQQ0qGZiMIpIdxYXlxkdHxMaYmx1hcWmYzGRK2JnFGx/nG00/QDFxSbZEqoK59tO6x\nvtZldKLGxP5b6HQ2EELh+JbV9jqmClN7J3AbEadev8DFxQ32TI/y2suX+cKXnqNR96kGiixJee28\nodGsIyubNOo+VANefGOBeiPgoQ8/jBAhna0hb75xiizt0utm7D14gI2tLdorG+yamMZzfUwYYJBc\n7fTxPR+/VmGyGjHMDbvn93BXErOydJGrl18mcB22NvsoMu5/94MEzRE6a33m5w8zdeh2guYEwnGJ\nPCBOyG1Blhsi1/9+3eFa+/+CGfwDIcTPAC8A/721tg3s4uaBf4XSQ/iepvQVlJuR6gautvhODald\nsr5EFLuQniWqNhByjTz+CtPTQ9TkGPHwFGma4thdmPwQeLfgVSZpNo7RMScIK23yNcVKsoUXQNE3\nFNkGSrQJIok3MkLh7Ua0JrjzQ3exZ/9DnH/zKXwtcaVk2D1PgMIPPApZI81cXFFnZv4hzNx+dPoy\nQrgYtVXWmAe7kLKyPRRiqOdATm7aCBr47i6sKNmHq2ETG5aMRZo+V9dPMWkznMEm2VZMjiXHpS00\nnU3FeDVEmISt1Zw8Ctl/90dpHf55ctuiUBU8wAi1rSnw/6aV0GJhS2qxNM1YWLjA1UsvoJM+aAfw\n8N0Ax40IgjrGSvxwhFp9Eset40d1XFfdVJxodZk/kqdd8mzAG6+t8+n/+CYnFmK6iWDfvil+8adv\n52tPnWJz6yL/+Nd+jNm5SawAB3WtjgEhkErg+BJjXKwTUGQZXlilcB0KYZBWY1LBxPQ0pshxXYfp\nqXE22ptkacHDD78PESd89+XvMDm3i167S7+3hc4SXNclTgPOvXGaZqvG7gN7yAdbzAeTXFna5Nzm\nZSZ3TTN/62FGRyZ58YXXSa5e4MiBcaYnJ6CwLC+v0817mAReOX0RoQyjIy3qu0YoMs3JM8soDFla\nEIQ+9fooUeRy4c3T2xRmNTrZgF6/R+j51GpN6s0mrhcR+AFJkqDTIcnWFrtH69hhHdcXDGLDWlZA\nc5Z2dZZo+ig/9zMfpzk1TWIUqSnwhANaUAkDhmmKkoo4TQn9dzYIf1Nj8H8B/9P29r8E/lfgF77P\nvm/Lzxz6moQMoToUhcHqGvV6Rpr2yGhjuiE6f4361APUpv4N3fU/oLv+ewRSYrMahW3juT3S3pNc\nuXKRbz/9LZq+Zt8duxg7eCeSKnl/kYuXvo7yBIHrYYIJZPRJ9u75JfbMh2ytnCLNFzh460dQ1gfZ\nIjVD8m5CnvtIGeM5k7hBDnIdB4sbHaCsbxBIvUqRbJHqFmE4RSH6YId4ahRX7in18EiROICL2C4t\nFhiUjZhp7EXnV8i9abRfox7ELC5laCCzkrYJObD3fvpxitO4hcbEx6hXdm8fo2zBNXLTnMwskmYO\nkT9Bkue4josvb0aQLVCgyzRum2OFQSpBPXRZoWC9u0a9PoYrfJQfoNyQJBXU6qPM7j5IVJtGetUS\n0JPXly91ATq1JSmJyXlzoc9nPneKr36nQ2Jzjh4b41/80/fx3DdPc/bEIv/jP/sJ7r13H466jm4o\nIAesECgVlMIggY/NSpq0LBkglEOtPkI66KO1wHMhS2JcKdBAa2SM7tYWT3z5CSZHKtz5wP2cX7hE\nt98nVIJKJaLTHdK+tAp+wJnzy2wsrzMz7hMqn5nGKEOTsL62SmJ9ziyssHR1k13TVRw/oD8cgnEY\nm96F191i5eoyDV3FkYKO3mLP/AxRELGxusHa6gaVSkBYDTFFQX9QkBeSVr1KlqVkw5TZ3bupVCOW\nL1+l1xsgpUcQVYnqNaJai057k1x67D7+HiqtSURlnGP+CFPzB5jdPUet2sBRDkqVvcsgSok7A77y\nS3pAoRHSod3tveOg/hsZA2vt6s62EOJTwOe3Xy4Cu2/YdXb7ve9p/+rffQ0hRiis4L67dvHAnXN8\n59k/4qWXTnPX0aPcc+dPkjBOOgjorb5ENhjHFT+LNgsov1zJ7vfPM8i/wML5mLXNnGHDY3hhyL70\nEkLOUZs9wsj+WarBUSreXrzaHmQwTZJvoswiUcsjEreh8xiTp6Ak1apHlgzo93KqzSZONIoUVcAD\nCiBHs4U1Z+lu/QXGNqiOfAClZhFiEkm+HcOrbQry69a41AXeQOoLdDuvE8oqRX8Tm62VnbQzoFYP\nWL2Q0hqbZO/BD1HoY+w5fDvR6C14UbO8529zPwUuvpzFCywCl4rnfU/Ogd3eEiYjy/oUeUqSlUu3\nkadwlUcQVClMyacsC01BxsTUAaamD+EHdRwvQro3oxSFBqMzknQTKwrOvNnhd//9K3z9+Q26heTg\nvpBf+9X38Nyzp3nqS2f5+Z99kHvvmUU6ZWXjToKSpOyQhQSsRMhtoRYXcmsIonIQ5WmKX2kg3ZB4\nOMCv+AzjPn5UxcQx9RHJ+z/2OOtrK0zOznK0N2Tx4iWef/YvWVu4jM5yqpGgSLrsq1e58567WLh0\njoEuaFQ8lq906Pdhfq7JVMvj4HiVKKzguAarJMYaVrfWSJKEWrOBUhLX8TGyYJD0QUrCapMWJeeh\nX2uSJZqiMExMtzh58jxJ1ufgoRlOXzjPrpndNKemwd3CFBbHi2j3cmw1Yu6+DzM+u59ebvGjBvv3\nH6JSq4Mqn0GapAyTLq6riIIIJRTSUWhjiNOEp//yL/n2898updf+GmxJWPvOwgoA25jB529YTZi2\n1i5vb/8j4F5r7U9uA4h/QIkTzABPAQfsW35ECGEHF/8Z0r+DVIyS6xWGy99m8+pn2eqvMz56nInW\nD+HVK6R0SOMtTAJWu+jCY2z2NoLWDHGa0+utcO67z/Dyt3+fe98dMLtvL0HlNowJ6LZfZ33lMkU6\nzcFbf47p+R8hLxZBr+MyTjLMidOV7Rx7DyMkldoUQTiGEM5291SULIKrlFTjI9tdtg8kFNpDGwfX\niZAi5DqOb3grpl/kJ1lbfZ2RkT10t75INvgWef4mqmdwspTOlkFV7kCGd4ESGL/GIHGp1OaJGrex\na/YIGRk+OxiBxdqMtOiiVER/OKRVHX/bZ7gjlmLJyJJNNtZfx+gBStVxVIUszVlcusiVpTM4rkGI\nKtb4TM0cYW7fvTh+DS/wcB3n2lWVoQYUCfR761jb5dRrS/yH3zvFN19pMxjk7Nnf5J//xkdYXtzg\n9z/1LB//2G387E/fz/hY5ZrLeCMX407T26+1zjFaUxQZOs8p8gxdFBSFRusCz1EM+h2EMOiiFFop\n8pwiy0iGCRcWFmg2mzTqDUyacvqlF/nGU0+QD7sURUElDOgOY5zAJY1zJkZrCM9hcmKakWYTCs3m\nxiZpsl28ZXIc12VsfIxKNcSVEHgBV6+usbm5yr69M5gCVte2mJiYoFGPyNOkxD3CCsJ3CGstTp+8\nwOrSAjOzY0xNT+N6FVY2OpiowcE77+PwseO0pmeRfoDj+nheRFFoJJrA9/Add7tiErr9Lt3BkJGR\ncTxHsaNXAxCnCUKpkhJNCKq+j7X2ba3CX2sMhBCfAd4HjAErwG8ADwN3bj+vC8AvW2tXtvf/J5RL\niwXwD621T7zNMW0xeAptBPEwQudX6S3+zwT6Kt1hm05xFzPjP09qv0M2fBKZZtj8CE7jFqqzD9OY\nuB/pjYHJSPuXGKy/xMnXfouVtRfZNV9jdtcd+O4Egj5+VeGH+2j3IqycJYwOo5QmUGEprGoSjHCw\nRYawFuU2wQ9J8x4m7eG4NaLqflxPY+KnEcUAE97DMOnQ3XiRkbHHCCt7MMJBEiLYIZ3cpiUrF9rA\nrjDMBgSuYavzWapmnWKwSHftGaQu6FmL4/8QTnA/uvAxcUBerCFUgnKmGJm6A6c5S2ZyRqozgEWb\nAYPeKmk+JKpN4ThVfLVDjGrL9OPtrYIEicQhQhd9er1TZHmPqDJDp7vFytUFNlaX6fc6SKHAjdh7\n8B527b6NQkR4YYiS5UqBBDxHUGhLVhQMh+v4puDFbyzz28OW6G8AACAASURBVJ9+nRdPDYjTHg+/\nd4b/4R89ypsXO/yHf/9NHv3AbfzkJ++gNVKe445HwA136kajkO98agxYTZHnaF2g86JkQDJlKbm0\nhjQdllWsRqMLjdY5xbCPY1OunH8Dt+gS99bwVMk1sbjY54knnyMrMvZMj+MGihxBu98jUpLAryCE\nw9hog/bWJspV+GGVbn9AWKnSaDaRWDwFvnIZxhlplpAN47J4zfcIwojQC7BZjDUxwnXIrWB9Y4ta\no8ro2BgLVweMz93CwWNHmdu3n+nZvdQaY+AFZZIbkMSlMalEIY6rSmp5QGtNt9ulUa+RZjmO6+A4\nLtiSdg5T3l8jwBWQaYPvqL+5Mfj/owkhrMnb9OOTtC++yNrCF6i7z5N1E7K+JK/so7rnl2lOjGCH\nrxFvfA2d+6jGQ1Qn3k+9eRSvNkaa9NhYepn+5glOnXyCjcvfZffuPmPzDkJGSMcyPXsUh7sYZhHV\nsTtp1D+AkhqdXyXur5DnQ4S0WJ0itYNUIamOSYYb2KyLJ1OK4irdzrcohqdxwlEm9v0UlZGP4Hoz\nSLlDEhIAikRv4khQ1kWzRjw8TRQ26Vz+LeL8Co49j8jWsKZMDXYouRVTPYLJHsR1jyFcQaLBFgWG\nHkbnDFKHPbc+TnX0dvIsw3UladqlMAPipKBVn8HzWtf4hSya3A6wFhxZip1kRcZwOETqBF1slbOs\ndRn0N1hZeZM03aC9leBHk+w7eB+j0/uRboQXhNdoz6/5PUVOmiX0Bz36/YS/+tJpvvBnC7x5NaPd\nW+FHf/Qe/v5//TAvvnyez3z6JR57/6387E/cSXM8uuZv7aTA7BiCGz2Enb+d/fIix+gCpSi9giJD\n5wXCSrTR5HmGUgpdpFidkmY58XBA0dvA9Fe5eu5FfNOjUfNZWu9h5ShBdZKllU0WLlxkebEsVPN8\nnzzNaVZrmKKgKHLC0EcYQxBFrG1sUKnWCIKIPEtRShL6IVEUEUQV8sJQaEu706Xf71OtVojCCD+s\nYJWiFw/RUtHtD3DcgPsfe5zj73qM8dGJkuxGCowRWGMJnG0CGSBOUqy2BIFHHCf4notyFEvLy0SV\nCo1aHaFk6QEgUFJcA3eNNcRxTBQEOMr522cMCp1i9TKvfOWXGFHfpujndK5qlq8K6hN3894f+hQD\n36fQbbZWXsWNJNbWyJM+9fpuVDiF9Ko0mlMgJFne48Qrf8STn/9XzE60OX5bhfHJOwgb/wWydpyg\nNo1gBIsLZFjTo0guo4fr5EVOZoeYXOB6FRxHkiZ90kEHEa9D/ALdznOsb27hRJJWawxZu5WJfb9K\nJbodJTOEChEiorAF2A56uEU2vIJbreEHE2Sdpxl2fpuqOEuRGTKrybXCOIKk45BuWU59S3J5ocLx\n97+XXbfdt80ulGBMm6SwiNodHL7jEwwGA6JKSdWudY6rPCQB5gYq1CzPWG9v0Gq0CDwfjSbXMWnS\npt9Zot9bQwiJtIIi6bG6tsRmb0C9uYeDhx8gqk9jZIAbOEjJdQE2CyZPGQ47ZHFC0vP43OfP8idf\nfIlLVzcZaTj8yi8/wqMfuZOvPPkyf/qnr/DYB+/gpz95F1PjNy+Baq5rKV4XeCvbjjG4MegqjEEI\ngTUF2mzrQxpLmsQYXWxjMhahM7I8x1hBHifofIBIuoTSsLx8mUGqGZnYQ0Ep6CKFQWcD1lcuc/nS\nJa4uLjHsdXGsZXNzDV0UuFJQpDHSQr3ewFqIogjX9UBJNJYk1/hRhSCs0B8m9IYxOC7Tu+c5cusd\ntMamaI6MUau18IOQsFonCEOELFUZS96tbWTHWjTiWigAUOQao0sx3DRNqNfrOK7L6uoKSipGR0dQ\njqLQlrTICb0ylNgxtkk8pBJV/vYZg2H3EquLX+bk0/8NMw1B0pcsdxzieIqHfuifElX30S8W0Lkk\n3koRfkilXiftbwI59dYU2oZE0RjVZhM3qIHUDIcdNldPobiA44zQGv0gKprG0kUxAFpYPEx2lbz/\nHDp5jcIajJygKKoURuEEFWzhYhJJkV1k2P8iw/a36bcTOluWRrVKNBLRmnuUscn3Y22KdDp4zgyS\ncfLsEpgz6MF5hqmk3vpR/OY8yfDPofdbkHXQeChGSIYuWSboDyaI8zFqtfuwcozCv1DmOaSSeJgS\n1GYYmboLtzJNWKmh8Ngh5nynbMOdpinI8pLyvNe+hM5ijBZk6ZA06dLt5bQmbmX3/mPghDhBhHSu\nVyAKysGYpTl53KdIC65eGvJHf/JNvvTMApu9nNldPr/wKx/k3vcc4+kvn+SlZ07z4cdu4eMfO8po\nM7wJExCUKwc51wOqtzMIO5I2O/UOBsiNxZFQ6JJgFGsp8hTIWV+5Sr3SxPNCkizB2LykajMSU6RY\nk/PtF55HG83x43ejNSDUtmZkgbU5RZFRpFmp62B1OfjiIWkSk6ZpSTrr+GRZTpokuNukLlG1TrU5\nSlip4/oBSjlEtSphtYrj+viOd+3ahSgJ+KQ15fUJedP9ybP8mnK1vYEoRmvLoD8AKSmMJgojAlex\nsrqCMZaxsVGUs83PSFnVa4pS8boUthHf1xj8wGoThu0FKqEiS5u0NwZEnqF/WSCjCq4JGQzfQMdr\ngIOrNNkAjJqhWtmFUR7KrxBGVSpRg0K36W+eJqrMUKkcpLL3wFt+zVKi+jkwROCQZosMOl9Empco\nsPjiNkLnAVI7jc1qGCUxTobIutjBBioztAIFvuHi+R5TqUOe/RmuPoXj1+n3T+O7klpjN9KpYHUH\nhzaeHZK2T2KGHiocpZdGSDfEd3bRH1ylcOs47kM0RucZqx7F9Y4hCXG8nJXll+gmy1Qnx5mYvoet\n9atEdYtEkdsCF4UUf70psBgwBUVRIK1fFlc5mqxISDJFUNvH0UP78StzWAXS2y7PZrsTWtDaUGRD\nkuGQOLGceHWR//QHL/DCqx0KIXj8Q0f4L3/xvRR+xKf+3bfYvLTOj/3wcT74gQPUa95NYcDOE9nW\nrbrOk7j92Y10tTcpMu3sL0VZ1qxcKLVccV0XXRTsmWuQFQVa5wSuoMgdhFIUeY4xDgp478OP8sKL\n3+aZZ77Gu9/zHnxfoaQD1sfY0sSqhiCOYwpdELlqmyBGoDwf4boo5SGsQKFAlicRRhHKdZFS4Tnq\npuvdMdoKKLbp3x2hUEJi7A7n1vWQyfFc8qK8MzuiuQZQSlCtVNjqdJBKIkXJWDU1OcnGxgYLC+eY\nGJ+gXm8ihKCwkJoCRxc46p2H+w/MGKy+8Y+pNo8RRhHLlw1TUUbFqdCcO4L0I9JeH0REng3QWqMz\nSXttlcakhxeNYXIfR/pIp4HvNRGigiQE3q4yS1Aag51lvoLBsE233aHugHUsa9k5nGiEVmOy5CO0\nkqLX4fK5J+m3T7BreoJ6K8QbX2P6kKa/lTAYZHQ3+ozM7GNz8U1Wriyy5/AWe275EH74EMP2F8q6\ndt3GZBu4RZWqP4ZhHuF9lCLv0h8IWmOP4VfqJc4hA4wwDNKY5sRdjO66G0yCRFEfG8PgAgJlM4bp\nJqE/hZTfP+VohwbdWo0jDEppkjyjKAzKrzI1MkG1tRcjI3Alzlsoy7CWrLCkaUwaD+huJnzzqbN8\n5nOvcm65y9hoyI/8+Lt47GN3cPnsOn/1xHdI4i4/9VN38a6HDuCHHgVsn/XOOd0w0wGJMXiiHGxm\n+70dI3CjV7DzJD2gENx0XLut1iRUqRuhHAeExTNg0KhMo4Qgz8vViYfe/QhvvnmGbz7/bT706GNI\nxy3Fd4WDQOC4Ls1xlyIvMAY8zy0Z4K3AFinkfYo4ppAe9ZEpXM+/aZVl59zLbYvJC4wos1QdsaNT\nCcaWwjJs/y/vS2kaPKc8YmG3vT+xbRAcwdhokzQvyLXBVRaEYHR0FMeBfr9X3qcgIggCAuFjjCHP\n83cckz+wMOGlPz8GI+9ifv79nDv9Cv2LX2Xv3PtpHfkI/V5Mlm+RZ10oBiVj7FAj8BFBlYldBxBB\nDev4NFpzhNFImUCjLVJ5vJWz/3ubpcgusvLmH7Oy8CxRa4xdBz6KXz1MlsaksUXaNpfOfJbFU5+j\nFm0yPhvhV3wqVSiylP5WihKCqGYQnmBrQ7J22bD3lgr+eB3PUUTRUQY9nzy9SM3fJDcFRu1ia1Dh\nzGXJ2SvfpbOZ40UNQn+MSjjO5MQcY6PzNGq72b3nAaRo4nku1+cZibY5vc4lirjPyNRRpPjembds\nBm0ShvEWRmuszUmzHoNBhjEejbEpHL+BEUFJuHoDNiBsyZqcFznDOKc/KLh4Zokv/MnzPP3NFYaZ\nx933TfGTv/ggrdE6f/GnL3PljRUOzLX4kU8e5/DRaXI0tsxYoMxpvFGp4rpBKLb/nO3PE23JjSFy\nJM72s9yZOeG6EdHcbFRcIN/uznJ74LjbxwbQphSEFbYUdnUdxdWlRb713De5/Y7bGRsdxQ0CPD9E\nOV4pSCNKXgZjLMWwy+bCCS69/g2G65eZmd/PxJEHaO2+FTe8Lk6zg3XsnK/ZHmPm2lgr9Q+UAGMM\nRmuEsCjlIIUs+TJE6ZHt9GV9w3XdWCZudCl0u6NUJaWl1+2xublFVK0yMjJCrg2O46KwSCn/9mEG\nJ574BHtu/U1i3cC4a9C9xObmKv7YXtysw6C/hkCjdEqW9cniDLTAqoCRqVsIR3YjA5eoMkoYjWFM\nQZoMcJSD6wWU9Ogle+/3NoO1CZAghCHtvUY8eBonECg7i7Q1NjZOcvGNP6doX6JWgaCuaU4qXEeR\nJ4LN1fLBRHWBEynCoIGJDZmyeJVZQn8UzzlMu9NlOFyhWonIlc+V7jpfffFFNpIhwyygu6XxvBTX\n1/S7ijBQzExMUJGj3Hnso9xx9HGq0RyOqt9w/hZjunQ3lojqu3H9ynZsfbMjbkjJ8phet43n+2xt\nbSCUxA2bJQ2cChAuyOtMZUgD1lgKk5FnMXlsWF9OeOapE3z2C9/lwqql2XR49EN7efQTx7l8qcdz\nf/EKyhjuOL6bDz92C/v2jJUJMdYSG4sSAm0tQgoCUWbK7bi9O6HA9bMu30u2Z8obPYq3e5L59nEc\nrmMKNxqcG1dAdnIXBJRFUdseUG8w4I3Tp6jWq4yPjeG5HqFfEpJ6qkyTzvKc3sZV1t58nvbi69Ra\nLUZ230pr5jai+sS1s/x+4c7b5VTseEBZus205XpY6ZUDG0izrMwulGUooQuD40oKbXHUtmbF9jUP\nen2yPKPVbBDHQ6SQOJ6LtQIrygDLd94ZM/iBGYPTz/5X7Dryy+RJgFQxw+Qi7aUlqtE8WbqCEQk2\n15BnFDYnz1N0WpaiupW9tKZvBcfFC0NqjZGSqizuYYscIz0cN8D1Ihy3ehMiC2BtQp5dAHECRy3R\nW3sWm7xEGDURZhLkKGmR0Nl8jbS7jLHlY0sHLlFFUq9PoJwxrIrx/FGCaA8bnSso2yGq7ke4FXQR\nQdFkc7iCsofIgz6vn3uBv/j6N3Arkqm5caQ6jNUD2p3TbK2n2KKKKTKEsYyMCcabY0zUb+PBu3+a\nA7vf95a7WCYWg4OlwGK3dRR3PtXktkTZ8wKSJCu1IdwAHA+pZLkWbbdnKGvR1lBkUGQ5WR7T6eS8\n/PxVvvhnr/LiySsUgeD4XbP88Cfvwam6PPXZF+gtDpmbH+Xjn7iN+4/P4zo33+vMGIZ5jnAcrJJg\nIBQC74biJrh5oO9gCDnXcYK3Axh3mqZ0pQWlgVaUdK83ApM33rEbj7Xz+0mS8Ob58wyGfQ7s34+U\nklqlhqvK8MMChSlI+x2ypE9UbxEG9Zuu4Zon8JbXb10y3XlvB+DL0z7ttcuEYUittQvhXBfRhXL2\nLwpdYgRq2zAYiyvFNY+gKDRplhEGPnEypNfp4ocBtVqdLDdYIYk89Y6ewQ8MM6iHITL2SNUqgXEo\nEkW1VkXqPogMYwyOLAEfbcpH6AiF0ANsfIG841AZmSMdxkBeCqvYogSYbILSGcaWsI3rhdu/WjqV\nQjh4foRFgtkgqLVB9SFfITfLOP5eTKpRpkN9QpAaxeqVHJsqwjAgL8bxwqOElRGE30A4klq1gSUH\nuRtrxshtjvInqEvLxvoV3lxdJq9OEU7M8MYrCyyeX6DXWWDv/BgH989wx93TKK/G8uo6eRYQhQ5S\ndMtEFX2jeu4OFCUo6d4tqd7p4CnWWpRUaJszyHKyrEA5Lo7fQCoP4QjsW6bYTFt0kaP1kDTO6G4Z\n3ji5wRc+/zrPP3+eWEa0Zsd59P1z3PfuQzz/nQu88FdnGW9GHL93ksc/coyjh+e+J5NQQCmQ6/sM\nC02xnQQTFwYrwVfXQcobp6Sd2XsH4dkJCXbi8J19di5DAp6AQaEJtoG7ndDhRs9AUHb4nd+6EbSL\ngoCjR47QGw7JkiHGaDq9DtWoiu+VoaeSDtX6KKI+etO57hx7p+0kU70daGptabB2PCMpBXlhEW6D\nYVbg5QW+Y7dhw3KJUSqBsJAVGZ5wcVRJ75elZYGWVBKtNZ7rk6YZvheShQW9ThtdaBqtEQySNH9n\nFeYfmGdw8okGWs8SVY7hVedxo91kxTReOIE1mjztYtM3KQbniDNDoasIrbGFIUkUqCZTc4exyiOo\nVHHCAIODUB5SCVzHw49q+GGpUce1xCAXMFhzHm2eQHIWkV+hGJ5AW4Pw5inMDDrJSPqn6fcvsbGU\nkvYt9bpDc2QXzdY+sIIsdnCCEay/yqDwqFceIQgPY50ISwWjLVsb59haP8FqfIU4spxbWef1117D\nJaXXH2BiEFYzNePRHwxptRrs3XeAgphQznNg+nEeOP5J3LcFRkv9wqzokwza2GzIMDf4lRaOF2KE\nh1UeSrrXpsNrqap22xsoNDbPyJKY3mbC+bM9nvjyGzzz7FnaiSUa97nrgQM88rF7kTrmW0+8wPri\ngJnZMT7ykf2898F9BK577Xx2BsBbBzeUs3c3SSlkCZg1XJdA3UyrfuN3bgwjiu3vO5SezFvBSOCm\n3E99w/cV3zsoDWVMnhmLr65/qq0Fa4nTIXEyxGhDpVIj9Mt8gLd6FDcag7d6AG/1DApjS+0LJRFS\n3PCdUpNDIK55ITvf3wFSS3xAk2cZvueRZSWVn5SSIPAQArK0QCpJkqZYLNUoJI4HaCuIKtVS/O5v\no2fgu5CKM+SDiyQdSbV2J+HUo2gPhBH4ekicnqCbfhXfv4Wa9yCDfkxu+/gVn6hSpSh6xLEh0zFe\nFpWhQVhDqRIE8ryA8kaX2gpClpCTtV3S5DzDwTKepwi9aZRr0FpSsB/lHyTpXSLpvUmnp9nMS80/\n40iszVhbP8vqZkxr8ijDfp+pqQ8yOfNuYBTHa4LyEbh0e4toG0HhEeaK7soa02GVyu3H2Vxv0+6s\nUSSCIo9xVJtaXVKpwcXLF5BylA/c82Hec/wn0cjvmWUor4zcpOg8ZzgYIoXFrzRRfh3lBGV9hbo+\nE0E5AKwxWG3Q2hAPUvq9mLNn13jySyf5+rMXWe87hPUqx+4e5wMfv4dqKNi6uEh7qY8e5rznXbv4\n2ON3s3u29bYd6EaDcKPL7AoYCX22hgmFkvRzXRYnKfk9IcBbDcqOR7AzeHeQ+oLrqP3Ob+14BDvb\n2oJ/w8F3DAQCPCVuytVwtrXkA9enKHK0KOh2O2RhTq1SA3Xd89jBKG70PAC0KZf7lLz5iUn5/zD3\nZrG2ZOmd129NMezh7H3OuTfvzbw5VGVmDXbZVeWhymPjtqHdLQtXG2SGBwsEjcSoRjzhRrzwBq3m\nBR4Q8AKNGLoFtJFALQOS225L7bJxueyyXa6qdFZm5XSnM+05ItbAw7di7zjn3swq6IfMeDn77CFi\nxYq1vuH//b/vU7hB5CdmK6EXAkMCFgysCwkWSD/LUnoxVGXJer0lBE+MjhgTzlkSibKqaLqGbdMw\nGk1oug5iwocPjiZ8aMIgxJlkppktdJ7Fxe/wePH71NPPcOf2j3O1fJerx/+QqgrYWFOZY1JV0bQr\nutSx6VZUtmA0noOpSNoS8KiwhlASVUVMI3Sq95MtU9sQ4xVaF0yP/gJdaNhEyXE35YzC3SOkElN/\ng3VYs9g+Sz1+hqPRHeqywk6PsFrTXnyDN7694NM/9Je4/eJfwbnedOyXZEdRjZg983E0HVxa2Mxo\nlaFKLevwTdrLEqUKIayMJlhX0ezgmemLfOkv/Qd8/O6nr5nG/ZEQZDymyLaLRF1TnryIcQUaI/UW\ntWjQvU+eICXpldg1Dc12y+X5lte+teA3fvOb/Ppv/DEXm4LRfMrHfuyUn/jZT3DvdIRebfjO1x/y\nrT97jVdevsW/82//Rb7vU3efQMyH0YH+eD8//2RUcf/ikqAsm1QyKaV34817HLoCCgH8PIltSIyy\nRWGAbYRKywgMElGw6hCd8OogIG6OZah9h5856xhVY1brFXVl2KxXpOCZTWc4K52kArKseuM6ZovF\n5ArRIBu+xzJuzo9WIqh6Pa36Gx+MLSSxGqxWpARt21EWcn1jjDzTEFFa6nO6QhKVNtuOs4sz7j17\nT7qJKrDFBxdE/fAYiGf/NW+++fepzZLl44ekrZamFqVnt/CospSJtc+iyy/y3Mf+Ij5EFpf3aVYb\nNJJMEpUmKs3R7Ihmt6XtGsrxLerRCVU1w5hKSltri9JDisuTxmNKYlyKb9eRUqTzEZWiPEyF9CLU\njqQ6lGpJKWLUKDdOPegMIZNqurCj61a07YLzswcUlbTj3mwbXnj+4xRmBmgWzSVVWVPw9AIUCfas\nsgCs2h0hRZwTNyAlsf9dvqVDw1MRArqLNF3Hdt3y+J2Wb3zriv/rN/+I3/xHf8RqV1KfTrn3yjGf\n/f7n+cyrzzMtFa99/TXeevMt7r10yi/8/Of4yc99/H2f6dC0vWmS3/zbb/DFZsdqu6MoK2ajEp0F\nwk3QT0FOSmqwVnPeNMzqKToJ/75JYFSkXW/ZbDfU89uMTMQojfSMlvM+bdMPx3bTzO/H2rYti+UV\n3rdoZTg+PsU5t+dE3ORC3BSSN4+h+9D/xueGtqT+mSWMAjJAGCJYrfYCRyN9HYV4pNhudzhX4JwR\nwDsl3njzDW6d3gIU9WRCYe1HM5qwuPwySWkszxB35xilccWYRm1ofEKFEeNyJF17iLShQcctm+UF\nTQvWGWLYEkJL9BGj5X9DJKmScnzMeHaHspaux8ZIItHTD4GoYvT46Am0JL+ha1bC3Nu1JC8TXIxq\nbH1EUZ1gzYgDRNVbHk/3fg96C6RTnhosU/A02EEkvt/IAoIqvFJsG2mCqq1DKSPcgMEVYzYnAaFY\np0T0ia7puHq84PGDDV/5vbf5+//7n/Cn37rA3prhbheMa8crL9/iC1/8OOGq5Su/9SfE1ZJPft8t\nfu6vfJaf/rFPUh1ggSeOIUoOTxcIgeubrl/Q7z18RBcj0/mcqnBUWj+xKQHazZpH7/wRJ1PL229t\nOLrzApM7L1GUBpdn7Q//5Gt841t/wi/+3D+B326w5YhYVLjRyd4EDumAOajB2IZjioj7kRDrQgPN\nbsdqvSAp6Hzk9OQUZ11+nk+a9wxePzEfiRz9YR8eXO8a0DpHB6SudUwRa6RPh1IKqxU+SojWcBAa\nbdtQFAXBi7WotbgkIQRQ8vsy17f4SAqDb3z5X+P4+Iexk5cx5RwfKwpdoYs5nd9Sq4KgpHVaG1sK\nW1JYzfLyEaPJnKRGLJbnELbosIGwousu8c0OKCjGtxifvMj0+EWMGfH0oBQkPCGsgEY0bCyEzUZL\n12xRBAgbgu/wXswx42qK+hhjRxn1NRwivkO98PRrPjmGJISUhABISdEpAfiarkOjMYXtP96ftdd0\nGlkUIQuOGBNd07A4X3P2cMW7bzX89m99i9/+rW/wxqNL7L0jipOa05M5n3v1BZ599jb3v/Mef/Z7\n38C5Hd//uTv80i9+kZ/6kU9Q2oO2/sc5PAdTvffxh7O02G4JQGUMhXOgFE3whOBJnceEHe9++X+m\n3l3y3//tX2f+6vP84l//j5idPk9dWEiaoCC1C976yv9EPD/HTe8yfvaHuf3qZ1FIuLEBVIy4FCkH\n9NzrIb9DU9p+rAHYbbdsNgtJBuo889kJLtcVCHkf9VWihySpw3mFvBRBkp+Mls2ev9N2HpMzD5US\n8kcvBLoQhQ49aFKjkozTey+cDGNZb9aM6gofgmQoIlRyYxQqKexHMYX5wWv/DeeL32C9vuT4+Ed5\n9tkvocxzuLFDpYawOWfZLtHGkWKJUdDuruiawMkzr1KMb0uyR2jZXL3N8vw1ut0ZwbdoVeJGp1Sz\nF6mnz1HXR+8zkkiIW3bbh2jVYI2haxPaTbB2TIzS3NU3GxIeZcAoIxrBGDAOsBjl0Bh87NBqmC9w\n8JpTCrl3grwvml8eZpckCy9FRVKOgAYtxBIGZ+oX1MF0Digk7OR9Yrtbs7zacv5ox+P3On73d9/g\nt377a7z+5mPS9Jjqzi3KmeLZ50a8+srHqMKEN3//dd57+G1mty2fevk2/8I/+xP80GefxWn1PSVA\nQY9hiHPcb4YhGt4fQw0ss3+4N4DHiwXb3Y7T2RHGOKFArxecn5+jNmvOXv8tdg/e41M/8KN880/+\nD+790C9z61M/TTk/QUVNaRIaxeOLN2nWl9w+fQldz/fkpYRYWpvtGgWMR4euxCFF4m5NWF+QrMLW\nM4ryiJQSPohVaJ2jbXcsF5fsdluUspzcuo2xko+Qrfy9ACFr7pjnZRhybLtOxpWLxYR0oBwDUgqQ\nhDViLYTIHjtgcJ3O+32jWxDGZEoSjo8pUhhHjEHyIbxEGD5y0YTpK/8yc36FFJq8qDWRQLQGEzu2\n3Ypmc87x8fN0XYVvN/JAbEnXeXS7koaf2lFNb9N2K0wxRisj4aCjW4zmz6H05ANGIeaYtQUphy1J\ngXZ7SauWxKSISWO0Q2sLCUJSqKBEWyuF1ooUO6IKNK34bVYXol1SQimHQuNDJ3H+nJ0WUiLGXH4k\naYx1e3NQ78c2QK3TEKFP6JDodi3dLnBxtuHRow0PGkNKuAAAIABJREFUHq34wz/4Nr/z5df59rev\naJVh9MyM+fe/iJs7Xnr5Hi/eucej19/h//m138E6ze27Y37hS5/gn/urP87z92YCNPH+lsBN/xpk\nk68l/Y/aHoqk3vTRDYfw39OE2+NHj5kejdnstjgdqKqC0p4Q247YXbDlMbZ8myPzDJ//8b/A+JM/\njZ7cpmtanEvsYsD4xNhvefjOt3F6wqScYQ3olEBpQpLMv8l0LOCcUugUSe2Oq2//Actv/DZ6ekT5\n0o/wzMtfIAKrzQaVIrPZnKKoODo6Ztd07JqGzXqFtY7xeIzO/Ql6+zCmSOcDGI3KTMZe+ZbOiQDN\nYxiCvamPHmTl4UNeFzlBy+bzBKUkB4ODyxD3eIPkmfjOk5I0gU32gxX/h2YZrFIaGNRJDO0kiCx4\nUlgLTTZZtpuGdreG1GK0wmqHsXLTrjzCFhOk8+2AtZ0UqO9F1kUSG4K/IjRbog9IfQhNQmOMtBiP\nSbw8rQwpKZTWWCdlwn2IGGvFZVAG0NmqSGhtUdriU0RpQ1RPak3FdbQ7DRDmPrE15sD5ZtOyvNzx\n8P4VVxcd77y15Kt/8Dpf/cPXOFu2rJOmms1RFZTjxLP3Tnnl4y/QLBv+/I+/zuJiyfGdKfeem/KX\nf/YH+Kf+wg8ym4vva3iy+tBQk183pQ/vtUl+U6iDdlFcxzAYfD8OzjG0EvrX2+2O5XpJ0gGr4NE7\n91Fn36Fa/SG7i29y+96r8PxPcvrqz+GV2UOuDYnL80ek7l3GdU01eglsJeXeQsQad+2+upgFa/Sk\nruG9174Cy7cpi4quusOdV38YXZTEENEG0f4cKgl2Xct2s6ZtW6xzTCdHmMxY3D/XDPqqzB8YUojh\nIDCfmNdEbiqs8DEKcxCVyXiKLgSMMXuLI0QhJ3kfxO6MYX9N0DgrdKuPZG7CJib6aNI+Ppx6Hzjt\nK7zIg9vzsUhEVIz4sCOGiDMFzlZ783R/jf8P40mpwXdXxNAQE5LYYQqcK0EZ8fOySa8w+aEIkKO0\ngZwxh9IoZa6ZynDdPB4CVkPNGVPKDzbRhXynUdHsGtaLHWcPV1xdtLzz1mNe/7Mrvvq1b/H6/XMW\nDaALyqJE20gaw2w+5ZmjY5rVmuXiMXWlOXnmhMks8clXTvjLP/PDfOrlu1Sl2QuA/VxwANAYjFsP\nXvefK8CnxC4kCq2otLqGB4QkZnv/nG9u/i6xt0TS4NoGePONb1HYQDk+IgTL8vF7xNU7dN2aj33i\nB6lPXyEqtwf5+rnt8oUsifV2R1WVuGxG9+tMxLrKBVPACUuaECNKJYqsVHotn0KUXIZcwLXHaXpX\nbbvdsN1uMcZS1zWuKEFnbkjGgmKMJIXUKBisj2GK9vBQQAwiSEw+l2j+RIoJbcUyjVEsTKPVns2o\nFaTg88aHpJIoq5hw5iMoDLp83es+MEKK0TE/roOGDIOVdDOE8zTCyvcC4e0XZgqEJPhxUhBj1gJZ\nyw8l9s3rDx/kTRN6qPnj4Ds3hYOK4vM3PrHZbFldLFmetSzOG967f8Vrbzzij7/+Lq+/ecZy42mC\nJtmIHWl0cbAmnC3QPhIaz7h23DodceduzUuv3OIHPv8iP/5DL/PS6WQ/tptaaXj0G/MmENbff/St\naC0MXQRnBOgaBmyf9rvhfd98rxcwBmi2G84evUeXYDyZUE/GFM5hdXmNktyfYxgn8iR8kIW/aTsm\nRe7aDagUSQjSbvf+eiLGiI+glKayh+pAISV8ShQ5yhFTklTkHJXoj6ZtWC2XxBApqop6NMJZi88b\n2mopz5ZQEg7k+joVPONgSSkgdF5ITjm82HYBYzQxxsyGFAXSeTmvlbRFUpRMRo2Ah7awgjMkhdMf\nwWjC+f0Fo/EYW2iSBVRO2xyYYXBYlNeERn+ewWcM/pLN7JvMsJt3OgwIDjf1TeDsu83Q8Pw3x3ft\n2kkeuA+Rtg00m5blcsfiYsNy0XD2sOHROxd881tv8dqbS77z3mPaVrPuOkxlaPCYStPEiAoKOoNO\nFt/uiDowv33EvdmIF54b8YnvO+bHfvwTfPb7P4ZzBp8itSsY20SRuyUP5/Rpq2OorfeaN0aILd3l\nd9Chw4yfRRUTXI4APM2tGL5+mqAIg+cV0iHZyMfIV37/93jhxXso56gnM0rjKJS+TuzhpjCA7a6l\nrop9eNZqjUqJZreh2azx3Y6iqhkfnZDQeN/hrCVmFD/HboTWnMRazS4+zqgn7gHAdx3L1ZLtbktR\nOCbjKa4sJZCcRGPvN/zAlSDJb5PS2Iy5DNOUfbaiE0pyFZTwK3wn1ZCs1oSQ9lEDYww+BozSe/6C\nBkzmJHzkhMF/+h/+PZ59/g7P3B5RzwvqsWN2NGI8meBqR10WKGcwBpIGpdN+sQ3NzeEmvikYnsYq\nGH7nJs98P77v9T5uXL8/Z4piXfiY8F1gt+3YblvWix2LxYbFxYbzRxsePVjy1juPeePtxzw4W/L4\nYsNmJ3Sl1rUopSn0mK6V5KOu80RaYrdCqYpJdcTdO3NObjuee77m05++ww9+7kU++6l73JmPDqg2\nUips3XlaEtOioFQHssxNn/XmffUCIfiO7eKc9vxN/OOvQbNAn/wA5b3PM5mfXKPavp9w6f9es4yQ\nsJ95yvuBxKOHD3BOE4lU5QhFwWRUPZGI1B/XCVfQRY81Bkfi6vKc+2/8GZVLRFvywqufR9lCtGno\nxOUzGpUiVkvYr8ssQJ2tRKXEbR26P71w7UJgt12yujojxsTR/Bb1eCoYVHYt925CnpAYE5v1Buts\nzjM4CAqxRnLORK8olQgnpRS+a+laT13XKKVo205wi/57RhNiwntP4Sz2o4gZjN2XKAvL8fERt+ZH\n3Do+5d5zt7n33Cm3To+YH8+YzmvqiaUcFVQjRz0qKUtHWTnK0uGswRYa4yS1UxJAROj2deP6VTn0\nVYekkJtavX89/L9fxf3iIgoiHX2gazu6LtA0Hbtdx27TsFu3rFcNm3XH1dWWs4sNjx6veff+OQ8e\nXvL4bM2jqw3bqAidRxuDcpY2tPLgfcDqEhUghh3WBerKMpmMmM/nnE4qjp8Z8dLHb/GZz7zAK598\nho+/eIujQu8TdEpuFMHgAPZ1SfzkAg4hN65jGDdN+S4ENssLNvdfY/nGH9BcvU1ZaE4//oOcfPJn\nsKM7TwXCbq667W6HMRZr7TWT2A++OxQGvda/vDhj06zQrsAWI2bVGOsOAPFNaxIksSnFmC0bQey9\nb9hePobYMZmfYKvZfjPHGGhaT1EUQt65kYegUnYdYkJrhVWHtdRXcU6Ajh2b1QUX549BFcyPb1OP\nx6BNri3AnjQUcgRAqxx+VNddht5ykPcSKbsnWilQCd95FlcL5vM5Wotd3XaS0u6cFSG0X8B8NDED\nZ34EsBjjSDg0FSmKyeO0gIKjUcn0aMr0aMJsMuV4NuNoOmI2HzGdjqhrRz0pGI0d9aigHpXUtaEo\nLLawOCeCwlhDobVMVs8r3k+HvE4kSeDJIE0MkRACPgQ6H/KGD2Le7wLtzrNbt6xWazbrhsW6ZbFs\nuLhcs1isuLxasVg1XK42dEkRsslprJiN0Wk2zQ7VJUqsgFRNR2EdMXZMj2qmRyNu3x1xelpyfDrm\n+XszXnjpHq+++iyvfOw2s6NiXyjEw76yz3BT9bp6uNkCsEuyuEf6sPl6srbiSX7Arm1ptiv87pKw\nOqNdXlCoyPzZZ6lPXyEZwSKe5ooNj7PLS8qyoixLrD5YekOLZOjCDK24y6tLNs2GoiowOOq6oswN\nRYebcijYdl2HD5GqKvEhUZvr17w5xn4jom6QpPLGVTfBP3WYrx50NSnRdg2h3dFuG7oQKMcj6tEE\nrQWA7rNHYxIQFg7C4NpOTWI5oJUUf83WQVISGSD1NRITIQjvJEWxCGK2HoxW4kIohfkoCoPx+AcJ\nnSTOpGRAW1IU0E4pg05S9cXqkn13oyRhuohkZxntMMbgjKV0jkldMq0rRqOaona4wlKUlrK0VM5Q\nGoOzWjpM6ANBRmlISTr09Ew+n7X+tvPsfEfrA7tdy3YnJn/bBFofaIIXzas0ygmKjBHkFx9pd2J6\ndiESgtCPlTbopNEERrXl6GjMqHIcTQ3z+Zj5rSOOTh3Hx1M+/vG7fOb7XuDF555hPrXXtHhfUBQO\n2j9yqAJ5E1iNiLYEKSq+iQmrFJXiA4uNDDdOAHwMpK7DGI21Dn19+V4LTfahyqdhCX39P5817ftx\nN4cgZtPsWG6WKALaOMpiRFWWoA6j6LWqhOE0m85TOivJPTewpHTj7/B+e2tqyJoM6TD/cAPwyxEn\n00fCUiKFwGazovUdVVULD8XYPVFof614+C0DzKDnHMT8ZaVk3kKIuLzhDbBarlguF0KCMm5fK0Hq\nKgrOEEOSiMhHTRj8/M//S1wtdiwXW1bbhu3W0+yk5HWMHSQjsfWkQBlSZokrreT2lUbrApJG5eCS\n0gqt3V76xpRQWswkYQ1kK0ADWlwLqR8nZlrKNljSKYeBLKSM/maOeMoPOmYTlNgJ8OUBDF0XxFVB\no0KSv1ZhnaEoDVVZMDuZMTuquHtnyuntI45mNdNxyUsvnvDSS3d57vk7FJOCysr56tJg1cEUlfr6\n10NqfUOSfvG6G+/34bsuJbYRRkbuYxMghUStwWbq8U2kuz/v0PzvN8vw+ze/tzfXYwYFb2i9LmY0\nPGaat7oejWBwnsO1JaNjs74Si61tGY+kUYnRhi7JZjFaUPbKmoPG7sedJKtxeI3+PobvDV3K4biG\nwm4/tl6JNDu2l5cY5xjPj6XqskpsNlJq3VpDWUr4MelDanpvJQzLopNDs70w6IVZiOIuCOEtoFPi\n4YP3ePT4IZ/89A+AsflziYrFINVvCm1k/3zUGIj/4i9/gdXGc3G15uxyx/nFlsvlhqtFy3IZ2K6b\nDI4EmjbRtYrOe2L0pNBnGDYC/EQJD6oo4NseVspWRspCQJ56P9siAVQPMgjEi0QTJSaclEVHJXUB\newKHyvZ1jusaQLmCwlqKssQ5Rz2uGNUls8mIyaxifFQxP54wOSqoKsNzd25x99k5z79wm1u3jhnX\njpAiVaH3G7yLUGvoEN/UD3zIIXp+U+PuC4VmP5T8P3khGaWwWnCDAhgp2KbEOkBlxEror8HgGr2Q\nGa6iJyyPgXrtX/Zpvv04hqCl0xLLt+bQM2CodYfnGf6vgel4xnq7JcbIcrUkpsS4HuMjVE6Av9Ka\na9hDiELn9Vmox6x2UzrgS8Nrq5SuVWzuV9Z+vnsrIfvw3necvfcd3n3tNU6fvcdoNhelojST8QSt\nFLvthlW7pB4FynqUG6IO5jQd5uumcOhDmSbPW0LCnlZpTk5vc/vOXZKRFSTFUg73FWOfu/L+x4cm\nDO7MHM8cO/yzFT5CFxRNaFlvI6tl5PJyybZpaTtYbT3bbWKzjXRtJLSBtu0IPuK7hO8SXefpUqQL\nUW48CKIfE9KuKgWIQtjozdNeEAjVUwt3yOicqqywRYF1FqMVrigoRyXOWYrCUJSO0aikKAyjcY1z\nBq0SR7Mx01nF/GjMndNjTk5mnNw+ZTIZUVQ2N81Q1GMnLDFtZbNnodZ3L/JRuhH3pcH6zWIRU7VT\nBz9/SBxSCPGmQayD/gH3NQE1IgC6JALHaBiXml3MzUmUbNIuJRrvMSgqZ58wpeG61t4LkHTdAtCw\nD5X1WjkOFvhNkHNoHovcjmybC84v3mKxvE/brbDOYFQFacJLL3yOVYTdrsk+NAQjJe2UHgjNBK2P\nKKslHp/HYiCvmUjdJ4MN7m+fntwTfjKR5wA7ic8ek9QpDMowvfMcs7v3UFYQnP7ZlWWdrZoN6/WG\nRKKqKrS2RHU9OkG2bsjXHoZte4EY85kDoGxB7AWbOoQxdRZGGFlfH3R8aMLAuIIYpRFkqSMxWRIj\nTqYBnvG0jaXZbAlJYVwFpsQHR4x634W3aTtSMrRdxPtEGyO7EGm7RGil551PCZ+bUeyjAIl9WWql\ntLC5nMnmmEjasiiEG18XuMJQFpZ6JHhEVTnG4xGTcUVRaMpSGIBNuyMRMQasNUyqWsCyakTTBZSW\nTLJ225I6zaSw+CSb22mDD4f5Ke11lN+pQykvqw6fDewgyH8LoFGS5486lAqDgVBRgpX0Kb2VhiZI\n8QyvIDrLzkdsAqMNJEmcUoNFOSQtJQ6mtw9Co1VaXdPMezM7yWa3T3EdRBgkNu0Z54vXuVi+x6r9\nJm8//Crv3P86TXvBeDxG+ZL1YsZf++X/gsnoJRq/JYQG7yHEFmtHVHV1MOMVWJPj9FndmuweanK5\n8cE4RRMfNnJvEIo7IBWRlDpUpE5AUda88LFP7kuiq9Snqcv1tNGUZYW1js16yXa1pG12jMZTYS3m\nzdyf72nCl8F7Gk1hNT53WEaJ1dBXWurrQxitiCFrmg84PjRhUFSVmFl9/zwE2EtJ/MeyrJmOfe4K\nrCickk1rC3y0tG2LKyeU1ehQD18nUIf200Il7oEq3fOyJX7vSlmsSoPWYq7pvLyTwhiLc46iKgQv\niCk/zBJIUoraWkxOgy2qGufmrNdrmu1WaKgeogEVoXIWHyRWrY3G+0BrDIU5LAB7CA9fA99C1kQ3\nwa7+9U3URyEPtk/ZNVyvFNxrYasOroQGaqPYBsW26XDaMLKasF2ybReYcoox5bUFec1FYKCxsvnc\nL66+R+I+dJbE7elSEA6Jkgi+ARp/xbuP/pS3z/8h711+mceX76DKM5QFc9wxUoHd9hFKdzzcdLz9\n6Gt8/tVPktqITpHdbk3sztm1Gnf7OXQ5PWAbWu8jAyD1CAsr5e+tMQfgkWEDlHyvWmFR+xqJMRdW\nOQCJipjt+72QTCkLRrDG5NJlEgInjVj6jt2uwcfEdDylKksBohV7ujSwz2d4P3TPZi7Bft0o9kK7\n5zY4Y4SA9AHHhyYMnC1AaUKvDrUIA4U8NJQkMo1GE9arFb5rBYTSCWUMu23AasvRdLKPAsSY1R2S\nSIQSskhfbNIZt//MWkvKmEEEUpQyUtbYbJYJgGiLQpprKg1assSMdQdTNmUzLEmprJPjYy5T4Ory\nEpPk++vtjqOjiURAjMLmXnhdTMQQKAq73/DDTR6BTdMSlaZ2h7h8X/evBwWHmxIOFkShMmCYEmUu\n5tkfKUHHAcHuz1EWgl80ywsu3/w6izf+iPFkzvTlH+XolU89IXyGi7SPsxdW06WeOSc+eg/e9pZI\nYWDdJkkU0gpL5Oz8O1z6P+Krr/89Xn/wmyi3QRkNbWKkb6NtjWLLerOgLBW7VqGZSI3Dosag8FVk\nu9rR7jyXF4aTWzXK2qzVhUF5SA1PtJ3HObsvL1aY6/ME14WwzvUFQkwEMmtWqWsbdr8RFegsJW9q\ndesKxpMjiq5jtV6yWFySJlOKakQyg5qXSYhX+wjF0zYTYJQ8mUNtDfYp8r3LZT6qloExVtJ5I7I5\nel+y9+GNbOqqHFOUY1arK2LwIr21pR4f4QOUxYi2a+gbWSaMEDOMIcQOsiVgjUUriUKIFaBkE+fm\nln2/O2ut4AdKiTuhe09brAkhjWhcUaC1yS6Lhyi168tCstcuzs9Yb5Yoqyidoe06KVFmFDFqmt2W\nrm2k6YuTzrl9Oy04+P7rRmiytrCyyYM4+r0vfBNsgwOn4JDNN2wsl3+npES6j5HK2b0fHFIEFUh+\nzeLxfTaXS6piLolZXN/8/TWH/++R+4xLJMjVeg5huZiTg5yxtJ0HVrz5+Hd468FX2LnXuErfQI0i\n61VHWVY4PaXdFWizY7e9RClYNxGl7nL39AcAJcVCrKUaj/HNKcWx5mq94XJxQekqxpMJnfcS2TBW\nrBRjaFqPDpIVGFLCRyk35lPC5e7IPdGn34w6T0QIUdKItdoDfb0Q37MprdmT1YZhSKM1dV1jnIj0\n7XbDYnHFOAbq8eTQMi8LFDlv34qtFzpyXq1gu12jVKKqJmj03hXTOrtGA0vj/Y4PVRiAoii0hD9S\nkjTfLFWNsmhjCSFQ1mOOq5LNekXbbIkhMqpKYhCCReEqfOxwVvRlCAlrDQ5D9OJ1t11LiC3ToyNh\nv2m9LzaijMIWjoj4VgqDsVl4IDnhwYsv3actp+ilS3FZoKyV1Gcf8TpSVjUnp7dZLi7ZbJZEFdEY\njDIYZVFaUThL2zREBV1OQOmJK730V8CkLrGZW54QtBkO5n5fjWy4KYehQQvsYiL1wOTgs0JJt51d\n50kh4qxh0zWE0NKGyPHHPsXHfvDHsNWU+uj4Wg/EoYYaglr9ZrCIX92HxEA0XEqBGBXBJwpraJr3\neP2dX+PP3vlfUUWLL7Z0yqL1nFFt0Noyqud0jcbYll3rCZ3lauv53Kd+iVvzZ6WJSBbmCcX01ot0\nzZaxrnnw4F2O56doY9DG7hN9Fqs1s9kRzhmazlOXBU5rKXKCKIKnuV/9vQu4J9+XDNPso+ffDYuc\n9gBg9kCvAZTWOsaTKdY6FotLlqslIUYmkykm10fQGXtJ8Xq0JWZ3Z8+MRV4oLRfa82i+i1XRHx+i\nm2DzDYjkstZRGC3JF0iyhbCucs03WzJ1Jc16xWZ1SYotVTlGk7CugqAhl3ZyRjrHgEI52eBt16G1\noa5HYjUE6XW/222J0TOeSD1DYySCoFV2B7TkgnddS7NrUCRKU5G6RtwMV1KUTkA3Hwkx4hMc33qG\nyXjMg/vv0G23dFgKJEFIWUNVlYzLgnXXCnFk0JJ7b0oCRUby+0NbjUPwgGF/wuHCHT5UDdh0MM+H\nwkNrRaEM287jU0LFRIGSCEcxhkpBfUR01Z6vcJNQNMQ3GHzHc9CmbRfQWuGDcEhMMnSNZ6sW/Pk7\n/xtf/fP/kvPtO4yPZsS4IHCMs88QG8/V8pKm22KdhsbnZBzL2H6af/KLv4JH5qN0jpBdDp/AuYrj\no5Ltak2KkcurC6qyZHZ8mvkhUireakXMWJKkkIvPTs/DWK05vzjnzp07FEVx3UVSStyfLIiiz26D\nlqT7/TPITMswACH2URPELS6rmiOlWK0WrFcrurbjaDbfJ4AB+wSl3hoQGSORjHo0Ram4dxG0ug7a\nfi/HhyYMYvQYIxWEOt+BVriywroSUiRFKVCqrSJ0LVpVWFegpzNQifXySjRYs8O4ktFogk+Rrm0E\npLEWKU4i7bJd7k9fFGUWFB1d2xAywNM2O5qmxZU1x8cl1kplGpRYDcootDU5fi8WSdt0FLbEaIUp\nLJ0KhBDYrDeMRxV1PebWyW3eeedNKd2tNTGNKMcjghbgamKl29Oe+5XdpSG7sF9DfdX7vmxqQITC\nsC4zXDfltVLUzmTATt4bIvhGSVxemUxIUZA6zWh2SnV0Ihl/1tJ7ME/TMDeBTcipvyGitSL6Fh87\nUmrodmLZ+bDl/uIrfP2dX+fB6hHRGtrtjtR5nN0yqyu8Nmi7ZrNbo7oWElg35urC8gtf+Ne5e/J9\n+40yvHZPuFJG89zzL7C4vGSzXXF29hBrLNOjGSfzOU3IeQta5SangyhJSlLkBglH9xPaXyMiG9Eo\neUohJkKSpjRaK3QW4mowsH6D9tbTtWemFXVdo5Viubiia1uWiwVH0yNcIQLhJm+kz2PQSuoaaHUI\nQw/nY49vfRfJ8KGGFvuij33IQ6MwhSMQ8cFDl3nVXUvMmt06x2R+ijWG1dUVbbMlKYUrHEVRoXNV\nIrSVsJE1OYtLuN199KIoSoyWLLFEyDXjFE3TcnFxwWx+iislEyylJOCiFUZi78q0IUg9+8wIK5zF\n68RutxNfWGmms2PmywWb7ZKmXdN1LT4ERtMprdF7GnDPSQ9I9V1rr3fvCRyaizJ43XEAFIdZmv2C\n6LWI+MHy3SEnoV/8fWShSZFVs8VFx3Q0otR2f749JyBKTYCkB4j34Jq+aXOFnyjmdGjpdku69orN\nasWuXdCpSx6svsJGP+SibbFYxtUYY24xqub4dkZRFsyLMe8++iY+XFJVkdU5fOET/wY/+f3/DEo7\nxLmTZqQ6N1dRSp6N0lIafDaf45yl2S45P3sISjM7OsJaTZd9mJQzEYdRG6VgNJkwnkyuCbvI9c2W\n8gM04j9es556ATOc787L2rDmUCOhv3ZVSZ/FFCOXF1cs4oLp0RRXFPjgs/uqcp3DQaVkdV3IMBxD\nP8bvwjb+8NyEaiRZVQnCagUhokIkqZBNNIUrRxirMPWIs8ePcTEydkfYomZ+OkKbku1qie8aNssF\n02OLtY4QY3Y15H+hYAKqDy0qjNbY0hKCpfMt2jq0KdCbLW3n2WzXTJ2T1mQZrREro4+E9xaCLPg2\niOXgColWhCj176wz3Hv+JR4/fshieU5RaJpmBSim8xleawa4IU4J4aj1QQQZhw3bb8aeydcnJfWL\nc4gbDBfB/vTq8F6MkV3nsVpTOkuhxIwtrGVUF0KgiR6n3T686UMgeqn9p7VmNBqhTF/+/bAQ2xBo\nmy2FFe78dnNFs3nMcvGQtvN06ZJl902W3Ws4V/LqCz9CF0qKegKmQEVLE7c8vHib8WjMaptoQ6RS\nL/DDL/4i//RP/JuMiuMbm1LtsYo9gp4FYVCKejRmPj/lwYP7LK7O6dqW+ektSZZKGh8CxuoD8Ukd\n6hLugcM8jT1eM5zrXli4QSbl0ywmOY+sH6kEJWNtu4bCWowxlFVFSomJjywWl4SrwHQ63dc77AWV\nEOpSthrU/jkMLY7+EBLSB5sGH15HpQTjskYrTQyBzXpL27bgwdUVZH/IGEdVFrhqxW67we0KKlOi\nyxEnd+5xae+zXS7FFN1tKcdWkj6yRI6dp6gqbFnQdq3UsxvQdLQ1OFsRQ8KYiFGWygdZxNu1hH+c\npLQmJEqhMpdBIcxHRcSZMoNIoJTGWY1GaM1tSkzmt1FGc7U4x9jEZrdCryy2rCQlWw0RaE0Tw95P\nv1k9qEW6RipEIAx7EsBAs5EjBPl1n9HXn1fdWDV9IpMxBdoeNnigBx6lZPdqtRINGDyj0XjvS+83\nROGE8Zk8bbNhuzpjs7xP165p/ZZNfMROP6S6Hbs6AAAgAElEQVRREW1e5N6tl2m6xNVqSdMsKeyE\nLizxesub713g25LPv/pLfOL2z/JTn/4So2JGSJJk1T9Ha65r7MIK+NaGgDWGoDXT+Smb3Zbzxw+p\n7lrWV2e4sqKuRuik2LUdReEOAjbjCP3cDNmRPZrfz7cma+eUN7y6/kyGm9RmmnTIhUeMUjQx0nhP\nmSNZKMV4OgadOD87I8VAWRZMj+bCZ1DCpOyZkSEkSeFX14XP8NAfLAs+PGHQtS3NdsN4PKGsxyhT\n0DSttBDvOqqqkvLO0eCT4mh+Itl+xolcVWBtye0791iW5yyvLtnt1lhnKaoRXYK2ayGCK0qRtgq6\nps3kF7UX9UZJS6rgA9oZjBVG2raROou2tvgYBACKYGyunYB0TGpDoiAQ05Kr9RXVaI5VUwpn5JpI\nvH82vyVJTSmK9bFaUcVEUVg8igxv4DQoba71EYTDwxpaAkPO/HDBMvjdMBV3jydozSg31ugPq2RB\ndyqHyvKq32MMRmOqknR0xGazYbVZo7TGGMF9+iKxRksYtusSMSRSSLR+yzq+izdrutSQ9BFV4WiD\nptl2eN3gwyWNf8DlynO1OqNyc169++O8fPeH+dyrP8np6GMUyuQ5keKgw9j5kBE5dLt6+jna8Mzt\nuyQf8LsV5/ff5vT2Xdp6wmR2ggpPJ/cM5yhj3sJIHMznfgNmgRAHnw1lbv/dHnNQiItSFAU+RELO\nh5BzScPUFBNdu6NrGy7OH1OPJ5RVTeJQbzOq66O+eQ/9+vig40MTBlVZSrfYpCirmqKqcPWIrmkI\nvkOhsFoYhCFErCuYHM2kYqzNPeZioKoqTu88hzKOxcUZi9WCqTG4UgQCGhKRZrulbRpC6ICEc1LC\nXBqyGql56AyJhMrYQq1zP3vIYU6JFuiMIRijSKEgelmY3q+IaYU2NaQJbUpUiLmprWLrW2bHp6gQ\nWK/XPD47JxSWdm0oxyN8VGJZDB5Mv5B6AfC06MHQMnhaX8bhYuxtoi4mtj6JYNOHvIUyuy0+xoOQ\n6Re+gmQMo6mU81pvNnKerhOWXU9V3u8ehTGKuhrh4wkpbUCPUa1jtdlQ+BVtXOO3LcrCxExwVcls\nXPLynVvcHn8fn3z2s9yav4xTDmIippA5GVK2PtwQCP1c9ULRGYNPKWMJEv25dfd51pePMMWOkBLN\nZkXShsn46Np89U1srMm4Uy9U+rnguiAY3Pb7Wmr9/30kQMYr7NZCaynIkg5z6L1nPJkQQs1yuWBx\neUEIHpUS9WhM0nrPbARxG/roRT+GmOKgl8f7Hx+eMKgqvLd0PuB3u33IrxqNiCGQYiAloVm2TUtV\nl7ii2CO7RmuICd9FyqpgfnqLGBOL88dsri4ZTaGoj0hKk5Tkwe92DU27FbzAWFCKEKRUtrZgiuyF\n+wAqYp0wCGNMmMLm/IEczVYyhlIXNNGj0Fh9TKFnGF1ISXUGG1tDpzUdMLKWyXjMZrth10p5dmeg\nqsd0HDT4sIefj4fEFTgsLMuhWxFcT1rqvzdcGMPfBQ1tjKiQGLlDVR+LRD72AY6Y8DEzFSW3C1sU\nHBlJj01aOgPZvIKt0igLMTmSLQlERrbi+OhTspC9IhyXrH2gDVsavyYYjy1qSjdjOj5hWt+isjMs\nem/eSsjOCvEKifX7eN0a6o9h3kbP3hPtKMK/ns6pJ3Pa6GkXSzaLFSqAq2vqutrPYR/+huub+v3A\nxKfN9XBM/fv79mhK3K/+gz4bYo9TaC1JTM5Rj8YoBZvlgouLM9quZTqbSa8QcmGUgetyGKd66nhu\nHh8oDJRSLwB/G3gmn/e/Sin9Z0qpE+DvAC8BbwD/fErpMv/mbwD/KrI+/3pK6f982rlTlDbp0+mR\nUG4ThLaTCjjO5AwrhYmBuNvRtZ6qrLDO5O5DkaSFedY0iaoqOZpN2S0eEXaXbPwGpaEYz4lJeiq4\nsoDUQrfDeItzo5zMYzDaYpDwmnYm16rPjStiREfpqNPnih20gMrjjWg9ljBPXjEua4ielTe2h+k2\n1nJ6csqji3O2ux1XK1hvN7iy4ng6peO6n2/UkxYBg++Qr9MmuW4MkfVux2Q82n9veGgtpc27KIk3\nw3qCw4UNspl86Nh24lO77FdrYyiMyQVTDr9RcgGJ/beymEejIybjE9quofM7xtN7nOiKLnkIAaUT\nxjicKbHK7BdyiMPGMnnsg0xOo9Q1TXrY9Aet3c9hn26srCFFIYAVpkajaDdbdrsNXexwTuOMNLWx\nzu3L9rNnuR7m6ea1hvNwc94TOZOQ62HG4W80as9/SFpA6369VVVF4RyFK7i8vODi4oKUEkfTOdrm\nUGY/1uF8fY+kow8sbqKUugvcTSl9VSk1AX4f+CXgXwEep5T+plLq3weOU0q/qpT6fuB/AL4A3AP+\nb+CTKV1PnlRKpW987XfpfGB+fExICh+CEEq0oa7rnFMgkrHX6NZaXFHIBOWQkNZGyDNlQUwdFw/f\nolk8xncdbnLCaHabopgQU0763D1m++h1VNgxOb5DNX8BO74jNfjzSta2yBZFhJT29etdUUifhCSm\nv+3j8qpvhwWF6TsmyabsE1+C94x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FzzNvLUUeNPY7UYZSIpOA1VfGkiSSHlslWq/o8cor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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f75d05ad810>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_index = 1\n",
+    "image = test_net.blobs['data'].data[batch_index]\n",
+    "plt.imshow(deprocess_net_image(image))\n",
+    "print 'actual label =', style_labels[int(test_net.blobs['label'].data[batch_index])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top 5 predicted style labels =\n",
+      "\t(1) 99.76% Pastel\n",
+      "\t(2)  0.13% HDR\n",
+      "\t(3)  0.11% Detailed\n",
+      "\t(4)  0.00% Melancholy\n",
+      "\t(5)  0.00% Noir\n"
+     ]
+    }
+   ],
+   "source": [
+    "disp_style_preds(test_net, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also look at the predictions of the network trained from scratch.  We see that in this case, the scratch network also predicts the correct label for the image (*Pastel*), but is much less confident in its prediction than the pretrained net."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top 5 predicted style labels =\n",
+      "\t(1) 49.81% Pastel\n",
+      "\t(2) 19.76% Detailed\n",
+      "\t(3) 17.06% Melancholy\n",
+      "\t(4) 11.66% HDR\n",
+      "\t(5)  1.72% Noir\n"
+     ]
+    }
+   ],
+   "source": [
+    "disp_style_preds(scratch_test_net, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course, we can again look at the ImageNet model's predictions for the above image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top 5 predicted ImageNet labels =\n",
+      "\t(1) 34.90% n07579787 plate\n",
+      "\t(2) 21.63% n04263257 soup bowl\n",
+      "\t(3) 17.75% n07875152 potpie\n",
+      "\t(4)  5.72% n07711569 mashed potato\n",
+      "\t(5)  5.27% n07584110 consomme\n"
+     ]
+    }
+   ],
+   "source": [
+    "disp_imagenet_preds(imagenet_net, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occasionally down because it is run on a research machine.\n",
+    "\n",
+    "http://demo.vislab.berkeleyvision.org/"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Fine-tune the ImageNet-trained CaffeNet on new data.",
+  "example_name": "Fine-tuning for Style Recognition",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  },
+  "priority": 3
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/03-fine-tuning.ipynb b/examples/03-fine-tuning.ipynb
deleted file mode 100644
index cc90b16b..00000000
--- a/examples/03-fine-tuning.ipynb
+++ /dev/null
@@ -1,947 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Fine-tuning a Pretrained Network for Style Recognition\n",
-    "\n",
-    "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\n",
-    "\n",
-    "The upside of such approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful feature that you can treat as a black box. On top of that, only a few layers will be needed to obtain a very good performance of the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we will need to prepare the data. This involves the following parts:\n",
-    "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n",
-    "(2) Download a subset of the overall Flickr style dataset for this demo.\n",
-    "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.chdir('..')\n",
-    "import sys\n",
-    "sys.path.insert(0, './python')\n",
-    "\n",
-    "import caffe\n",
-    "import numpy as np\n",
-    "from pylab import *\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# This downloads the ilsvrc auxiliary data (mean file, etc),\n",
-    "# and a subset of 2000 images for the style recognition task.\n",
-    "!data/ilsvrc12/get_ilsvrc_aux.sh\n",
-    "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n",
-    "!python examples/finetune_flickr_style/assemble_data.py \\\n",
-    "    --workers=-1 --images=2000 --seed=1701 --label=5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's show what is the difference between the fine-tuning network and the original caffe model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1c1\r\n",
-      "< name: \"CaffeNet\"\r\n",
-      "---\r\n",
-      "> name: \"FlickrStyleCaffeNet\"\r\n",
-      "4c4\r\n",
-      "<   type: \"Data\"\r\n",
-      "---\r\n",
-      ">   type: \"ImageData\"\r\n",
-      "15,26c15,19\r\n",
-      "< # mean pixel / channel-wise mean instead of mean image\r\n",
-      "< #  transform_param {\r\n",
-      "< #    crop_size: 227\r\n",
-      "< #    mean_value: 104\r\n",
-      "< #    mean_value: 117\r\n",
-      "< #    mean_value: 123\r\n",
-      "< #    mirror: true\r\n",
-      "< #  }\r\n",
-      "<   data_param {\r\n",
-      "<     source: \"examples/imagenet/ilsvrc12_train_lmdb\"\r\n",
-      "<     batch_size: 256\r\n",
-      "<     backend: LMDB\r\n",
-      "---\r\n",
-      ">   image_data_param {\r\n",
-      ">     source: \"data/flickr_style/train.txt\"\r\n",
-      ">     batch_size: 50\r\n",
-      ">     new_height: 256\r\n",
-      ">     new_width: 256\r\n",
-      "31c24\r\n",
-      "<   type: \"Data\"\r\n",
-      "---\r\n",
-      ">   type: \"ImageData\"\r\n",
-      "42,51c35,36\r\n",
-      "< # mean pixel / channel-wise mean instead of mean image\r\n",
-      "< #  transform_param {\r\n",
-      "< #    crop_size: 227\r\n",
-      "< #    mean_value: 104\r\n",
-      "< #    mean_value: 117\r\n",
-      "< #    mean_value: 123\r\n",
-      "< #    mirror: true\r\n",
-      "< #  }\r\n",
-      "<   data_param {\r\n",
-      "<     source: \"examples/imagenet/ilsvrc12_val_lmdb\"\r\n",
-      "---\r\n",
-      ">   image_data_param {\r\n",
-      ">     source: \"data/flickr_style/test.txt\"\r\n",
-      "53c38,39\r\n",
-      "<     backend: LMDB\r\n",
-      "---\r\n",
-      ">     new_height: 256\r\n",
-      ">     new_width: 256\r\n",
-      "323a310\r\n",
-      ">   # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer\r\n",
-      "360c347\r\n",
-      "<   name: \"fc8\"\r\n",
-      "---\r\n",
-      ">   name: \"fc8_flickr\"\r\n",
-      "363c350,351\r\n",
-      "<   top: \"fc8\"\r\n",
-      "---\r\n",
-      ">   top: \"fc8_flickr\"\r\n",
-      ">   # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained\r\n",
-      "365c353\r\n",
-      "<     lr_mult: 1\r\n",
-      "---\r\n",
-      ">     lr_mult: 10\r\n",
-      "369c357\r\n",
-      "<     lr_mult: 2\r\n",
-      "---\r\n",
-      ">     lr_mult: 20\r\n",
-      "373c361\r\n",
-      "<     num_output: 1000\r\n",
-      "---\r\n",
-      ">     num_output: 20\r\n",
-      "384a373,379\r\n",
-      ">   name: \"loss\"\r\n",
-      ">   type: \"SoftmaxWithLoss\"\r\n",
-      ">   bottom: \"fc8_flickr\"\r\n",
-      ">   bottom: \"label\"\r\n",
-      ">   top: \"loss\"\r\n",
-      "> }\r\n",
-      "> layer {\r\n",
-      "387c382\r\n",
-      "<   bottom: \"fc8\"\r\n",
-      "---\r\n",
-      ">   bottom: \"fc8_flickr\"\r\n",
-      "393,399d387\r\n",
-      "< }\r\n",
-      "< layer {\r\n",
-      "<   name: \"loss\"\r\n",
-      "<   type: \"SoftmaxWithLoss\"\r\n",
-      "<   bottom: \"fc8\"\r\n",
-      "<   bottom: \"label\"\r\n",
-      "<   top: \"loss\"\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!diff models/bvlc_reference_caffenet/train_val.prototxt models/finetune_flickr_style/train_val.prototxt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For your record, if you want to train the network in pure C++ tools, here is the command:\n",
-    "\n",
-    "<code>\n",
-    "build/tools/caffe train \\\n",
-    "    -solver models/finetune_flickr_style/solver.prototxt \\\n",
-    "    -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n",
-    "    -gpu 0\n",
-    "</code>\n",
-    "\n",
-    "However, we will train using Python in this example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iter 0, finetune_loss=3.360094, scratch_loss=3.136188\n",
-      "iter 10, finetune_loss=2.672608, scratch_loss=9.736364\n",
-      "iter 20, finetune_loss=2.071996, scratch_loss=2.250404\n",
-      "iter 30, finetune_loss=1.758295, scratch_loss=2.049553\n",
-      "iter 40, finetune_loss=1.533391, scratch_loss=1.941318\n",
-      "iter 50, finetune_loss=1.561658, scratch_loss=1.839706\n",
-      "iter 60, finetune_loss=1.461696, scratch_loss=1.880035\n",
-      "iter 70, finetune_loss=1.267941, scratch_loss=1.719161\n",
-      "iter 80, finetune_loss=1.192778, scratch_loss=1.627453\n",
-      "iter 90, finetune_loss=1.541176, scratch_loss=1.822061\n",
-      "iter 100, finetune_loss=1.029039, scratch_loss=1.654087\n",
-      "iter 110, finetune_loss=1.138547, scratch_loss=1.735837\n",
-      "iter 120, finetune_loss=0.917412, scratch_loss=1.851918\n",
-      "iter 130, finetune_loss=0.971519, scratch_loss=1.801927\n",
-      "iter 140, finetune_loss=0.868252, scratch_loss=1.745545\n",
-      "iter 150, finetune_loss=0.790020, scratch_loss=1.844925\n",
-      "iter 160, finetune_loss=1.092668, scratch_loss=1.695591\n",
-      "iter 170, finetune_loss=1.055344, scratch_loss=1.661715\n",
-      "iter 180, finetune_loss=0.969769, scratch_loss=1.823639\n",
-      "iter 190, finetune_loss=0.780566, scratch_loss=1.820862\n",
-      "done\n"
-     ]
-    }
-   ],
-   "source": [
-    "niter = 200\n",
-    "# losses will also be stored in the log\n",
-    "train_loss = np.zeros(niter)\n",
-    "scratch_train_loss = np.zeros(niter)\n",
-    "\n",
-    "caffe.set_device(0)\n",
-    "caffe.set_mode_gpu()\n",
-    "# We create a solver that fine-tunes from a previously trained network.\n",
-    "solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
-    "solver.net.copy_from('models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
-    "# For reference, we also create a solver that does no finetuning.\n",
-    "scratch_solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
-    "\n",
-    "# We run the solver for niter times, and record the training loss.\n",
-    "for it in range(niter):\n",
-    "    solver.step(1)  # SGD by Caffe\n",
-    "    scratch_solver.step(1)\n",
-    "    # store the train loss\n",
-    "    train_loss[it] = solver.net.blobs['loss'].data\n",
-    "    scratch_train_loss[it] = scratch_solver.net.blobs['loss'].data\n",
-    "    if it % 10 == 0:\n",
-    "        print 'iter %d, finetune_loss=%f, scratch_loss=%f' % (it, train_loss[it], scratch_train_loss[it])\n",
-    "print 'done'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's look at the training loss produced by the two training procedures respectively."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fbb36f0ad50>,\n",
-       " <matplotlib.lines.Line2D at 0x7fbb36f0afd0>]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-       "ZGZWR3rl4iMzM+sfDnUzszriUDczqyMOdTOzOuJQNzOrIw51M7M64lA3M6sjDnUzszry/wFBsEB8\n",
-       "UlvRigAAAABJRU5ErkJggg==\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fbb37f20990>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(np.vstack([train_loss, scratch_train_loss]).T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice how the fine-tuning procedure produces a more smooth loss function change, and ends up at a better loss. A closer look at small values, clipping to avoid showing too large loss during training:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fbb347a8310>,\n",
-       " <matplotlib.lines.Line2D at 0x7fbb347a8590>]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-       "MNz09TAVr7gy10TEmfznJGnx4DHgnKTFAJJup/iK75Z0il9rf70rH7MOIuJ8ZMA7XHlk4FgWIGke\n",
-       "Kbm/HxG78+FS+ucgEvz/hVKS5pMKpfYM4HNrQdICSbfk/ZuB1cARUgw35Ms2kKaHsOI6xW8PsE7S\n",
-       "fElLgWXAvgraN2fkBNTwEKl/gmPZkyQB24GjEfFG06lS+mffl+yLDoVS/f7cGhkCPk39gJuADyJi\n",
-       "r6T9wC5JTwATwKPVNXF2k7QTuA+4TdIp4GVgG23iFxFHJe0CjgKXgafznanRNpZbgHFJK0iPCk4C\n",
-       "jSJIx7K3lcDjwGFJB/KxTZTUP13oZGZWU16T1cysppzgzcxqygnezKymnODNzGrKCd7MrKac4M3M\n",
-       "asoJ3sysppzgzcxq6j+vUsbacqJa4gAAAABJRU5ErkJggg==\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fbb37f207d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(np.vstack([train_loss, scratch_train_loss]).clip(0, 4).T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's take a look at the testing accuracy after running 200 iterations. Note that we are running a classification task of 5 classes, thus a chance accuracy is 20%. As we will reasonably expect, the finetuning result will be much better than the one from training from scratch. Let's see."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy for fine-tuning: 0.570000001788\n",
-      "Accuracy for training from scratch: 0.224000000954\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_iters = 10\n",
-    "accuracy = 0\n",
-    "scratch_accuracy = 0\n",
-    "for it in arange(test_iters):\n",
-    "    solver.test_nets[0].forward()\n",
-    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
-    "    scratch_solver.test_nets[0].forward()\n",
-    "    scratch_accuracy += scratch_solver.test_nets[0].blobs['accuracy'].data\n",
-    "accuracy /= test_iters\n",
-    "scratch_accuracy /= test_iters\n",
-    "print 'Accuracy for fine-tuning:', accuracy\n",
-    "print 'Accuracy for training from scratch:', scratch_accuracy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Huzzah! So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n",
-    "\n",
-    "http://demo.vislab.berkeleyvision.org/"
-   ]
-  }
- ],
- "metadata": {
-  "description": "Fine-tune the ImageNet-trained CaffeNet on new data.",
-  "example_name": "Fine-tuning for Style Recognition",
-  "include_in_docs": true,
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
-  },
-  "priority": 4
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 663d7360..43bbcb83 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -19,11 +19,12 @@ foreach(source_file ${examples_srcs})
   caffe_set_solution_folder(${name} examples)
 
   # install
-  install(TARGETS ${name} DESTINATION bin)
+  install(TARGETS ${name} DESTINATION ${CMAKE_INSTALL_BINDIR})
+
 
   if(UNIX OR APPLE)
     # Funny command to make tutorials work
-    # TODO: remove in future as soon as naming is standartaized everywhere
+    # TODO: remove in future as soon as naming is standardized everywhere
     set(__outname ${PROJECT_BINARY_DIR}/examples/${folder}/${name}${Caffe_POSTFIX})
     add_custom_command(TARGET ${name} POST_BUILD
                        COMMAND ln -sf "${__outname}" "${__outname}.bin")
diff --git a/examples/brewing-logreg.ipynb b/examples/brewing-logreg.ipynb
new file mode 100644
index 00000000..0f87185a
--- /dev/null
+++ b/examples/brewing-logreg.ipynb
@@ -0,0 +1,1164 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Brewing Logistic Regression then Going Deeper\n",
+    "\n",
+    "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import os\n",
+    "os.chdir('..')\n",
+    "\n",
+    "import sys\n",
+    "sys.path.insert(0, './python')\n",
+    "import caffe\n",
+    "\n",
+    "\n",
+    "import os\n",
+    "import h5py\n",
+    "import shutil\n",
+    "import tempfile\n",
+    "\n",
+    "import sklearn\n",
+    "import sklearn.datasets\n",
+    "import sklearn.linear_model\n",
+    "\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZsmQhkpQhEGjir//6Ty6pBHu1mI5x9/v9yLKMy+W67OvV7Xbz29/uJBBQOHLk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\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luroFURRRlDwTE+1cdlkFb7xxknRKxGi0YTIbaGx0cPfdX/pY1lH5pP9RUhSFn/zk/5BO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BaDQabrr9dqp7euizWvEFArT399PgdGLKyOCDrVs5sH8/DodjzHaSJNHW1sbJkyfp7e4e9yJQ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S5dN9iyZT8PP/wEx47d8IYo2UgJ/+2/we///pXek++HEIk6cvw4vOMdV3pvXjtc0UrZK43z\n/iBks1mOHTvMmTMbDA7uJJMpsLh4gW9+8z66XZU/+ZMHeOyx57n77rfw4Q+/k7/6qy/Sap1ESigW\nVTKZMkIoqKrGLbfcyOjoXXz5y39Kt3s/y8s211+/h9/4jd++LE2Xy2UmJ3OcPfkCvpfBiWK6NPom\nZ4n7oguMkFCBUaBCUnaJSehDmwwxOc4jcdkDVJB0oN+uOoLDDCUMqqRxcOOAOF4BxkhSbmwS9eMU\nUOubhpsk12NW/zYDOCT9v2eBEaQc6G/LIESMlF0MYwAhUrjuBRTlPEKMoesb9HozSDnP0aNHGB62\n6HZzTE295OmQTh/g1KknmJ2dZWpq6sde5x8H9UKBjbk5UiQUKzU2xpt+zE6ujY0NHnzwCZaW1lDV\nmLjZJJCSKI5RFYVyLsfK5iaNdg8tN8L27ddy7sIpLtUFz33LxbKeI5MJsQsF6gsLlKVOmogSm8yj\nELIHSQmDJQxmKNHGIkajh4NFgEIenwoqLyBYpExAiM8KDYr9huQYaNLFIsRGJyCPhoKP6Heb5Ihp\nA1UCLOoUqLNCD4cCAQvEOJh4+LTQGSaDQBLhEbCCoIVGQIoO4yhsQcchYppmKIh7PcqKhSN0DM/A\na7XIGAbdKGKlqaEaJsGrQDxLpRKln+LMYhzHl4lIFEV84esP8NhZhzDYgRfMsjYfstqZR1Ha7M6k\nWQlbtIRKPpYESoCl5bnU7rDuCxq9OlcjKEmFbujj4FFD5bCRxws3WZIesZQM4tLou4NU+wrERSo0\n2IrspxLFzJMkeafQWabHJh5NbDp94zkLi5gOPULyCGIiukgEJh4eAZBGoYFCCwUTnwv06PQTkTqE\nLDClR5TwSKkWmzKNHYGCZEitUI8j6nKFafykfV4k6pwVqnQjk43VVapDR1FDC8Vq4lSzLAG7Dx1i\nZHQURVE4t7rKte961+Xj7bounicxjJeXxnXdIAw1ut3uG4KMPPxwoj685aefVvFTweHD8NRT/0pG\nXtdwXRfbLlMqDVGv1zhx4jSFwkFSqRDTbFIu7+ev//o+fvM3f4WPf/w3mZz8HNPTNTKZm3niie+w\nsQGmGbB161X0ehu8//1v59//+19GUZTvu/o+MT1NoZhnxX2SfKyQEIhEl3jR51WSZI/kSALRIKEP\n8yQJvj16dFDosgNJGkm3X/3PsoZLi5AuENAhokEcByQdA8MkNmhpkjLLnv47qP3/X9N/h27/cR6J\nMXWRpIC0DKwCIyhKFlhFVXcQxwG2bTE8nKZWm8W2C6RSKpOT13L11ZOk0zrnz4c888wpcrk0IyPD\nmKaJrpeZn1981cnIRz76UWZmZmi1WpRKJSYmJr4vDfgH4dSp03zqU/dRKu1mYuJqms11Hpp7gikL\nXqjX2VEosL1a5Svz84hAsmt0G7NLMzw+s0jDuBbPr+B5LYSocvLi86SlQYmYq7F4BIUyQ3Tp4tFg\nCI00g2g0GWSTNVxUXApYaJisYdCmiMIuAtYw8PEBnx4qHQRdhghQ8VnDZxWJjsIyEYMIAhRcJFuR\nbJIQ3gnq1GiQRusnQqdZ6Y+TWggy+Ogo1MggGMUlj0aOkAY+HgZVnLBFQ7bQjAARhJgyj+6HiDjG\nVhQCx2HGaXHH+DhPPfUUruOgKAo7du68olbgvV6PS5cuATAxMYFt2+y+9lqmv/hFBvJ5VlZWeHa2\ngRBTqEGdgfJuNppnsRTBkBeT11z0dIrhOOZS1GM9ytP0R9gMbPJ+gy2yzCZtXJIcqBVCGoQoYciE\nnkfxlhlAINU0M1GEisJFFFZR6DCEQxENA4GKygg+Z8liEeKi02AADwuNYQwcfFbQiBA4eGj0EMz2\np2uSdBqBh4ZHlXXy6LjEbFDDIYdHizQRNjliW8UhIPJdcoqCkBEbUZecOoAhUlixwmqsYKfHcMIs\ni76Hox5C1ZqE0SpRNA6UeceH3s/86dPU2m2aCwu0gLGDBzl0+PDlNUiUMBXX7WJZ6cvbfd9F1+Mf\nWKp7PeG//lf49V9PRmlfjzh8GD7xiSu9F68t3nBkZGhoCCm/jZSSxcWLaNoQqmrQam0yPl7uf0EG\nOHPmBW688Sgf/OD72LNnmkcfPcHOnRqbm2cZHt7G+vo0Y2M57r77Pd+Xl7GxscGnP/lJ7v/0p/Fr\nNZQ4pkHMGLCThAKkSEozs8Aa9OdmktO/6P+dRMb7tLEQZFHw+8N6HhoDhGRwKRCTx2eZgDKJ2lEm\nUTsWSWjPi3m9FsmSdUnKMSoJ6aD/uFT/eWP9PQHYwLK2YBgZQOA4s1QqDr/wC3dxzTU7cRwXwzDY\nvn0rpVKJj3/8D7h4MUexuJUgWOL552c5duwQUeSSSr36VtCqqv7Ejo9SSu699yEqlX2Xzdjy+QFu\nePNHOP7QnyPyJucWFoiBzNVXs9HRWM2WOL18mmbmCEQVbD1HFPloWoYoHCEmwqfOHCEuKQxs1L4Z\neA4TgY6DhSBRyEJgEpilxxlcJBV8uoRkUPppJTDOAM9QxkKjRwqVHDku4HKBFAE6Nuvk0RCEbODi\nAxEK54gZQhCi0UWQwmErIYu0KaNS0VN0Y5XlKDmVpZUsKFl6UUQsczhIenQZVTMMqR1CbwMosClV\nrEiS0nVavTautk7z6af571//OtlymYNXXcVTisKRO+/khv4E02uJEyee4UtfepAwzCKERNPu5T3v\neTNXX3MNZ0+e5PjZs2wuLLHZC2m7TabKI+RyZS46KwxqdTrYzMuAPSMjpEyT2soaSjxFt2OSNlSq\ngU5O5okQGOioaEh8HBqsxgGGTOIZVCTNyCdLiiYqOiEeNh5Jkm3ctyZLCm5pDFax6ZDtJ9OsY6AR\nU0DDRmMdlS4uCj0yaH1qWkYhg0aDUYoU6KBhY2GTw+McG5TpMU6aiaBDGLq0MxXi/ACN7gb5UNCT\nBlG8TE5RuYBgVdMpKAU2wx6OMoUu8vhBjONcYv/+G+l0YGFhmf/tYx9jZmYGx3GoVqsMf0/Tgqo+\nWyreAAAgAElEQVSqvPnNR/niFx9neHg/tp3BdXssLT3HnXceek36yv6lOHMmUUbuuedK78k/jyNH\n4CMfgTh+/RKmnzbecGRkeHiYgwcnOH78aZrNOlJmaTRWsSyXiYnEiVVRdBwnCftSVZXDh6/j8OHr\ngCT2vlarYRgGlUqFdrvNffd9g5Mnz2NZJppwOPnoo1x85BEG63UarRbjJAN5ZRICIkhO/QYv6RAO\ncIGkayPs3y+hcADJCuvMs4YkhSDAooqCRoMmEUUENj6bJCSiTjKWO0ZCQs6SLJPsv7pJMhXd6t+H\nREWJSEhKUhxKtBoDVX2WIDAAE0U5z9DQJn/8x7/DsWM3ceLEs5w5c5FcziCbzXLffd9maOgAtdpZ\nbDtFNluk06nz5JPH2bNHZ9eul3tHvN7Q6/VoNFzGxl6edjUwMMLe636O9773FhRFIZfLUS6X+cM/\n/HPK5cOcW/kkVjhMqxUhpYNtZ2g2m8RS6Se3jtCg0/d6EAgEFhARoRGi4DJKshovADYRFholFCR1\nPAroOAg0AgIs5ihjIvBpIgn7Vt8WaVIM4JCiQ8AwIRmSVF4NldOEfZ+SEh4KNjE6bWxabAIrRHQD\nH8M0WI86hARYIsCPJJrIEksfjzYmA2wEa3R1SYYYTVvBUcdw1RTSCLHMJlsNgVqvc9fUFDP1Ot12\nmyN79/LE177G1u3bXzZZ82qjVqvx+c8/yNDQdZhmQohdt8vf/M39/Mf/OMz7PvQhzp49y71f+SrB\n9Aq2USDWQi6uvUCn12CLnqdYSpHKCDzLwjQMEBrG4Ah7d1xFc/lptIZEUQRRnMYgREPDwiPCYgOV\njN8hR4gQKo5UcLEZpcQiG2wSohHi4vSHem1MIKDJID6FfnG1i+AFMtSABh00VDbRKRDSZR6FASRF\nMnTpsoIFlMgi0YlZxUJBRzJFjwAFnVZSfpEm+bbHhhEh7BwLTpcBs4jvR6zZZdZji06oEXEVnlon\njioYiiStBlhqHsPQ0HWHOFbRdf1lsRuvhMOHDyGE4P77H2N9PcS2Nd71rus4evTID3ze6wW///vw\nG78B6fQPf+yVwtAQFItw+jTs2/fDH/+zgDccGQF497vvYmJimi984R+4dOk0+/ffxLZt+7EsCykl\nQbDB1NThV3yuruuMjo7S6/VYX1/nL//y8zSbOQYGdjEzM8/0Qw9QiC5Rchyqqko9ivpuDS+d4oF+\nRTchJCZJacYk6XOoAVOo7MLGIMYgJmYFGOr/xBj08JF08ciRKB4F4ByJsqGSNKh2+u9Y7d83++9c\nJWmP7ZK0z7ZIlJBJEnXkPDCGYSgYRo5KpcnAgM11113Nxz72USqVCv/jf3yaRiNDoTDE+nqP48e/\nxPr6Ajfe+EuoqsGzz04TxzmkjGk0TvOf/tP/+bqXYJNyksD33ZfVtKMoRIiIXbt2vazZ8u1vv5kv\nfvFh0mmTpaVL+H4K09QQYhDPa6Eoddx4EJdlzP74bosmBnY/E8ZD0qFAh6QQlhTUYkIyWBRRqAOB\n2AQJtpgCuURMBY11BA4mGXQqQNxPNkmTIsMGJgPo2GhUcJHYrKHi9LtTICaFR44YA50RAjLAU4SE\nQRebEEObZSNcxWE3hhwiYgUbFRsXU1oEbmKwd0QLwFpi3c5QMRSKls53WoKFtTVUKanm88zNzXF4\n3z4GVZXz5869pmTkuedOo2lDl4kIgGWlEaLCqVNnuOWWY+zdu5eJiQlOn1/ha199nC7jpI0tBLgs\nd8+QMkMMV2OgUsELAuoywvPWEKuPo0UdNkQHM9IwCFGFhpQR6/TwKPf7es4xiKSjalyKDMawQQpi\nJNtQaONho9OghQ+E1BmgSx4Q5IlokUZSJWQdgyyDtGiygcCghE0bhwtIRlDIMESKNl1iIlRU8iiM\noSKQ+Ch9Q7uYOjlsJCY6gd9lKbbIjb2VpaBDp+2SKQ2xdfgavvP0V/H8RbLpcVqdDkFUR1cNPHed\n9fWTjI/nuOqqH02RFEJw+PAhDh06gOM4WJb1hhnpnZuDr34Vzp+/0nvyw3HzzYmC869k5HWMF9WO\na6+9hk9+8q+ZmfEIQ5dms8vm5hz79w8yOTn5is9dXl7m7//+Aebm1pibu0C3W+DWW/dj2zYbK+uM\nZCc5e+YZrhWCdhDgk5xgKiQlmRRJgeTF/pBNXiqoeEBMiWHSWGywiQJE2MRUqXEWjw5FBD6CgMQI\nfASJQVLsyfdvCyTtsZP9V10haYmtkFCdNgkRCUmIjNLfC4uEFklUdRPLCikUNO644yj/5b/8zuUT\n8b333k+zmWN8/EU/kRK2nefJJ49z8GCPyck9DA9PUq8nRSfXTbHt9Tb/9grQNI2bbrqG++8/zcTE\nNSiKShzHLCyc5siRlxORKIoYHBzgLW/ZTzodsLR0L4qSQspRms1LqGqLTKaM67ZZCnoMyRI2ApcV\nWkIQShONDlM0qOCzBKyjMIROB4WQkC4ChS6VjGDOKRGGbWwydMjSZpEBXFR0JBKwqNPuu88oxOjM\n0KFEi5gUbl87kZRYoUkOiY5HFo05AkAlhU0JB02CZWTRfJMqERc4TZN5LGw0TFKKJEuRHE1Q4FTQ\nY2cuxWQ5Q7fb5XS3SzYIGI9j7GaT5zc2aGazRHGMIgRR+KMZlf200O06aNr3N0VqmkWn07t8P51O\n85GP3M0jjzxPfdknjmoomo6fz7HcXmP7WJXRwUHO1+sUilmytVly6e2kM0NcTM2z1F5kEIkuU2zg\nMYtGlwolbRVNh5UwR2CotB2YiQMKrJInokKBeZZwSJGigMo5AjpMAEWyuARs0CJHhNVXTnrACgaC\nQwTEOJxFUkHBJGSUiHbf+H2FYt/3VUMlJKKOoIyCQCGkQpsuHdKsotMSu6BlEwQuup4nnU2haSuY\nZo+hgkraNMhoAV6g4oSrCLHC1q23smPHAAcP/ngW8IqikH49ywuvgP/8n+FXfzVRHV7vOHYMvvEN\n+Hf/7krvyWuDNyQZeRG6rvOhD93N9PQzTE+fQVUVbr/9MFdfvf8Vmx7r9Tp//Mefod0uETgZ5i66\nxBg8/PCTDJZznJmephwE1NZ71NUmQgpUEgLikhCQF0iowibwPIIekjIJQalhoVOmxSYWPqMEmCgs\nEzJPBo0KNhE9OsRUEGT7J6JFEvIxTuIMkCVRO14kGSFJGSYGYgzDx/f3AAskZlcWCWl5saF1DSkv\nMjKylzvv/GVMM2Rtbe1yzszJk+epVPa+7NhkMlny+WEuXjzJ3r1HME2boaFJ1tYW2LZt+BVdNV+P\nuPXWYziOy+OPP4IQaeK4y8GD27jjjjdffszKygpf/vSnkZubGIpCGvjwh+7g7IV1Tpx4nna7RjY7\nwtatEyhKwLPPdjjfMbEIyBb3oSt5euuPorGGJyJOS4FEZT8qJhpNNGrobNClp2qMDO4iuz5Po/k8\nKiVieqzjEuAxjA+s0yZLgzIaNj0WiRnAIc0yNhpZBD2Mvhq2gkWHNiHRZZq6HUGPLhViMnoKqVjU\nNYmtFBiNQtwoTw6VFAXSpkEUzVHUbVQvYh6bk9iUsUB2GAJ2b92K0e0yYFkYrssT3S6u77MWBBzb\nvv01XdMdOyZ5/PGHSL4fL8Fxamzbtu97tvncffev0G6HnD99GiWOabYrtGpneM538H0fs1rlQBCw\n0FlkYeMFms0Clm6RSemcdFx0JcZVtmBbw+wbUBgoSK657R38wxceYH7ZIJQ6Tdps0OEq6qzTRiXP\nMCouG4DKMrJvdeYjEf22c5UWPZoo9NBoMEXis1snMaAuE3MOD/AYRKXWd97doExMHUGNGIMU6/Qw\n+2VDE5MVHBxlnDi2cN1LRNEmnpfl0qUyS0sRcTyGkC0sbQFjwKbT3mQAaMochw6N8OY338zm5iZC\nCHK5HD+LWFhIvEVOnbrSe/Kj4eab4bd/+0rvxWuHNzQZgUSaP3r0eo4evf4HPm56+gR/8ief4tsP\nncWQgu3VEdROl0ZvndryMrtHTLYNDXHu1Gk6Ms0pr84WTSVN0iOy2L/1SXpDasAoRSZpM0jAkyi4\njBMy3L/6kZxDYKJg0KOAzRwqNhOkqFFng5hu/5UgISBpXupG0frbXjQ28wAHXc8gZYp0eoow9PG8\ndWAKVdWJojqJaqKhqoLx8S0MDk7Ras3ied7lY2FZBr4f8L3YsWMcXV/j0qXnsKwCntcklerwzne+\n7yddntccmqZx1113cOutN1Gv18nlchQKL/WQBEHAF++5h0kpqfaD3oIw5Mm5Of7Nv/kFVPUX+bM/\n+zTZ7FWMjm4jnc7ziU/8Ho6zjzCMCMMQtxOQLd9Jq/FZdioSKzDokOUUNQQhPQxc0tQBLxpCa5YZ\nHxzC4hQrTZcUVWwsuvgs00Rngya9fifAGiExkkMI1oiokOIU45QpYhPjskmGdUZYY5ExYsq4pIEm\ngiwqSiwwpA1qC1mq0Nu4iBq3EXKCSDFp+GsU9Bb1IGYlhmZphGp5P422S6O+SNGKsAwDhGBmfR1X\n0xjJ57n/1Cluffe7qVQqdLvd1+yqeMeOHWzd+jQzMycYGJgEYG3tIjt35tn+PcTIti2kDNmzZw97\n9uwhjmPOPv88Cyc0br7a5MZ9u3l4eprnag0y9jhbd6aYazRYbnQI9CLjYzrddpqSyJBLGQg1ROaK\nbNRBsQ9QKZoE7R5R5FL3T9KkhCoikCPE1DGp0UAhhyBGJUVMCocuMQFp2ki6iH4G8HYCFkkuOJKw\nP8kEgjUkq4QEhLhs4HAeKKGTxiJDjxU8LmBREiGeVGgpWXzho+vn+xlOoyjKfjTNQlW7FIoHaYfL\nDJkqO7ZMIuOtbLgO6eEpymmNb3z2s9iKQkdK9t10E7e/7W0/1hTbGwG/8zvwa7+W9GO8EbBjB3he\nUlr6nkzKn0m84cnIj4KnnnqaL3zhUVZW8ujxLgYyKRbXZxku2XTdTWorPZxCDlMpMNOKsPQ8gVHk\npHOOAlw2pLoe0FFoELOMRpcOBQIuAl1MRnDYFBdRJHTFIXTpExGwjgPU8Ajoihgpy/3w+AXgAPQT\naZJ36ZKoIJn+9pCkR6QHtAiCVRRljDBcQMo0L+o1cawixCJC2ICGEIL1dY8HH3yaIDjJBz5ww+Xj\nceTI1XzhC0+TTieNaGEYcuHCC7Tb83z4w7+IlFCvdxgaGuWqq/a+4aRYSDxpXqnHZXZ2Fq3ZvExE\nAHRNY1uxyKWzZ3n/r/wKqqrx2c8+iJQSz3MwzTT1+nnGxw+yseGSywkai/cSKhrnRIBJl5gidXay\nKUxCkph2WERRcrR6G3i9kJRukFYqdOIWKRaYQkEwgmCNIlnmUKlzkBiDhPIOouFjYGChUcBC4qHT\npEObLj4BLbp9u7QAA19XqMQKgYxQQo9m4yLCKKKZJZzuDJaSZjiVRw9z9FSTDcNnpLqDW/YcwDJN\njp/oMWU1WO50GJ6YoLptG7qmMd/rcfSd76TjRvzu7/6/xDGMjpa4666fY2xs7FVdS03T+NCH7ubp\np6d5+unTCAHvfOd+Dh068H29Cnv27OLrX38c1+1hWSkURSFfLHLSX2LrSNJg6YYhTVdgazFzLR/D\n2MXUaJaV5irLnVVuf8ubKeZHuDT3PCtra8xcqnN25jRBICkaU6QzaRothZy5k6Z/mrT0+6qVjcTA\np8MQOSQBy/hofW/mTSKGgGFCBCu42Lx0SbDGi6VWyT4SpXQVGMWmjSSkTQcVSafvQ7PBEA1pJClX\nehldtzCMZTzPwvclQjTJ5w0ymQJh2GO9GXG2VsPTTCa37qSyJcVQUUdbWOCm/vchjCKmv/Ut8sXi\nD3Q7fqPh7Fn40peS2zcKhEhKNd/+9r+SkZ8JRFHEffc9xsjINZx9/ttoMsTQ0ij2dmr1afSwRs47\njzOXYzGKsFSbLZUDxKGktryOGTdJEWGS/FwUiekACiF54ASgINhJHqlayNinTRqkSkiRiDYRNi4R\nMQ6oNoqQREGDhGDMk/R5bJI0o6b6e66REA2HRBm5BOhY1mHiuInva0jZItFr8ki5iqJsQVUnieMN\nQKAoKlJ22LbtGj7/+fsZHh6mVCpx8OAB5uYWmZ5+DM+zOHHiBGHY4eDBo3z962fJ5Vw+8pG7L+e+\n/CzBdV1eycs1ZZpcqtX4oz/6E+677zjNZhtdf5zt26fIZGLe9KYDLC5ewnFWUDsX2IbEV4sIrciq\nP8ciHo44jBBZ4vg8EGGqVcqmgh7M4gWrOJiYlGnTpICJRp6YEEkJQZoSMRtEJOpYETDQ+70DMREx\nEgOVgb5tno9GrBtciCSDikXaztMLPPwoQEqfUGo0nZB5JcYzLWItjxBdDNFCipAmTUJjiF67y7dO\nPIRFB+m1eGpznVt3bGN4fJy9+/bR6nbxmk3m5mrUajYjIzeiqhr1+ip/9mdf5Nd//X991T8rpmly\n441HufHGH3yCLBaLvO99b+Zv//Z+oihpH4U6b33nDcw260m8gKJwMZCkwi5+kEWjST3eoOb7qJkR\n1tbqFIsFnj/3AuvrMY6TqIphkCMw5tg/dYSev0gc2vgiwwV3ExsHgY2CxyAaWfIIWlTRkQgcurg4\nFFAJ0RFkqLGGxygRAyTE4xzJhccoApCEQI5lsv1WVYUOMU0U2uSAITzKaKYJygqKvEAcjxDHORSl\nCJSp19s0NldI2Tl0YRIHGugDLNdOccedd7Fx5iQ7RkYuHz9NVdkzPMzxhx76mSIjH/84/OZvvjF6\nRb4bt94KDz6YhPn9rOOKkREhxM8DfwisSylvfrXep9Pp4DgxAwNpqkODzJ6+iBcUMDSL+uY8B1Ia\nQxlBLhtSb0QsRz69dpNMtkBJj9geGFgEFInpCcELUjJIEoMFghDJLgwcVLpqQBA1yRBh8AINDDoM\no1LBRcNnDcIaQhgkVmljJOrHNAkJGSH58XwpbyQp01wk0WdGCAIJOEg5ixAaUnokV1EqlrUXKbtI\naWDbI7Ra8xw9ehVXXXUjy8vnmJ5+lttvvw1VVXnve9/FTTct8xd/8Wl27drG3r03oOvJaXptbYEv\nf/kf+bVfe2N9A8IwZH5+niAIGB0dfUVFp1qt0oS+lP2SDL2wscE/TJ9jY3OIavUoxaLG5uYMy8vL\nfOADb2V2VmX//sN87tOfZMQco7U0SxzrRGSwNJWpsM6a/DYdkcfXJKZaYZwuxTDEliEhw6zgsITE\nQEOlSESahOImCo6Og0qLiBeVhvOE+FjEgKBLiI6PUNJgCPKqgmnbpEwDt+0wbti0QoVn4w4dNBwM\neph4sYJwdTKZfWSLZdY6F3D8F4ASo8WjGDImaq8SEFE1u4wV80zPzLCm60TZLE1d55pbbuGBB15g\nYuLA5WNWLFZx3Q5PPPE0d911x6u1rD829u/fx9atU8zOzhLHMePj42SzWc6dO8eZZ56hMjDAzrbJ\now88SEEKpCYQZpqxVJ41p0W7HXD//V9hc3McTZvEtj16vRlUdRPXEyxvLjEwUOCF+e+gKkWymTxu\nbKDHRZTgOxhsoEQBMTYOLUxsVokQSBxCNnHpEmCzlxRN2qyTjOdPkkzQXUBSQ2EKBQ+bHmVMLHQE\nCikkIRYdDHSrzv6rDwDDnDnTJoqqKEpMHLcwTRWv56KjkspGZDMq45UJzJzN1N5bSaUseqr6fREP\nactibW6Oc+fOUSwW/8WW/VcaDz0Ejz6ahOK90fDWt8If/EFiX/8qJ3FccVzpoLxrgAdezTdJpVKo\nakwQ+ExOTbEw8jxuZ51ao4XWW4FMhnrssr7axYkUbL3KYuM0MxsxO1SNntBIyRApDLYognaUBIG7\npOkgCIjpEuLSIZA6ByybjmcRSkGamEUatBgiUgSqGCGOF5AygxB+P+xuhISQNEhIx2D/fodEFfFI\nxnlHAJ84bvdLMWq/TPNii62D617AsvLkcjrF4lbCcJmTJy+wvl5n5849rK+/PBi5VCrhOAr79x9D\nUV6SuwcGRpmbe4Rms3nZGv/1joWFBT71qb+j3VYRQkeIFm9/+w3ccMPLr+48z2PNdfnzz3+e3aOj\n7Nixg04YMr22xmrNZNeuWy8/Np2+lrk5l1arzfh4nosXT2IbHdzVSwRBjUgrgX+e7VIFzSQbe7Tk\nArORTkE2qcYBvnRBHcPEYpg6KwgCJvFYx2SYZG19wKBNRIRBooitkXiAdogJkMS08ZAESby9L1jT\nDDqBYLct0PQSZ7wGftihLibwlTE81SKMgKgOCHQ9QxSp6PpuPG+JQmES15P43WV2Z/OEocGau8y7\nD+2iVKtRq1QY+7mfo+q6nJyeZnVVMjoaon1X3no2W2ZhYfFVX98fF+l0mquuuupl23bt2nXZQ6Pt\n/ndeePQxSulhbCODjAKEcDGMDJcWThKLLFBF1y3iuEcmU8VxII6XWK2fY1l2cUOfjGkS0IWwRigh\n0gZZ99YxaJBlgDoaXZqk8Nnab1ffTch52nRoIImBrSSE9MXPQg5oIYjRaTFIjM0oyfc8i4lDlQXa\ndPB9m42NHtXqThSljGlI4iiPI+dwnW+iyRSxogMxo+UB9k7uYr3VodeL6PUCHEXBDwKMvllZGATc\n99CjPNeJueeeh4jjNldfPc4v/MKdbwib9+9FEMBHPwp/+Ievb1+Rfw67diUk5PnnYc+eK703ry6u\nZFBeA3jVg9d0Xefmm6/l/vufY3z8GnYeOMil0yfxg4uQAdFpMhokrote5HDOn6dHDpUhdJHFV2zW\n4gW6sosbSTxgEwtJhhU0DHx0NNpsUA5dKlYFL2zTjdIIWaBAxJqoY6cknieIojJC6KiqJAwXSMoz\nkuRHaJykX8SBvlCb1JHnSSTcEaRsI6UPHCaZtvERAlR1DFW9hKo6mOYgmcw2HKfDwMAher02Tzzx\nDd761l952bGJ47jPuF/eqPbimsRx/Kqty08Tnudxzz1fRtd3MDGRXMUFgcdXvvIkQ0PVyxb2Z8+e\n5Wuf/CQHCwU2DxzgudOneeLSJd7ygQ8wki1in1n9vtfOZrdw+vQsn/jE/8P8/Dx7d+d49FOf5tQT\ndRrdJSYiULUCrpQouko2chiJPQqKTsXMsOkoqJFHkMTUYRDhsIVl1lBZw0bve6J2WMMmmdVaAxZQ\nKaIzyDqz9NjERjAvcjhqilidwEjvJgjOcyY+j9FrEAmd9SDXz0cpIuI80EJRikh5lk6ngBA+Q0NF\nTHMbO3bspdn0WX7+DK2uSzFrk0pXuLS5ydDkJFEqxdP33ceYYWB0OiyfWeKJruDwTTdejk7odOps\n2/bGu3I+duwo5x+4j2dfmEETo2imgaIqOJ0G3eZ5jOxRXHcdIWKy2RS53DDr6yFra8fxehuU9Jis\nOYGgyXrPQ6GMogwQ+xl61HHYwOorm4KIEXRcAkoIdAyGgVnWiS9nTEHynYfkAiMi4gIBXSxyvOQ/\npAMWBmHi/hrnaDYvsnPnjcRRTOzDjtERhDLG4uoT0DmP1PPsG92PGRhcOHk6sSAIGrz//XczOT7E\n01/7GrsHB8mmUnzr8Sc5vtDiyF3/lnJ5GCklzz77HJnMN7nzzre9xqv0L8cf/VGSfPve917pPfnJ\nIAS87W3wT//0r2TkZwK33XYzvh/w6KOPks7abNlrs/PQIY5/8TxlJ6YrQop2ltjM0mzUuIjAxyeK\nzmMTEZLCVRVqms28V6dOBkNRGCEgJmRTOmhammzcIKV4DGRVlMhns9PAiJNI8jA8hKpmCYIZpDQI\nQ52XekIE0CRpXh0lGdGNSH6kQpIegjywBNQRYhdS5oAAIQxUNYWUSwhhIUQWMGk0zjIwkMYwMnie\nj+cFDAyUX3ZcbNtm27ZhlpYWqVS2XN5er9eoVjMvm0J5PePChQt0u/ZlIgKg6yaZzARPPnmCqakp\n4jjmG1/9KvvLZQqZDBPVKgd27mSj1eKS4zA8PNTv9Xg5HKdJpZJHCMH4+Di/+N738p1HHmPxiRl8\nmUfoaRqyRWwYZFNb6TSfZUfZYqbVY92L8GWqb+peJyYiIEOMT4cK50lj0wBsetSISKPxBIJ2YlqG\nhYqJwzh1JljnIinlegy7SizTCKHheSaYk6QGriZlNFm/+BDIgX7vkIKUMXEs+2pRA8tKMzQ0TBSt\nIgRs374Pb3OWXRNDuJ0Om50aE9dcw9iWLTxy7728Zc8e8uk0URxzYanBsxe+w5Oqx1X7D6AoKr6/\nwJEj/8truNo/HYyMjLB9316K+WW+8tg5Gis6YeRjaC127j3ARkPF9zfJZAbI50tEkU+ncw7CJraa\nwleG8KM8quKiKgZ2XCJWJE4kUKgS4rNBih5VJmmD0AnkOVRiBBEpInzaxJj9TpBhkiFtk0QNFUCV\nkLM4GKRxiDH62gp4KPhowADt9tPU6xfJGCGSENfvMVQaJShtw3fm6CktZhfOM1msUs1WmG9s0F6d\nRRHv5dgtt5AvFHjym99kc3GRp1sBh9/+a5TLiQ28lKAoBf78z79ArbbJddftY8+ePS9Tx16vWFqC\n3/1deOSRN3aJ461vhb/8S/gP/+FK78mri1f9EyWEqAKf+57NK1LKD/yw5/7Wb/3W5b9vu+02brvt\ntp9oH1RV5ed//i3ceutNtFotcrkctVqNlSceofnCHFocAYK21yESCraIKMkWkeww1K/1tqKIVtSl\ngSAgoCxVbMWkKFS6UuFC3EOqGtLtUCoMUw4DNmWPLjkCJomDUaJok0Tl2Ely6CdIpNllkv6RBslo\n7kD/7ymSH6dFEjKSBrpIWQMchDBRFAMpe4CPZTWIYw/LkoyMlDHNKvX6OQoFlXS6yKlTp0in0ziO\ng2EYbNmyhTvvvJ0//dO/5tKlJplMmV6vgaZt8MEP/uKrrlr9tOC6LrxCW6pppmg2E7Wj1Wrh1+sU\nxl/uVVHO5TgzP8+td97JPff8A/X6RQqFSYQQOE4Tz3ued7/7/2BhYYFHHnmK8+dnObvoEaT30fMW\nqEc9bG0MSZugtwrSoOb06CklWiSTNikEKjGLuGRQ6ZIYHYRYtBn7/9h78yC5z/rO//V8r3h46lEA\nACAASURBVL7v7rnvGY1G0kiWJUuyLNnY2ICBGGyDMeEMBgJZjmSXbJLdLLskW/klW0VCqNpUWNil\nEog3AQKYy5jDxiaSJV+yDuuaS3Nffd/f+/v7o8eyBUkIxLZkNu+qrur6TvfM08/T0/15Ps/7oMX5\n6SSMSztLdOLhR6WChEOQKnnWSWKKFJpooJglGuY6jYaOLHcCMq6bZH19ZaMw9eG667iuuhG86AAQ\niYyg6zYTE0+STjexrAK2bSACAYp6k2hUcNNV42wdG+PhEyfo6+4mttHbNi0Lz3OoN4ocOnSE6Qvn\n6evz8/GP/yYdLxOtpGVZPPbY4xw5chLDMClU6jxzfp2RtnGW3Tz5Wp6sHqF6bo5EIkYmMwjkKZeL\nVMtThBsn6XZcUoE2mpJg2s1RtgbxSX6aLBMWXQi5TpAodUfCk/qIomF7FYRr4hJgjRoBLGxUZGER\n90zyLNHqQTZpbUAStDYnGpENb9YMTcLY6HjUCLJ2kfhcQHJNTP0st+w+wLHJkxRrR3HcPkw7RxFI\nSd0EtQRThRpThWk6Mz5+41dezamjRzlw8CA7rrqKHVddRalU4k//9ItkMt1AK+vp+PFTzM0V0HWN\n+XmF8+cPMz5+jl/91TuveNfV//gf4dd/vXXU8XLGzTfDe98Lug4/EaP2S4UXvRjxPG8NuOkXee7z\ni5EXAsFgkGCwpVaJxWJoyTRuh0SxXqFq1kEISpJC0LVp8wKYSKxsCC1LOHgbGRPrpEFsorFhYuRT\nVhkJhmiGHdZyNVzdpVitUvU0VnAACcfJAzO0PmieDbqL0TqmqdPiijg862DSukm0uCNpYAVZ3owk\nqbjuLJK0hG13oqoentckGHTZtu16lpePsXPnGDfffBuWZXHq1FFmZ1eo1Sz+7u+O86lPfZmxsS20\ntbWRTAre8Y47+OhHf42TJ0+xuLhOe3svO3e+/mXTFQE2rMkP43neJQVUqbTK3r2tIxpN07CFwHFd\nZEkiWypxbHKepWyZnG1wy1tNfvd338unPvXXzM6eBhR8vhof+cgbiMVifOYzf4/fP0A262N5OYxh\nN1AiW8gZE/RKGp7hxxdwWbM9CpZKRNnEmlhAE0WaHhhoQJQ2NCQazDNOa31nAAmZKD1MEyWKumH5\nHqTGOiXaRS+6KFCXNXySguwVUGSPPFEQfmxbxnXB8+IIIRMINNH1ALbt0XqPTeN5KzTqp8G2kI11\nRruHWK+e4cjUEcLhDqaKTYKFPG2ZUQ4vL9O1bx+x53lm//jkOcr1DNeOjzPfbLL/xpvI5WaYmppl\n69ZLDfSuRHiex5e+dB+nT5fp6NhKMKhy9myOkttBrlRhvrCO7fYSDA/jOAXK5fPkco8yODhMV1eY\ntcpZ9m8eZ25qkbASIy6r4KxxzFhGd/pxyOI6LkFJoepAxTPAKRJW4uieRo4yGQRJ/GhIFPC4yhei\nrK9xjhxlpYFuR3guhTtFgCn6CVPA5gIKwQ1OSRaTOiHEhnNzJuJjbPMu/HWLazZ3s2/LFhzX5ccn\nTWpdY6xmCyT8fqJOFJ0Y1+9JsG1ggEMLCzQajYsS+Gg0SiSi0GhUCQYjFAoF5ubyhMMZQqEiHR2D\nCDHE6dOPMzU19TMzbC4nHn64ZaX+2c9e7pH865FMws6d8MMfwq/8yuUezYuHy6mm2Q38CTAuhPg+\ncJvXkoa8JEgkEmw/uJ9HF+4n0TVMRG8yszJDwamTIIwPiCCRJIiJRRqbKhoVNCqApvlI+EJIIk7O\n8NC0RapEKMTamK2uUnddGmzDVXrAPkmr0LBp+YpYtI5cqjxrdtT6YvLT2hFBq0gJ0Irn8zZ+XgBA\nljUyGT/5/BRCRJCkHMlkEoCDBzfT0eHjyJH7mZ29wIULeSKRAcbGeqhUBJ2dr2d5+RTj41tpNmv8\n9V9/jf/wH97PgQPXvfiT/iKhu7ubnTt7eeqpp2hrG0FRNHK5BWKx2kWL62AwyPCOHUyeOkUsEOC+\nQ+dQlR7qephAW5L/+T+/TCKhMTY2Qq1WYnx8kLvuejORSISPfey/MT9vo2lZlhen0Yth2sJpCpUa\n/vg4s405JE/gOA0agRQ0TYJOBMcOkiKBioyESasNHyNKDI0iJls2rikEeAY/YRRkfLgbiUZhZBYx\naaB6VeJymhV9moAII3sKnjBwvQqadhXl8grBoIqqtuN5RYTIIkkyQii4bgnoAsMm7NvwO8EhWc7R\nH43z6rvfSSLRhuNYTE8f4RW338qOHTv4y//xP6jU66iKwsxylbb4MMvFIn1bthAKBfH7x3jqqUe5\n9dZbLnJIrlQsLi5y5swaAwPXXixYg8E4imaRK5xBt3vw+XppfSSGsKx2HMchHB5geLgNafYU7ZkM\n+bUyZtXAh4RiefjJYTKIwI/wglSdOrbXwBNpFNqw7AgNdEx8FIVNyaug4kOICLLZRMJmQIqwHlJZ\nrK1hOzvwyAAlgsj4MMjQQYE6Tfx4gEcRgQpIyMLBH/SIJaNMzv2YV1/VyXBbG8dPnyO7bkJYomdg\ngOFEAlmSUGSZ1fwxdNMEVb0ksVySJF73uhu4994fEo9vYmUlh2la1Gpn2bfvmovzFgp1cvbs9BVb\njFgW/Lt/B3/+5y9P0uo/hje9Cb761X8rRl4UeJ73FPCqF/r31ut1jh59ghMnzqNpKnv3bmf37l3/\naEvx197/fiYmZzn8wEM0s0tgG8Rx0TBwRAXJM5AxCCOho2HjoGx4skqyScXUUYREMhKmYNjMVi0k\nXzsVI46NgSRFUCQfsjyC4zRo7XietTlaATbRWoJnc35tWjujEEK4eN6zEt8VZLkPIXzAM7S3C7Zu\nHaVUajIz8ziaVmFwsIuDB5Ps338LDzzwEEtLVYpFi0hkjFSqnYmJWXp7x9G0ELVakvX1Rfr7x5ib\nW2R2dvannCxfbrjzztvo7z/GkSMn0HWL/ftHOHDgVy6xsX/V61/P18pl/uar9+PoPSgBiVBHB71D\n3Rw+fAgIcPvtb6BWK/LUU/+AbX+ZhYUVfvTQPJHgGMgy+RULz52nM7mb5fwCTj1MrZnEQScUirJj\n/FeYnzvO6mIFD5kmHrKSRjhVPA8ghkcDjwStYrMbWVrDcyVaGbr1i0F4EgIblZK3jCcFaJoGEgJF\njlK3dYSoIAkJvDrhsJ8tW/o4e/Y8jcYKknQ1mlbDMFYRog2fkiSMS1cmScO9QNHL09HRTVc0jeNY\n+P2tjmFv79WcODHB7t27ed1b38p3/uZv0KpVSrUmultATaUZHBoCQJYVHKd1/HE5i5HFxUWOHT1K\nYW2NzoEBdu/b91Ny1PX1dYRocX9c12Vy8gTnzp1iYmKNWq2M5/WiqkEajSaGMUck0ouqguMoyHIc\nSYkyNzdNMh0h70GjWqFpNvHhx2ABTwhkKYFlr4FnE4luQ3ILaB7Y5iY8FhBqP8sNmygdKJ6PnFcl\nFq3is6rozSzCjSFJRqvLhX/DgyaMgksHbbQO3HJYKJg00aQ5/GGZG19zgDvuGKf5mn4OfevbfPFb\nD6E7KnPlJooF9toijVie7SPDSAIkSXB6aYmdr3oV6oaK5lmMj2/jfe/z86MfHWVi4gTBoM21177y\nEk6ZbVtoWpArFZ/+dMsk7PbbL/dIXjjceSf89//eKrR+Ysl+aXDls5B+DjSbTT73uf9LPh8gnR7D\nMCy+9rVjXLiwyFvecvvFyr5er/PMyZM8fvgw1eU5ehQTPeSj6plIoQClskWv5wImAWQsXJaR0YCm\n7FEnTiYQZMf4KGazyczSDPN1i7oVxdHB81rZra4LjrOIEElUtRfLegiYQpYHcJwKrc7Is2ZRRVpR\nfBKSNIPnJWh1U04DBq7bRjDYAHK0tfWxtvYo4bDG7//+Hbz3vfewuLjIl770Pb7zndMcO1YiHB4h\nEpkglRoiFEqxujpLoVAmHk8jhIzntXgEQvg2OBcvb8iyzN69e9i79x9Pa4ZWd+Qt73oXj59cIJHY\nTTAYIh6Pcfjw9wmFxmg2Sxw//hgXLkzjunG+//0fUCktIAsPrS2I40TQzAxNkWNy8XF8ah896RSz\na+cpVEtYWobVVQtEBuGbwfDaydpF/K6J55VpOXSWKWFhi/FWspGXRRYVDBGk4pVRUKjRRBZgey55\nBAoKJRc0KUC3kkSWwriqiiQPEQwUEOoCgbCGaWbp6tKZnZVx3XWwVwCHQGA3QZ8PWS+RL12gLeiy\nvlTCU0t4tQoh67mGpKYFqNVWABgZGeHdv/VbnHnmGZ4u/z2J1DYGBoYvFvblco6OjtjFo8/LgbNn\nz/K9L36RvkCA3lCI3BNPcO+TT3LX+99P1/PMvFohia3XOTV1ktOnFwiHdyFJD6JpAQzDRtfXEcJp\n8WiEh9/vsbAwT71eoVFrYjXz9HT2EI6qNPQmVXQaShRZGGhSN3hVJElFkzIMDl6Drs8hN/MsrxWx\nbYOmvowqBjd8ZjwaIkNdDFILrBFQJeR8HsddQ0gqnhuijo8GJUKEaaUfVdCpYOAjLGuE/Am6x0L8\n6Z//GeFwmHw+z2f+1/9loSio1wvUTIdUJIEa6mWhMU/+1FlSCZdNm2QGb7iBg694xT86p8PDwwwP\nD/PGN76aT3/6XmKx5wo7x7ExzRW2b78yDdEWF+FP/gSOHHl5k1Z/Er29MDzcOn561Qu+hb8y8EtV\njBw/fpJsVqW//zkNVDi8mxMnjnDddYv09vZSKpX42899DrG+zrljx4jlcsQsk9lAmHMliVJTbYVa\nyXWCjk4BE2sjW7UqJGa8OgR6WXaKpM0K4UiCnFxEpwPYhiS14zjP2rvbOM46qlrGcVrcACjhOOdp\neQmEgCmEAM9TaB3JtMLuhGgiSXE8T8V1I3heFtv2IcsKr3/9B8hkumk0yqysnGdiYpIHHjhEJDKO\n6+aIRHSSyUHK5VWmpo4iy100Ggb5/BSKohIIZEkmr9qQ9pZe0jj4yw1FUWhrSxKPp9C0Vos6n89j\nWWGmpyeYnKygqqPUanUq5RjRwCCmtcRKPkc4WMbvxbEMD0m2aU+UCPp0hFNHSJup1XTm5pbw+ZLo\nehBYpyiB58yRQkalSo0mOnF83iw6Koq0iiw0bK/OEhKWMIhLETxZI+u6FBwdRA2EhusFWPUcTNvC\nFX5sO0fDrCErK1w9tI1XvGIEx7mBwz++n8KZGdKhFDO1OnVrnoYbQDHXGI+EGevuIl/PsZk6ZysF\nauXcxfkpFJZ5xSueS2iOx+Ncd/AgyXSaL3zhAQqFIOFwnEolj2HM8+Y3v/GyEZ0dx+HB++5j54Ys\nFSAeDhPI5XjkgQf41XvuufjY4eFhQqGHyOVWmJiYJB6/iuXlBSKRTSQSy1y4MINlhQmH24Eoslyg\nUDhHQFaoNUxcO8iCuYBcXGTT6CDLVo2VmksmNkC5ISHTgQc0zToCC0Xxo6oh4pko87lp6kYOHz7C\ncoa669DwqnhenGYzSDxhsGfrKN97/CjCBY8azWYWE5ijQRqbEOvUCZCjDYMOPCeL617gne/8TwQC\nASzL4rc+8jvMnqzQHd9EVs/iWGXWV79Hpv06dKOBGqrjS0n83h99kv5/gb94JpPhttsO8K1vHQaS\nG529Aq9+9a4XPQLgF8V//a8t0uqmTZd7JC883vQm+MpX/q0YeVng/PlZYrFLmf1CCCQpyfT0DACP\nPvwwyVoNS1Xp9fmoCMFjhSbzjXZsbzNxV6PkNci78+Txk2CVTjRkPJpCwZU6keU40WQHFVtCURaJ\nxEOs5yMEAhksS8VxbIRI43nrSJKGqhax7SLgoKojOM4srpuhxRFpR9NKeF4Dx1FxHEHryEZBUQZo\nFSgCz1tFlhfp7LyKubkc3d2DaFoGVfXx5S/fj22HSKddXFfgeU0AgsEEk5OPEY368fuT2PYCMzOH\n2LKlGyEkZmefYv/+Tb80tu/lcplKpUIikfgnU4YlSWL//qt48MGz9PfvRAiB4xhMTp4jEkngugk8\nL4aug2NbKIpK0DdIXZ+kXMuSs5YxLZd4NEgqGqScMzCdToLBNPX6NI5To1qVgRCKoqP4/JStUTBP\nEvTCNAAZB5kCPjwSkRGS8UEMc52l3I/IeqMUtS4CwSC1WpFQSEHXc6hUwU5jewI8D8sNIOjGE1kU\n2U+zKWEYBqFQmL6Aj7a4RrPpElclgsjUKBCIKHSlEhQaBZJBcFHJ+IOszpxiZPM15HKLRKMVdu9+\nHQsLC7iuS1dXF6qqMjY2xm/8RojDh59gZeU8Y2PtHDx4N52dnS/pGj8fuVwO0WgQSV0qWe9KpXhk\nZgbTNC8eH/l8Pt7znjfx+c9/iUJhGcfpwLazpNMxarU4PT1LZLPnkaQwrjtPobCEX2ljIDROUAth\nWnXcSDdZ9xyRUIz9d1/Hqc88jmmG8NwmQtYQnockNXGERKWSQ5YrFIsGkdg4euMMGjZNLwjeOi4h\n/CIOElQaDQ4/M48qj1AzqkSjGXT9FHgRmtRYwAC6URlAIKNQQpFDRKLb+dY3nsDvDxEKacyfKzAQ\n70eRfQSUMLFgNxeqp9Erj9Iuw1Xtg3iNEt/4whd40z330N3d/TPneN++PYyOjjAzM4PrugwODl6x\njqznz8O3vgWTk5d7JC8O3vY22LGjxYW5jM3IFw2/VMVIJBJkYaF5yTXXdZmdepLvZX/MQFsbDz78\nMK/cvBk5GkUTgrVGg6oRQRYpkr4E67UCGgaeFyBOFlsE8CQLx7PwgoNsiQxBOsmmq3cRjUao12dp\nNl0kqYnnZQkEtiBES27qeTpC5DGMJqoawHFkfD4Nv//VFIsncV0Zz7uAZQlUdTOyXCIY3I5pygQC\nNWq1KSTJQZIkVLWComTZufNu1tez1Go1wuEwfn+IkycnKRRcUilw3SaVyhK2HebcuRN43hiuq1Au\nH6e3N053dw+VygqWdZa77jrA1VfvvEyr9cLBMAwe+OY3mTl+nKAk0QC27NvHwMgIzWaTTCZDd3f3\nxR38DTccoFAoc/z4IYSIUq8vo6oB2tt7WVmp0GzqSFKrEPFcC0/24VNNHDdJLJSkUFklHh7g9FSW\nSnMeW+zApyRR1RlMcxkhPKCG657H84JIboKMpxKRw8hyg6YisaK7yG4M07SJhSwimRg+bYilbBWb\nJVQ1TSaTQpK6WFs6QlQVlEUT4bbjeCVkYmg4CKmIX+0iGBzkhz98hC1jBt2BMJk9B5mbPw8rRS5U\nztCdGkL1h/ESCl4zx45tI4yOjqIbBl956gSOc47rrx+hq+sqPvvZv6VWkwCBz2fw5je/mrGxMXp7\ne3nrW6+cHbGmadie91MqKsu2kRTlEp5YtVoll8vx2tfeQDZbIBbrJxDYxle/+gOEyJBMbsO2ZxGi\njM/no1JRUSwN4ToYRglNg3R6mLm1ClNnJwmnxghHFdaWZ1ClKKY5iYeOP5QiGlNYW/s2mmbjOEk8\nTxDyh5GdHDiNDU6Qg+XZOHoOSThIYgtCKRMIuDiOg6b14lgNcMexnw3hE6AIGaH0I2s1Qopg6ewS\nn/zjz9HRlUAvSoRDJrajAh5CgOqoBBoVbt57A7FYGFn2M+rzcf9XvsL7fvM3/0VdrUQiwe7du1/4\nBXyB8YlPtPJnXkZCwJ8LPT2wbx987Wu/nFk1v1TFyDXX7ODJJ+/DstpR1ZZ18ZlnDiMtneJ1+28j\nGAgwGY/TWFjATqepuC6GZSNEEscTILn4ZIOwo2B4YWJqmF5VUBI2shzF1lK4wqNiuszOlikWp8lm\np/G8JWR5GMPI47rH8bwwklTD887h8zloWhu1moLn9dNoVDHNcwSDUer1tY0smT48r4rPZ9PePsbq\n6jSepxCNagSDNTRNIhqNoKojaFqIZrOErhsEg0GOHj3K6mqZTKaPSKQNWfZhmh4zM4eo1daIxfbh\neQax2Ajlso9IJIEsN7jhht3s3r3rkvmrVCqcPn2WQqFEb28nmzdvfllYQP/g/vspHj/Owd5eJEki\nVy7zuT/7XwR7dtLbuwmoMDbWxt13346maaiqyl13vZGbbsqRz+eRpByFQoCJiUl0fRHbbsPn01CV\nTiI+m0p9DsspgTeI6ZTYvG2IpeUSItzfYhRZDRxnDduW0LR9KEoTxykTjw8RDDoU1wooboOIP0bI\n18+aWUCSAjhCYFs5ZJFgMVdibq2MEN0kUm00GmtUKk0ss4nqlvERQnKbmEziEsRHAUkukQx3ITwf\ns5MruLJNPvcwaddi16YtDPePMTAwxs7aLCIe5vELC9y4u4ftw3cQ2lBRLKyvc8fb3sJd73gH1WqV\nP/uzzxMOb6O3t5Uo1mzWuPfeB/joR1NXXActkUiQHhxkbmmJged5nUysrLDtuusuFiPHjj3Nffc9\nvNH18iiXqxSLx9i9+1V0dyc5c2aeQmEOkEilttLb28eJE99BX9ep1VZIxFPEE+0YpkmtopPoTqBp\nESKROFZGxXV1FKWBLGvIcoh43EQIF11P4DgpbLuEpa8xIAXRWaRJcIOJVmvFArhb0V0BiiCkaei6\nQjQ6RLN+Asu0kbwkqhpCuAqSSBIIxDDtOfKlFdJhH7VmjUKgjOTEqDZNSnqNfKWJIqo0jRwDHSHS\nqST5wgJ79gyRiceZnJ8nm83S1tZ2mVbvhcXJk61Auc997nKP5MXFPffAZz7zb8XIFY/+/n7e+MZr\nuf/+I7huBLBZOv9D3vXKAwQDAQBGhoYoTkwgFQqE2tspnTuHpFoI1ybbLBGTZHxCxvaaKJ5FWHLI\nhMLkInFW15pYlgZuk+ncKZpeCmjDthcJBASOE8JxTIQooWlFhJCIRnsplfyoagemqSLLg9j2eRSl\nhiQ5uO4SEMTv70KWY6yszOH3q9TrZWIxmZ6ebSQSEcLhGrFYjNOnj1IsNqnV8uTzBXK5C/T0ZND1\nOktLD6Eo7ayuLpDNLhGJyITDBqVSi4+iKDKNhkw87ud73zvMnj17LrLpFxYW+Pznv4ZlJfD5Ijz6\n6ONkMo/x3ve+9aIPwZWIWq3G5FNPcWCjEPE8j4eePkcyuI1KRaO3dyuSJDh79jjf/vZ3AYlz52aJ\nRIIcOLCLnTuvYs+e7Tz5ZIU3vGEXJ04c4uzZedbWTEwTytY6kpzFaOq4VOjrGiMQbEeWpzcks1WE\nWMR146jqOI7jYNtlgkGIRjuJxwM4zjS1RpZuKUDFzLGsF6k7KWyRQfeaPDExDxh4dheaLGgWHPD1\nYppzCAr4hAOWiiYFEPZZbNYIKVHiyTGEp2CaTRTXoOaYDAxcz/qFGZ48W2JqZZVd/RGi7RmenMuS\n7h3i7OICiaCPTDJJsdFgBbj7llsAOHv2HJYVJxJ5Lto0EAgjSR2cOPEMt9zyC9kF/Yvw0EMP89RT\nZ3Fdjz17trJ//74N0uk/j9fdeSd//4UvkJ2bI0iLEp4YGeHgjTdimialUomvfe0ROjr2XuQIdXRs\n4umnv87q6qMkkwU6OkrU6wrp9E0kEp2srS3hOD6kgMBxFAxDZnVlCdezcVin6XSwvCyjKGEajQU0\nbZB0eiuVyjyKkqfREPj921HVMK7rR9OGqTQepemWiRNC4gIJ8hQIYYs0imJTa6wRDnvIcgQhVOr1\ndVTVxXbquFYajwKmGyESCCCEi2EWERSom91UdR1zPUW9cp6APERf+xgDfSFW1hYwjDW6Er2UyhcY\nHe2kt6elihG0VEjLy8tEIhEGBgaueBOzfw4f/zj83u/BP3E6+0uDN7wBPvxhmJiA0dHLPZoXFr9U\nxQjAtdfuY3x8G0tLS9i2zf3uHD3POxvdvmkTDxUKzM/MsHdsjPToKDPHzhNL9mOWPWJSGLNZwzSW\n8ESTrAtBR1Aq5TFdk0CoBz8BNNdj1Srh+Q1CoU3U66cRIoGu26iqSSQCsVgfuRz4/YNYlgk0cF0f\nkhSi2SwjhA7YCFGgXnfwPANJascwFGS5jiTFOXfuOwwPdyPLo0xPTzA/P0Eq1cfqqo5lGYBJMrkF\nSZK4cOGHmOYshuHH80K4rsrExMP4/XvRtCiu26BQWOC223bTbPqZm5tjZGQE13X58pfvJxgcex5z\nvo/FxQl++MNHuOOOK1fc3mg08AmBvJHAW6hWyZU8OpIdFAt5HMdGklSi0Q7+4i/u5YYb7iSd3o1h\nNPnyl4+wuprluuv2cvz4vRQKCps27SSXW2Nu7hClUpl0updkew/VCznCwQ56BzbjeR6RyNBGMuoc\nfv84y8s1HGcF217DcXLYdoxGI4hhVEkkJGazFudrZ7AIY3gZGpSxPT8uEWx3EVXE6I3FUQXkqlk0\nKYprW8jKOiH/IH5PpWbNYFJGExJVr0bQ8vCsGsFQgLXyKWIdSUZG9uDYBgszx1nPuUwXl0nEdULJ\nHpxygolsnSPnHqanI8PYji28/0Pvv+igWqnUUJSfPoz2+UIUi5UXdR0ffHCW9vZtG/cvcP78Bd73\nvnf8lPT0J5FIJLjnwx9mdnaWarVKNBpldnaBP/3Tz2EYNvV6Acdpp6/vOT8Nny9AX99errsuza5d\nV/H5z3+Jb33rCRKJLkxTZ35+ls7O7ZRKT7G2WsB2PPxAzZhGlwwkulhamicQCNPbm2Fy8gkUpZMt\nW/bhur2cPPk4stykWi1iWTJCdKDQTp0iHiXCFMng0efzmFcrzFqn8Rhn7943sro6z7FjT+N5WUzT\nh98fwfVWMG2Q5QJ1vYku1fC8VSL+HupGk6BvB4n0GLXadyk1Z9GXcySjCUJxmfHBq1GUAjffvO/i\npmKlUOCJqQWWvnIYWY7heQ3a2mTe9a43k0gk/omZvnLx+ONw7Bh86UuXeyQvPnw++MAH4FOfgr/8\ny8s9mhcWv3TFCEA4HL5oyHOko4NCpUIyGgXAr2m8Ys8evhePM/z617P97rvZ8dBDnHz0KWbmS6wv\nTGPqOdJKmO7YDmzX5WR1Fttns2s0zezqIk27k4bZxLaWsEih6wFMM4OqhvH7PWKxNhzHoFRawDQj\nSJJAlgWRSJhqdQ3HWQPW8PtrxGJ7qFYlmk1vQ92ygixLdHbuIxTqQpLmWV2dQ5JM9lie7gAAIABJ\nREFULCvD+PhearVzuK7E0NBNnDlznmeeeYh4fBvr6+C6MprWg6aptHwsVHT9CTyvhus2kaQyR48+\nSSYTxeer8v73vwNN0ygWLfr6LiWmdXYOcfz4Id74xtchSZeG6V0piMfjWKqKbpr4NQ3bcRBCoWHo\n+MORixkas7OTWFYbHR0tq3dV9REMXsPhw4e57rq9fPCDv8r99/+QL37xr5DlPq6++jYsK45hrOF5\ni2zffgu5XIWZmXOMjY1j22U8L0tvbw87d17Lfff9DcvLefz+OLadwLJ60PUIjlPGdSGcHKXmM2hW\nm+i2AhxAuPlWHKIXwfFsVCCqRkBzcChQlop4siBnT5G3iggRRpJ3EnRNdLHKamWNoBpE2DKammXL\nljuYnPwHVtcKOPIIkuIjX7pAvtYgrfuZnllHCJ10eoCxa15DKBTmG994kI98pA9N0+jt7cI0J2il\nyD6Hej3L0NCLyy3q79/+vPvjzM4e4/z584yPj//M58qyzPBwSwH01a9+kyefXKe7+xo0zc/Ro4eY\nmjpNV9cwyWTH856jbuQ1pfngB9/JwsIi58+fAFSSyTCuW0DTBvDHZzEVC8MpYno6qu96YrHrEELC\nMApUq0tomopt55mbe5R4PABIlMsejuNDltuxrCweVVTK+KnQRhJdKJTNOiVhEJE8fP4C2exJlpZW\nkeUVZLkHIQZxXQPP03DdpwEFvEVkScenpig1V9CUNgQVqtXTCNGDL7CLUDhLqitBKuVx002v4okf\n/RVnlpfpTiap6jqHZheIZnYzOPic0eHq6ixf+9r9vPe9b3+hlvQlw3/5L63bL7NV+vPxoQ/B2Bj8\n4R/CFXZy+q/CZfuGEUL8uhDiyMbtZ+bU/KK4/jWv4XQuR67ccjbNlst89/hxekZH6ejqYvfu3Xz4\nt3+bj/3xJ/jgx95Fx2iC3rYuuru20xCCdUzqgU0EItvZNjbGeMbPYLxJRF1FoOK5KXRdwXFSOA44\njh/L6kaShnAcgePMo+vn0PU8oZBDd3eUaNRPMhkgFGrH87oJhbYiyxkkKQm0oSgG6fR2JMlPLrfG\nwMCNtLf3EAj4KRTyZLMSy8tTOI6BzyfRbKoIkcTzJIQYQJIy+HwKjUYTVR0D2vD7O/H5ooTDeygU\nugmHN6MoW/g//+frLVXCP0Fka8n5rlxomsbem2/m6cVFSrUayUgEyy4xV8yzaXzbxdc1MzPB4OCm\nS15ny6E0xtraGplMhuHhPq699rXceeddOI5CMtlJV9cuGg2VSnmRTCqJEItUKo+TSKwQDnukUp30\n9W2ip2eA7u5dxGIJQqEhQqEklUqOSKSTTOY6fL4tdHTvx8QG0YksSyClkeVRNHkbEgor1So2NgKP\nqllGkCYSuYlU5q1Y8iZsOY0a2oTW3svOTTexe+gqYtoqmzokeke2U6tlmZ2doV4P4boBGo05XDeE\nEP3kckuYph/Py1As1pmaOkNHxyCFgszExATQkr92dUlcuHAcw2hiWQYLC+fIZBy2bXtpLd8DgTQz\nM4s/13Py+TzHjs0wMLDz4pHM4OAoQqSYnDxzyWMbjTVGR1tRAdFolN/+7Q+yZ08b11wzBhSp11UU\nJcn27a/ihle8j9Etr0XRBggE4uh6Hdd1AB+5nIGqdjIw8Fr27HkPfv849foynudHksLIcgpJGsTB\nRKJBnDiOiOBpKdalDpoM0vTStGXiaFqZaFQiGr2aaHQMy1rGMFbxvAY+Xx8Bf5R05nocbwdNO4Us\ntSPUaxByB6XSEpBEklpHh7reZHExx9NPH2bz7n1cdccdKFu30nfLLSR6xhgbu9SPp729nwsXchQK\nhV9wxS4PHnkEpqbgPe+53CN56dDe3koh/ou/uNwjeWFxOTsj3/M877NCCAU4Cvzti/FHNm/ejLjn\nHo48+CBHzp7lwtQUo21tJLJZDt97L4czGd7ynvcwNDREJBLhew89yYSwKboOricIRQcYl2IsTR2h\npHuMDGY4eXKNhgeG04Yrj4A3i/B6wdGx3Gew7Tp+fwe6DtHoOKXSIp6nUCyayHIdyDE0dC1nzjyG\nLPsJhwNoWgzLauJ5IYRYx3UNZBlM00ZRfDzxxCGqVQu/v51wuAPDWOXcuUdxnCDpdApVBdMsEgpt\npb29DdNUN0ycQti2BsyiaSkUJYNpTtHVFaezs4+VFYvp6TliMYlKJU80+pxMcnX1AldfvfmK7Yo8\ni2uvuw5/IMATDz9MeWmJoV0jLBcUJMmiVitRKq3j9zcZGPjpQDfPMy5aYs/NrRKJpBFCoCgKrutQ\nzJ1FZKdJmVlipoVplBnbtIftO6/n6NHv0tbmsLT0CJIkEwyaXLhQQlFGURRQ1TZqNZdw2KDRkHAc\nBUXtQHcsXNdGliO4rgkigCqpuKyTa3oY5gqmFEJWFCRJxbbrCJFEkhVkpc7m7fsRwsQqriObi4RG\n+lDMCMePH8O2R5GkNKGQn3weYAnPC2wUql3Ydh3TPE2x2NpSqWqUbDbP4uIiD3372zQWLlBbWuap\n+SP0Do2yf/9Orr9+/yW24S8FLKtxyXvxX4JCoYAkRS8pODOZNAMD3UxOHmVsbAcA+fwcW7cmL3Ed\n7u/v5wMfuJMHH3yUr3/9HEJsZ2Cga8Mm36ZQWCAU6iYcdggEXBqNIoZRIhTqwnVzpNMZZFkFkqjq\nALr+NK47iGNVUFhCZYEGfZwVBkk1iOlEcEUQv6KhyessrK4RzQwSjSYpFCR0HWQ5CUTw7GlcfZGw\nmiZmgqUksZQkQqwTjVroegDHiSLLs0hSAFneRCQyimGUOXbsMDt3HuDAwYMb82rx/R88gaJcevwl\nhEAIFcuy+EkUi0VyuRzhcPiySrl/Ep4H/+k/wR/8AVzhaQQvOH73d+Haa+EjH4HUz/dvcsXictrB\nz23cfTa05UXD6Ogoo6OjfPFzn+OqVIq+5zHIzy0s8Mk//EOSwSDCslg5dwJJjNC96fUXP9Rs22Z2\nxsawba6/6QCW8wgP/3AB1O0oXgEPB0my8PsSNM0IVnOespPDtutomsvu3a8ml1sin58mFpMZGtqK\nz9eP338C0zRoNPIbjqh1ZLnVQnYcnXq9giRVefrpf0DXi0hSjGo1S70+j6KUaTZ7qddPs3XrQcLh\nDLqewbYdAoEArlvD71exrCbt7W0kEh65HECW3t4EIyOtVnwkkmJpaZG77nodf/VX91Eup/D5wjSb\nBVIpi1e+8rUv5tK8IBBCcPWuXVy9axeu6yJJEnNzczz22NPkcoscONDDrbe+n29+8xiO04Mst972\n2ewimYxy0cCprS3BxMQ6iUQ7Q0PdHD8+gZQ9zog/TLo7ztpagSGfyvrpf+CU3OTmm8e4++7b+fSn\n/5JmcwHXjdDevoNazcOqLxMUAhoexbUSFT3P0NCNWNYAi4vHgBaHR5IauEhIqIRkj6pxGkVTgXaQ\nQtj2PIoiIcsGQmhEoy7pdAZJkkgkMsiBJT78kXdy+PBjnD49BzTw+VygjiRJCNGN560iSamNuYph\n2ybBYCu0w7IqSFI7X/3f/5sRv59tw8PYAwNMrazgdMe49dZbXpJi9NlwNmgpeCDL9u23/ly/IxwO\n47r1S65JksTYWD+9vWXi8Za52ytfeQ07dmz/KcJmX18f73lPHysr65w8aVGpVCkUGqyuzuM4Eo1G\nnUikg2x2kr6+TRSLCtWqjaa1iL7QMtWTpCia5uJacwSpEXYlBD5MKUhDSVMVPiJimahSwnQtDFUi\nFIwzN3ea/v592PYctp1EVSOYRgPZzaGKJFEtgc+FjOSnqjQQwU50fZpIZBzT9OHzlVCUTfh8Gs1m\nCcMo0t8/hmGIi54rqqoyNNTN6uoy6fRzXLpms0Yg4F7iIWLbNt/61gM8+eQUkhTBdRsMDiZ561vf\n+E/6+LyU+M53oFJp+W/8v4aREbjrLvjjP4ZPfvJyj+aFwZXAGfkgcN+L/UfK5TL5uTm2/IRz4Mrq\nKvmzZ3nt3Xfj8/lwl9a479DTLPvb6O7ZC0C1mmVg2M+BO27g5NISbdftY0ddsLiYRDNkdMtHrlpF\nSBqKDJ5bwjDW8Pk6CQY7aDSWSSYjdHffgCzLdHU5rK7OEQjICFHE8xJomksgkKDRmELXC5TLZwiF\nZBTFoFJZRJZ3IstduK6BZbXUF7K8QjhcR4gGg4Mhdu36Nb7xja9QKHgoikZnp8zCwgzBoI94fBP1\n+jypVCcdHdJFolqtVmJoKE1/fz+/9Vu/xjPPnCafL9PTczVbt255yXfE/1o8+8XZ399/icuk53k0\nmyYPP/woEMXzDNraVN7+9jsvPufqq3dw6NC9VKtpRkaGOX/mcait4/o8/P4eurt9dHakqXseA7s7\nedvb3sTKygq5nEsoJKjVwoRCUSrZH5DyMsiOSiQao6LPYrhL6PpO6vV1QqEU9XoBRRlAVcN43hSq\nWsYScWKBIP0DQzhSF/H4EBMTT2IYNcLhGNVqDs/rZH5+kp6eAfL5MwwMxNi7dy8+X4BarZOHHz7F\n+rpJItFDs1nBceobHKUUlpVFlnUCgRC9vX2src2SSFhUCwU6gI6NsEVFlhnr6eHxuTnm5uYYHBx8\n0detVjtJLteSkft8Bm97262kfs4tX2dnJ8PDSebmztPVNYoQAsNoUi5P8573vOlfHOx2/fW7qdXO\nk0oNMT09jW33EokkmJi4j87ODjo6hqhWZ0mnPSRJ5+ab38zExDKFQhYhmsA8gUAftjND0OsCOUzD\nmsViFZ/chc+cp09LEFD9uBRQIxEWVR1HFZTLU0QiNYLBGI2GhdGYxy8X8SntaFII16ujCEFMlene\nMoJhgKLIGEYeEGzfPkYkEqVer2IYJq961X5qtbNUq9WL8/na197IZz/7ZZaXm8RiGWq1Ms3mHG9/\n+6svKdAOHz7C44+v0N9/4OL/yMLCBF//+v28851v+bnW5oWG48Dv/z780R/By1gE9K/Cxz8O27fD\nRz8KfX2XezT/erzoxYgQoh34u5+4vOJ53tuEEPuAW4EXPdLIdV2k1nguXqs2GmSXluiJRC5e37Nr\nB2vZHA/OPcK8W8F1XYLBGv/5P7+fG298LsuhY9M3+OP/7+tABE3zkSuX0I3zeEyiiAiynNnofgxS\nqSxSLJ7GNFO4bpF6fZhIpI1MJk0ut4wQWTZtGqNUWsU0oatrC6lUgFgswKFDXeh6iWq1juuuIYSD\npgVQlA4SCcE99/wG5XKDer2Vv3Hw4F5OnTrCwMAwiUScYlFHiAiqGkMIFyHW2Lv3NciyTKVSwLIW\n2bev9cESi8Ve1um9/xyEENx8843s3bubtbU1fD4fPT09l7wf0uk07373bXz1q99jddUlmdBJDAUZ\n7m5ncTGL35+mVJYpNtdpb+rIskw+n0dVk1x11W6+/e0HEV4bfapLw1tAdxuoTpSeuEwvbUzVnkTX\nG8TjwwSDBRqNGXw+H11dnXR3D1MoFHnDG95MIBDhoYceQFFsVDWI50Xx+y1se4JKxeTs2UWWlx1G\nRjK8730f2pBm9qGqx3jDG17NN7/5ffL5SWS5iOtOIEl+/P4gQtSxrAuEww6qWqOvz+a22+7mO1/5\nCl0bBO/nI0yrRf9SFCO/8zsfZHGxxRHp6en5hYP37r77du6777ucPXsIITQ0zeZNbzr4cyXM7t17\nDRMTs0xPn2N6ehYIYRjr3HbbW1hdXWJ1dQXHWeXqqzdhGJtIp1MMDIxQLlc4d+4kk5NVCoUSqtWN\nJoLg6ST8vTSVFRR5lpStEI9FcJwKrithFPMYbhm17wBbtgxx4sST1GrnSaf9KO4s7aZDxS5hmGGC\nAZBVGymYQNdzXHvtPkKhEAcPRjh27AyGsU69XkXTPPbs2Uk4HKTZtC/pZHR2dvKhD72dxx57itnZ\necbGElx77ZsusXj3PI9Dh56mq2vXJZ2xrq5NnD9/mGKxeFmVN5/9LMRiLanr/6vo6mrJfP/9v28l\n+r7c8aIXI57nrQE/ZVAghOgGPgm8wfP+cZrkJz7xiYv3b7zxRm688cZfeBzxeJxQezvZUonMhkVf\nXdexm018qRQnT56hXKqRSEa4+Yb9uBcucM0tN5NIRNi/fz8AR448xtpanq6uDPv3X8Pua37M6cOz\nVIou3XFBsbZKQ3cQtOELh5CkKrXaEooSxrLSGIaOz6dRrRq0tSXo6roJTfsRW7b0kMl0o2ld7Nw5\nwk03HSSVSnHs2DHOn/8sS0uCcLgT11VQlAiua2GaFyiV5rj11hvJZDKcPHmKhYU1rr12D3/wB7+G\n4zioqko8Hmdubo7FxSVgL1NTC0xPn2V+foJk0s+73/36K+oc+MVGJBL5Z31ThoaG+NjHPkA2m2Vx\n8Voe+uIXWT09i+OkyGYtNA2yIsjJMytks1nC4TCe12TLlr0sLMxx/tQknZEwES2EP2RhWzlSiTDH\nz05iyxE6O19BPL6Fej2LJE0wMjKAJEEu9xTxeA+Vikkk4uO6667n4Ye/Sy53AdsWDAwMMjr6bs6f\nn6BQmCEaddi+/XoeeeQYIyPDdHd3s3v3AE88cYHbb7+J5eUVfvCD7+DzafT27sUwbDStVSR3dRX4\noz/62MV5SHV0UDxxgvhPtN4btArUlwKapjE0NPSzH/gzEAqFePvb30y5/P+3d97BbV1nov+di94I\ngA3sFKlmqlAk1SxZkiVZtoq9lmQ7TrLuduzYWW/8NnnZN0ne2+Tt5M3uzk422U3ZxNk42djjxHGP\nW9xkWZLVeydFUiLBBjYAJACin/cHFFlUsRokkPT9zWAGvMT97of7Hdz73XO+4iccDpOdnX3B9OAz\nMRgMPPjgl2hubqar62c4neMpK5uEyWRl3LgqIpEhWlp288gjK9Hr9bz00p9pa6unoaERn2+AqqqZ\n7NzZiUhq0WsUHPZx6PVmfAMG0OzAaQ4wOHgAIUyYTFnodArZynj6YyYKCq5j+vSZvP76Hygvr6bd\nXYy1swFjwEtvuBFX9kRKS0rZ3VIP4W4aG71Mnz6ORx+9jxMnWnnxxW0UFFThdOaQTCZwu/ezdGn1\nWcULc3NzufXW5ec9B4lEgqGhGLm5w2dGU7El+ow21+zqSvWgWb9+bDXDuxy+/e3U7Mibb8JtI7cC\nw0WRyWWa/wPkA6+cfDpdKaUcNsJPd0auFCEEt6xZwyvPPENfIIDDbKbT66UpGERgJSsmMRrz6egI\ncPjYFkoWz+GLX7wTgK6uLn7965cIh7MwGu3s3HkIq3U7Tz75AN869D+woOAwWYn5s2nwaxjSFWNy\nZFNSUsSuXVtwOksIhTwUFbnQaFxEo4KGhvXYbAZCoT5crunMmjWOxYsXDatyWVJSQm6uHikDWCxO\nwuEA8fgAsVgfer2X8ePzTz21zp8/77zfvaKi4tTnFi5MdS2ORqM4HI5LbnIWi8UIBAKYzeZRUZ31\nclAUBZfLRX5+Pq+++BIf7K8nS5oQQhDQKESzx2EZsnLw4GEWLVqAy6XB42lh4cKVRIYGiRzZTTIZ\npCAvh1mzlqLX63APBgkbJtPb5yMUCqAoWkpKJjBlSgmHDu2momIq3d1B9u51U1/fwpw506iqms6+\nfW6mTJlFWdlUWlrcmEzlFBbmo9HsYfLkufT3d/HKK3/ma197kDVrVjFp0mF27jyE3a4lGKykoOAu\njh49TDRqAOIUFRWSk1M4LFCxbu5cXti5E8fgIE6bDSklx7u60LhcjBs3LmN2uBLsdvsVOVIajYaJ\nEydy550r2b7deyomBFLXEoslSXFxMWazmW984zHq6+v55S8HWLLkHnbs+IgdO9ox2icwFGrHEk8g\nlBAAfcEkM6fU4O3uZnDQRCIhAQshReJwlPPnP29m6tRKKiquY9++9ygrm4MvYKDAmsWMvHyautrp\nCvYxfXYZRUUzsdsLSCQi/OY3r/Dgg2tZsSLAxx/vJBi0odEkWLKkmqVLz92d97PQarWUlubh9XqG\npURHo2G02gjZJ5f0rjVSphrhPfooTJ2aERVGFEZjqt7IV74CN94II7g+5QXJZADr49f6mKWlpdz/\n9a+zb/du+j0eJl9/PZ909uNrD5BvsqHX6ogm4ngGzVijmlM9L1599V0UZRylpX+ZQSjB42ll9+5D\nTLt+If1tQbqbj+FFg3DkYtTmoigmhFCwWBwYjZCVpVBVdRPhsJ9g0E9LSy8Wy3xycqZis01n375+\nOjtf5atffeDUTd7lcrFsWS27dx/D692FopShKBGysgKMH5/H4sWpGgzxeJxDhw6xZ89RAOrqqpg6\ndep5KypaLBYsFsslnTspJZs3b2Xduh1EowpabZwbbpjBkiWLRl3lxlgshlarPa8jFo1G2bdvP/v3\nN/D2e9tok0U4LSVoFD0JrRG9CNPQ0E5vrw+NRsP993+Bl19+i+PH9zGtehJHIieYVZrPwlmziEQi\nvLdxE36TndW338GuXZvp6uomN3cSUibZtu0TXC4rN9xwO21tzezde5jm5j6OHt2KyaQjEmnBaJxD\nMinx+4OYzfkEAh40GvB4WgCB292Hz+fD6XQybdo0pk2bRjQaxeP5GYWFkygvn0woNIhWq8dgMOF2\nbxo2W1BQUMCtDzzAB6+/TsztJiElhZMm8YXVq0edbdPNggXXc/jw87jdR7DbXUQiIQKBFtasmY/5\nZLeyVLPFBBZLMRqNhsLCCWRlbSAeDxHQGQmFOsnW2OiPh7C6iujVG9DoDYwbNwlPz3H6ooOEjZMQ\nkSz0+gSxWBZudycWSxZz59YQCk1AJhPEg34mTogzONRGVdVyCgrGndLT42nh+9//IUVF5QhhRqOJ\nsHr1TdTV1V72d1+x4kZ+9atXSSTiOBz5hEID9PXVs3bt/Et6EOnv72fnzj20tHSSn5/NnDm1lz0b\n+2//Bh4PvPTSZe0+Jlm2DJYuTS3X/Nd/ZVqby2ckBLBedTo7O/lk3Tpa6usxWa1MnDGDpatWIaWk\nsGQv8TwzBxr3QCyK1mpn8uIvEIu1EQwGSSQStLf7KCsbXnwpP7+UxsYNlJWV4nKVoLNPJNEapiQr\nm2PHtuD1nqC5OQuvt4dotAONJswnn/ye7OxphMM+/P4oFks+8XgfXm8n7e1trFvXxLFjLXzxi7cy\nb95cNBoNDzxwDwcPNtLQ0I/H48ZkMjFpUjVWq8KCBbM5cuQI7777EZ2dguzscYDk+ec3U1NzjLvv\nXsvQ0BDbtu1g374GdDotc+dWU1dXe8k3mW3btvPGG7spKZmJXm8kFouybt1BpJTcfPPS9BnrKnLo\n0GHef38zvb0+7HYLS5bMYebMumFOSTQa5Xe/+yNNTWF0OiceTy6JhILUObE5U2MgFGqlv38vDsfJ\ntvUOBw8//Nds2rSJrVv3M+OGxXT2unn6/Q/p7QsQ0eQSkAVs3ryBGTOqmTAhTnNzIz09HZjNfSxc\nuAaNRseECdM5cmQ3fm8/0SFJ3vgCClx1NDbuJhIJEYvFCQR8JBItBAIhtmw5BEAgcJijR284tZwI\nqWWPmpoJ7N3bSEnJdVitqaXJrq5UWfAzl6omTJhA5d/9HT6fD51Od1EtAKSUdHV1MTAwQHZ29ojr\nXXM5DA4O4vP5SCQS9Pb2YrPZePDBuzh06Aj19S2UldmYPfuvzoqjSQV6RwDIycmltHQSPl+QaNSO\n0WgjEunH33eMSHcWm31daGIe8ockJquLtugQmqSeiG8/8bifwkITihKiq6ufLVv2YDLlIOUAJSX5\nlI2rYOvW4+TnD49YbG5u4NixBFOm1JwMYB3gxRc/xul0XHbMT3l5OY8/fhcff7yVEyd2kptr57bb\nbqaqquqiZXR1dfH0038kkcgnK6uAzk4f27e/wP33X3qW3osvwg9/CJs3f/5SeS/Ev/871NTAq6/C\n2rWZ1ubyGPPOiMfj4Y+//CXlWi3j9Xo2b97M9tde40+lpcy+6Sai0RATJt5A5cQa4vEYOp2BZDJB\nZ6cbnU5HIpH4TPmLFs3khRc2UlRUjtvdgEajJy+vgHC4AYPBisORJCenjGAwiterIxjUEggIotHx\nbNz4PKWlZTQ2HsRgKCE/v5pIxM7rr+/F4+nhjjtux2Aw8M1vfpX//u/XiUQs+HxBWluP4PH08uMf\nn8BqzaGhwUNRURW5uQbsdjsORz779m1j+vQjfPDBZnp6jOTlTSYSifHyy7tobm7l7rvXXvQSTTKZ\nZN26HRQVVZ8qJqXT6Skpmc6mTVtZuHD+iM+6OXjwEM899wF5eVMpK3MSCg3y0ktbCYcjLFjwadDu\n4cOHaW4eoqKijvb2dqzWMrRaM15vPQZDHnq9nXg8lYZ9elDku+9+yEcf1SOlnUOHvHg8MdzudnJz\np1FePh5t3I+iVLB3736WLVtBUdE4tmx8Fq97kKZP3iCm0aDLdtF48AATrVPQZUUZX+hifzBIr3AC\n7RQUWAkEoKPDi9NZS3+/BbNZQ35+LW+/vY1JkyYNy0BZvnwp3d0v0tKyHSFsSBkkP19h9epzZ0Io\ninLR0+/BYJAXXniNpqZ+FMVCMjnIjBllrF172yXHaIwEBgcH+clPfsmHH+6kp6OFuLebfEceNpeL\nkusm8sTfPMBXv7rovPuPGzcOp1PS19dJTk4htbW1HDhQT39/PXa7lr07t2AT0ynOngoCugeP0zJw\njCml42EwxNBQFCkdGAwumpr2YDYPIsQENJoy4nEjXq+etrZmWlsPUVIyvFKy399LV9cAWVmlKErq\nkm6xZJGVNYENG7ZfUQBySUkJ99xz12Xv/847H6HRlFNQkOqJY7M5CQazee21Dy9JznvvpSqPvvce\njNKVw6uKzQbPPgt33JGqPzIawwBHdjWrNLB1wwZKFAWz0ciWLVuo0mq5raKCkoEBxPHjBHuaaG8/\niqJo0OuNCCFob2+grm4SBkPq5l5a6qS3t32Y3O7uFiZPLqG2tpY777wBrbYFh6OL1tbXUZRW4nEb\ng4M68vIm4/W2EQ7rsNmy6etrAAYQIoGijKe7O4RePwOz+Trc7lZaWo5w+PDYZkOoAAAbNklEQVRx\nfvSjP/Cv//pzmpqaKC8v51vfepSaGjuhUCuFheOIxSoIh6s5eNCDxTKZZDKbrVv3kEgkEEJgNLp4\n772P6O7WUlY2BZPJis3mpKJiJvv2dZzKXLgYwuEwoVAco3H40o5WqyOZ1BEIBNJhqquGlJJ3391I\nfv60U03gzGYbJSU1J5edoqc+e/BgI1lZRQDodDqys81YLGZstkJisWaE6MFqjVJbO/FU9kF/fz8b\nNx4kN/c6Dh3qwGyuIBg0Eo1OJhjU0doaR6PJprX1IF5vmP3732f/7j8yJSvJLRPKGafTM1Gj5cg7\nzxLu7cPX56Orr489DS3EkgYCvgH6+93U1pbR27uNeNwOOPD5/LjdDSenzPM4eHB4lVGLxcJjj93P\nww/fwtq1VTz44FKefPLhtASlvvHGuxw/DuXl8yktnUFZ2Q3s3etl/fqNVyz7WhOJRPjOd/4fb77Z\nRshrxNYTZJwcjz2QxbiklUBTJ7/4xR/weDznlaHRaLjvvjswGjtoadmO3R7B5erFbo9x9NA+kols\nFEVhwO8jEU9QaJ+EVrhoanoHozGf7GwXFRU5VFdPZerUxQwN6cjPL6a5+QDNzS0Eg1FCIT0nThzH\n6TTh8Zw4deyhoQDhsMThMGGxfNpbyGZz0tHRczVP3WcSjUZpahpezwTAYrETCFx8aeetW+Gee+CV\nV1JP/yrnZv78VN+ae+5JpT6PNsb8zIi7sZHa7Gz2Hj1KoaJgP/kEb1IUsk0mprpy6Nd10tIyiBBm\npAxSWelg+fJPlx7WrFnOM8+8RGtrPwZDFuGwD4cjwqpVqSfM2bNnUlNTjdfrpa+vjx/+8Ge0t2cx\nYcI8LBYLDQ0R+voMaDRxCgtLqaycxM6dO1EUI8lkH4GAgsUiCIU8dHQUMG3aPKAYrzeHZ555gyee\nuAuXy0V9fQe1tbeze/cWbLYJWCy5eL3ZdHd7cLkq6e/30dvbh8uVTyIRw+Ppx+kcP+x8CCFQFCdt\nbe3DUvk+C6PRiM2mO1kY6dNAvlgsilYbG9FdfSHlTHm9Q5SVOYZtTy036fD7/aeWGEwmA/F4Krgz\nJyeHwkI7yWSESCQVtGe1mhgaOsZjjz12amaps7MTIey0tXWhKHZisTCBQASzuRwpOwEzNls2ZrMR\np3OQJUsq6DlWz7yCAqLhMBs2bOPokTaccS29CT/RpCQo7US8RooLC3DadFjsGsrKTFRXz8Hvz0dR\nDBiNWWRlVdHf347LFSYQCJ313RVFOdW3JV0MDg5y8GALJSULTm0TQlBcfB1btuxg6dIbR1Wsyd69\n+9i3z4fLNZOufb/HqSvCaMwjEumnv8fP+MnlHHX3sHfvQZYvd51XTn5+Pk899RXa29tpbGwkEvGi\n0Uyk8YiCxVgIDBJKdJEcAJM5il6xYs92cuutKzlwoJ3c3PEoioaBgTZiMS1CBCkrm4BGYySRSGCz\nVRGLSWIxHVK20tIyiNHooL+/jWSyjZqaZcP0GRjoo7T0/PpebTQaDRqNIJGID6v4KqVEyourc3ng\nAKxeDb/7HSxYcOHPf975h3+AW26B730PfvCDTGtzaYx5ZyQrO5vAwAB+v5/C04KuYlJiMBhwRKPM\nXbkEl8uF3+/H4XCcVX/C5XLx1FMPcejQYXp6+ikoqKCq6rphLc51Oh35+fkkk0mysysoL/dgMKRO\nr8PhwufrxecLUlHhoKVlB8nkIENDjWg0UZLJFgYHBzCZzBiNZQihEAj0EYu50Gpz2bhxO4sXzyMS\n0WI0WgiHh9BqU/NwBQWT2Lv3feLxWkBDPB4jGg2TSHiYNKmS9vahc5yVGGbzhduz/wVFUVi69Hpe\nfnkLRUXVGI0WotEwbW0HuOWW2hGfVWMwGDCZtEQiQxgMn37vRCKOEJFhwby1tVPZufMtEolCNBot\n8+fPZNOmLUQibvR6I729+5k2rYYPP9xDf/8gK1cuw2g0ImWUYDCBTmdgYMCH0ehgYMCLXq9DUbRE\nozEsliyGhpqorV3D+mP1GHQ6DDodVVWV9PZG0SWT+HwePHRjNtQgpIJvsI9Iop1l825j8+btlJZO\nJBYbJCfn06l3rdZKV1cTlZXXpkZMOBxGCP1ZlVl1OgPRaIJoNDrstzHSOXjwGFptNslkFGMyiaLo\nAIEQBqLRIAatnsSAn4GBC88AKopCaWkp77+/CaOxHL+/hSy7k77uMGb9OGKJgxiNGhLJfoS+j6lT\nx+Ny5aHXm9m+fSednW6iUcnAQCtWq6C4eCo2W8pRDgZ7cDjyMJuLWLVqCjqdns7ObnJz5zN9eg7N\nzW5sNisajZbBQS+BQDOLFmUugECj0TBrVhXbth2jrOzT3kY9Pa2MG3fhgnbNzbByZSoeYuXILwQ9\nItBo4PnnYeZMmDcPbr010xpdPGPeGZm5YAHrfvc7rDYbPp8Pu9FI38AAOrsdh8NB48ngu7ILlLAz\nm83Mnj3rgscLBAIYjU6qq13s2bMfvb4Es9mJEHsIh7vw+ysQohxF6aGg4DqSyQG83t2UlKyktXUI\nvV6wZ88n6PUGDh/uIx73097eyc03L0LKGFJKCgoKaWzswWCwotdbKSzMw+/fy8BAkIEBDYlEM6tX\nL8DlyuMXv3iNWKwAnc5wUj8fev3AsL4cF8OsWXUAvP/+Fnp6EhgMglWr6obFW4xUFEXhxhtn8uab\n+ykvr0WjSfWdaWs7zNy5k09lRUCqzsiyZdNZt24L4CQej5BM1lNa6qK9PYrDMYNYzE529gx2724h\nFHqTL31pLXZ7Aq83daPW6bTodEY0mnoUJYtIpBchcujtPcKiRXlMnjyZj/T6U52GY7E42dn5WKx2\nWhqHsMWCxBINxBOSeDDMdXUzmThxOh0dRzAYTOTmhujtPYrVmlqH7+9vYMoUGxMnTrwm59PpdGIy\nybNmyvz+XgoLnaPKEQHIzXWi1SYALVGdgWQkCDhJJmNotQpDiQiKQc/EieUXEnWK9nYPBsNEhNBT\nVjkVX99mInETiST0hg4TiRqxOLRYrQXs3v0udXXLMRgSlJdfjxBahobKaW/voL5+PdXVKwmHB0gk\n3EyZsojBwU6sVitTpkyh9mSyzIwZ1XzwwXq2b99MMqmQk2PmwQdXDatCnAmWLVtMV9dLHD++DUVJ\ntUDIzZXccccXePTR8+/X05N6wv/ud+FLX7p2+o4FXC74/e9TzfS2b4cMD4GLZsw7I1OmTMF76618\n/MYb1Pv99AQCFBUXM722lvr2dgwlJWmtpZCTk0MiMUBZ2Q3YbA6OHz9GMBhk7twq6ut76e5OkJur\noNNZSSSMCOFEiB683u1EIlF6emLY7WVMmDAJjUZDIKDF63Vz7FgT48fn0dp6nIqK62hpeRevVxCN\nJpg2bRpGY4CyMi1Ll86noqLiVFzA6tXX8/bbm0km7UgZx2QKc999f3XJqb1CCGbPnkldXQ2hUAiT\nyYRWO3qGz/z51xMOR9iwYTNgQsows2dPZMWKZWd99qabFjNjxjTcbjebNm1Dyhvo7OxGp8vHbM7B\n7+9l374jzJs3iyNHNuP1ernvvrX8+tcvUF9/mKEhM9FoIxUVU5ASEoku8vIMmM1GHn/8QfR6PbOX\nLmX3G29QXVSE3Z5FKOLGK4yUzlmBf88ObMLFUGSI0gnVLF62kmQyQX6+FUUZZObMhXR0NHHiRBPx\neIzx4+M89dRj18weWq2WVasW8sIL67HbJ2C1OvD7ewmFjnPnnaOvJGZd3XRKSz+htbUX4RzPYGgX\nsUALMhnHmVfCoW43NcsXXlIWicuVQ1dXDCmjuFyVTJwySGP9AQKDx9BpC3Hk2Hjk0ftwufL5+OOX\n+fjj5wgESrHZ4phMMZYuvRm3u4233noFr/cTKiquY+LEpWi1OqLR4FlBqQaDgVtvXc7NNy8hGo1i\nsVguuYbQ1cBkMvHII/fQ0tJCf38/NpuNysrKzxyrQ0OppZkvfxmeeOIaKjuGWLgQvvUtuPPOVGfj\nS7zcZwRxnuKnGUcIcb7CrJdFKBTiwIED7NiwgajPh9BomDBjBktXrLjkG/P55Ot0OnQ6Ha+//hZb\ntrRTXDwFvd6Iz9fNwEA9VqvC7t0hgkE9yaQOr7eTUGgAiyXJsmVFDA0NsXlzkMrKhQihEI0GGRw8\nxMyZ1WRnD/LII1/iuedepqMjQjicxO0+jEaTZO7cOhYsqKOurvacTc0CgQBtbW1oNBrKy8svu9T2\n1UYIwdUej6FQCL/fj9VqvWCsSzwe5wc/+An5+fN4991XMZvrTnZmhf7+RpYvn09vbz0PPHAjEyZM\nIB6Pc+DAATZu3MK+fUdpbu7G5Rp/slR7iFtumcOcObMwGAxIKdm+bRs71q0jFgyy88BRTDnV1M1e\nTmPjfjZu3EZ2dgm33bYcrVaD232A+fNLyMlx8NZbW5DSiRCgKD7uuGMJNTUzrup5OxeNjY1s2LAD\nj6ePsrICFi2ae9FxSKdzLex+IbZt285vf/s6jY0+utz1JAdPUJiXTV5ZOcvvuJ27775z2AzahWho\naOCZZ97B6zXS0hIiK8tFW9suOjoOU1o6k5UrF5Gbm1qq6Ovr5MiRP+NyzcXhyCM7OxuNJlXnaP36\ndzAY+ikurgHiGAwB7r33r6isrDzZb2kIvV4/qh4M/sK57J5MppwQRUktN4wAf2rUIiU88AD4/ang\n35EQxnXS5ue06ufGGTmdUCiERqNJS6xDa2srb765jo4OL4oCs2ZNZunSRezatYdNm/YwNBSjtDSP\nFStupKGhiY0buzCZcgkGg5hMJnJycmht3cVDD91EOBzme997mnDYhBA6DAbJ9Ok15OeX4PXu5H//\n768jpaS1tZVAIEBOTg4FBQUXVnKUMBJuSqcTiUT4x3/8GWVli9i8+X0GBrKxWFLr915vM0uWzMTr\n3cc3vnHfWU3dpJSEw2FaW1vxeDwcOtREe7sXIWD69EpWrrwJm81GIpEgHA6TSCRYv34Tu3YdJZFI\notfHGRoCrdaGosSYO3cKN9+8BJ1Oh8/no6WlBUVRGDdu3IgPIL4QI8XugUAAt9uNoigUFxcjhMBs\nNl/2DMOePXt5++0NNDR00NraikYTRogylixZOaw77sBAH+3t67HZplNSMmmYjJaWHdx+eyouK9Vx\ntxKTyURDQwNvv/0xvb1BdDqYP7+axYsXjqq06nPZ/TvfST3Jf/hhqrqoypURjcKKFTB5MvzsZykn\nL5OMSGdECHE/8AhgAJ6WUj5zxv+vmjOSLjweDz//+R8wmyfhdOaTSMRpb2+gslLhoYdSfa0TicSp\npxav18tPfvIsOl0l2dkFSJmko6MJl2uIxx9/gEgkwr/8y9PY7dMAgdlsQ1E0dHY2UV1tZu3aUd58\n4AKMlJvS6Tz99LP092eTTCbZuPETLJbJKIqBSMTNlCk51NXl8YUvrD7v/n6/n//4j/9GUcrJzS1G\nyiSdnc3k5AT42tcePOuJNplMIqVEo9EQi8UYGBjAbDaPujiMS2Ek2j1dxONx/H4/BoOBgYEBfvrT\nFykrm4eifPqY2tJygAULCtixo2HYtaGzs5H8/DCPP/7AsOyk48eP86tfvU529lSysrKJxaK0tx9m\n5sy8UbVMdqbdf/Qj+PnPU0XNxkD9vBGD358KAL7uOvjlLyGT/upnOSOZ9JOel1LeCMwHvpZBPS6b\nLVt2otEU43TmA6DRpGp6NDX5aGtrQwgx7GbjdDr5ylfuIifHi9u9kY6OT6iutvDAA3ej0Wgwm82s\nWHE93d2HCIdDDA0FaGurR6/v4cYbR36g6Fhk1aolDA01EosNUVMzhWBwN+3tb1BaOsDixRWsWbPq\nM/ffs2cfsVgOeXklJ9OqNRQXT8TjSdLU1HTW5xVFOXXj0el05OTkjGlHZKyj1WrJycnBarVSVFTE\n/PmTOXFiB15vN4GAj9bWg+Tnx7jxxkVnXRumT7eeujaczkcfbSUrayJZWakCdTqdnvLyGezZ00x/\nf38mvuYVIWWqzPtPfgLr1qmOSLqx2+H996G7GxYtgoaGTGt0bjLmjMhPE80NQDAdMtevX58OMRct\nv7XVg812doqaEFa8Xu85ZRQVFfHYY/fx3e8+zne/+zXuuuv2U+29169fz7x51/Poo7dRVhZBp2th\nwQIXTzxxT9oaU13rczQajvVZckpKSnjyyb+mpsZKUVGchx66id/85v/yox99n+XLbxo2LX4uOW63\nB4vlbNtpNFn09PReki6XwliVky5ZVyrjcve/9dbl3HvvjeTl+WhpeZ/lyyfw6KP3YDabT10bvvOd\nr551bTid9nYPdntqmae+fifwl266tvNed67Gd0nH/h9+uJ4nnoBnnoGPPoLLCDnKqP4jScZn7W+x\nwJ/+lMpMmj8f7r0XXn8d3O6UM5guHa5ERkZXkIQQ/wA0AM9c6LMXw7W+0RYW5hAI+M76nJQhsrKy\nPlOWyWQ6K2blL/LHjx/PvffexZNPPsQtt9yEw+E4h4TLQ3VGLl1OXl4eq1ev4m//9mHuvnsNkyZN\nOufa/LnkFBTkEAr5z9qeTAZwOs+260i7+Y80OemSlakbmBCCadOm8fDDX8bpNLJw4Q1nBcZeqCN2\nfn42g4Mpp6OhYReQilFKJgMXvO6ci0zezDdsWE9lZWpp5nJTUFVn5OL2VxR46ilobITZs+GnP4U5\nc0CrBbMZnE5YtWo9BQVQXJyyR2UlVFXBDTekMpwefhi+/W341a9SzmNrayroOB3f46qHYAshXMAf\nztjcJaX8spTyH4UQ/wx8KIR4WUo5rKrQ97///VPvFy9ezOLFi6+2upfE/Pmz2Lv3RQKBLKxWB8lk\nkq6uZoqLjResW6Ly+aCubgaffPIcfr8Tuz0XKSU9PW4cjtg1qwuiMrZYvHguv/3tu6cK+KVq5hyl\nqqpo1DUrFAL+/u8zrcXnC4cj5ZQ89VTq70QCIhEIh+Gf/gm++U2Ix1Pb4/HU9v5+6OuD3l7o6ko5\nj88+C01NqW1FRalZrZKS1DLQ4CCYTCknp6YGVn32ajZwDZwRKaUHWHLmdiGEXkoZBWJAEjgrqOV0\nZ2QkUlxczP33r+RPf1qH2x1HyjhVVaXcfvtd50yxVfn8kZOTw0MPreHVV9+ntbUeKZNUVuaxZs3d\nIzbFWmVkM3nyZO6+e4h33tmE399Ge/sn1NaOZ9WqmzOtmsooRKNJOQ1mc2o551ITNMNhaGv79OX3\np5yTUCjllAwOXpycTGbTfA9YTCpm5A9Syv844/9jM7xeRUVFRUXlc8qIS+1VUVFRUVFRUYEMB7Cq\nqKioqKioqKjOyOcMIcScTOugcm1QbT32UW08dvi823JMLdMIIYxSyvBVPoZBShlJk6xZwDzAAfiA\nLVLKnWmSfS5HUwDvSinP7g53eceYBsSllEdP23a9lHJrOuRf4NhWUuP3IsOjLijvisfO5YyNdIyB\ndNk6XfYUQtQCPinlcSHEzYAeeEdKmbzArheSmzabZ8LeV2LrkWTjdNh3NNvySn+zY82WZ8j7Gynl\nzy5r39HojAghvgx8E4gDrwH/IqWUQoiPpJRnZe6k+djvSSlvSYOcH5My/AeAH7ADN5EaXE+lQf4Q\ncK5BOUNKecUV1IQQ/wbkk8qGygMellJ2Xy0bCCEeJlWpN0iqLs1XSGVhvXxm8PMF5Fy1sXOpYyNd\nYyAdtk6XPYUQ/0kqKN0EhIFBYAAokVI+eLFyTsq6YpuPFHtfqa1Hio0v175jxZbp+M2Odluetv9G\nQDI8E3YqcFBKuehidBiGlHLUvYAtpNKSBfAE8DrgBD5K4zE2nuflTZP8DZey/TLk7wYc59j+QbrO\nz2nvq4GPgdnptMEZx9tKalnRBLhJXRAEsPlaj510jY10jYF02Dpd9jxdd+DAae8/zoTNR4q9r9TW\nI8XGl2vfsWLLdPxmR7stT/vc3wG/BZactu2diz3+ma/R13f6JPLTcvL/KYTYDfyJlKeYLnJJearR\n0zcKId5Pk/xdQoingfdIeaRZpDzs3WmSfyswdI7tK9IkX/lLrRgp5X4hxFrgOVKe8dUgIlNTh0NC\niF/9xS5CiEteMkvD2EnX2EjXGEiHrdNlz9MbqXz3tPeXMwWbFpuPEHtfqa1Hio0v175jxZbp+M2O\ndlumPiTlj4QQBuARIcTjwPOco17YRXO5XkwmX8BjQPkZ24qBX6TxGCs5t/c6M43HqCPl4X+b1BRm\nbabP7SXoPhdwnbFNC3z5Kh3vfkB7xjY98L1rPXbSOTZGyhhIlz1JXQzPZafbM2HzkWTvTNs6HTa+\nXPuOJVtm2o6ZtuV5ZOmAh4F/vtzvNCpjRs5ECPG8lPKvr/Ixfi+l/PLVPIbKxZMue6Rj7Khj49qQ\njvOs2ntkoNpS5UzGSmpv4TU4xiUWyVW5yqTLHukYO+rYuDak4zyr9h4ZqLZUGcZYcUZUVFRUVFRU\nRimqM6KioqKioqKSUVRnREVFRUVFRSWjjJUAVpeU0jPaj6Fy8aTLHumQo46Na8NIsZVq7ytnpNhB\nteXIYUw4IyoqKioqKiqjF3WZRkVFRUVFRSWjqM6IioqKioqKSkZRnREVFRUVFRWVjKI6IyMIIcQK\nIcRRIcQxIcT/yrQ+KlcfIcQzQgiPEOJApnVRuTYIIUqFEB8JIQ4JIQ4KIb6eaZ1Urj5CCKMQYpsQ\nYq8Q4rAQ4p8yrdNIQg1gHSEIITRAPbAMaAd2kOozcCSjiqlcVYQQC4EA8Dsp5fRM66Ny9RFCFAAF\nUsq9QggrsAtYo/7Wxz5CCLOUMiSE0AKbgP8ppdyUab1GAurMyMhhDtAopTwhpYwBfwBWZ1gnlauM\nlHIj4M20HirXDilll5Ry78n3AeAIUJRZrVSuBVLK0Mm3elJdc/szqM6IQnVGRg7FgPu0v9tOblNR\nURmjCCHGAbXAtsxqonItEEIoQoi9gAf4SEp5ONM6jRRUZ2TkoK6Xqah8jji5RPMS8NTJGRKVMY6U\nMimlrAFKgEVCiMUZVmnEoDojI4d2oPS0v0tJzY6oqKiMMYQQOuBl4Dkp5WuZ1kfl2iKl9ANvAbMy\nrctIQXVGRg47gYlCiHFCCD3wReBPGdZJRUUlzQghBPBr4LCU8seZ1kfl2iCEyBVCOE6+NwE3A3sy\nq9XIQXVGRghSyjjwJPAucBh4QY2uH/sIIX4PbAYmCSHcQoiHMq2TylXnBuBeYIkQYs/J14pMK6Vy\n1SkE1p2MGdkGvCGl/DDDOo0Y1NReFRUVFRUVlYyizoyoqKioqKioZBTVGVFRUVFRUVHJKKozoqKi\noqKiopJRVGdERUVFRUVFJaOozoiKioqKiopKRlGdERUVFRUVFZWMojojKioqKioqKhlFdUZUVFRU\nVFRUMsr/B18y5d10ojlGAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8611575a50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X, y = sklearn.datasets.make_classification(\n",
+    "    n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n",
+    "    n_clusters_per_class=2, hypercube=False, random_state=0\n",
+    ")\n",
+    "\n",
+    "# Split into train and test\n",
+    "X, Xt, y, yt = sklearn.model_selection.train_test_split(X, y)\n",
+    "\n",
+    "# Visualize sample of the data\n",
+    "ind = np.random.permutation(X.shape[0])[:1000]\n",
+    "df = pd.DataFrame(X[ind])\n",
+    "_ = pd.plotting.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.781\n",
+      "Accuracy: 0.781\n",
+      "Accuracy: 0.781\n",
+      "Accuracy: 0.781\n",
+      "1 loop, best of 3: 372 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "# Train and test the scikit-learn SGD logistic regression.\n",
+    "clf = sklearn.linear_model.SGDClassifier(\n",
+    "    loss='log', n_iter=1000, penalty='l2', alpha=5e-4, class_weight='balanced')\n",
+    "\n",
+    "clf.fit(X, y)\n",
+    "yt_pred = clf.predict(Xt)\n",
+    "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the dataset to HDF5 for loading in Caffe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Write out the data to HDF5 files in a temp directory.\n",
+    "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n",
+    "dirname = os.path.abspath('./examples/hdf5_classification/data')\n",
+    "if not os.path.exists(dirname):\n",
+    "    os.makedirs(dirname)\n",
+    "\n",
+    "train_filename = os.path.join(dirname, 'train.h5')\n",
+    "test_filename = os.path.join(dirname, 'test.h5')\n",
+    "\n",
+    "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n",
+    "# To show this off, we'll list the same data file twice.\n",
+    "with h5py.File(train_filename, 'w') as f:\n",
+    "    f['data'] = X\n",
+    "    f['label'] = y.astype(np.float32)\n",
+    "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n",
+    "    f.write(train_filename + '\\n')\n",
+    "    f.write(train_filename + '\\n')\n",
+    "    \n",
+    "# HDF5 is pretty efficient, but can be further compressed.\n",
+    "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n",
+    "with h5py.File(test_filename, 'w') as f:\n",
+    "    f.create_dataset('data', data=Xt, **comp_kwargs)\n",
+    "    f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n",
+    "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n",
+    "    f.write(test_filename + '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's define logistic regression in Caffe through Python net specification. This is a quick and natural way to define nets that sidesteps manually editing the protobuf model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from caffe import layers as L\n",
+    "from caffe import params as P\n",
+    "\n",
+    "def logreg(hdf5, batch_size):\n",
+    "    # logistic regression: data, matrix multiplication, and 2-class softmax loss\n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
+    "    n.ip1 = L.InnerProduct(n.data, num_output=2, weight_filler=dict(type='xavier'))\n",
+    "    n.accuracy = L.Accuracy(n.ip1, n.label)\n",
+    "    n.loss = L.SoftmaxWithLoss(n.ip1, n.label)\n",
+    "    return n.to_proto()\n",
+    "\n",
+    "train_net_path = 'examples/hdf5_classification/logreg_auto_train.prototxt'\n",
+    "with open(train_net_path, 'w') as f:\n",
+    "    f.write(str(logreg('examples/hdf5_classification/data/train.txt', 10)))\n",
+    "\n",
+    "test_net_path = 'examples/hdf5_classification/logreg_auto_test.prototxt'\n",
+    "with open(test_net_path, 'w') as f:\n",
+    "    f.write(str(logreg('examples/hdf5_classification/data/test.txt', 10)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we'll define our \"solver\" which trains the network by specifying the locations of the train and test nets we defined above, as well as setting values for various parameters used for learning, display, and \"snapshotting\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from caffe.proto import caffe_pb2\n",
+    "\n",
+    "def solver(train_net_path, test_net_path):\n",
+    "    s = caffe_pb2.SolverParameter()\n",
+    "\n",
+    "    # Specify locations of the train and test networks.\n",
+    "    s.train_net = train_net_path\n",
+    "    s.test_net.append(test_net_path)\n",
+    "\n",
+    "    s.test_interval = 1000  # Test after every 1000 training iterations.\n",
+    "    s.test_iter.append(250) # Test 250 \"batches\" each time we test.\n",
+    "\n",
+    "    s.max_iter = 10000      # # of times to update the net (training iterations)\n",
+    "\n",
+    "    # Set the initial learning rate for stochastic gradient descent (SGD).\n",
+    "    s.base_lr = 0.01        \n",
+    "\n",
+    "    # Set `lr_policy` to define how the learning rate changes during training.\n",
+    "    # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\n",
+    "    # every `stepsize` iterations.\n",
+    "    s.lr_policy = 'step'\n",
+    "    s.gamma = 0.1\n",
+    "    s.stepsize = 5000\n",
+    "\n",
+    "    # Set other optimization parameters. Setting a non-zero `momentum` takes a\n",
+    "    # weighted average of the current gradient and previous gradients to make\n",
+    "    # learning more stable. L2 weight decay regularizes learning, to help prevent\n",
+    "    # the model from overfitting.\n",
+    "    s.momentum = 0.9\n",
+    "    s.weight_decay = 5e-4\n",
+    "\n",
+    "    # Display the current training loss and accuracy every 1000 iterations.\n",
+    "    s.display = 1000\n",
+    "\n",
+    "    # Snapshots are files used to store networks we've trained.  Here, we'll\n",
+    "    # snapshot every 10K iterations -- just once at the end of training.\n",
+    "    # For larger networks that take longer to train, you may want to set\n",
+    "    # snapshot < max_iter to save the network and training state to disk during\n",
+    "    # optimization, preventing disaster in case of machine crashes, etc.\n",
+    "    s.snapshot = 10000\n",
+    "    s.snapshot_prefix = 'examples/hdf5_classification/data/train'\n",
+    "\n",
+    "    # We'll train on the CPU for fair benchmarking against scikit-learn.\n",
+    "    # Changing to GPU should result in much faster training!\n",
+    "    s.solver_mode = caffe_pb2.SolverParameter.CPU\n",
+    "    \n",
+    "    return s\n",
+    "\n",
+    "solver_path = 'examples/hdf5_classification/logreg_solver.prototxt'\n",
+    "with open(solver_path, 'w') as f:\n",
+    "    f.write(str(solver(train_net_path, test_net_path)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Time to learn and evaluate our Caffeinated logistic regression in Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.770\n",
+      "Accuracy: 0.770\n",
+      "Accuracy: 0.770\n",
+      "Accuracy: 0.770\n",
+      "1 loop, best of 3: 195 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "caffe.set_mode_cpu()\n",
+    "solver = caffe.get_solver(solver_path)\n",
+    "solver.solve()\n",
+    "\n",
+    "accuracy = 0\n",
+    "batch_size = solver.test_nets[0].blobs['data'].num\n",
+    "test_iters = int(len(Xt) / batch_size)\n",
+    "for i in range(test_iters):\n",
+    "    solver.test_nets[0].forward()\n",
+    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+    "accuracy /= test_iters\n",
+    "\n",
+    "print(\"Accuracy: {:.3f}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do the same through the command line interface for detailed output on the model and solving."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I0224 00:32:03.232779   655 caffe.cpp:178] Use CPU.\n",
+      "I0224 00:32:03.391911   655 solver.cpp:48] Initializing solver from parameters: \n",
+      "train_net: \"examples/hdf5_classification/logreg_auto_train.prototxt\"\n",
+      "test_net: \"examples/hdf5_classification/logreg_auto_test.prototxt\"\n",
+      "test_iter: 250\n",
+      "test_interval: 1000\n",
+      "base_lr: 0.01\n",
+      "display: 1000\n",
+      "max_iter: 10000\n",
+      "lr_policy: \"step\"\n",
+      "gamma: 0.1\n",
+      "momentum: 0.9\n",
+      "weight_decay: 0.0005\n",
+      "stepsize: 5000\n",
+      "snapshot: 10000\n",
+      "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
+      "solver_mode: CPU\n",
+      "I0224 00:32:03.392065   655 solver.cpp:81] Creating training net from train_net file: examples/hdf5_classification/logreg_auto_train.prototxt\n",
+      "I0224 00:32:03.392215   655 net.cpp:49] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TRAIN\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/train.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0224 00:32:03.392365   655 layer_factory.hpp:77] Creating layer data\n",
+      "I0224 00:32:03.392382   655 net.cpp:106] Creating Layer data\n",
+      "I0224 00:32:03.392395   655 net.cpp:411] data -> data\n",
+      "I0224 00:32:03.392423   655 net.cpp:411] data -> label\n",
+      "I0224 00:32:03.392442   655 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
+      "I0224 00:32:03.392473   655 hdf5_data_layer.cpp:93] Number of HDF5 files: 2\n",
+      "I0224 00:32:03.393473   655 hdf5.cpp:32] Datatype class: H5T_FLOAT\n",
+      "I0224 00:32:03.393862   655 net.cpp:150] Setting up data\n",
+      "I0224 00:32:03.393884   655 net.cpp:157] Top shape: 10 4 (40)\n",
+      "I0224 00:32:03.393894   655 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:03.393901   655 net.cpp:165] Memory required for data: 200\n",
+      "I0224 00:32:03.393911   655 layer_factory.hpp:77] Creating layer label_data_1_split\n",
+      "I0224 00:32:03.393924   655 net.cpp:106] Creating Layer label_data_1_split\n",
+      "I0224 00:32:03.393934   655 net.cpp:454] label_data_1_split <- label\n",
+      "I0224 00:32:03.393945   655 net.cpp:411] label_data_1_split -> label_data_1_split_0\n",
+      "I0224 00:32:03.393956   655 net.cpp:411] label_data_1_split -> label_data_1_split_1\n",
+      "I0224 00:32:03.393970   655 net.cpp:150] Setting up label_data_1_split\n",
+      "I0224 00:32:03.393978   655 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:03.393986   655 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:03.393995   655 net.cpp:165] Memory required for data: 280\n",
+      "I0224 00:32:03.394001   655 layer_factory.hpp:77] Creating layer ip1\n",
+      "I0224 00:32:03.394012   655 net.cpp:106] Creating Layer ip1\n",
+      "I0224 00:32:03.394021   655 net.cpp:454] ip1 <- data\n",
+      "I0224 00:32:03.394029   655 net.cpp:411] ip1 -> ip1\n",
+      "I0224 00:32:03.394311   655 net.cpp:150] Setting up ip1\n",
+      "I0224 00:32:03.394323   655 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:03.394331   655 net.cpp:165] Memory required for data: 360\n",
+      "I0224 00:32:03.394348   655 layer_factory.hpp:77] Creating layer ip1_ip1_0_split\n",
+      "I0224 00:32:03.394358   655 net.cpp:106] Creating Layer ip1_ip1_0_split\n",
+      "I0224 00:32:03.394366   655 net.cpp:454] ip1_ip1_0_split <- ip1\n",
+      "I0224 00:32:03.394374   655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
+      "I0224 00:32:03.394386   655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
+      "I0224 00:32:03.394395   655 net.cpp:150] Setting up ip1_ip1_0_split\n",
+      "I0224 00:32:03.394404   655 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:03.394424   655 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:03.394443   655 net.cpp:165] Memory required for data: 520\n",
+      "I0224 00:32:03.394450   655 layer_factory.hpp:77] Creating layer accuracy\n",
+      "I0224 00:32:03.394462   655 net.cpp:106] Creating Layer accuracy\n",
+      "I0224 00:32:03.394479   655 net.cpp:454] accuracy <- ip1_ip1_0_split_0\n",
+      "I0224 00:32:03.394489   655 net.cpp:454] accuracy <- label_data_1_split_0\n",
+      "I0224 00:32:03.394497   655 net.cpp:411] accuracy -> accuracy\n",
+      "I0224 00:32:03.394510   655 net.cpp:150] Setting up accuracy\n",
+      "I0224 00:32:03.394536   655 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:03.394543   655 net.cpp:165] Memory required for data: 524\n",
+      "I0224 00:32:03.394551   655 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:03.394562   655 net.cpp:106] Creating Layer loss\n",
+      "I0224 00:32:03.394569   655 net.cpp:454] loss <- ip1_ip1_0_split_1\n",
+      "I0224 00:32:03.394577   655 net.cpp:454] loss <- label_data_1_split_1\n",
+      "I0224 00:32:03.394587   655 net.cpp:411] loss -> loss\n",
+      "I0224 00:32:03.394603   655 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:03.394624   655 net.cpp:150] Setting up loss\n",
+      "I0224 00:32:03.394634   655 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:03.394641   655 net.cpp:160]     with loss weight 1\n",
+      "I0224 00:32:03.394659   655 net.cpp:165] Memory required for data: 528\n",
+      "I0224 00:32:03.394665   655 net.cpp:226] loss needs backward computation.\n",
+      "I0224 00:32:03.394673   655 net.cpp:228] accuracy does not need backward computation.\n",
+      "I0224 00:32:03.394682   655 net.cpp:226] ip1_ip1_0_split needs backward computation.\n",
+      "I0224 00:32:03.394690   655 net.cpp:226] ip1 needs backward computation.\n",
+      "I0224 00:32:03.394697   655 net.cpp:228] label_data_1_split does not need backward computation.\n",
+      "I0224 00:32:03.394706   655 net.cpp:228] data does not need backward computation.\n",
+      "I0224 00:32:03.394712   655 net.cpp:270] This network produces output accuracy\n",
+      "I0224 00:32:03.394721   655 net.cpp:270] This network produces output loss\n",
+      "I0224 00:32:03.394731   655 net.cpp:283] Network initialization done.\n",
+      "I0224 00:32:03.394804   655 solver.cpp:181] Creating test net (#0) specified by test_net file: examples/hdf5_classification/logreg_auto_test.prototxt\n",
+      "I0224 00:32:03.394836   655 net.cpp:49] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TEST\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/test.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0224 00:32:03.394953   655 layer_factory.hpp:77] Creating layer data\n",
+      "I0224 00:32:03.394964   655 net.cpp:106] Creating Layer data\n",
+      "I0224 00:32:03.394973   655 net.cpp:411] data -> data\n",
+      "I0224 00:32:03.394984   655 net.cpp:411] data -> label\n",
+      "I0224 00:32:03.394994   655 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
+      "I0224 00:32:03.395009   655 hdf5_data_layer.cpp:93] Number of HDF5 files: 1\n",
+      "I0224 00:32:03.395937   655 net.cpp:150] Setting up data\n",
+      "I0224 00:32:03.395953   655 net.cpp:157] Top shape: 10 4 (40)\n",
+      "I0224 00:32:03.395963   655 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:03.395970   655 net.cpp:165] Memory required for data: 200\n",
+      "I0224 00:32:03.395978   655 layer_factory.hpp:77] Creating layer label_data_1_split\n",
+      "I0224 00:32:03.395989   655 net.cpp:106] Creating Layer label_data_1_split\n",
+      "I0224 00:32:03.395997   655 net.cpp:454] label_data_1_split <- label\n",
+      "I0224 00:32:03.396005   655 net.cpp:411] label_data_1_split -> label_data_1_split_0\n",
+      "I0224 00:32:03.396016   655 net.cpp:411] label_data_1_split -> label_data_1_split_1\n",
+      "I0224 00:32:03.396028   655 net.cpp:150] Setting up label_data_1_split\n",
+      "I0224 00:32:03.396036   655 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:03.396044   655 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:03.396051   655 net.cpp:165] Memory required for data: 280\n",
+      "I0224 00:32:03.396059   655 layer_factory.hpp:77] Creating layer ip1\n",
+      "I0224 00:32:03.396069   655 net.cpp:106] Creating Layer ip1\n",
+      "I0224 00:32:03.396075   655 net.cpp:454] ip1 <- data\n",
+      "I0224 00:32:03.396085   655 net.cpp:411] ip1 -> ip1\n",
+      "I0224 00:32:03.396100   655 net.cpp:150] Setting up ip1\n",
+      "I0224 00:32:03.396109   655 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:03.396116   655 net.cpp:165] Memory required for data: 360\n",
+      "I0224 00:32:03.396138   655 layer_factory.hpp:77] Creating layer ip1_ip1_0_split\n",
+      "I0224 00:32:03.396148   655 net.cpp:106] Creating Layer ip1_ip1_0_split\n",
+      "I0224 00:32:03.396157   655 net.cpp:454] ip1_ip1_0_split <- ip1\n",
+      "I0224 00:32:03.396164   655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
+      "I0224 00:32:03.396174   655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
+      "I0224 00:32:03.396185   655 net.cpp:150] Setting up ip1_ip1_0_split\n",
+      "I0224 00:32:03.396194   655 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:03.396203   655 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:03.396209   655 net.cpp:165] Memory required for data: 520\n",
+      "I0224 00:32:03.396216   655 layer_factory.hpp:77] Creating layer accuracy\n",
+      "I0224 00:32:03.396225   655 net.cpp:106] Creating Layer accuracy\n",
+      "I0224 00:32:03.396234   655 net.cpp:454] accuracy <- ip1_ip1_0_split_0\n",
+      "I0224 00:32:03.396241   655 net.cpp:454] accuracy <- label_data_1_split_0\n",
+      "I0224 00:32:03.396250   655 net.cpp:411] accuracy -> accuracy\n",
+      "I0224 00:32:03.396260   655 net.cpp:150] Setting up accuracy\n",
+      "I0224 00:32:03.396270   655 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:03.396276   655 net.cpp:165] Memory required for data: 524\n",
+      "I0224 00:32:03.396283   655 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:03.396291   655 net.cpp:106] Creating Layer loss\n",
+      "I0224 00:32:03.396299   655 net.cpp:454] loss <- ip1_ip1_0_split_1\n",
+      "I0224 00:32:03.396307   655 net.cpp:454] loss <- label_data_1_split_1\n",
+      "I0224 00:32:03.396317   655 net.cpp:411] loss -> loss\n",
+      "I0224 00:32:03.396327   655 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:03.396339   655 net.cpp:150] Setting up loss\n",
+      "I0224 00:32:03.396349   655 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:03.396356   655 net.cpp:160]     with loss weight 1\n",
+      "I0224 00:32:03.396365   655 net.cpp:165] Memory required for data: 528\n",
+      "I0224 00:32:03.396373   655 net.cpp:226] loss needs backward computation.\n",
+      "I0224 00:32:03.396381   655 net.cpp:228] accuracy does not need backward computation.\n",
+      "I0224 00:32:03.396389   655 net.cpp:226] ip1_ip1_0_split needs backward computation.\n",
+      "I0224 00:32:03.396396   655 net.cpp:226] ip1 needs backward computation.\n",
+      "I0224 00:32:03.396404   655 net.cpp:228] label_data_1_split does not need backward computation.\n",
+      "I0224 00:32:03.396412   655 net.cpp:228] data does not need backward computation.\n",
+      "I0224 00:32:03.396420   655 net.cpp:270] This network produces output accuracy\n",
+      "I0224 00:32:03.396427   655 net.cpp:270] This network produces output loss\n",
+      "I0224 00:32:03.396437   655 net.cpp:283] Network initialization done.\n",
+      "I0224 00:32:03.396455   655 solver.cpp:60] Solver scaffolding done.\n",
+      "I0224 00:32:03.396473   655 caffe.cpp:219] Starting Optimization\n",
+      "I0224 00:32:03.396482   655 solver.cpp:280] Solving \n",
+      "I0224 00:32:03.396489   655 solver.cpp:281] Learning Rate Policy: step\n",
+      "I0224 00:32:03.396499   655 solver.cpp:338] Iteration 0, Testing net (#0)\n",
+      "I0224 00:32:03.932615   655 solver.cpp:406]     Test net output #0: accuracy = 0.4268\n",
+      "I0224 00:32:03.932656   655 solver.cpp:406]     Test net output #1: loss = 1.33093 (* 1 = 1.33093 loss)\n",
+      "I0224 00:32:03.932723   655 solver.cpp:229] Iteration 0, loss = 1.06081\n",
+      "I0224 00:32:03.932737   655 solver.cpp:245]     Train net output #0: accuracy = 0.4\n",
+      "I0224 00:32:03.932749   655 solver.cpp:245]     Train net output #1: loss = 1.06081 (* 1 = 1.06081 loss)\n",
+      "I0224 00:32:03.932765   655 sgd_solver.cpp:106] Iteration 0, lr = 0.01\n",
+      "I0224 00:32:03.945551   655 solver.cpp:338] Iteration 1000, Testing net (#0)\n",
+      "I0224 00:32:03.948048   655 solver.cpp:406]     Test net output #0: accuracy = 0.694\n",
+      "I0224 00:32:03.948065   655 solver.cpp:406]     Test net output #1: loss = 0.60406 (* 1 = 0.60406 loss)\n",
+      "I0224 00:32:03.948091   655 solver.cpp:229] Iteration 1000, loss = 0.505853\n",
+      "I0224 00:32:03.948102   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:03.948113   655 solver.cpp:245]     Train net output #1: loss = 0.505853 (* 1 = 0.505853 loss)\n",
+      "I0224 00:32:03.948122   655 sgd_solver.cpp:106] Iteration 1000, lr = 0.01\n",
+      "I0224 00:32:03.960741   655 solver.cpp:338] Iteration 2000, Testing net (#0)\n",
+      "I0224 00:32:03.963214   655 solver.cpp:406]     Test net output #0: accuracy = 0.7372\n",
+      "I0224 00:32:03.963249   655 solver.cpp:406]     Test net output #1: loss = 0.595267 (* 1 = 0.595267 loss)\n",
+      "I0224 00:32:03.963276   655 solver.cpp:229] Iteration 2000, loss = 0.549211\n",
+      "I0224 00:32:03.963289   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:03.963299   655 solver.cpp:245]     Train net output #1: loss = 0.549211 (* 1 = 0.549211 loss)\n",
+      "I0224 00:32:03.963309   655 sgd_solver.cpp:106] Iteration 2000, lr = 0.01\n",
+      "I0224 00:32:03.975945   655 solver.cpp:338] Iteration 3000, Testing net (#0)\n",
+      "I0224 00:32:03.978435   655 solver.cpp:406]     Test net output #0: accuracy = 0.7732\n",
+      "I0224 00:32:03.978451   655 solver.cpp:406]     Test net output #1: loss = 0.594998 (* 1 = 0.594998 loss)\n",
+      "I0224 00:32:03.978884   655 solver.cpp:229] Iteration 3000, loss = 0.66133\n",
+      "I0224 00:32:03.978911   655 solver.cpp:245]     Train net output #0: accuracy = 0.8\n",
+      "I0224 00:32:03.978932   655 solver.cpp:245]     Train net output #1: loss = 0.66133 (* 1 = 0.66133 loss)\n",
+      "I0224 00:32:03.978950   655 sgd_solver.cpp:106] Iteration 3000, lr = 0.01\n",
+      "I0224 00:32:03.992017   655 solver.cpp:338] Iteration 4000, Testing net (#0)\n",
+      "I0224 00:32:03.994509   655 solver.cpp:406]     Test net output #0: accuracy = 0.694\n",
+      "I0224 00:32:03.994525   655 solver.cpp:406]     Test net output #1: loss = 0.60406 (* 1 = 0.60406 loss)\n",
+      "I0224 00:32:03.994551   655 solver.cpp:229] Iteration 4000, loss = 0.505853\n",
+      "I0224 00:32:03.994562   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:03.994573   655 solver.cpp:245]     Train net output #1: loss = 0.505853 (* 1 = 0.505853 loss)\n",
+      "I0224 00:32:03.994583   655 sgd_solver.cpp:106] Iteration 4000, lr = 0.01\n",
+      "I0224 00:32:04.007200   655 solver.cpp:338] Iteration 5000, Testing net (#0)\n",
+      "I0224 00:32:04.009686   655 solver.cpp:406]     Test net output #0: accuracy = 0.7372\n",
+      "I0224 00:32:04.009702   655 solver.cpp:406]     Test net output #1: loss = 0.595267 (* 1 = 0.595267 loss)\n",
+      "I0224 00:32:04.009727   655 solver.cpp:229] Iteration 5000, loss = 0.549211\n",
+      "I0224 00:32:04.009738   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:04.009749   655 solver.cpp:245]     Train net output #1: loss = 0.549211 (* 1 = 0.549211 loss)\n",
+      "I0224 00:32:04.009758   655 sgd_solver.cpp:106] Iteration 5000, lr = 0.001\n",
+      "I0224 00:32:04.022734   655 solver.cpp:338] Iteration 6000, Testing net (#0)\n",
+      "I0224 00:32:04.025177   655 solver.cpp:406]     Test net output #0: accuracy = 0.7824\n",
+      "I0224 00:32:04.025193   655 solver.cpp:406]     Test net output #1: loss = 0.593367 (* 1 = 0.593367 loss)\n",
+      "I0224 00:32:04.025545   655 solver.cpp:229] Iteration 6000, loss = 0.654873\n",
+      "I0224 00:32:04.025562   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:04.025573   655 solver.cpp:245]     Train net output #1: loss = 0.654873 (* 1 = 0.654873 loss)\n",
+      "I0224 00:32:04.025583   655 sgd_solver.cpp:106] Iteration 6000, lr = 0.001\n",
+      "I0224 00:32:04.038586   655 solver.cpp:338] Iteration 7000, Testing net (#0)\n",
+      "I0224 00:32:04.041016   655 solver.cpp:406]     Test net output #0: accuracy = 0.7704\n",
+      "I0224 00:32:04.041033   655 solver.cpp:406]     Test net output #1: loss = 0.593842 (* 1 = 0.593842 loss)\n",
+      "I0224 00:32:04.041059   655 solver.cpp:229] Iteration 7000, loss = 0.46611\n",
+      "I0224 00:32:04.041071   655 solver.cpp:245]     Train net output #0: accuracy = 0.6\n",
+      "I0224 00:32:04.041082   655 solver.cpp:245]     Train net output #1: loss = 0.46611 (* 1 = 0.46611 loss)\n",
+      "I0224 00:32:04.041091   655 sgd_solver.cpp:106] Iteration 7000, lr = 0.001\n",
+      "I0224 00:32:04.053722   655 solver.cpp:338] Iteration 8000, Testing net (#0)\n",
+      "I0224 00:32:04.056171   655 solver.cpp:406]     Test net output #0: accuracy = 0.7788\n",
+      "I0224 00:32:04.056187   655 solver.cpp:406]     Test net output #1: loss = 0.592847 (* 1 = 0.592847 loss)\n",
+      "I0224 00:32:04.056213   655 solver.cpp:229] Iteration 8000, loss = 0.615126\n",
+      "I0224 00:32:04.056224   655 solver.cpp:245]     Train net output #0: accuracy = 0.8\n",
+      "I0224 00:32:04.056236   655 solver.cpp:245]     Train net output #1: loss = 0.615126 (* 1 = 0.615126 loss)\n",
+      "I0224 00:32:04.056244   655 sgd_solver.cpp:106] Iteration 8000, lr = 0.001\n",
+      "I0224 00:32:04.068853   655 solver.cpp:338] Iteration 9000, Testing net (#0)\n",
+      "I0224 00:32:04.071291   655 solver.cpp:406]     Test net output #0: accuracy = 0.7808\n",
+      "I0224 00:32:04.071307   655 solver.cpp:406]     Test net output #1: loss = 0.593293 (* 1 = 0.593293 loss)\n",
+      "I0224 00:32:04.071650   655 solver.cpp:229] Iteration 9000, loss = 0.654997\n",
+      "I0224 00:32:04.071666   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:04.071677   655 solver.cpp:245]     Train net output #1: loss = 0.654998 (* 1 = 0.654998 loss)\n",
+      "I0224 00:32:04.071687   655 sgd_solver.cpp:106] Iteration 9000, lr = 0.001\n",
+      "I0224 00:32:04.084717   655 solver.cpp:456] Snapshotting to binary proto file examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
+      "I0224 00:32:04.084885   655 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/hdf5_classification/data/train_iter_10000.solverstate\n",
+      "I0224 00:32:04.084960   655 solver.cpp:318] Iteration 10000, loss = 0.466505\n",
+      "I0224 00:32:04.084977   655 solver.cpp:338] Iteration 10000, Testing net (#0)\n",
+      "I0224 00:32:04.087514   655 solver.cpp:406]     Test net output #0: accuracy = 0.77\n",
+      "I0224 00:32:04.087532   655 solver.cpp:406]     Test net output #1: loss = 0.593815 (* 1 = 0.593815 loss)\n",
+      "I0224 00:32:04.087541   655 solver.cpp:323] Optimization Done.\n",
+      "I0224 00:32:04.087548   655 caffe.cpp:222] Optimization Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!./build/tools/caffe train -solver examples/hdf5_classification/logreg_solver.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you look at output or the `logreg_auto_train.prototxt`, you'll see that the model is simple logistic regression.\n",
+    "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n",
+    "That network is given in `nonlinear_auto_train.prototxt`, and that's the only change made in `nonlinear_logreg_solver.prototxt` which we will now use.\n",
+    "\n",
+    "The final accuracy of the new network should be higher than logistic regression!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from caffe import layers as L\n",
+    "from caffe import params as P\n",
+    "\n",
+    "def nonlinear_net(hdf5, batch_size):\n",
+    "    # one small nonlinearity, one leap for model kind\n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
+    "    # define a hidden layer of dimension 40\n",
+    "    n.ip1 = L.InnerProduct(n.data, num_output=40, weight_filler=dict(type='xavier'))\n",
+    "    # transform the output through the ReLU (rectified linear) non-linearity\n",
+    "    n.relu1 = L.ReLU(n.ip1, in_place=True)\n",
+    "    # score the (now non-linear) features\n",
+    "    n.ip2 = L.InnerProduct(n.ip1, num_output=2, weight_filler=dict(type='xavier'))\n",
+    "    # same accuracy and loss as before\n",
+    "    n.accuracy = L.Accuracy(n.ip2, n.label)\n",
+    "    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n",
+    "    return n.to_proto()\n",
+    "\n",
+    "train_net_path = 'examples/hdf5_classification/nonlinear_auto_train.prototxt'\n",
+    "with open(train_net_path, 'w') as f:\n",
+    "    f.write(str(nonlinear_net('examples/hdf5_classification/data/train.txt', 10)))\n",
+    "\n",
+    "test_net_path = 'examples/hdf5_classification/nonlinear_auto_test.prototxt'\n",
+    "with open(test_net_path, 'w') as f:\n",
+    "    f.write(str(nonlinear_net('examples/hdf5_classification/data/test.txt', 10)))\n",
+    "\n",
+    "solver_path = 'examples/hdf5_classification/nonlinear_logreg_solver.prototxt'\n",
+    "with open(solver_path, 'w') as f:\n",
+    "    f.write(str(solver(train_net_path, test_net_path)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.838\n",
+      "Accuracy: 0.837\n",
+      "Accuracy: 0.838\n",
+      "Accuracy: 0.834\n",
+      "1 loop, best of 3: 277 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "caffe.set_mode_cpu()\n",
+    "solver = caffe.get_solver(solver_path)\n",
+    "solver.solve()\n",
+    "\n",
+    "accuracy = 0\n",
+    "batch_size = solver.test_nets[0].blobs['data'].num\n",
+    "test_iters = int(len(Xt) / batch_size)\n",
+    "for i in range(test_iters):\n",
+    "    solver.test_nets[0].forward()\n",
+    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+    "accuracy /= test_iters\n",
+    "\n",
+    "print(\"Accuracy: {:.3f}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do the same through the command line interface for detailed output on the model and solving."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I0224 00:32:05.654265   658 caffe.cpp:178] Use CPU.\n",
+      "I0224 00:32:05.810444   658 solver.cpp:48] Initializing solver from parameters: \n",
+      "train_net: \"examples/hdf5_classification/nonlinear_auto_train.prototxt\"\n",
+      "test_net: \"examples/hdf5_classification/nonlinear_auto_test.prototxt\"\n",
+      "test_iter: 250\n",
+      "test_interval: 1000\n",
+      "base_lr: 0.01\n",
+      "display: 1000\n",
+      "max_iter: 10000\n",
+      "lr_policy: \"step\"\n",
+      "gamma: 0.1\n",
+      "momentum: 0.9\n",
+      "weight_decay: 0.0005\n",
+      "stepsize: 5000\n",
+      "snapshot: 10000\n",
+      "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
+      "solver_mode: CPU\n",
+      "I0224 00:32:05.810634   658 solver.cpp:81] Creating training net from train_net file: examples/hdf5_classification/nonlinear_auto_train.prototxt\n",
+      "I0224 00:32:05.810835   658 net.cpp:49] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TRAIN\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/train.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 40\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu1\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip1\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip2\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip2\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0224 00:32:05.811061   658 layer_factory.hpp:77] Creating layer data\n",
+      "I0224 00:32:05.811079   658 net.cpp:106] Creating Layer data\n",
+      "I0224 00:32:05.811092   658 net.cpp:411] data -> data\n",
+      "I0224 00:32:05.811121   658 net.cpp:411] data -> label\n",
+      "I0224 00:32:05.811143   658 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
+      "I0224 00:32:05.811189   658 hdf5_data_layer.cpp:93] Number of HDF5 files: 2\n",
+      "I0224 00:32:05.812254   658 hdf5.cpp:32] Datatype class: H5T_FLOAT\n",
+      "I0224 00:32:05.812677   658 net.cpp:150] Setting up data\n",
+      "I0224 00:32:05.812705   658 net.cpp:157] Top shape: 10 4 (40)\n",
+      "I0224 00:32:05.812721   658 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:05.812729   658 net.cpp:165] Memory required for data: 200\n",
+      "I0224 00:32:05.812739   658 layer_factory.hpp:77] Creating layer label_data_1_split\n",
+      "I0224 00:32:05.812752   658 net.cpp:106] Creating Layer label_data_1_split\n",
+      "I0224 00:32:05.812762   658 net.cpp:454] label_data_1_split <- label\n",
+      "I0224 00:32:05.812774   658 net.cpp:411] label_data_1_split -> label_data_1_split_0\n",
+      "I0224 00:32:05.812785   658 net.cpp:411] label_data_1_split -> label_data_1_split_1\n",
+      "I0224 00:32:05.812798   658 net.cpp:150] Setting up label_data_1_split\n",
+      "I0224 00:32:05.812808   658 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:05.812816   658 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:05.812824   658 net.cpp:165] Memory required for data: 280\n",
+      "I0224 00:32:05.812831   658 layer_factory.hpp:77] Creating layer ip1\n",
+      "I0224 00:32:05.812841   658 net.cpp:106] Creating Layer ip1\n",
+      "I0224 00:32:05.812849   658 net.cpp:454] ip1 <- data\n",
+      "I0224 00:32:05.812860   658 net.cpp:411] ip1 -> ip1\n",
+      "I0224 00:32:05.813179   658 net.cpp:150] Setting up ip1\n",
+      "I0224 00:32:05.813196   658 net.cpp:157] Top shape: 10 40 (400)\n",
+      "I0224 00:32:05.813210   658 net.cpp:165] Memory required for data: 1880\n",
+      "I0224 00:32:05.813230   658 layer_factory.hpp:77] Creating layer relu1\n",
+      "I0224 00:32:05.813241   658 net.cpp:106] Creating Layer relu1\n",
+      "I0224 00:32:05.813251   658 net.cpp:454] relu1 <- ip1\n",
+      "I0224 00:32:05.813258   658 net.cpp:397] relu1 -> ip1 (in-place)\n",
+      "I0224 00:32:05.813271   658 net.cpp:150] Setting up relu1\n",
+      "I0224 00:32:05.813279   658 net.cpp:157] Top shape: 10 40 (400)\n",
+      "I0224 00:32:05.813287   658 net.cpp:165] Memory required for data: 3480\n",
+      "I0224 00:32:05.813294   658 layer_factory.hpp:77] Creating layer ip2\n",
+      "I0224 00:32:05.813304   658 net.cpp:106] Creating Layer ip2\n",
+      "I0224 00:32:05.813313   658 net.cpp:454] ip2 <- ip1\n",
+      "I0224 00:32:05.813321   658 net.cpp:411] ip2 -> ip2\n",
+      "I0224 00:32:05.813336   658 net.cpp:150] Setting up ip2\n",
+      "I0224 00:32:05.813345   658 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:05.813379   658 net.cpp:165] Memory required for data: 3560\n",
+      "I0224 00:32:05.813401   658 layer_factory.hpp:77] Creating layer ip2_ip2_0_split\n",
+      "I0224 00:32:05.813417   658 net.cpp:106] Creating Layer ip2_ip2_0_split\n",
+      "I0224 00:32:05.813426   658 net.cpp:454] ip2_ip2_0_split <- ip2\n",
+      "I0224 00:32:05.813434   658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
+      "I0224 00:32:05.813446   658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
+      "I0224 00:32:05.813457   658 net.cpp:150] Setting up ip2_ip2_0_split\n",
+      "I0224 00:32:05.813465   658 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:05.813473   658 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:05.813480   658 net.cpp:165] Memory required for data: 3720\n",
+      "I0224 00:32:05.813488   658 layer_factory.hpp:77] Creating layer accuracy\n",
+      "I0224 00:32:05.813499   658 net.cpp:106] Creating Layer accuracy\n",
+      "I0224 00:32:05.813508   658 net.cpp:454] accuracy <- ip2_ip2_0_split_0\n",
+      "I0224 00:32:05.813515   658 net.cpp:454] accuracy <- label_data_1_split_0\n",
+      "I0224 00:32:05.813524   658 net.cpp:411] accuracy -> accuracy\n",
+      "I0224 00:32:05.813539   658 net.cpp:150] Setting up accuracy\n",
+      "I0224 00:32:05.813547   658 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:05.813555   658 net.cpp:165] Memory required for data: 3724\n",
+      "I0224 00:32:05.813565   658 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:05.813585   658 net.cpp:106] Creating Layer loss\n",
+      "I0224 00:32:05.813599   658 net.cpp:454] loss <- ip2_ip2_0_split_1\n",
+      "I0224 00:32:05.813616   658 net.cpp:454] loss <- label_data_1_split_1\n",
+      "I0224 00:32:05.813627   658 net.cpp:411] loss -> loss\n",
+      "I0224 00:32:05.813642   658 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:05.813663   658 net.cpp:150] Setting up loss\n",
+      "I0224 00:32:05.813671   658 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:05.813679   658 net.cpp:160]     with loss weight 1\n",
+      "I0224 00:32:05.813695   658 net.cpp:165] Memory required for data: 3728\n",
+      "I0224 00:32:05.813704   658 net.cpp:226] loss needs backward computation.\n",
+      "I0224 00:32:05.813712   658 net.cpp:228] accuracy does not need backward computation.\n",
+      "I0224 00:32:05.813720   658 net.cpp:226] ip2_ip2_0_split needs backward computation.\n",
+      "I0224 00:32:05.813729   658 net.cpp:226] ip2 needs backward computation.\n",
+      "I0224 00:32:05.813735   658 net.cpp:226] relu1 needs backward computation.\n",
+      "I0224 00:32:05.813743   658 net.cpp:226] ip1 needs backward computation.\n",
+      "I0224 00:32:05.813751   658 net.cpp:228] label_data_1_split does not need backward computation.\n",
+      "I0224 00:32:05.813760   658 net.cpp:228] data does not need backward computation.\n",
+      "I0224 00:32:05.813772   658 net.cpp:270] This network produces output accuracy\n",
+      "I0224 00:32:05.813787   658 net.cpp:270] This network produces output loss\n",
+      "I0224 00:32:05.813809   658 net.cpp:283] Network initialization done.\n",
+      "I0224 00:32:05.813905   658 solver.cpp:181] Creating test net (#0) specified by test_net file: examples/hdf5_classification/nonlinear_auto_test.prototxt\n",
+      "I0224 00:32:05.813944   658 net.cpp:49] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TEST\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/test.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 40\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu1\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip1\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip2\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip2\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0224 00:32:05.814131   658 layer_factory.hpp:77] Creating layer data\n",
+      "I0224 00:32:05.814142   658 net.cpp:106] Creating Layer data\n",
+      "I0224 00:32:05.814152   658 net.cpp:411] data -> data\n",
+      "I0224 00:32:05.814162   658 net.cpp:411] data -> label\n",
+      "I0224 00:32:05.814180   658 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
+      "I0224 00:32:05.814220   658 hdf5_data_layer.cpp:93] Number of HDF5 files: 1\n",
+      "I0224 00:32:05.815207   658 net.cpp:150] Setting up data\n",
+      "I0224 00:32:05.815227   658 net.cpp:157] Top shape: 10 4 (40)\n",
+      "I0224 00:32:05.815243   658 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:05.815253   658 net.cpp:165] Memory required for data: 200\n",
+      "I0224 00:32:05.815260   658 layer_factory.hpp:77] Creating layer label_data_1_split\n",
+      "I0224 00:32:05.815270   658 net.cpp:106] Creating Layer label_data_1_split\n",
+      "I0224 00:32:05.815279   658 net.cpp:454] label_data_1_split <- label\n",
+      "I0224 00:32:05.815287   658 net.cpp:411] label_data_1_split -> label_data_1_split_0\n",
+      "I0224 00:32:05.815299   658 net.cpp:411] label_data_1_split -> label_data_1_split_1\n",
+      "I0224 00:32:05.815310   658 net.cpp:150] Setting up label_data_1_split\n",
+      "I0224 00:32:05.815318   658 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:05.815326   658 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:05.815335   658 net.cpp:165] Memory required for data: 280\n",
+      "I0224 00:32:05.815341   658 layer_factory.hpp:77] Creating layer ip1\n",
+      "I0224 00:32:05.815351   658 net.cpp:106] Creating Layer ip1\n",
+      "I0224 00:32:05.815358   658 net.cpp:454] ip1 <- data\n",
+      "I0224 00:32:05.815367   658 net.cpp:411] ip1 -> ip1\n",
+      "I0224 00:32:05.815383   658 net.cpp:150] Setting up ip1\n",
+      "I0224 00:32:05.815398   658 net.cpp:157] Top shape: 10 40 (400)\n",
+      "I0224 00:32:05.815413   658 net.cpp:165] Memory required for data: 1880\n",
+      "I0224 00:32:05.815435   658 layer_factory.hpp:77] Creating layer relu1\n",
+      "I0224 00:32:05.815450   658 net.cpp:106] Creating Layer relu1\n",
+      "I0224 00:32:05.815459   658 net.cpp:454] relu1 <- ip1\n",
+      "I0224 00:32:05.815469   658 net.cpp:397] relu1 -> ip1 (in-place)\n",
+      "I0224 00:32:05.815479   658 net.cpp:150] Setting up relu1\n",
+      "I0224 00:32:05.815486   658 net.cpp:157] Top shape: 10 40 (400)\n",
+      "I0224 00:32:05.815495   658 net.cpp:165] Memory required for data: 3480\n",
+      "I0224 00:32:05.815501   658 layer_factory.hpp:77] Creating layer ip2\n",
+      "I0224 00:32:05.815510   658 net.cpp:106] Creating Layer ip2\n",
+      "I0224 00:32:05.815518   658 net.cpp:454] ip2 <- ip1\n",
+      "I0224 00:32:05.815527   658 net.cpp:411] ip2 -> ip2\n",
+      "I0224 00:32:05.815542   658 net.cpp:150] Setting up ip2\n",
+      "I0224 00:32:05.815551   658 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:05.815559   658 net.cpp:165] Memory required for data: 3560\n",
+      "I0224 00:32:05.815570   658 layer_factory.hpp:77] Creating layer ip2_ip2_0_split\n",
+      "I0224 00:32:05.815579   658 net.cpp:106] Creating Layer ip2_ip2_0_split\n",
+      "I0224 00:32:05.815587   658 net.cpp:454] ip2_ip2_0_split <- ip2\n",
+      "I0224 00:32:05.815600   658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
+      "I0224 00:32:05.815619   658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
+      "I0224 00:32:05.815640   658 net.cpp:150] Setting up ip2_ip2_0_split\n",
+      "I0224 00:32:05.815654   658 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:05.815662   658 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:05.815670   658 net.cpp:165] Memory required for data: 3720\n",
+      "I0224 00:32:05.815677   658 layer_factory.hpp:77] Creating layer accuracy\n",
+      "I0224 00:32:05.815685   658 net.cpp:106] Creating Layer accuracy\n",
+      "I0224 00:32:05.815693   658 net.cpp:454] accuracy <- ip2_ip2_0_split_0\n",
+      "I0224 00:32:05.815702   658 net.cpp:454] accuracy <- label_data_1_split_0\n",
+      "I0224 00:32:05.815711   658 net.cpp:411] accuracy -> accuracy\n",
+      "I0224 00:32:05.815722   658 net.cpp:150] Setting up accuracy\n",
+      "I0224 00:32:05.815732   658 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:05.815738   658 net.cpp:165] Memory required for data: 3724\n",
+      "I0224 00:32:05.815747   658 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:05.815754   658 net.cpp:106] Creating Layer loss\n",
+      "I0224 00:32:05.815762   658 net.cpp:454] loss <- ip2_ip2_0_split_1\n",
+      "I0224 00:32:05.815770   658 net.cpp:454] loss <- label_data_1_split_1\n",
+      "I0224 00:32:05.815779   658 net.cpp:411] loss -> loss\n",
+      "I0224 00:32:05.815790   658 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:05.815811   658 net.cpp:150] Setting up loss\n",
+      "I0224 00:32:05.815829   658 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:05.815843   658 net.cpp:160]     with loss weight 1\n",
+      "I0224 00:32:05.815867   658 net.cpp:165] Memory required for data: 3728\n",
+      "I0224 00:32:05.815876   658 net.cpp:226] loss needs backward computation.\n",
+      "I0224 00:32:05.815884   658 net.cpp:228] accuracy does not need backward computation.\n",
+      "I0224 00:32:05.815892   658 net.cpp:226] ip2_ip2_0_split needs backward computation.\n",
+      "I0224 00:32:05.815901   658 net.cpp:226] ip2 needs backward computation.\n",
+      "I0224 00:32:05.815908   658 net.cpp:226] relu1 needs backward computation.\n",
+      "I0224 00:32:05.815915   658 net.cpp:226] ip1 needs backward computation.\n",
+      "I0224 00:32:05.815923   658 net.cpp:228] label_data_1_split does not need backward computation.\n",
+      "I0224 00:32:05.815932   658 net.cpp:228] data does not need backward computation.\n",
+      "I0224 00:32:05.815938   658 net.cpp:270] This network produces output accuracy\n",
+      "I0224 00:32:05.815946   658 net.cpp:270] This network produces output loss\n",
+      "I0224 00:32:05.815958   658 net.cpp:283] Network initialization done.\n",
+      "I0224 00:32:05.815978   658 solver.cpp:60] Solver scaffolding done.\n",
+      "I0224 00:32:05.816000   658 caffe.cpp:219] Starting Optimization\n",
+      "I0224 00:32:05.816016   658 solver.cpp:280] Solving \n",
+      "I0224 00:32:05.816030   658 solver.cpp:281] Learning Rate Policy: step\n",
+      "I0224 00:32:05.816048   658 solver.cpp:338] Iteration 0, Testing net (#0)\n",
+      "I0224 00:32:05.831967   658 solver.cpp:406]     Test net output #0: accuracy = 0.4464\n",
+      "I0224 00:32:05.832033   658 solver.cpp:406]     Test net output #1: loss = 0.909841 (* 1 = 0.909841 loss)\n",
+      "I0224 00:32:05.832186   658 solver.cpp:229] Iteration 0, loss = 0.798509\n",
+      "I0224 00:32:05.832218   658 solver.cpp:245]     Train net output #0: accuracy = 0.6\n",
+      "I0224 00:32:05.832247   658 solver.cpp:245]     Train net output #1: loss = 0.798509 (* 1 = 0.798509 loss)\n",
+      "I0224 00:32:05.832281   658 sgd_solver.cpp:106] Iteration 0, lr = 0.01\n",
+      "I0224 00:32:05.859506   658 solver.cpp:338] Iteration 1000, Testing net (#0)\n",
+      "I0224 00:32:05.862799   658 solver.cpp:406]     Test net output #0: accuracy = 0.8156\n",
+      "I0224 00:32:05.862818   658 solver.cpp:406]     Test net output #1: loss = 0.44259 (* 1 = 0.44259 loss)\n",
+      "I0224 00:32:05.862853   658 solver.cpp:229] Iteration 1000, loss = 0.537015\n",
+      "I0224 00:32:05.862864   658 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:05.862875   658 solver.cpp:245]     Train net output #1: loss = 0.537015 (* 1 = 0.537015 loss)\n",
+      "I0224 00:32:05.862885   658 sgd_solver.cpp:106] Iteration 1000, lr = 0.01\n",
+      "I0224 00:32:05.883155   658 solver.cpp:338] Iteration 2000, Testing net (#0)\n",
+      "I0224 00:32:05.886435   658 solver.cpp:406]     Test net output #0: accuracy = 0.8116\n",
+      "I0224 00:32:05.886451   658 solver.cpp:406]     Test net output #1: loss = 0.434079 (* 1 = 0.434079 loss)\n",
+      "I0224 00:32:05.886484   658 solver.cpp:229] Iteration 2000, loss = 0.43109\n",
+      "I0224 00:32:05.886497   658 solver.cpp:245]     Train net output #0: accuracy = 0.9\n",
+      "I0224 00:32:05.886508   658 solver.cpp:245]     Train net output #1: loss = 0.43109 (* 1 = 0.43109 loss)\n",
+      "I0224 00:32:05.886518   658 sgd_solver.cpp:106] Iteration 2000, lr = 0.01\n",
+      "I0224 00:32:05.907243   658 solver.cpp:338] Iteration 3000, Testing net (#0)\n",
+      "I0224 00:32:05.910521   658 solver.cpp:406]     Test net output #0: accuracy = 0.8168\n",
+      "I0224 00:32:05.910537   658 solver.cpp:406]     Test net output #1: loss = 0.425661 (* 1 = 0.425661 loss)\n",
+      "I0224 00:32:05.910905   658 solver.cpp:229] Iteration 3000, loss = 0.430245\n",
+      "I0224 00:32:05.910922   658 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:05.910933   658 solver.cpp:245]     Train net output #1: loss = 0.430245 (* 1 = 0.430245 loss)\n",
+      "I0224 00:32:05.910943   658 sgd_solver.cpp:106] Iteration 3000, lr = 0.01\n",
+      "I0224 00:32:05.931205   658 solver.cpp:338] Iteration 4000, Testing net (#0)\n",
+      "I0224 00:32:05.934479   658 solver.cpp:406]     Test net output #0: accuracy = 0.8324\n",
+      "I0224 00:32:05.934496   658 solver.cpp:406]     Test net output #1: loss = 0.404891 (* 1 = 0.404891 loss)\n",
+      "I0224 00:32:05.934530   658 solver.cpp:229] Iteration 4000, loss = 0.628955\n",
+      "I0224 00:32:05.934542   658 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:05.934553   658 solver.cpp:245]     Train net output #1: loss = 0.628955 (* 1 = 0.628955 loss)\n",
+      "I0224 00:32:05.934583   658 sgd_solver.cpp:106] Iteration 4000, lr = 0.01\n",
+      "I0224 00:32:05.955108   658 solver.cpp:338] Iteration 5000, Testing net (#0)\n",
+      "I0224 00:32:05.958377   658 solver.cpp:406]     Test net output #0: accuracy = 0.8364\n",
+      "I0224 00:32:05.958395   658 solver.cpp:406]     Test net output #1: loss = 0.404235 (* 1 = 0.404235 loss)\n",
+      "I0224 00:32:05.958432   658 solver.cpp:229] Iteration 5000, loss = 0.394939\n",
+      "I0224 00:32:05.958444   658 solver.cpp:245]     Train net output #0: accuracy = 0.9\n",
+      "I0224 00:32:05.958456   658 solver.cpp:245]     Train net output #1: loss = 0.39494 (* 1 = 0.39494 loss)\n",
+      "I0224 00:32:05.958466   658 sgd_solver.cpp:106] Iteration 5000, lr = 0.001\n",
+      "I0224 00:32:05.978703   658 solver.cpp:338] Iteration 6000, Testing net (#0)\n",
+      "I0224 00:32:05.981973   658 solver.cpp:406]     Test net output #0: accuracy = 0.838\n",
+      "I0224 00:32:05.981991   658 solver.cpp:406]     Test net output #1: loss = 0.385743 (* 1 = 0.385743 loss)\n",
+      "I0224 00:32:05.982347   658 solver.cpp:229] Iteration 6000, loss = 0.411537\n",
+      "I0224 00:32:05.982362   658 solver.cpp:245]     Train net output #0: accuracy = 0.8\n",
+      "I0224 00:32:05.982373   658 solver.cpp:245]     Train net output #1: loss = 0.411537 (* 1 = 0.411537 loss)\n",
+      "I0224 00:32:05.982383   658 sgd_solver.cpp:106] Iteration 6000, lr = 0.001\n",
+      "I0224 00:32:06.003015   658 solver.cpp:338] Iteration 7000, Testing net (#0)\n",
+      "I0224 00:32:06.006283   658 solver.cpp:406]     Test net output #0: accuracy = 0.8388\n",
+      "I0224 00:32:06.006301   658 solver.cpp:406]     Test net output #1: loss = 0.384648 (* 1 = 0.384648 loss)\n",
+      "I0224 00:32:06.006335   658 solver.cpp:229] Iteration 7000, loss = 0.521072\n",
+      "I0224 00:32:06.006347   658 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:06.006358   658 solver.cpp:245]     Train net output #1: loss = 0.521073 (* 1 = 0.521073 loss)\n",
+      "I0224 00:32:06.006368   658 sgd_solver.cpp:106] Iteration 7000, lr = 0.001\n",
+      "I0224 00:32:06.026715   658 solver.cpp:338] Iteration 8000, Testing net (#0)\n",
+      "I0224 00:32:06.029965   658 solver.cpp:406]     Test net output #0: accuracy = 0.8404\n",
+      "I0224 00:32:06.029983   658 solver.cpp:406]     Test net output #1: loss = 0.380889 (* 1 = 0.380889 loss)\n",
+      "I0224 00:32:06.030015   658 solver.cpp:229] Iteration 8000, loss = 0.329477\n",
+      "I0224 00:32:06.030028   658 solver.cpp:245]     Train net output #0: accuracy = 0.9\n",
+      "I0224 00:32:06.030040   658 solver.cpp:245]     Train net output #1: loss = 0.329477 (* 1 = 0.329477 loss)\n",
+      "I0224 00:32:06.030048   658 sgd_solver.cpp:106] Iteration 8000, lr = 0.001\n",
+      "I0224 00:32:06.050626   658 solver.cpp:338] Iteration 9000, Testing net (#0)\n",
+      "I0224 00:32:06.053889   658 solver.cpp:406]     Test net output #0: accuracy = 0.8376\n",
+      "I0224 00:32:06.053906   658 solver.cpp:406]     Test net output #1: loss = 0.382756 (* 1 = 0.382756 loss)\n",
+      "I0224 00:32:06.054271   658 solver.cpp:229] Iteration 9000, loss = 0.412227\n",
+      "I0224 00:32:06.054291   658 solver.cpp:245]     Train net output #0: accuracy = 0.8\n",
+      "I0224 00:32:06.054314   658 solver.cpp:245]     Train net output #1: loss = 0.412228 (* 1 = 0.412228 loss)\n",
+      "I0224 00:32:06.054337   658 sgd_solver.cpp:106] Iteration 9000, lr = 0.001\n",
+      "I0224 00:32:06.074646   658 solver.cpp:456] Snapshotting to binary proto file examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
+      "I0224 00:32:06.074808   658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/hdf5_classification/data/train_iter_10000.solverstate\n",
+      "I0224 00:32:06.074889   658 solver.cpp:318] Iteration 10000, loss = 0.532798\n",
+      "I0224 00:32:06.074906   658 solver.cpp:338] Iteration 10000, Testing net (#0)\n",
+      "I0224 00:32:06.078208   658 solver.cpp:406]     Test net output #0: accuracy = 0.8388\n",
+      "I0224 00:32:06.078225   658 solver.cpp:406]     Test net output #1: loss = 0.382042 (* 1 = 0.382042 loss)\n",
+      "I0224 00:32:06.078234   658 solver.cpp:323] Optimization Done.\n",
+      "I0224 00:32:06.078241   658 caffe.cpp:222] Optimization Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!./build/tools/caffe train -solver examples/hdf5_classification/nonlinear_logreg_solver.prototxt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n",
+    "shutil.rmtree(dirname)"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
+  "example_name": "Off-the-shelf SGD for classification",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  },
+  "priority": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/cifar10/cifar10_full.prototxt b/examples/cifar10/cifar10_full.prototxt
index 446479da..83cf0d86 100644
--- a/examples/cifar10/cifar10_full.prototxt
+++ b/examples/cifar10/cifar10_full.prototxt
@@ -1,12 +1,11 @@
 name: "CIFAR10_full_deploy"
 # N.B. input image must be in CIFAR-10 format
 # as described at http://www.cs.toronto.edu/~kriz/cifar.html
-input: "data"
-input_shape {
-  dim: 1
-  dim: 3
-  dim: 32
-  dim: 32
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }
 }
 layer {
   name: "conv1"
diff --git a/examples/cifar10/cifar10_full_sigmoid_solver.prototxt b/examples/cifar10/cifar10_full_sigmoid_solver.prototxt
index 7dd3ecb9..a8e55399 100644
--- a/examples/cifar10/cifar10_full_sigmoid_solver.prototxt
+++ b/examples/cifar10/cifar10_full_sigmoid_solver.prototxt
@@ -17,7 +17,7 @@ momentum: 0.9
 lr_policy: "step"
 gamma: 1
 stepsize: 5000
-# Display every 200 iterations
+# Display every 100 iterations
 display: 100
 # The maximum number of iterations
 max_iter: 60000
diff --git a/examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt b/examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt
index a57b280f..a4dabd67 100644
--- a/examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt
+++ b/examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt
@@ -17,7 +17,7 @@ momentum: 0.9
 lr_policy: "step"
 gamma: 1
 stepsize: 5000
-# Display every 200 iterations
+# Display every 100 iterations
 display: 100
 # The maximum number of iterations
 max_iter: 60000
diff --git a/examples/cifar10/cifar10_quick.prototxt b/examples/cifar10/cifar10_quick.prototxt
index 9352fbf6..cf3b2a35 100644
--- a/examples/cifar10/cifar10_quick.prototxt
+++ b/examples/cifar10/cifar10_quick.prototxt
@@ -1,10 +1,9 @@
 name: "CIFAR10_quick_test"
-input: "data"
-input_shape {
-  dim: 1
-  dim: 3
-  dim: 32
-  dim: 32
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }
 }
 layer {
   name: "conv1"
diff --git a/examples/cifar10/cifar10_quick_solver.prototxt b/examples/cifar10/cifar10_quick_solver.prototxt
index 5de276f7..14b4401b 100644
--- a/examples/cifar10/cifar10_quick_solver.prototxt
+++ b/examples/cifar10/cifar10_quick_solver.prototxt
@@ -20,7 +20,6 @@ display: 100
 max_iter: 4000
 # snapshot intermediate results
 snapshot: 4000
-snapshot_format: HDF5
 snapshot_prefix: "examples/cifar10/cifar10_quick"
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/examples/cifar10/convert_cifar_data.cpp b/examples/cifar10/convert_cifar_data.cpp
index e1b89f42..7385a74a 100644
--- a/examples/cifar10/convert_cifar_data.cpp
+++ b/examples/cifar10/convert_cifar_data.cpp
@@ -91,6 +91,8 @@ void convert_dataset(const string& input_folder, const string& output_folder,
 }
 
 int main(int argc, char** argv) {
+  FLAGS_alsologtostderr = 1;
+
   if (argc != 4) {
     printf("This script converts the CIFAR dataset to the leveldb format used\n"
            "by caffe to perform classification.\n"
diff --git a/examples/cifar10/create_cifar10.sh b/examples/cifar10/create_cifar10.sh
index a42725cb..7ee1d6ad 100755
--- a/examples/cifar10/create_cifar10.sh
+++ b/examples/cifar10/create_cifar10.sh
@@ -1,5 +1,6 @@
 #!/usr/bin/env sh
 # This script converts the cifar data into leveldb format.
+set -e
 
 EXAMPLE=examples/cifar10
 DATA=data/cifar10
diff --git a/examples/cifar10/train_full.sh b/examples/cifar10/train_full.sh
index ef112e1f..fe46e60d 100755
--- a/examples/cifar10/train_full.sh
+++ b/examples/cifar10/train_full.sh
@@ -1,16 +1,17 @@
 #!/usr/bin/env sh
+set -e
 
 TOOLS=./build/tools
 
 $TOOLS/caffe train \
-    --solver=examples/cifar10/cifar10_full_solver.prototxt
+    --solver=examples/cifar10/cifar10_full_solver.prototxt $@
 
 # reduce learning rate by factor of 10
 $TOOLS/caffe train \
     --solver=examples/cifar10/cifar10_full_solver_lr1.prototxt \
-    --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate.h5
+    --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate $@
 
 # reduce learning rate by factor of 10
 $TOOLS/caffe train \
     --solver=examples/cifar10/cifar10_full_solver_lr2.prototxt \
-    --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate.h5
+    --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate $@
diff --git a/examples/cifar10/train_full_sigmoid.sh b/examples/cifar10/train_full_sigmoid.sh
index 9cff06d3..9b5d5213 100755
--- a/examples/cifar10/train_full_sigmoid.sh
+++ b/examples/cifar10/train_full_sigmoid.sh
@@ -1,7 +1,8 @@
 #!/usr/bin/env sh
+set -e
 
 TOOLS=./build/tools
 
 $TOOLS/caffe train \
-    --solver=examples/cifar10/cifar10_full_sigmoid_solver.prototxt
+    --solver=examples/cifar10/cifar10_full_sigmoid_solver.prototxt $@
 
diff --git a/examples/cifar10/train_full_sigmoid_bn.sh b/examples/cifar10/train_full_sigmoid_bn.sh
index 011387c9..05547f3a 100755
--- a/examples/cifar10/train_full_sigmoid_bn.sh
+++ b/examples/cifar10/train_full_sigmoid_bn.sh
@@ -1,7 +1,8 @@
 #!/usr/bin/env sh
+set -e
 
 TOOLS=./build/tools
 
 $TOOLS/caffe train \
-    --solver=examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt
+    --solver=examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt $@
 
diff --git a/examples/cifar10/train_quick.sh b/examples/cifar10/train_quick.sh
index 6b7d2288..257479e0 100755
--- a/examples/cifar10/train_quick.sh
+++ b/examples/cifar10/train_quick.sh
@@ -1,11 +1,12 @@
 #!/usr/bin/env sh
+set -e
 
 TOOLS=./build/tools
 
 $TOOLS/caffe train \
-  --solver=examples/cifar10/cifar10_quick_solver.prototxt
+  --solver=examples/cifar10/cifar10_quick_solver.prototxt $@
 
 # reduce learning rate by factor of 10 after 8 epochs
 $TOOLS/caffe train \
   --solver=examples/cifar10/cifar10_quick_solver_lr1.prototxt \
-  --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate.h5
+  --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate $@
diff --git a/examples/cpp_classification/classification.cpp b/examples/cpp_classification/classification.cpp
index 974662e5..6b67c537 100644
--- a/examples/cpp_classification/classification.cpp
+++ b/examples/cpp_classification/classification.cpp
@@ -159,7 +159,7 @@ std::vector<float> Classifier::Predict(const cv::Mat& img) {
 
   Preprocess(img, &input_channels);
 
-  net_->ForwardPrefilled();
+  net_->Forward();
 
   /* Copy the output layer to a std::vector */
   Blob<float>* output_layer = net_->output_blobs()[0];
diff --git a/examples/cpp_classification/readme.md b/examples/cpp_classification/readme.md
index a086db1a..4f683aa6 100644
--- a/examples/cpp_classification/readme.md
+++ b/examples/cpp_classification/readme.md
@@ -10,7 +10,7 @@ priority: 10
 
 Caffe, at its core, is written in C++. It is possible to use the C++
 API of Caffe to implement an image classification application similar
-to the Python code presented in one of the Notebook example. To look
+to the Python code presented in one of the Notebook examples. To look
 at a more general-purpose example of the Caffe C++ API, you should
 study the source code of the command line tool `caffe` in `tools/caffe.cpp`.
 
@@ -19,7 +19,7 @@ study the source code of the command line tool `caffe` in `tools/caffe.cpp`.
 A simple C++ code is proposed in
 `examples/cpp_classification/classification.cpp`. For the sake of
 simplicity, this example does not support oversampling of a single
-sample nor batching of multiple independant samples. This example is
+sample nor batching of multiple independent samples. This example is
 not trying to reach the maximum possible classification throughput on
 a system, but special care was given to avoid unnecessary
 pessimization while keeping the code readable.
@@ -42,7 +42,7 @@ script:
 The ImageNet labels file (also called the *synset file*) is also
 required in order to map a prediction to the name of the class:
 ```
-./data/ilsvrc12/get_ilsvrc_aux.sh.
+./data/ilsvrc12/get_ilsvrc_aux.sh
 ```
 Using the files that were downloaded, we can classify the provided cat
 image (`examples/images/cat.jpg`) using this command:
diff --git a/examples/finetune_flickr_style/readme.md b/examples/finetune_flickr_style/readme.md
index 4e9d41f1..dacfd01c 100644
--- a/examples/finetune_flickr_style/readme.md
+++ b/examples/finetune_flickr_style/readme.md
@@ -9,14 +9,14 @@ priority: 5
 # Fine-tuning CaffeNet for Style Recognition on "Flickr Style" Data
 
 Fine-tuning takes an already learned model, adapts the architecture, and resumes training from the already learned model weights.
-Let's fine-tune the BVLC-distributed CaffeNet model on a different dataset, [Flickr Style](http://sergeykarayev.com/files/1311.3715v3.pdf), to predict image style instead of object category.
+Let's fine-tune the BAIR-distributed CaffeNet model on a different dataset, [Flickr Style](http://sergeykarayev.com/files/1311.3715v3.pdf), to predict image style instead of object category.
 
 ## Explanation
 
 The Flickr-sourced images of the Style dataset are visually very similar to the ImageNet dataset, on which the `bvlc_reference_caffenet` was trained.
-Since that model works well for object category classification, we'd like to use it architecture for our style classifier.
+Since that model works well for object category classification, we'd like to use this architecture for our style classifier.
 We also only have 80,000 images to train on, so we'd like to start with the parameters learned on the 1,000,000 ImageNet images, and fine-tune as needed.
-If we give provide the `weights` argument to the `caffe train` command, the pretrained weights will be loaded into our model, matching layers by name.
+If we provide the `weights` argument to the `caffe train` command, the pretrained weights will be loaded into our model, matching layers by name.
 
 Because we are predicting 20 classes instead of a 1,000, we do need to change the last layer in the model.
 Therefore, we change the name of the last layer from `fc8` to `fc8_flickr` in our prototxt.
@@ -57,7 +57,11 @@ The prototxts in this example assume this, and also assume the presence of the I
 
 We'll also need the ImageNet-trained model, which you can obtain by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet`.
 
-Now we can train! (You can fine-tune in CPU mode by leaving out the `-gpu` flag.)
+Now we can train! The key to fine-tuning is the `-weights` argument in the
+command below, which tells Caffe that we want to load weights from a pre-trained
+Caffe model.
+
+(You can fine-tune in CPU mode by leaving out the `-gpu` flag.)
 
     caffe % ./build/tools/caffe train -solver models/finetune_flickr_style/solver.prototxt -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel -gpu 0
 
diff --git a/examples/hdf5_classification/nonlinear_solver.prototxt b/examples/hdf5_classification/nonlinear_solver.prototxt
deleted file mode 100644
index b4aacf6e..00000000
--- a/examples/hdf5_classification/nonlinear_solver.prototxt
+++ /dev/null
@@ -1,15 +0,0 @@
-train_net: "examples/hdf5_classification/nonlinear_auto_train.prototxt"
-test_net: "examples/hdf5_classification/nonlinear_auto_test.prototxt"
-test_iter: 250
-test_interval: 1000
-base_lr: 0.01
-lr_policy: "step"
-gamma: 0.1
-stepsize: 5000
-display: 1000
-max_iter: 10000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 10000
-snapshot_prefix: "examples/hdf5_classification/data/train"
-solver_mode: CPU
diff --git a/examples/hdf5_classification/solver.prototxt b/examples/hdf5_classification/solver.prototxt
deleted file mode 100644
index 8587b5a1..00000000
--- a/examples/hdf5_classification/solver.prototxt
+++ /dev/null
@@ -1,15 +0,0 @@
-train_net: "examples/hdf5_classification/logreg_auto_train.prototxt"
-test_net: "examples/hdf5_classification/logreg_auto_test.prototxt"
-test_iter: 250
-test_interval: 1000
-base_lr: 0.01
-lr_policy: "step"
-gamma: 0.1
-stepsize: 5000
-display: 1000
-max_iter: 10000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 10000
-snapshot_prefix: "examples/hdf5_classification/data/train"
-solver_mode: CPU
diff --git a/examples/imagenet/create_imagenet.sh b/examples/imagenet/create_imagenet.sh
index e912ac43..1bf08b1a 100755
--- a/examples/imagenet/create_imagenet.sh
+++ b/examples/imagenet/create_imagenet.sh
@@ -1,6 +1,7 @@
 #!/usr/bin/env sh
 # Create the imagenet lmdb inputs
 # N.B. set the path to the imagenet train + val data dirs
+set -e
 
 EXAMPLE=examples/imagenet
 DATA=data/ilsvrc12
diff --git a/examples/imagenet/resume_training.sh b/examples/imagenet/resume_training.sh
index bf7945c0..4aef2043 100755
--- a/examples/imagenet/resume_training.sh
+++ b/examples/imagenet/resume_training.sh
@@ -1,5 +1,7 @@
 #!/usr/bin/env sh
+set -e
 
 ./build/tools/caffe train \
     --solver=models/bvlc_reference_caffenet/solver.prototxt \
-    --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate.h5
+    --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate.h5 \
+    $@
diff --git a/examples/imagenet/train_caffenet.sh b/examples/imagenet/train_caffenet.sh
index 94558ec5..a5094d44 100755
--- a/examples/imagenet/train_caffenet.sh
+++ b/examples/imagenet/train_caffenet.sh
@@ -1,4 +1,5 @@
 #!/usr/bin/env sh
+set -e
 
 ./build/tools/caffe train \
-    --solver=models/bvlc_reference_caffenet/solver.prototxt
+    --solver=models/bvlc_reference_caffenet/solver.prototxt $@
diff --git a/examples/images/cat gray.jpg b/examples/images/cat gray.jpg
new file mode 100644
index 00000000..43c5ce37
Binary files /dev/null and b/examples/images/cat gray.jpg differ
diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp
index 16d28093..57ddef77 100644
--- a/examples/mnist/convert_mnist_data.cpp
+++ b/examples/mnist/convert_mnist_data.cpp
@@ -22,12 +22,15 @@
 #include <fstream>  // NOLINT(readability/streams)
 #include <string>
 
+#include "boost/scoped_ptr.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
 #include "caffe/util/format.hpp"
 
 #if defined(USE_LEVELDB) && defined(USE_LMDB)
 
 using namespace caffe;  // NOLINT(build/namespaces)
+using boost::scoped_ptr;
 using std::string;
 
 DEFINE_string(backend, "lmdb", "The backend for storing the result");
@@ -67,43 +70,10 @@ void convert_dataset(const char* image_filename, const char* label_filename,
   image_file.read(reinterpret_cast<char*>(&cols), 4);
   cols = swap_endian(cols);
 
-  // lmdb
-  MDB_env *mdb_env;
-  MDB_dbi mdb_dbi;
-  MDB_val mdb_key, mdb_data;
-  MDB_txn *mdb_txn;
-  // leveldb
-  leveldb::DB* db;
-  leveldb::Options options;
-  options.error_if_exists = true;
-  options.create_if_missing = true;
-  options.write_buffer_size = 268435456;
-  leveldb::WriteBatch* batch = NULL;
-
-  // Open db
-  if (db_backend == "leveldb") {  // leveldb
-    LOG(INFO) << "Opening leveldb " << db_path;
-    leveldb::Status status = leveldb::DB::Open(
-        options, db_path, &db);
-    CHECK(status.ok()) << "Failed to open leveldb " << db_path
-        << ". Is it already existing?";
-    batch = new leveldb::WriteBatch();
-  } else if (db_backend == "lmdb") {  // lmdb
-    LOG(INFO) << "Opening lmdb " << db_path;
-    CHECK_EQ(mkdir(db_path, 0744), 0)
-        << "mkdir " << db_path << "failed";
-    CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";
-    CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS)  // 1TB
-        << "mdb_env_set_mapsize failed";
-    CHECK_EQ(mdb_env_open(mdb_env, db_path, 0, 0664), MDB_SUCCESS)
-        << "mdb_env_open failed";
-    CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
-        << "mdb_txn_begin failed";
-    CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)
-        << "mdb_open failed. Does the lmdb already exist? ";
-  } else {
-    LOG(FATAL) << "Unknown db backend " << db_backend;
-  }
+
+  scoped_ptr<db::DB> db(db::GetDB(db_backend));
+  db->Open(db_path, db::NEW);
+  scoped_ptr<db::Transaction> txn(db->NewTransaction());
 
   // Storing to db
   char label;
@@ -125,52 +95,19 @@ void convert_dataset(const char* image_filename, const char* label_filename,
     string key_str = caffe::format_int(item_id, 8);
     datum.SerializeToString(&value);
 
-    // Put in db
-    if (db_backend == "leveldb") {  // leveldb
-      batch->Put(key_str, value);
-    } else if (db_backend == "lmdb") {  // lmdb
-      mdb_data.mv_size = value.size();
-      mdb_data.mv_data = reinterpret_cast<void*>(&value[0]);
-      mdb_key.mv_size = key_str.size();
-      mdb_key.mv_data = reinterpret_cast<void*>(&key_str[0]);
-      CHECK_EQ(mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0), MDB_SUCCESS)
-          << "mdb_put failed";
-    } else {
-      LOG(FATAL) << "Unknown db backend " << db_backend;
-    }
+    txn->Put(key_str, value);
 
     if (++count % 1000 == 0) {
-      // Commit txn
-      if (db_backend == "leveldb") {  // leveldb
-        db->Write(leveldb::WriteOptions(), batch);
-        delete batch;
-        batch = new leveldb::WriteBatch();
-      } else if (db_backend == "lmdb") {  // lmdb
-        CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS)
-            << "mdb_txn_commit failed";
-        CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
-            << "mdb_txn_begin failed";
-      } else {
-        LOG(FATAL) << "Unknown db backend " << db_backend;
-      }
+      txn->Commit();
     }
   }
   // write the last batch
   if (count % 1000 != 0) {
-    if (db_backend == "leveldb") {  // leveldb
-      db->Write(leveldb::WriteOptions(), batch);
-      delete batch;
-      delete db;
-    } else if (db_backend == "lmdb") {  // lmdb
-      CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS) << "mdb_txn_commit failed";
-      mdb_close(mdb_env, mdb_dbi);
-      mdb_env_close(mdb_env);
-    } else {
-      LOG(FATAL) << "Unknown db backend " << db_backend;
-    }
-    LOG(ERROR) << "Processed " << count << " files.";
+      txn->Commit();
   }
+  LOG(INFO) << "Processed " << count << " files.";
   delete[] pixels;
+  db->Close();
 }
 
 int main(int argc, char** argv) {
@@ -178,6 +115,8 @@ int main(int argc, char** argv) {
   namespace gflags = google;
 #endif
 
+  FLAGS_alsologtostderr = 1;
+
   gflags::SetUsageMessage("This script converts the MNIST dataset to\n"
         "the lmdb/leveldb format used by Caffe to load data.\n"
         "Usage:\n"
diff --git a/examples/mnist/create_mnist.sh b/examples/mnist/create_mnist.sh
index 06ecc27d..f5e2e796 100755
--- a/examples/mnist/create_mnist.sh
+++ b/examples/mnist/create_mnist.sh
@@ -1,6 +1,7 @@
 #!/usr/bin/env sh
 # This script converts the mnist data into lmdb/leveldb format,
 # depending on the value assigned to $BACKEND.
+set -e
 
 EXAMPLE=examples/mnist
 DATA=data/mnist
diff --git a/examples/mnist/lenet.prototxt b/examples/mnist/lenet.prototxt
index dff7123b..8cf78e62 100644
--- a/examples/mnist/lenet.prototxt
+++ b/examples/mnist/lenet.prototxt
@@ -1,10 +1,9 @@
 name: "LeNet"
-input: "data"
-input_shape {
-  dim: 64
-  dim: 1
-  dim: 28
-  dim: 28
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 64 dim: 1 dim: 28 dim: 28 } }
 }
 layer {
   name: "conv1"
diff --git a/examples/mnist/lenet_auto_solver.prototxt b/examples/mnist/lenet_auto_solver.prototxt
index fa4bbf02..481c8449 100644
--- a/examples/mnist/lenet_auto_solver.prototxt
+++ b/examples/mnist/lenet_auto_solver.prototxt
@@ -1,6 +1,6 @@
 # The train/test net protocol buffer definition
-train_net: "examples/mnist/lenet_auto_train.prototxt"
-test_net: "examples/mnist/lenet_auto_test.prototxt"
+train_net: "mnist/lenet_auto_train.prototxt"
+test_net: "mnist/lenet_auto_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of MNIST, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
@@ -21,4 +21,4 @@ display: 100
 max_iter: 10000
 # snapshot intermediate results
 snapshot: 5000
-snapshot_prefix: "examples/mnist/lenet"
+snapshot_prefix: "mnist/lenet"
diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index b87a0f53..35952155 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -248,7 +248,7 @@ These messages tell you the details about each layer, its connections and its ou
     I1203 solver.cpp:36] Solver scaffolding done.
     I1203 solver.cpp:44] Solving LeNet
 
-Based on the solver setting, we will print the training loss function every 100 iterations, and test the network every 1000 iterations. You will see messages like this:
+Based on the solver setting, we will print the training loss function every 100 iterations, and test the network every 500 iterations. You will see messages like this:
 
     I1203 solver.cpp:204] Iteration 100, lr = 0.00992565
     I1203 solver.cpp:66] Iteration 100, loss = 0.26044
diff --git a/examples/mnist/train_lenet.sh b/examples/mnist/train_lenet.sh
index 1b6bf7d9..f7f9b861 100755
--- a/examples/mnist/train_lenet.sh
+++ b/examples/mnist/train_lenet.sh
@@ -1,3 +1,4 @@
 #!/usr/bin/env sh
+set -e
 
-./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototxt
+./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototxt $@
diff --git a/examples/mnist/train_lenet_adam.sh b/examples/mnist/train_lenet_adam.sh
index a32ecf2d..7b4e9056 100755
--- a/examples/mnist/train_lenet_adam.sh
+++ b/examples/mnist/train_lenet_adam.sh
@@ -1,3 +1,4 @@
 #!/usr/bin/env sh
+set -e
 
-./build/tools/caffe train --solver=examples/mnist/lenet_solver_adam.prototxt
+./build/tools/caffe train --solver=examples/mnist/lenet_solver_adam.prototxt $@
diff --git a/examples/mnist/train_lenet_consolidated.sh b/examples/mnist/train_lenet_consolidated.sh
index c8554678..c5f02666 100755
--- a/examples/mnist/train_lenet_consolidated.sh
+++ b/examples/mnist/train_lenet_consolidated.sh
@@ -1,4 +1,5 @@
 #!/usr/bin/env sh
+set -e
 
 ./build/tools/caffe train \
-  --solver=examples/mnist/lenet_consolidated_solver.prototxt
+  --solver=examples/mnist/lenet_consolidated_solver.prototxt $@
diff --git a/examples/mnist/train_lenet_docker.sh b/examples/mnist/train_lenet_docker.sh
new file mode 100755
index 00000000..e946ba0f
--- /dev/null
+++ b/examples/mnist/train_lenet_docker.sh
@@ -0,0 +1,119 @@
+#!/usr/bin/env sh
+set -e
+# The following example allows for the MNIST example (using LeNet) to be
+# trained using the caffe docker image instead of building from source.
+#
+# The GPU-enabled version of Caffe can be used, assuming that nvidia-docker
+# is installed, and the GPU-enabled Caffe image has been built.
+# Setting the GPU environment variable to 1 will enable the use of nvidia-docker.
+# e.g.
+#   GPU=1 ./examples/mnist/train_lenet_docker.sh [ADDITIONAL_CAFFE_ARGS]
+#
+# With any arguments following the script being passed directly to caffe
+# when training the network.
+#
+# The steps that are performed by the script are as follows:
+# 1. The MNIST data set is downloaded
+#    (see data/mnist/get_mnist.sh)
+# 2. An LMDB database is created from the downloaded data
+#    (see examples/mnist/create_mnist.sh.
+# 3. A caffe network based on the LeNet solver is trained.
+#    (see examples/mnist/lenet_solver.prototxt)
+#
+# For each of these, a step is executed to ensure that certain prerequisites
+# are available, after which a command that actually performs the work is
+# executed.
+#
+# In order to provide additional flexibility, the following shell (environment)
+# variables can be used to control the execution of each of the phases:
+#
+# DOWNLOAD_DATA: Enable (1) or disable (0) the downloading of the MNIST dataset
+# CREATE_LMDB: Enable (1) or disable (0) the creation of the LMDB database
+# TRAIN: Enable (1) or disable (0) the training of the LeNet networkd.
+#
+# As an example, assuming that the data set has been downloaded, and an LMDB
+# database created, the following command can be used to train the LeNet
+# network with GPU computing enabled.
+#
+# DOWNLOAD_DATA=0 CREATE_LMDB=0 GPU=1 ./examples/mnist/train_lenet_docker.sh
+#
+
+
+if [ x"$(uname -s)" != x"Linux" ]
+then
+echo ""
+echo "This script is designed to run on Linux."
+echo "There may be problems with the way Docker mounts host volumes on other"
+echo "systems which will cause the docker commands to fail."
+echo ""
+read -p "Press [ENTER] to continue..." key
+echo ""
+fi
+
+
+# Check if GPU mode has been enabled and set the docker executable accordingly
+if [ ${GPU:-0} -eq 1 ]
+then
+DOCKER_CMD=nvidia-docker
+IMAGE=caffe:gpu
+else
+DOCKER_CMD=docker
+IMAGE=caffe:cpu
+fi
+echo "Using $DOCKER_CMD to launch $IMAGE"
+
+# On non-Linux systems, the Docker host is typically a virtual machine.
+# This means that the user and group id's may be different.
+# On OS X, for example, the user and group are 1000 and 50, respectively.
+if [ x"$(uname -s)" != x"Linux" ]
+then
+CUID=1000
+CGID=50
+else
+CUID=$(id -u)
+CGID=$(id -g)
+fi
+
+# Define some helper variables to make the running of the actual docker
+# commands less verbose.
+# Note:
+#   -u $CUID:$CGID             runs the docker image as the current user to ensure
+#                              that the file permissions are compatible with the
+#                              host system. The variables CUID and CGID have been
+#                              set above depending on the host operating system.
+#   --volume $(pwd):/workspace mounts the current directory as the docker volume
+#                              /workspace
+#   --workdir /workspace       Ensures that the docker container starts in the right
+#                              working directory
+DOCKER_OPTIONS="--rm -ti -u $CUID:$CGID --volume=$(pwd):/workspace --workdir=/workspace"
+DOCKER_RUN="$DOCKER_CMD run $DOCKER_OPTIONS $IMAGE"
+
+# Download the data
+if [ ${DOWNLOAD_DATA:-1} -eq 1 ]
+then
+$DOCKER_RUN bash -c "mkdir -p ./data/mnist;
+                     cp -ru \$CAFFE_ROOT/data/mnist/get_mnist.sh ./data/mnist/"
+$DOCKER_RUN ./data/mnist/get_mnist.sh
+fi
+
+# Create the LMDB database
+if [ ${CREATE_LMDB:-1} -eq 1 ]
+then
+$DOCKER_RUN bash -c "mkdir -p ./examples/mnist;
+                     cp -ru \$CAFFE_ROOT/examples/mnist/create_mnist.sh ./examples/mnist/;
+                     sed -i s#BUILD=build#BUILD=\$CAFFE_ROOT/build## ./examples/mnist/create_mnist.sh"
+$DOCKER_RUN ./examples/mnist/create_mnist.sh
+fi
+
+# Train the network
+if [ ${TRAIN:-1} -eq 1 ]
+then
+$DOCKER_RUN bash -c "cp \$CAFFE_ROOT/examples/mnist/lenet_solver.prototxt ./examples/mnist/;
+                     cp \$CAFFE_ROOT/examples/mnist/lenet_train_test.prototxt ./examples/mnist/"
+    # Ensure that the solver_mode is compatible with the desired GPU mode.
+    if [ ${GPU:-0} -eq 0 ]
+    then
+    $DOCKER_RUN sed -i 's#solver_mode: GPU#solver_mode: CPU##' ./examples/mnist/lenet_solver.prototxt
+    fi
+$DOCKER_RUN caffe train --solver=examples/mnist/lenet_solver.prototxt $*
+fi
diff --git a/examples/mnist/train_lenet_rmsprop.sh b/examples/mnist/train_lenet_rmsprop.sh
index 621cab23..adfa7ab0 100755
--- a/examples/mnist/train_lenet_rmsprop.sh
+++ b/examples/mnist/train_lenet_rmsprop.sh
@@ -1,3 +1,5 @@
 #!/usr/bin/env sh
+set -e
 
-./build/tools/caffe train --solver=examples/mnist/lenet_solver_rmsprop.prototxt
+./build/tools/caffe train \
+    --solver=examples/mnist/lenet_solver_rmsprop.prototxt $@
diff --git a/examples/mnist/train_mnist_autoencoder.sh b/examples/mnist/train_mnist_autoencoder.sh
index cfd67e82..724a0f14 100755
--- a/examples/mnist/train_mnist_autoencoder.sh
+++ b/examples/mnist/train_mnist_autoencoder.sh
@@ -1,4 +1,5 @@
 #!/usr/bin/env sh
+set -e
 
 ./build/tools/caffe train \
-  --solver=examples/mnist/mnist_autoencoder_solver.prototxt
+  --solver=examples/mnist/mnist_autoencoder_solver.prototxt $@
diff --git a/examples/mnist/train_mnist_autoencoder_adadelta.sh b/examples/mnist/train_mnist_autoencoder_adadelta.sh
index 4be0ebdd..a660dbb9 100755
--- a/examples/mnist/train_mnist_autoencoder_adadelta.sh
+++ b/examples/mnist/train_mnist_autoencoder_adadelta.sh
@@ -1,4 +1,5 @@
 #!/bin/bash
+set -e
 
 ./build/tools/caffe train \
-  --solver=examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
+  --solver=examples/mnist/mnist_autoencoder_solver_adadelta.prototxt $@
diff --git a/examples/mnist/train_mnist_autoencoder_adagrad.sh b/examples/mnist/train_mnist_autoencoder_adagrad.sh
index 95fe1b17..4c11dfa6 100755
--- a/examples/mnist/train_mnist_autoencoder_adagrad.sh
+++ b/examples/mnist/train_mnist_autoencoder_adagrad.sh
@@ -1,4 +1,5 @@
 #!/bin/bash
+set -e
 
 ./build/tools/caffe train \
-  --solver=examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
+  --solver=examples/mnist/mnist_autoencoder_solver_adagrad.prototxt $@
diff --git a/examples/mnist/train_mnist_autoencoder_nesterov.sh b/examples/mnist/train_mnist_autoencoder_nesterov.sh
index cf19ea74..fd0559d2 100755
--- a/examples/mnist/train_mnist_autoencoder_nesterov.sh
+++ b/examples/mnist/train_mnist_autoencoder_nesterov.sh
@@ -1,4 +1,5 @@
 #!/bin/bash
+set -e
 
 ./build/tools/caffe train \
-  --solver=examples/mnist/mnist_autoencoder_solver_nesterov.prototxt
+  --solver=examples/mnist/mnist_autoencoder_solver_nesterov.prototxt $@
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index ff780fbb..217c2d1a 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -22,7 +22,6 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
-    "import Image\n",
     "\n",
     "# Make sure that caffe is on the python path:\n",
     "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
@@ -3511,7 +3510,7 @@
     "print(\"blobs {}\\nparams {}\".format(net.blobs.keys(), net.params.keys()))\n",
     "\n",
     "# load image and prepare as a single input batch for Caffe\n",
-    "im = np.array(Image.open('images/cat_gray.jpg'))\n",
+    "im = np.array(caffe.io.load_image('images/cat_gray.jpg', color=False)).squeeze()\n",
     "plt.title(\"original image\")\n",
     "plt.imshow(im)\n",
     "plt.axis('off')\n",
@@ -4480,8 +4479,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "pre-surgery output mean -12.93\n",
-      "post-surgery output mean -11.93\n"
+      "pre-surgery output mean -0.02\n",
+      "post-surgery output mean 0.98\n"
      ]
     }
    ],
@@ -4489,7 +4488,7 @@
     "# pick first filter output\n",
     "conv0 = net.blobs['conv'].data[0, 0]\n",
     "print(\"pre-surgery output mean {:.2f}\".format(conv0.mean()))\n",
-    "# set first filter bias to 10\n",
+    "# set first filter bias to 1\n",
     "net.params['conv'][1].data[0] = 1.\n",
     "net.forward()\n",
     "print(\"post-surgery output mean {:.2f}\".format(conv0.mean()))"
@@ -5480,7 +5479,7 @@
     "\n",
     "Let's take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully convolutional net for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional network (convnet) structure by amortizing the computation of overlapping receptive fields.\n",
     "\n",
-    "To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding."
+    "To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 $\\times$ 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding."
    ]
   },
   {
@@ -5499,43 +5498,41 @@
       "< name: \"CaffeNetConv\"\r\n",
       "---\r\n",
       "> name: \"CaffeNet\"\r\n",
-      "4c3\r\n",
-      "< input_dim: 1\r\n",
+      "7,11c6\r\n",
+      "<   input_param {\r\n",
+      "<     # initial shape for a fully convolutional network:\r\n",
+      "<     # the shape can be set for each input by reshape.\r\n",
+      "<     shape: { dim: 1 dim: 3 dim: 451 dim: 451 }\r\n",
+      "<   }\r\n",
       "---\r\n",
-      "> input_dim: 10\r\n",
-      "6,7c5,6\r\n",
-      "< input_dim: 451\r\n",
-      "< input_dim: 451\r\n",
-      "---\r\n",
-      "> input_dim: 227\r\n",
-      "> input_dim: 227\r\n",
-      "152,153c151,152\r\n",
+      ">   input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }\r\n",
+      "157,158c152,153\r\n",
       "<   name: \"fc6-conv\"\r\n",
       "<   type: \"Convolution\"\r\n",
       "---\r\n",
       ">   name: \"fc6\"\r\n",
       ">   type: \"InnerProduct\"\r\n",
-      "155,156c154,155\r\n",
+      "160,161c155,156\r\n",
       "<   top: \"fc6-conv\"\r\n",
       "<   convolution_param {\r\n",
       "---\r\n",
       ">   top: \"fc6\"\r\n",
       ">   inner_product_param {\r\n",
-      "158d156\r\n",
+      "163d157\r\n",
       "<     kernel_size: 6\r\n",
-      "164,165c162,163\r\n",
+      "169,170c163,164\r\n",
       "<   bottom: \"fc6-conv\"\r\n",
       "<   top: \"fc6-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc6\"\r\n",
       ">   top: \"fc6\"\r\n",
-      "170,171c168,169\r\n",
+      "175,176c169,170\r\n",
       "<   bottom: \"fc6-conv\"\r\n",
       "<   top: \"fc6-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc6\"\r\n",
       ">   top: \"fc6\"\r\n",
-      "177,181c175,179\r\n",
+      "182,186c176,180\r\n",
       "<   name: \"fc7-conv\"\r\n",
       "<   type: \"Convolution\"\r\n",
       "<   bottom: \"fc6-conv\"\r\n",
@@ -5547,21 +5544,21 @@
       ">   bottom: \"fc6\"\r\n",
       ">   top: \"fc7\"\r\n",
       ">   inner_product_param {\r\n",
-      "183d180\r\n",
+      "188d181\r\n",
       "<     kernel_size: 1\r\n",
-      "189,190c186,187\r\n",
+      "194,195c187,188\r\n",
       "<   bottom: \"fc7-conv\"\r\n",
       "<   top: \"fc7-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc7\"\r\n",
       ">   top: \"fc7\"\r\n",
-      "195,196c192,193\r\n",
+      "200,201c193,194\r\n",
       "<   bottom: \"fc7-conv\"\r\n",
       "<   top: \"fc7-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc7\"\r\n",
       ">   top: \"fc7\"\r\n",
-      "202,206c199,203\r\n",
+      "207,211c200,204\r\n",
       "<   name: \"fc8-conv\"\r\n",
       "<   type: \"Convolution\"\r\n",
       "<   bottom: \"fc7-conv\"\r\n",
@@ -5573,9 +5570,9 @@
       ">   bottom: \"fc7\"\r\n",
       ">   top: \"fc8\"\r\n",
       ">   inner_product_param {\r\n",
-      "208d204\r\n",
+      "213d205\r\n",
       "<     kernel_size: 1\r\n",
-      "214c210\r\n",
+      "219c211\r\n",
       "<   bottom: \"fc8-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc8\"\r\n"
@@ -5611,13 +5608,6 @@
     }
    ],
    "source": [
-    "# Make sure that caffe is on the python path:\n",
-    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-    "import sys\n",
-    "sys.path.insert(0, caffe_root + 'python')\n",
-    "\n",
-    "import caffe\n",
-    "\n",
     "# Load the original network and extract the fully connected layers' parameters.\n",
     "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', \n",
     "                '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel', \n",
diff --git a/examples/net_surgery/bvlc_caffenet_full_conv.prototxt b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
index 0cadde9b..f8f5c3c3 100644
--- a/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
+++ b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
@@ -1,11 +1,14 @@
 # Fully convolutional network version of CaffeNet.
 name: "CaffeNetConv"
-input: "data"
-input_shape {
-  dim: 1
-  dim: 3
-  dim: 451
-  dim: 451
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param {
+    # initial shape for a fully convolutional network:
+    # the shape can be set for each input by reshape.
+    shape: { dim: 1 dim: 3 dim: 451 dim: 451 }
+  }
 }
 layer {
   name: "conv1"
diff --git a/examples/net_surgery/conv.prototxt b/examples/net_surgery/conv.prototxt
index 6b3e5c76..8671bb5b 100644
--- a/examples/net_surgery/conv.prototxt
+++ b/examples/net_surgery/conv.prototxt
@@ -1,11 +1,10 @@
 # Simple single-layer network to showcase editing model parameters.
 name: "convolution"
-input: "data"
-input_shape {
-  dim: 1
-  dim: 1
-  dim: 100
-  dim: 100
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 1 dim: 1 dim: 100 dim: 100 } }
 }
 layer {
   name: "conv"
diff --git a/examples/pascal-multilabel-with-datalayer.ipynb b/examples/pascal-multilabel-with-datalayer.ipynb
new file mode 100644
index 00000000..94b9b4fe
--- /dev/null
+++ b/examples/pascal-multilabel-with-datalayer.ipynb
@@ -0,0 +1,479 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multilabel classification on PASCAL using python data-layers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this tutorial we will do multilabel classification on PASCAL VOC 2012.\n",
+    "\n",
+    "Multilabel classification is a generalization of multiclass classification, where each instance (image) can belong to many classes. For example, an image may both belong to a \"beach\" category and a \"vacation pictures\" category. In multiclass classification, on the other hand, each image belongs to a single class.\n",
+    "\n",
+    "Caffe supports multilabel classification through the SigmoidCrossEntropyLoss layer, and we will load data using a Python data layer. Data could also be provided through HDF5 or LMDB data layers, but the python data layer provides endless flexibility, so that's what we will use."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Preliminaries\n",
+    "\n",
+    "* First, make sure you compile caffe using\n",
+    "WITH_PYTHON_LAYER := 1\n",
+    "\n",
+    "* Second, download PASCAL VOC 2012. It's available here: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html\n",
+    "\n",
+    "* Third, import modules:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import sys \n",
+    "import os\n",
+    "\n",
+    "import numpy as np\n",
+    "import os.path as osp\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from copy import copy\n",
+    "\n",
+    "% matplotlib inline\n",
+    "plt.rcParams['figure.figsize'] = (6, 6)\n",
+    "\n",
+    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+    "sys.path.append(caffe_root + 'python')\n",
+    "import caffe # If you get \"No module named _caffe\", either you have not built pycaffe or you have the wrong path.\n",
+    "\n",
+    "from caffe import layers as L, params as P # Shortcuts to define the net prototxt.\n",
+    "\n",
+    "sys.path.append(\"pycaffe/layers\") # the datalayers we will use are in this directory.\n",
+    "sys.path.append(\"pycaffe\") # the tools file is in this folder\n",
+    "\n",
+    "import tools #this contains some tools that we need"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Fourth, set data directories and initialize caffe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# set data root directory, e.g:\n",
+    "pascal_root = osp.join(caffe_root, 'data/pascal/VOC2012')\n",
+    "\n",
+    "# these are the PASCAL classes, we'll need them later.\n",
+    "classes = np.asarray(['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'])\n",
+    "\n",
+    "# make sure we have the caffenet weight downloaded.\n",
+    "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
+    "    print(\"Downloading pre-trained CaffeNet model...\")\n",
+    "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet\n",
+    "\n",
+    "# initialize caffe for gpu mode\n",
+    "caffe.set_mode_gpu()\n",
+    "caffe.set_device(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Define network prototxts\n",
+    "\n",
+    "* Let's start by defining the nets using caffe.NetSpec. Note how we used the SigmoidCrossEntropyLoss layer. This is the right loss for multilabel classification. Also note how the data layer is defined."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# helper function for common structures\n",
+    "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):\n",
+    "    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n",
+    "                                num_output=nout, pad=pad, group=group)\n",
+    "    return conv, L.ReLU(conv, in_place=True)\n",
+    "\n",
+    "# another helper function\n",
+    "def fc_relu(bottom, nout):\n",
+    "    fc = L.InnerProduct(bottom, num_output=nout)\n",
+    "    return fc, L.ReLU(fc, in_place=True)\n",
+    "\n",
+    "# yet another helper function\n",
+    "def max_pool(bottom, ks, stride=1):\n",
+    "    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n",
+    "\n",
+    "# main netspec wrapper\n",
+    "def caffenet_multilabel(data_layer_params, datalayer):\n",
+    "    # setup the python data layer \n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data, n.label = L.Python(module = 'pascal_multilabel_datalayers', layer = datalayer, \n",
+    "                               ntop = 2, param_str=str(data_layer_params))\n",
+    "\n",
+    "    # the net itself\n",
+    "    n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4)\n",
+    "    n.pool1 = max_pool(n.relu1, 3, stride=2)\n",
+    "    n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n",
+    "    n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2)\n",
+    "    n.pool2 = max_pool(n.relu2, 3, stride=2)\n",
+    "    n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n",
+    "    n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1)\n",
+    "    n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2)\n",
+    "    n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2)\n",
+    "    n.pool5 = max_pool(n.relu5, 3, stride=2)\n",
+    "    n.fc6, n.relu6 = fc_relu(n.pool5, 4096)\n",
+    "    n.drop6 = L.Dropout(n.relu6, in_place=True)\n",
+    "    n.fc7, n.relu7 = fc_relu(n.drop6, 4096)\n",
+    "    n.drop7 = L.Dropout(n.relu7, in_place=True)\n",
+    "    n.score = L.InnerProduct(n.drop7, num_output=20)\n",
+    "    n.loss = L.SigmoidCrossEntropyLoss(n.score, n.label)\n",
+    "    \n",
+    "    return str(n.to_proto())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Write nets and solver files\n",
+    "\n",
+    "* Now we can crete net and solver prototxts. For the solver, we use the CaffeSolver class from the \"tools\" module"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "workdir = './pascal_multilabel_with_datalayer'\n",
+    "if not os.path.isdir(workdir):\n",
+    "    os.makedirs(workdir)\n",
+    "\n",
+    "solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \"trainnet.prototxt\"), testnet_prototxt_path = osp.join(workdir, \"valnet.prototxt\"))\n",
+    "solverprototxt.sp['display'] = \"1\"\n",
+    "solverprototxt.sp['base_lr'] = \"0.0001\"\n",
+    "solverprototxt.write(osp.join(workdir, 'solver.prototxt'))\n",
+    "\n",
+    "# write train net.\n",
+    "with open(osp.join(workdir, 'trainnet.prototxt'), 'w') as f:\n",
+    "    # provide parameters to the data layer as a python dictionary. Easy as pie!\n",
+    "    data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'train', pascal_root = pascal_root)\n",
+    "    f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))\n",
+    "\n",
+    "# write validation net.\n",
+    "with open(osp.join(workdir, 'valnet.prototxt'), 'w') as f:\n",
+    "    data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'val', pascal_root = pascal_root)\n",
+    "    f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* This net uses a python datalayer: 'PascalMultilabelDataLayerSync', which is defined in './pycaffe/layers/pascal_multilabel_datalayers.py'. \n",
+    "\n",
+    "* Take a look at the code. It's quite straight-forward, and gives you full control over data and labels.\n",
+    "\n",
+    "* Now we can load the caffe solver as usual."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BatchLoader initialized with 5717 images\n",
+      "PascalMultilabelDataLayerSync initialized for split: train, with bs: 128, im_shape: [227, 227].\n",
+      "BatchLoader initialized with 5823 images\n",
+      "PascalMultilabelDataLayerSync initialized for split: val, with bs: 128, im_shape: [227, 227].\n"
+     ]
+    }
+   ],
+   "source": [
+    "solver = caffe.SGDSolver(osp.join(workdir, 'solver.prototxt'))\n",
+    "solver.net.copy_from(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+    "solver.test_nets[0].share_with(solver.net)\n",
+    "solver.step(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Let's check the data we have loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QFN44TWaiBFYSW67ATBkn5xuRuVUkhi6l+hUo07CNwKdlJCYkCypcljbHvoibNJJFCsBL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\nYb2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9ZrNesbnbo40lp0iYJsaL3LuYtFBPqcjKkfKAJzDEnsuU8UmhjEWlwMIcUjfP4v/H\ndW0kXu/zX7oyefHvSGn2WpEsyxbr05Tish6ucMrc35l/p1AQXzhGfiWgLHDK66D+Yp2XwzmWxOF2\n7kH5XdZWrNcbtrs9/flM6v3CsklI1n3llr9ssklDMZGJxZL29j7NidPVcfALRsgCKcxp/G2VMkMP\n8otkLdnlgPva/QeBLmfPotVqxTAMjMWKoGoqtBX1d99f8MEzhchlGARaXa2uo/ZmiIyvVWvXzHyB\nSFPGDyOX0xmnBUrpB482UZqgaAyFNphk9y4VTFkTkrcKc8kZRVVL3h+zCPVSCuXup6Wqen39LIH8\ndDrAcEF5T35VNs0PmeLMVlnDuqqoCxwgmWjJFsqINMlyy8IomYfWBquMBBIUiUzICZ0j/WXicrnQ\nNi1d15G1RSlNDIFpHDiHA5ckczCtcri7t/TNlkCiqy2ryvL43PPwdOYcByoViJpZmoS2Yv2plS7S\nbYEYcvAQIiom8dwxlmgsyZcz25ZOfcwErzhdzqxaaK1gpsTbUmx23fNkAkZLM3UYRkJxpHOVxTlL\nW3X89je/xBnxuXl+OjCURT/7dMwbfC7hgcU3OkYZPxdC5PB8kGx4HFFk+suZFD0Pnz8xFBgmRqEc\n2mywzhasUKCaGNNN5ipCp5wiHz/8mZyTUOCsK0KQRNU04vOSIlOYiMFDDBiraFYbbL1C11vcwzPq\n8YnL+YT3CL7LTIv7MhD/NZHPfH1tQ3+RKedyWIe4VFszdqtLRu6n+cDPi3VCzkKrnLELgRHsYvA0\nJwS5HA5XeEu/3C+v3lfOeYEjbtk1zHehICxKabrVmv3+jo9//lECTWluxwwkmRR0e/fmJmVWuQTx\nOWN/cUwIZFhELt77JaDPDd1FEKSvCY+6PTALJEO+UvTmpOLVrV/+f0qJoe8Zhp7VqpPMuEjsd/s9\nVeXo1p1wtWPA+5HD80jbddRti6kk8Qh+Kqjvl+tieSZF5WqdKXEjcTmfKUcfIeay/jRNVVPVDqUM\nMfiy5jXGqBsFsCZrjbAXynMstkwheGL01K70Iv7Cev15hi/HRI4zhsa1vJDaTd5rWZC73Zbf/8Pf\nc3e3RetiTlRO/jmgqcLDVWVU1nxK3+J7izsdCldXdFrhmgqrDMrVXIIsyKpp2TlNrXMxDnKcbMch\nWgksWtFYqCpDqxrsBKE/ipWsEUzMWLuo9Ix1y6FDFMGRQyAHnxJnH1Eh4ipH11Q0rXCGtbbiCjd5\nLGJPMLs3zk6OxmrWqxbWrSyinDgeBIMGCeRVXVPXLfvtindv7vjuu/f84V//yPff/8BPP31Yytp5\ngOxtQFioiCVT895zuZxLMSTUsOenB5RSTKMMJZizWSl1M9ZaqqaWJuvsErj8Iwq4rCClQE5RPNmt\nLnNNRVZNSsQAKopQTDtLXVUYW+yDbYutOtr1mtPpRH/u6YeBaRyE3hnnLOvL7PBrEMbXGqJfw9Rv\nm5AxRoH0XuHvS+BF1upMS0PdeOeokiWXimeGH1IZ0BBnL+ubZ/Pa//110/Tmjd80Fq9S+owwWKq6\nwlYVehRvn6S0CLlios4zTJJvkqV5bczV2pdDveevp3SFTZW6vYev7vsrWEZiQtFJF6jvq1DTTZWQ\nMwzjwPl8om1rUpLehKscw9ATw4Q1lrqykCu0kvU8DQPTNJKCX2T+i8HazRt72WyVr2pVKtfCBR+G\nEaXO5CwZtTGK5Af8JE37WTCmFgFY6YeoOXGCcfJiVTCM4rNuNM4ZurYtPlN/Qxj5MroJChWLUoqJ\nnWXOiH+Gc+zv9my7jtV2R9aZYRQGhNKarmlEsqBm9dTNGKbCwMj5ihtShiZXdU3dNtjKgbZknTgf\nPWiDrVvWuxVrK+O3lHX0FwinSF2lQi0CYw3O1pLl+gFbS8Bq63ZpagBFvQqQUDlhlHgPpxS5TBPp\nMpKMxThHu2ppWpkEglKcjicUEzkFcUK7YcSAZDhNW4vXgxU2ilEK58Q8yFaWpmlYrdZUriKmt3z7\n/hs26zXWWs7nM8fjeSnF4RqoXjZAAYT37r2XxZSlGljG8+kZC749mRXOVbiqxlpb3B9jybATMYgq\nMJFwVuNsoW05GcOXM9JwS6kYYl0zzqZqMEWQZKqOqlux2m25XHoupwvn85nL5cw4DPhpJMYgFUEI\nxWztmmsun7eYmn0taLxYv6++nvPsfngrS5/hA/m7FNWSXcswbDBGOPhz5mmMweo585JAOPnpxknw\n5e9caIc37+s2Y5+/b/n6/C+lXgTypm0499LwDzkLfkxiChFnEsleqyiW/TWzdWa+/qtseWFA5cUC\nV19T0Jt7VNbdNRkvwfxqo3zbCP4iU1bX1wrB48NIiLJGm6bCWiNDP6L4h8vINE3lDNM0STCfPBaF\n04akLUl55l7HV54+S0KgBH5VyizwWU4RlRNWZ5zJWB3ROUHMS3KXY1pG2c0wsUyVErjnfLrQ9wOV\ntdRNRdvUGCJEh13iycvr5/FasWXQQ2nUZKR8v5zPPD0/k8h894tfUDU1ruuonCVluAwjT88Hjscj\ndV3R/vIXUoIkuRE2S84Tk5Lp8TEVjEnGKmUCISXqclKjtTALUgSdRZ2oHVQ1sfiVK6tFbh7hbrOi\nq8tpniJj8igidVuz2e/Y7nbsNtslOKWcFy/l+SQWPC9yODzjRsemadkojbUVzlULpg6Z/X5LCpB9\nQKtY4LGSzeb5swkWt+5arBF73rathC1hNU1Ts15tyCi8D6QQ+c2vfomfZLRejH8mnc9LtgyycW7/\n2xi94OMKkTzH6BnH4eZ78ovgqLUMC3FOPpdxRt53VCWYa5yR4B4JtI3Qq3RhMxjn0MaJ7/oMS3SF\nDUTxjjearMG2LVXoaL1nUyYkBS9Te/rLmaG/MI0DQz/Q973AL9O0MBRm+mC4ef9LsH6Rif0FiKZk\npjFIJovNCxvJ+1GMm8rwEbFnPuJ9ZLO9k89Zejm2yPglLcnF72bEe/+STTHjyss4d/Xy/dz8/Zbl\nsfz/cnhYa2malvVmy/PxgI+Ji/f4lDDZwDBitQwAzqXChSsMEqMc5td1c71P0jMIKJVLw1d+UpID\nOdiM1mRKy7MEs/zinucXr/u1zyR7a4YGM84Z1usWjRFXR63F5K3vGfqBuq5wrsXoDSlFoo9Mw8in\njx85xITyUarPeHtsLb99+XOuNrQ2dG1HiB6jNZv1hvv7Hfd3O+7udzgzD8bI1FVNzplhHK6j8Ers\nCjmQNKzahrauSLGo18sAFaMzRE9Kr8fryfXzBPLKoBeMqJS8yMDaoe8F2UyZbLTg166BmNAOutWq\nnISlBEWTkiLEhAmpUPUcPibwHlc2gZSAcip3qzW73Y6Y4fn5yOl0ZkqGy+Q5+8jBW5IJOJ1QEU4+\n4ZPm3aahqwwpBmIG4ypWVcPGGrbbLd1qTdPUXIaecbhw6S9lJp9kYtIwtBhrsLamNW5hFigMOSmm\nkES9SaZrLEkp4mKRm1+uKxI+jIyDpTIKt2ppmgqtBb5q2pq6qrDOia9JDKhiJrTdrvj9739HVVX8\n+ONPPDw+EEKBUmJahh9IY8wuk2nkXoZlA8+bSu6vvCuloG5a1usNq9VansmCLUrvw1USwMoxzv2b\nHe++ecebN3uycijt0AUzlpmW0jjNJQtMKc2W24QUF/8WP/nyWRPrVUvcb8RrowTraRq5XC5M40Tw\nngxM00Tf95yOx5sMWA6DpYkJS+Z5/bMEFkogT+EKM5Qgo5UGLbBfSoHD4cg///M/czqe+fvf/Tv2\nd29o23apKE1xIixJLOM4MQwD0zTJhCBteFkRfB0C+tqVX32fUuJDv1kXKmLOTOOER6GMrKHKGOrK\n0eUrPJlz8QeJkkUuPPny7JdnvSDsiVsPdWccYv02B2SpxIe+RzuLW8yh5EB78RleHKYvqw1nDetV\nwzdv97iSRFhbM/lRDvWS5VaVpXYWo8V8LobE50/f8OP3P/L99z+Qnx45DyNTCHJovbrXcwUXg9gI\ndG2HdYqqdmzWK+7vdry533F/v6epKhSK4KeFaRbzujwDSZCCD6VKy4u7YQwB7ye0KmMLl3v5N4SR\nm4L9LAjEHACKJFlwPXnoCY0M/FYoZambhkyaezMlkIP3CaMT2ooPmw8RdBAa1pxdip4X5yq6bs04\nBbTqSRGmEOi95zhFnkaNrjOdliG6Q5AyaFsrKpMZgrxO07bs1g37phLhUpm8chl6jucLp4uoULOU\nDGilqJuG9WpN23VkElOQkVuySVSxEC1zSWeTJG6c55YHWZgRhd4WYyCmgFKSQeeccdYuXHkZ64VA\nGE6z322lY980rFcr/vBHx+PjE+dLj49enkcp901Rf86ZZwi+2JSyvJeXuLE8x27V0bbN0pRNMROD\nwBdWi2OgVGaZ+/0d37x7x/v375l8Kv4diqapaZuGpmmwRgJ58CJ9n+9GyonJe8ZxwvuALwF4xlvn\noCXT7T39IBikNOIEvpBAfmKcRvw0SkO3HySwBS+BP4QyHODKyJhVmRRG1NLEVddRZWRdqpzMMI58\n/vzA0+Mz3/3i12y2O2Z2stYza6tkvWR88Ax9zzgO5LyRZtt8n2cki6/j+rfXXGW9FtwYY9lsN3Rt\nh7MVRsmc04xUo2NMjEGmvKeUS1WoliDuY+IV6sMccERZLYH82h1QpRELIHzycRg4H575+PEjm92W\nuzdvaJruOmjjrzSkl6wdRV1Z9rsNv/rVexpXYYxDYfBBIJQQpAlfO0tTu6KUNJAUh/dv2a46yJl+\nGhm8hzDfp/JbXmH5MYTClFHs9ls2m46mrtjt1qxWDbXTVM5itSY5I7CL1lhXl4rI0TQNqQycQAnF\nF5UXGFApGZYxD2n+m5oQZI3FaiOijiRNLW0M3XrNZrtdfKVl82X6YeTq61o8htXcssnEDMEnsivY\nazFpVzos5ZpSpYEG+JiYxkAIUFUtm/We54+PTD4zhMTTMLCpHco4zhMEFMaAyxMqWzIZqy3bzYa7\n/ZrOwDiNHA9HDocjl3FiilGydmOxVg6tob/QKMVq3dG0K4axZ7oc8WFCobG6pnItttYkEjqHZa+W\ncF5KT7kTxljut3fF2D6LAZWfmKaJcfTMsyRd5WRRkFl14pGxXgemKfD+3Tf8+pe/5Lvv3vN//qf/\nm+9/+JFQMhFjDNbKoAlyXgLy4qPNdY+9LH8FPqtchXPzUNzENMI0xfI6iaoyJTPPdF3H/f1bfvWr\nX3M8HTmfz0xjz7qzrFc1XdcUBocBKmasUilZTyLKSRJcfGCaPNM0CS5fNomfPMM4UteWoamYSiYk\nH6T8UVR2MXj684X+cmHoez59/szxdFwUgAuUkjPOuaXBOENomus4r3mSTUaa365uqNuJqhYH0HkA\nBQUTnjnDoMkxcrlc6C89lGxYIAr1IpjPVY+8hy/x8dfXTFM01sooud2O/XbLbrNhOp6YUsa1LUnB\nGBODD3Q1si8xTD4xTrO0/aVp1rwWdOHFS+VyrWKWFa0UWWeOpwP/+s//zH/5z/+Z3/6b3/K/NhV1\nVYuHzl8N4tc1oFSmbWu+ebvn93//GyrniCFxPvVMk0HrFXUta6iylrapynQfRU6K+/s9lRMfoD9/\n+sTj6SS/Qavr2niVDccUmcaB0/nIr37znrfv3hCmAWsVMXieHh/QGJqqZr3ZoJTASZU1jMMIWlNZ\nXaxFxD46Rtnz2kjvSykZACJJyrTMHH19/TzDl9P1hly5p+XhLtN/kohgimR48eGN1yWziImyZOu3\nGckLyXPJdkRIlOn7ns/5EaUsqdgB7DZrfll17GNmW0e+u1uxbhz9pyOdkdF0oR+wqi6/T8Qy0zSh\nVCpNOEu76nBNQ+TKyybLe20bMdgJKXIZLgTv5TOXzz6vcelHXRfQ0uwpn23GKFOSTGnukDtr0Koq\nk2VqnLNlI+US3AROiClD6bgbo3j7dk/bVTgnY8X+63/970yTzGLMCZKSbPMKUc1ZwbXcW2CGkrqk\ngpFqPTe7HH5y9P0oizFHJu+oKoVxhp8+/ES3XvPrX/8GoxSrpqJ1AiNEPzGcRcI829LqkmGSI1WB\nj7Q2QvGMCasSpjIYK2W6VppxHOl7g7PQOIMPwryx1oqfuHHYUs0EH3h6fKa/XAgh8P79OzkcSjUy\nT8ABmZizWq1ZrVYvMNyZzSL3Rzzd15sdv/uH3zONnvu3b6jqaklKNts13/7iPYmwUBlTymiVGYcL\nfhrJxqKNsKLmNFGgjS+Vn1+7XnDSZXGB0mw2G969ecvn4zOHYaTve8bgUc4ykjlOE11MtChiUpzP\nA+fzZTnU89wHUtfDXarL29F0L9fMHAv6vvS+TsK4mgkP8zuVJiEvf4750LuKkWLwBfKLTFPCD57h\nfOZ0PgsktUm0TYNVWpwFC6V2GCZiCNSV4927N+zvd3x6fuIyDEv1IOv/y0MlZZlv8Pz8RFUprIba\narrasd1sqV0tNORCk8ZIpVxvRVjYtW2hZEq1OPQ9obCsnktMqZzMYLBW6MRfu34mZWeSzu3MkV36\nSjPuzRVjWybRS3mfFGVx3NSVRd59zWgEfxdbUCn1lcplWn0mhJG+z2hTIVOGFG1leGMs26xodGJl\nW2pjaczAvgFSprIKrWSCNklsd5/zhPYiLHCVo6lFWFDq4wJFRCYfBPPPmculXz5jiBIUIJNUwKpI\nUomYE5WWzzhjrXO4V+XvMUaGS0+whlw56qpbStYQfDlkxBtGqIyCt2bEAsEYK2PcjGW3XfG73/0d\n4zTy9PjEhw+fGUpWj5JufCzTVq6xQL3YuJIdyntOxV9ZqVQ67SX7Xg6WyDh6qsJ1f3x6ovvpAx8/\nfqSpLFYrdM5y2MWIqoroIwTxpPCBECZSDIWZs6Juasah0CC1Wdz5pEmrhKebLDk7KlNoZkpRFVzW\nOWEFxJi4XHriNOGKN6qxMig5xMj5fGHyXtg6SyNL/DsoAWcerJ0KVp7LM6wby/tvvyPnLFBRgVAS\nsL3b8tu/+zVv7neyD1Jmmibu7/coEpfTEWMcVV3TtO21QqMcourLrPyrV3lo17CY6dqG+/2OdbvC\nGWFhjMGjrMbkjBontiGwigmnNadzmUqV5kP81R7PN/vzmtIiB09hwOR5judVoFQ3rcwGYBb6cTOv\n8zU+fk1toAizkhws0zQtfZAUhPUl82zLEk2gWav2AAAgAElEQVQykKLvB46ns0B9VrPfb/n2/Ts+\nfH7g8fl41VSkG9fWm0ua2tds2VoNSfQQu+2Gtm4hK4ZhLHYNRsRxtiSDQqu7GpSVpC2VZ2/MzeAS\nJ/Tmr10/D488iFDA+ECuG1lQSgmmpVQpIaPM2VRS6qI1KLtIdqUYVaCEx8nspz1Tr9ScbGi0Mig1\n48wZrRJGScaZoiJG8R2pgBqNTYbpdCGMhk5r2k2Hs45VU+FjYDyeiT5weD4wpInp+MRms2K/Lw2O\nBVKAviwmoTkJVhdDKCN/AJJg0llhVYXK4u8cYsRsWnF4nO0GykE1468xRBFXKVh1Lauuw3uxiP3w\n4SN9P6KNZrvdLtliLJleGEcmf2IceowxrFYd+/0d//bf/JrT8cTl3DOOT+X7Z/73bZAo7dd8m4Xl\nZX+lGMnJY0xGWIKq4POWYdRMo6cfAtYl6kZK9U8PD/y3f/xHfv3dt3RNRQwTMSZW3Yq2bamaphgc\nHXl6fhYxUoqCwxc62Pl8giyGaF1Xk3MSmGnOHHMSMYYW1W7bNsyqyqpypJQ5X3r6y4lY+hfaOrqu\nw1jh9g9DT56uY9qM1lgnDcp5DV7xclWw4qvYarFImGGfLEFiv9+z361pnKNtarRWDNNIfxnoLwNP\nTw9oZVgVlz9BWcRrSLLel5XuX5T5q2uQTEhl11SuzIa0aNQCo6lkUUkq5PM0sfaeSmtOl4HLZVh8\n5G9+Q2HnSLM0RvEiMsW1cRERZXHZDCnRdWt+8atfU9UNb96+oW3X5DKlKmWWIJ6Xg+D289xk+0r2\nujaOsR+FAOEc290ea8T1cH6r2hiGaeR0PnM4HthvN1TOsll3/Oa3v+GHnz7zp+9/KvTAklAtVQHL\n78xZ+mhd13G322NSpKlq6qqW52Qd1li2my1+mqQqHAfOlzPGWlnXcxJhLdt6sySz0zQtz9GWfpdz\n7qsx9WcJ5M5o4QvbMvkjUeCWGe+ieH1IaWadeBTEGLhcRj59/oQxhl/9spMXVIqZw6S0QmXJyBdX\ncDVn6LlMQjE0VQWImVHQCWskC07SPcNHmQWaCoY/KoUfNDlriPBmu6M1CYcntjWb7Yrtds1qsxJ2\nRcx47zkejjw8PPH5+UBIguOmnJamZO0sbSciIGcqqsphUkaH4pFMycmXJlgWf+MMkEgBuT9KMY5j\n8WxRrNbrsoCFj6+UKoeJNALHcaQfBpzR1JUsEq3gfr/j9//wb/n++x9lSMPx/ArCupVKFxwoz2nO\njASXR1JsTtvGys/mjJ+aUoVMTN5z7nuUNaSkOJx6/vs//U+8H9nv1tRO0zYdKysioWHoufQ90zSw\n6ho2mw5nDcZYttsNq9WKzWazVHpKq8VKYJomnHNUVY2rKhHfaC0iLlvolVaTpmtz3AfPME7EfOF8\nOWGdw1WWpnVYCyEEgcusZMm2ONPNWbH0GGzhGF8D3kxZzFzhQYVANAppRuckwjenwXYtVhnCdOBw\nODFNnqpxQs+t6huf8lcGVTdY+WtBEeRZXwalJogxcD73+BhL4iRKT59lfuvZTzxdzqiUeT6dOV+G\nm7VwbXjP+gMJ5AmtUzlsSlIwO2xqjdWOzW5H3TTc3d9TVWJDPU+Nf/1Z5u0+V95q+dySIPZ9z/PT\nM9MwkKMwwpytFt8SpSVWZCXNxu1mg3OOHCPTFDj1PcfTkcmPGFOqAK0wSpOSedVEl7U/ec/z8zP7\nzZo32y1+9Dw9PANwf3fHfrtjtenELtsolCkWwWV/h8kTfEBpRe2qRTPRDwNKKxlvaYr6PP4NNTsV\nEc3V+Akotp1Fkky+Qgl5NpdRJdgrgi8l0pKB82IBz+q4+eWVKlkRUqY4V7Nq5ww14MOIDwkfi1Kt\nyNbJCoORyTcxM/pEzpaULbU2OK2xWkQH2TguMTH2A9Y4jDIYDFk7tHVUzokQaG4aRo8uB1ZTNVRV\nLUHXWnRWaC0d/dvmkFxp2XxGi6+Ec4aqKSd1FhVeCAFVSlThuxaXSGNRKKJNOBvZrFesVy1t1yy2\nvgC/+/vfMk0Tfwg/0Bd/73xzn6/PkpKBZnKOy1sVGmNApYRViP1AbZlayzRZplHwSe8n+mHAWEua\nPE/eM377DdoYmpVYKNRNjTLi71w5i6KhroUpVDnxnZ4Dc9W2kmkWfNrZaclixPvE0rUN1rqltHXO\nSlIBeCVBp6krYteWrNjjgwcS1kLXNuiuBTKrrkVbC8osDKH50sWzZmElzNeMNMxAcJaz0FmHtU7U\nhylAnqfyOBSWy2UixgPH0xH9Wcksz82O1UqaYlqrL5/PK7jlhWgpl19c9Ag+Bs7jgE9JJONKBkZH\nEtFoLjlippHory6Wf+maD47Z82j+3TOdU6oTI7qBes5gVzc/DzkWCueC/b/CNRTMKkoQfYb3AT95\nUkwYrWR4eNGLTNNE5RweRYgSa1KKaDTnYeBwPPL49MTpdEZpxXqzXuJJBoZhpC9DUObbLJL8yOF4\n4vn5wK7tishx4ng4s2pXpLVwwm2xtbXWEjNL3yrdsKCSF/hFaUUottMxyqg5rcMXt2C+fh5oxY/k\nIldXOaNLnFZEopehAs41UFz/UpgwpkZpoQ3e3d0DkvHN9ipQuspaYbDXIQ7kkqXLdyg0rmpZbe5w\nzhDCwDCe6C9ntE8EQJXZfxkl028yhJjBy4DUECAkTTKScRrneOwPnMJET2C73XG/3fN2s6d98w2r\n/R3fhRFtgJSJPnA6nxi9J2deZCDGGAwSdEOaCqwosol531GWblVX3O++LYfUPFknEU+BTx8/kcqw\njdVqxd3dnqqq2GzWzGKOlBL3+z1tWxUaoCoBK/Mf//d/D8D5cuHDxwfGcZJD8+ZQWe57LoMq8lUV\n6ifP2I/4YSDVBqcdTQVtoxgnzTQaxjGRiIQo90ZpizUV796+57e//Q37e9lIomJXbLcblNpCFk6+\ntaVvkiKn05nT8UDbdlKmak1lLU1V8eb+HnJinCZSStR1LY1RBOO01hSed6GnVRX7/Z7tZlvopBeO\nRTRlrWXdtrRdR9u2dF1DSDIcQyn3ouE4N9q/jiGzNG5BDpSubejaisrqMuosFvdOQ86W1XpL1Rx4\nfHrg+Mc/cnd/z3sU6/Vu8TGZ8dXbZ3P9nVe17lwVqJTIhYEzBU8/TUxZBnfPB3TIGa9gMOIddDic\neDyeGMeR15XGbfZ8CwcoJQZQs/7AWivPDhF7KXip/MxKKldd7uGrGHKF8q5fmRlq1jnhi1tL17X0\n/cjQD0zDSNd26EJLnsZRNBMxcbr0/PDTB3788BNKw2rV8f79uyJoE+bV8+HAx4+fhHor7NhlT57P\nF56enrlbbVh1ApcYLWSDlMXcrnKOuhIl7eQjlyzeMNfnlhjL4aiNWYZQBx/oS/Xzwn7h5vp53A/9\nSPATMchpqJUYtZ8vR/78ww9EH/nd734v2VaK+HEQ+MOBrRvapmWu3+eWhxHdRVE8vxyGq42WLBdZ\nzH0/8Iln8ZlwGmvXrPdrcpwIY894eib6UUpilRZvk5QS2Sa0hjqDLrVpTpnTcOFfHz/zjx/+jK0b\nuqZl165YV5ZNbVnXIrVtrKNRDqc1la1xxlDXswhAOKlzg3T2QVE5k3MgF5/oOTnJMZNCLI0TuRHW\nWva7PfXvW8ZJ5MfeSwCbJl9omTIQYLVaoa1aJOOQySlirebN2z3/23/4X1ht1vyn/+u/8PT4RM6w\n227Y7/e0XVf4zWJA9vnhQVSIheJHhqEfODwd2HSOqqvQThOCI3pHGitCVclhWWANayxNXVPpTJpG\nxrMMHzbGLmwS730Z9SdZc11XaAWVteSqwE6oIqxRi/NkSgmrDaoElxBj0Rjk8pwFs3bWYVdC16QE\nvbVfsdtt8cETg9ABnx4HDs+C+ccMMcF294au09KsyzPEEJd7DmKKNAuhJGQJhGGMMH0u5zOnMKD1\n1TY4Zk1yCt021OsVrj8zjGKNMNtPSCJwhSfhCqHMAXyOOgqKM6Jwk0mZfuh5Oh44XI7Ss9GzkEmT\nlWJKCatlKhcoklJkrZZh6bfX9cC4iqco8OBsj7wEpFwSMHXjAyOYk9wjpb6YYnVT0szY0PL7tJIR\nbEaBUapAO1r2ujalEToyTaHssUzwkU+fHzDG8rt/+B3b3Zp//pc/8J//y3/n4eEBkATL+1D8hOQA\nutLjFaCxzrHbbXj75p79fst6s0JrxTgOxODZrtfUAFExjBMpRbq2pa4EntNay6jESQRMl8uZcfJ4\nH5bs/G8KI09Ih30emqvLwwjBczgc8GMgR3l3MQQupwOu7lDa4ZSiqmteND0KI0VpEf0oii8HIpRh\nbjbljCYRwsS5PzGFGlvVC99ZRUWaInmaMDFiRcux4O1RJbBgs8YUbnVGBhlM04WH4yN/+vwT0Vis\nrVhZmTa/qqoiva3pqoZ11bBrOnZtw7ZpWZur4KZxlhA9w1QabTGjsiITlnmQqozWSikyjj02ifLS\nWBFqZGRMniqN4RgrqqpkoVkWgnOOtmnK5ig+N16ycaMVq67j2/e2KM1k9qbWms1mzapbFcP+kRgi\n/TDy6ZM0V/tBGkhDL1WXmElJEFitGpq6Zt21bLqOcQxS4irQRiimddXQ1Q6rQKcs48RmPFuJ0Zjw\n3KWZFmKUv5eANkahNyqlFs6tnj2tixWvwHbFyjdnQkgLnObs7YRFaSzXVVVsImY1X+LS96IADYFc\nvPDzbBs7z2BcON8SQHPWZVUWSOUVrp1SJOVALrS/mOTePg8TU1JoW9N0MirvdHyWGanTxDQOZU+4\nK3ZcGnN5njZSDrSc09LwzsXoK6fIMFw4Xw6E7MlaWE1ZK0qphBhcFBy/rqGqUNaioqwZuCbHt1j5\nC2uA2wDO/LZyaZbffu8tHKTLLM65D5Bv/iy/9Obg0lqm9ag4K3mn6xDu0o+LIeLHkdEHhtFzOp/5\n4/c/UjU19+YNIQUOx6OIxE6X4ttSY21FVTU454lxrlB1gS4Vde3Y7tbs9mu6VYNScLkIzVgp6Ivj\nalXXpffnFuW1NWIpMMMvi88RV7Mta+zfViDH1GhbYVyxKy1NNK0N1jpygOISRfATx8Mjm62ibjdo\nLR8oczN3Us2WmLn03ySQxxjwsXR+Ebzd5IxKnljk6MMYgEE2k7+gxmfscKCtLE3bopwilM1nlyas\nqLUonipZQ0oXJn8i6YmghAaVvOe5l877DJvUrmZVN+y6FW/XG97vtry729E4S20s3632jFPgOE6C\n8WVQ6MLlliBdLGVIOdD3Z4x3ZVFYpslzGXoul7NIq7uO/Zt7nLWyYKxZAuHsSOi9ZxxDKZWhrisq\nY6lcZrte8R//w7+XRmFdgvrxxOV0odI7mlbYHJfzmXEKMhrueBK88eGBjz/+gNIGZy33+x1V3ZJQ\nhTt8kswk52L7K8ylzaqla2qaqqatmxeWrLMTiStrx/u5TBYhVAph6RGcz+dl4PB+v6fRRkr2VF6r\nON3FdGXlBD8sQSUEL2tGiVK1qWpMY7DGcLlchKIYhYde1R3a1OisISrxAFezx7gCdRXNzA3OVMhY\nzIGMhDFQOWlixph5ej7wPz98ImjDt9/+krptWa3XPD44KbnPF86nA0pvS7/ILP2jBS8vtgyqGJ35\nIH0AUpSmapgY+xPTcKauFC4pAhCFIQBKDrcYI8Eamu0K+7wiXkb8FEoEf5mZLw1ybvFyliBVvlK+\nzhLMF4YK5TMYjbFWGGzL6+eb1xWMIxdYTxtNt1ox9QOTF7rtnHlrFLV16BIbzucLHz4/8uNPH/jT\n9z+gtKb50594eHxgKCrhcRRrBGsrttsdoIvYLMhKVIqQIkZrmtqx3XZUtcX7iceHE+fLpRjNybDw\nrml4c/+GN2/esF6t6dq2VOCBmKbSV9G0bSs9gwJvTWVv/k1BK8fBU08eF6MEQbEswDnH3f090SeU\n1sScCCkWaa1soMXkCpmaPS+hlGetGktb2xRT+hAUSSt01qXJCkpntCqOZdkTpsDzx+85/fQn9OVJ\n8NVuTbO/Y7W/o92sadsNEcUUIqdLT9U6lDFMfuJ0OTL0J2xO5BCkgaEis+9dVErIOWkiTJFzGPh4\neuJ/fDR0dU1XVXR1zX71RxrrqLVh363ZVoroKrTLBDMRidecJCWm2DOdxcHQOUsOku0Z7WjqFU3d\nCPXOUIzEVBnxpjAmLosohFAcDOf+VyL4KxfdTyPD2IugyMdS3YihT+0c9W7L7I+XEcuDYRg4PT+j\nc6KpDJuuEYe9mGi6zHe/+q1k/Eoz9BeGoWeavBygSgOGvmS+wsiR4c+mMEFSyoLrDoP4t5QMR5kr\nY8QYszRxr3itYhgDl77neDotJmVzma+Kv8U8DMPoWXxEaZ6tyggvy9APZa0p7MxZfzGiTUQj5IzW\nwtjQSPReWCRJ1m4oDp2oTF0bqCxTpzjFP3MJgb3W0i+wFc1qjR96DucD+kFEVXLJa+uiyl0U0SXz\nFc+ZkaQ1pEAYL5yePnB+/gTjmberGk8kTIGksxxIM06dpPKZrKLab8Vg6tJDEFhq2XulL7Bk2epl\ngJ/vzfUe5Fdff920/bKJ+7URcVKhjjwfjwJHKkXVNFS5uEsqDVG44NpoPn76zD/9yx/4w/c/cDpf\nhPOvdfHTl888JwRTsZcVqb9Ha2RqkZIqWGDeMv0oF/pr8w0+BHwRkcUQqKxjtZIpWCF4+mE+xNUX\nwzNu6aruL8zqnK+fJZCfp8DKR7obC0wFGGfY7XeQFBhFJIKGummomwZrLSFMHJ4PoDT399L0LJUj\nL5ouiI/CXKpIPjTTsMAatRhI5ZQI04Vw+Mzw8Gd0f2DShsvpiCt82W6zpW5q6rZFW0etFF3dYGqL\nm0Z+8fYdI7A67zkNE6d+4HQe6MPElJLgIdaSiQQ0PsCpNLyMNqU542jcJ5qqYlU33DUrdo1m28C+\nhSpkbKqocThdYU2NsQ1WxyKUiRASlXFsVmvapll8V6QRJMEv+JKhLEb/xaa0bMaU1KJezCmhrRUx\njpeAqtFUxlFVwsYR21zJPLU1pSuf8TFwv98SpwmdM3XlxNS/2Bes1hsqV5PJNHXFMDTCLKjqQplE\nxD9lassSBCjtXnXl2BpjCgTiCm4cxZysGG/NlMJ5OIM4JAq/X9w4iw1BiTvaaCkKM1cox4YiSorL\n68wiJ+scxllmOf5NvX9tdpYZjfPwojkThwK3KvGorozwvE/jwJ+eH/nxfCTYim9SwqBRVUW73RLC\nxDgNHA6Z3W5bjMUcMWVU1ORspOE2Hxhlj2iVIXv81HM+PfL4+IH+9IQOI/tK0wdRc3oysQTzeXpw\nQpg99abFhoA7nAinM3m6QiwzfPQ1aOV1QE4lKVO6HKJz50BfG+dq2eV//cpJTL8eHx/FQVMVL/ME\naFExD+PE6Xzm+XDk8fDM4/MTT4cDMchBmnLCaIMrlN6UxZ1RoMepwHYs/G9QHM5nFvV02SPGGNpV\nR13XEp+UWDIbLa6faMUYJvppKJBJJcQJa9HzmijrdfY8+mvXzxLIxxCZvFhI6nKCzXSk9XaDxiyY\npzKGzXZP260w1tIPEx8+/ITWhv3d/gVUVpAVZiWkiD6KBzBZhi7njCs3JosSiJw82V/QocelEYuY\nI/kRLj7JCW8sVinevXvD22/esdm/ZVtXuK7DNyu6f9jwd7/+Oz6cjnx8fOKHnz7yrz/8yI+PI3Ea\nQYFOFlNZtLak0vxJRpNJDGHiEiZyf0I4KlAnzaqybNqKu03HfbfibtWxtzXrXLGxDbvVHV2GME0M\n/Rl8oK0q3r7ZE7NI0KViAQq9chxHaeYB3bqTU780WeeNN00SDGdxldbizDff06qqaNuWuhEe8zgO\noAw6S5c9pEBIUSpzZzGqeErkhImRkBKTDwxFMKVL1tW0DW0xTArBk3PCOku3WsmBVOhrcw+AQrmb\nqaypDOMIPhe+viqfZyrrRBSVM3vCaIFeqtqhclq8V5Q2aBTT6LmMZwCM96hh4Hw+L9WBsxZXucXb\nPiZNTCVQZwkuKl+zWmCR9guhuaxfMqbAY6va8jyN/OHxM//HP/0j35/OtJs7DtNEY6XP0G23XC7P\n+NOFYTgzDhe6psVWhuAnCbjBFMfE6wBmpcFZJR4uw4nD0yceHz8yDWcckbWGu9oxKsVpkqHgC4NF\nKcHKc8I0FWa/orrckXwglalUc0N1DsIzDp6XDFwgybl69j7IbFNtl/szB/zbg+DLQ+Hln/PPee95\nfnoSpWYRAaUQsVrGDz4/H3l4eOTT58+cLhdiymJ5oDU6yjAPUywBcpb+i6vcYtoGshfW6zXr9ZqY\nM2PwxCTCs3HwaHrIUDuhFdd1TdM1rLsOrTTTOHEezkV41guMYjSVrpdh2Vprceksdsuzn8/f1GAJ\nIWZLGZkL31iakuJrEHOaYT20tti6I2SZpVZVLe/ffydYuJoVajdlSb7JBpIoRBf5a57zcvF0MUoT\nQfjOzpKdI9mKGEYyCW0y1mSykqbQOAY++guHhw/YqmV7f8/u/g3bu3dUXcu7qmJ394Zv6w3v3Jat\n7ui05ePhkSGOKCf+2TF5QkpEpYnKELHl75qoIJXVGXXmnDxjH3iaRv74eKQyltZZVnXFtlvxZnvH\nru1YuZpKw/1mRbVq8Q40Bis5nHTsU2JMgrVhNM650gDUZZHmhdkSPAs1axrHxfUPFCkGvFfkti2C\nhZKVI7DN6AWOqcrItlB8uvuibDv3Queb+b85Z7qmZbvdcncnJmDjODL1wroxJVOZPVFmTvz8nOds\nJcUoeHeZ1NPU9bW5qfWyGXyM9MNAP4zMTBhjNF1bSxzimhg0jaF2jpnz7UMQjruTikQUtoFxGFFW\nlU60WbjBcTZtY/7a/8vcezbJkWVnms9VrkKlQAIlupvdFD1DWy5t/v8fWLO12TXjhx2zGZIz7Gah\nBIAUoVxdtR/Odc8ssjlfq8MMhUICKSLC/dxz3vMKxePjF06nM03TcjgcaNu24NpiBVC1Dafrme/O\nR/7l/MI1yTX6dDqx2RdKX12BVSQlh1vwnuAlfu/z5y/MPuDqhv1uz2a7pWmaFfhKMTD1Vy7HZ66n\nZ3L0qJI4ZYxmbzWT0Zxy5qI0/u3yEXldfE5QWeoPd8RLT5o9scBwLBBOiWL8uTCJguUL7FRVbv2c\nnymzWW/Xlcb5SnB4nbxXLF2JcObmsOdX33xFU1XoYjg1TxNh9szjzJenJ56eX7iOc2EbiX12ZWu2\n264cfJoQPcM4rA6ZcaFMGk3bNvzud7/l7u6WYRh5eX4W+IyMq8TsqrIVOQm11VWyzPTeFwOsiX7o\nhQ6rMuM8lYlHJsVFhr+wVBZ4RSi3f0bBEjolDJKWs6Kqip8l/MjYbEgkQo7oCCrKm7XZCpdYsTAM\nVHkhFpjm7Sn+Rgm1jGkZUsG1ppAYfUSZCnb3cBeZz4/YNGOIcqMmERuQRvyQ8MOZhKY/v3B+fGR3\n+MJmt6Pb7+n2B/amgW5LuntHHK60JM7DCeMUmExUws0NaAIWryumrJhiYogBn0SEobNMElPMDDGQ\nsweEMVNZTXu+sD2d2Ncdu7qhtY6Hw5bbTcfGVbRVTVs58YzRFgOkEIuIVpFTwk8TUWuMApXkPVnC\nBGNRwWktTCChghlIYLShrptiWGVXWCKqiE3F90bLskpniRtNMZIU5WaVRHNKIV7wbWvsKo5YLt6l\niBsrfN4QIzrJHkUpRfThZ5t9bCKqYmyQM7m4zgErjKSNpOMsXY5Wi+MmK2NAl1HYmCLNnyf6YURo\ninJTW60JJTnJqgplCgsDVr/0BUdZzM6+fHnk48ePbLdbtP4LmmX8LgVjCJEfXp757vmZZ++JxuKC\n5/F05KZuaXWGHFHOsNlvOVQ1deXkPUiReRoYRlnWVU6S3XNVif/NPOL7C+eXJy7nF/zUs4Q/5KxR\nOdGh2OvEjVfEqEkYYkmyKQi4eCUZA7uW6nZPnmbGJ2FnvCm7b+DM1xfj1XJCrqPl3/D25UosWNAK\nQ5WbWD62/nn5DFFwOlfJe6bL9e09quhVQggcjyd++vzI+dpzvvTihlk5drsNu+2Otm0l9H2ayDnS\nGyNEgroukWs1u92Wu9s9TWUZrrG4sWasM+xvdhzajsYJG6WpGyor1gCrj345tRfhTwxy/Yx6RAEz\nxTJ5FU+VZCP3Z1bITUpYxJM6sIztUihWn2etUEh82TQLn1sOKo01rhTl8iauJ35CLECX9z1Deg1I\nWLm7KRO8jJnTHOmngGs60v4rktkz1Ds2/kobB+J8Jc8TMXqMluDkHCVst58m+pcXPn/3HXXbsL+9\n5f1X37C9eY+tOu4qhz8caLPn2SScy2gH2iqSNgRl8TiCaelD5jSOvFyvXOeZKQpXJgFBKWHCIJa9\nKcPsA2c/8fl6wmFwSlgvu7pmXzfs2pbb/Z7DpmPfNGyMoysFvbIa8faJzCmiFVij0TmJOtJZtHOE\nnPAxUpl2pQDmbNbw3rpuiphGLD0XJkzTNOKUuC6utLA2tBZIxMjv1yKyqauKw80NbdMIT7yEgdi1\ngBtsVZGBaRwZ+l5+zqqisk7k99aVGC+HLYeE9zOp6AdQFFc5gTnaIup5e80tiIck3wScNihj1loR\nQuDay8/snJO0qZLJuoh7FGq9JBd/cdBrIUspcTwe+f77H9jtdtzf33N3d49CDtlhnBjCxL98/sx3\nz09MwtFlDDPP5xdO2w2dSiQCurLcHN7xm7sHpuuwFjxnDZOihH57cgzkKOwUP4qPzPn4zDz2KETZ\nSraFSROpMrQxss2Za4IRRXKLUAN0OeiS1nirqe4OMHmm01WK5hvfowVaWe5XxUI3Fn0EqnDRF8Wp\nKpPLwluAcvD/HEZ5symRr6uUHPTA5XphHg2xOBu21kra/TRzuVz59OWRT4+PjMMsuoubAx/ev2O3\n22Kto++v2B5yDpwvjqZt2e+3aJU57Hfc3R7YdBX95cLx6QvzOGJrg6vka91td9RW4hptiSP0JXgZ\nFh/yjA1CMZ3ySE5yfQUrlNLz+bzSaOzPxt0AACAASURBVJ2zdF2HtWIE96cevwyPXLEyHFhd/WTB\n9unTJ8Zx5MOHD1i7mNwIHzz4meTEclLwNwslB5G1l8/Fdzy/uRgWDqrMakqxwglmt6NuOy4zPI+e\nH2MmNO/Z3hsONnF++onz02eu0xMme9Lck/2MykV0AmQS0zDxEkeGywtV+6/U7Zaq67jtNty8v2d+\n2DOGM3OeSCbTbvY03S1Vc0PSFQHNGAI/fXni+XTi2F859heu08TVTwxxYkyeOQdititLx6gMKhGJ\njAnClDjNI/pyxD5/obaOzlRsqorOyq+Hw5aHw467/ZZd1+GspVIaFSVvUKLVwCqJXGvqCpLALKpA\nWanADCFFQMZoq+26eNRaY8pBlIpoZOXGlqJXVZXwtGtJMgLWNJxXJo3wiPU4MhZoxntP5RxNCMSS\nwCKHXlpNyUQIUsbaMmaHGCDl1xxV9brshVdqnKScyySxGJRlpYsydiceIUYolVWZIHyILDlOy2+h\ndFoyGar1exwOB775+mt2uz3bza7gxjK1nIeRj+cn/uXlmad5Fql8Wbhe85XBD1znRJgGNl1VFtoi\n7lIIe2u325KyhAHn6ElhIgUDMdI1jk19x6ZxHI8bLucjIc5yD2Zhm/zh4yc+fnzm+8czU9Ogdh22\n6Yi6sL9KT70YRlWbBnu3pz6e8efra4e9tN1lQlmLri4GX6s0XTxZhqEXVWZVlwmJQhP9jwKYU2nS\nlrhBsaDIaNCGqq1oui0W6M9XrsMguPQwMo0BaysOhx1ff/3Af/r9X/Hu/paqquivPZ8fH/nu44/0\n/UjdtNzeHNhuam72O+5vb3j37o6XlxPz5Nm0HVklKM9hLpRZMvg5FBbYa2PiCkS4dOIGI6ymEpyS\nUhJL7LZFVNjy756fjzw/H/9kTf1lMHIKtUZpIsvJLaPP6XTier1yd3eH1qUjnyZyVjhbCfZYFI+6\nmE8t5lplVcLP7HDJsq0pwojM4kqXV+wpa0f2gckHhlnRVFtc63BVpoqRGg11yyZ7nn78Vy5PX9A5\nYmPEKNAqkSP4KRP8xDD0OPdC07S02z2u3WDrio0z7Jottqlodze0m1vq9oakDHPBkKsQuatq+mnH\ncbjSzyNDmPDZcxwunIaROWp6H5hCIGXW1yTlgFeKmeJqN0vAgcPgrEAstbb8eGm5fdlws+242XTs\nmoatq+iqisYYaqsl4qtezKAMfpoY5lk8sZMqSSdWhFgKyJloxEtDlcUYsN60C2SxbOCNkYWp4pUb\nu05j5WOuFPdF0aeUWu1mF3qhLGrl8yXkQARnMUu6TtYadCjZjbI40l7+vSl7ApCl2zAM8nErP5sr\n3jTOObnhUlz//bKMWxg1wmkrGO96lb/Z1ehlYlTc3d3hXEVTt2w2WyhgYB8Cn65n/vuPP/Bp6BHD\n4aKJKPuN8/XIXm3YOcu7g3R/janQpeeJMbLZdOQcqSvHfrdhu+3ouhZVFr1aIQIiYzCuFntVrdcl\n349PI/P8mfOxJ41BAr23LdkKt3yBrZYCFa3BbBqa93ekEMCHBQBZLWgXq4KcX61/5d6UTvl4OvLH\nP/yR3X7H+/cf2G73a+H+3/msq4KtG6PZbDcFrrLC3FFgK0sKwibpx4lxmpnnQEoSF9m2LTeHgwQc\nGyPXXnkPjRJX0d1+z/v397y/v2XbtWy6lv1uR5gD267lsN/io6epBR/PKksASUoCPRohOFhj18Qt\nYwzZiN2thpXrnhF19mYj1544porHyuKi+acevwxGrgr2qBRevW6k3zqmLfLeRbBiTSV/XuLRKKKQ\nUsSlOy5vwJJrqJYlulqL+WLutFiIprLwyEqRY8DEyH23Z98oahfh7hbdNeT4wE2lmeLE8XoizCOw\nxGGw5jJCJvqR6Efm/sz55QVbtdTdhsPdnu72wKbbUqmOOle4pMlak4NI2zda07QNt3XD+92OkD1R\nBbTLfHn6zJfnZ4YAT9eB0zDjs2KOkSkE5hAJGSLI6wqEHPE5wOzREknL0+WEVRqrFdu2Yd+23Gy2\nPBwO3G46Do1AM3e24sbWBK0Y48R5mDkdz+RcVGaVo6odVVXirFIiZP+a91mKLbB2FqZQ9ZxbMF0p\n/iFFNMLNVZWjKtTRJfE+5Ywry8tFILQkb78KbBK2cmRkWuiHgXGe1ykhhEAqB42EQwsf3RrLOA58\n+vwJrTRN03C4OaBcReWqNc9Se4nAW8IiIkhaGcJysfUbNsXbhiKnYusqVLvb2xtub+9Q6HWjk1Tm\nOAx8PL7wPx8/c8qRqGWBrMr9EXLkej2SWsu79/d8/e4d26qFqdg5lGmgbZtCp0zstlsxHqsb4VGj\nZNGOpokZTA2oknFp0AZufnyh6X4UiOk6gNVUhy3aNUQnuxNpuIXW6lNE147q/S3+fIFzX5hovC4x\ny2Qt5mtvCjkyhX366RP/8A//wIevvsIaS9t2Kyyhyv2soBhSvcVdWN/L25sDh8NB6KXBk3NAG0vy\nntkHQkjMIZYFtPwMpmDg59OFsR/JGRHzvLww9APbTcv93YEPD+/49bffUJUpvDJOSATGcNhvmeaZ\nTdvRthtijhKw7AM37kYaHlRxgFSrXYTWBmUhWLPacmgjk59c+1L7csroyhac/s8IWjEqS/LPehMK\nNlZVNb/+9a/xPtA0LVqJ38SHdw9CEbISkPDTTz+hlOJ3v/0tcssYtDLCUEF4ukmx3gDrFrxc6Jm8\nyqGHYeYyTWTdUKvAXa35/bfv+d1Dy22rGOaRYbgy9hfm84nd7Z6b/h1pHIuMPBGmGZWUCG+CMAeM\nEtxfGxFejJcj83Dm+dNPuKZld3vH4e6Bw+0D28MNrVbkclE9DxfO15HtfotztZhJ2YjuNnRZgW64\nTjNzzDSbPXNKnIeRp9OR58uZ5/7KaRwYQ2QmE5UiK+EU19qytS0pZ87zRD9eeZxHqvOF5vMXNs6x\nq2tuthu+ur3lm7t7HvY7VIgMw8zLZWAYhMVSOcdhv+H2Zsf9frd2MovUeJHJL14RORYjsiRmTQAq\nlY7VmLWjrVwtWYpaYaJ5w/5QqzAMBTF6YWssPhSVKFx9CIzTyMvLC+M4CuOgYPe73Y539/dUzsn7\nU76n0Qb9Xg6AxWPEx8gwT2QtbBxl5JBNUeCAN869AjcB5CRWEYAxYr8bs7CepHtcilKZHZSYsg05\n8t3piT++fGaIgVhM3zQivNIpoUNgYy0PhwO//uZbNrYmzZ6+JOsYo2maFufEGVIXb5OMJkYxGvMh\n0o8zwxxIymDqZoWPKClat7e3PLx/h/3H/0mYJuIwMj+fULXB1BavFoNoys+fCUphmor69gDKvDZN\nSTxdXvnh4maa3xTjWMKzL/2V7nLmOvSkHFHalYbPSHTeG8ZK+VRQYKym7Rpu727Y7Tr8OFI5R+Vq\nEXBZ8fmfZoE3Ygk9SVgm73k5XugvV4EUlWK73QJCl62qmk3X0NSObddAkvBvay3jJMvvpu2kaQqB\nz18+y04mR1DSbMYgkJ61VYHvVlo+CyyZM2W6faUBiyq5pWleG5g/K2WnPNHXBUhO4sankCVUXb+y\nTqzRNJVYk0YUKcs4vLyJqtgfaqWJuYjp80Ify8XIvtw2WfBcVQq6NgpXWTZGUW/2NNs9c9J8uN9h\nbWKcBq7Dhev5mevpifPzI9N8pqoUaCkEKitU1VKZSjDlHAjzRJw9ycu2PKeZnAJhzvhRM/RX/DQx\nXnrOjy9stnvcpsN0Labu2NQbtO2ou5aYPTEM5BCwWbOxFltVdNagtONw+x5ta0JKnPsLz+cTz5cL\nL0PPaRo4TSPnaaSfZryXgpIY8TkTk8eXQ3RQnnNWHLWhGRxfxp7P1zMfnx55v91To0ghMM4zIQiG\n3iRQIbPBgGsk7ccIq0aoX+Kqt1BCldaoonyUjk4waqcdykiQQAieBFibMZgy5ShyzCUuqyzMtEjt\nQ/HOVuUgyCzeKk5ofbDycDcbGb2bUuRyFnGPKRz5rm0Ji7+FMcWrQ0yL3tLAyKKQjSkKR9lYrHX4\npdDk4qZUFnSpeIujFNYIw0LM0KTDHFPk89Dz4+mFL/2ZSSUibxz/csIp2FUV7zZbHrZ7brsthMgY\nJQJPFsRCy1zcHXWxICBEFJoUJi7XntP5TD+VkG2VxQOoHABNXXF7e8P93S1tU8vX9R5/OuN2DaZx\nRFPJoVSAbzmSICqFO+wkD2CYy1pq3Yax9OGvD5maXVWx2+/46qsPHA4H6qZ6VceWsVrln33Wv/s/\n4XZ33N4c2LetiKOMEYGaMlS2QinDN19/zePTC+dLT0qJaz/w+fEJXSL/6qYmpAKdYZj9yOxjsZEQ\nGqxiZpxG+n5g9p6maQBRpqeYRA1sLHVV42ypE8UfaRxHhn4oiEFGfKI0latoarECWGrfcu0KPdas\nkOKfevxCfuRAyqtUPGdWbFVu7uJyVjbgVVXUalmR0Nzc3PJK+H3dWi+YZMqvnNXle6AgISOo0kXO\n3bQ0RoPRdNsd75QjJBlJj8cX+tMTQ3/mfPyR8/ET19MT+BFNwjihxild47qKzeZAVzfURjFez4yX\nM8PxTJwG0jyISjULRz6FzHA5M/QDz+oL1tR0Nwd2Dw/cPXzD7u6Bw/ZAUIpx6hkHCHMoRUxh9eLJ\nYblpWrpuj3MVmSTpI9NIP8+8DFceLyc+vTzz+HLk5XLhPEz0wRNSpFLSHcWyT4jAlCUYeRg8z/2J\nj1/gtu6oC+/e1jXGCjukixodIo1PtD5x2xhsU2GVQSfweZa3qcAsGlBGeswQ0toxVk5MmWIKqyPh\nUnyXEXOa5SAS+bzEw8UkS826rouClbIoMqsMelk2OieijnoZW0tcXIwRnJPlt5WIuVSWuTnBHDx+\nnlDK4hyliAtXfYnP07V8bopJuvK1TS/QUQjrQfaWXkmS5uQ8T3x8eeLT9cTJTwQlUAu55KWQaKzl\nvun4sL/hvtvRastcBCvaiMKU9bpfvGJeXfNiSoQ5cjqdOZ5OjIWJpY10/Zu2FYy3ctzs97y7v+Pu\n9obZz1yHgXDtMecrtq1wtZNDs+wuZMgSSq3btNgMzk4oIxYab1MZl09YGjGyNG8PDw/8zd/8nqap\nORwOYpTFqyhoZZxl1j3E26ZtYRJtt1vuD4cVcksKnLHUlcJax1//7nccj2eeX07igDl7rpcrRima\nBoyruPYj1hpSypzOV1xV0fcj0zTTtQ1ozen5eVVw1k21UhiFbijskm23FbO2MvlN08wQJFpOa7W6\nqi44+lt3w8U6YxgG+r5/vX7r+k/W1F8msxOYvWccJ/RyUZfO/DVMYYFAAtM0oJQhF3/fpq7XTbdR\nyOi5hrwiHeDakQvfXD4eSTlhbc1+f8f9/R3KaEbvufY9l+uJy3VgGAbm4Ur0A1YH/DiQ/YRVEWUF\naUY7XL2hbXe0m1va7kBdiZhglwJxGpkvV/qXL/THR/rzs/hyRHH0w5giegqA5np+ofczUVnuux2H\n2/fUTUe32xP9LdPwwsuj4Xp5ZC0SMTH5QLhe0XrAaFmUHDYb7vY7vsq3gp97z+l65fF05vPLCz+9\nvHDse4Z54jyPXMeR6zzjUxTcV+SYJKUIWhMryyUIPzmHqUAIYJXmDy+fufm84cPNDR9u97y/OfCw\nv2XrKmyGOSY0FqXFyizEyORn5mmiqRtq54hZvEIq59YF6BL0HBbPlsuVcfRiNKU0TVtT146mqei6\n7o17o3RGdS2KOu/9WtBylq5exyiiHiuju+D4mdnPAs3FZWmrqV1N5ZrCs45FICK+5U3T0FaNHDiu\nIoeIj4lYCngonbIPUeLllFqLjvhzaPoU+DJe+ePpkWc/4bV47C+OmzpmWqO4a2t+fXvLh92eXVVD\nELl+2zRY92qbm8v3XXYPS0cnhT2iDNzeHVYevCmYbO0cRit5X5qab775wN/+7e8Fpvr+e3kNTxdU\n7ai2G5ITBsuCWSdEJKStRnUNddNB5QozSBVGzBuKIVLOM7Jv2W63/O53v5MFdLE9KP9orQdvGnA5\nKrN6xem953q9cj6d2BY1pzYaV1dEAjFIsMRvfv0tMUWM0Xz8/nvQmpubG+7fvRMTvXFAK835cuF8\nPjIMA0/PYK1h0zbc3d6glOLL05Gm7fi226BQpCwhLe/u7nClILdLzkCR96PAOnmuoskAZRRtK2pd\nW6Dj5T1brh/glYr759SRw8JaefsuLeKdVymzLDczMQSUWeAWYRXkLKM7hTGhtUSwvX6D5SRPoMx6\n0ZApVKeRx8dnIkmEHn3P9XplHAeiD+QU0DmAiagwY7J4mGjboIwjacf2cMd+d0vb7dG6QinL0jbY\nusVtDrQ3N+yHrxguRy7HI9fzkeF6KcUqkH1JAlJJFKSVxhPphwEdZOwyGur6hts7Q7fZk/PEPPak\nCGhXxDahLFEUJhtcWcTUxrBxNY0WH/Q6a/a2FsaLUszJ088Tp2Gk9xPXeaKfRq7TxOi93MjDRVJK\nYiBHvU48oLmGkZfxyufrkT8+Ntx0G+53B7ZVTesqamvpqpraWExWK6c5x0gXEm1d0TQVVRGlLPae\nWqmCrwqOXFdVCb4WHFdEGlbELm+60HWyK+wYvTAEyt/FGAnlY846TFOXUAnpYGcfCFm8cSrboIra\nbiG3aq1wtsY5oZGprFZIKEaZBiUeMBc++pIOVCCipUtPmaQUT/2VHy5HHqeeIadV1avJmJzQZG7r\njt/c3fF3v/kLvt7dsKkbZj8XmqbshWTo/PnzXn4tz9uYhalT0ZTJRIq5MClIopFojOXd/R3/x9/+\nZ4Fhhp7n52fSOJFOV/KuR+06VKOX2aPsCWAmi4GXq8nOCuzFKwUxl5tY9p/lnlRSqLbbrUCeZW+x\noDDLAnktFbxp8bMq6k3PDz/8yB/ub1EhUlkrhdRosdtV8tx3246/+PW31JXjN7/+Gq0Nu/2e3X6P\n957L5cw8z7y8HHnedYzjUIpvR4yB5+cXpnnm8fGRh/t33N3eCGnDzzhn8dOIqRswwl0X08bXE8gY\nS9sJ5BdixMeZaZLJNbmV67MeZP+WofUfPX6ZQl4uNmstsbBLQPBsipfzcsrlMvprLaqzVDbRcmMl\nUWi+kQHLZ8vXyyTIEaUr1qSZ8qIfT0c+f/nE7D1z8CUAYCLFmdponNFYnSHNqJwwWmTR2tZkUzEr\nx+bmA4ebWypXE3wihHLwpCSjeOPodns0if080r48YT//RP7yY8HMJ8IwQQRtDaarcNuaqBKXy5lE\nX2h6ju3mQNPe020OxDww9GfBbquNsHCShzCLYZQXMc6iokR6YhpjOdQ1W+Mw1lF3LcZqfAxcxpHL\nOHLsLzydj3x6fubleuUyTcwxMpNAC/wRSwcWUULZDBPH4cJPSlFrJ4XbOtq64Wa3Zd9t6FyNywqb\nM7VWdM4yxYgng9Wr7YhVSTIW1WsRcsVQLOcyeRlZRhorCj7pQMspXgqlXGZqVYc654qlaSgpUApn\nPZlc4JxEP45rElJV18ViWWiMtiyiqqLWs9ZitAQXTNMs1gYZEpqUlfxauPeFeb10zSlJ6PCUE5+v\nZ348v3BOHl+uXk0WL3bAKXjYbPjtuwd+/+2vaLVFpYwvFq0+LD71efV+WdkeSmiiKr1JYreWuqpW\nFe0rPbBAYOVzNm3Lb//i1zw+PYldbwhch4HYj8TnM8Y5tHMk87q0VIjlc9AQnCXZ4mleJpGslvsS\n+UOBjyjXqdbLXmEp0vysZi+PvC499TrBex/44fufuNnv2XcbdpuNwCMxQvFSsc5CytzfHbi72fO7\n3/4KrTV108iuYhy5Xi+czxd22467uz3jOILKAudZy/F44unpheP5xO3hhqpy1NYyG0XOkcvlzMKc\n87NHufJjFpqstUKAiCEJ5bUfGRB4pWtkunNlUlSKtRH53xVx+MWWnapkEWqUseIPnmNJKJ9JMeNs\nRc4RbcopZh3GWELMayK60UagiQwZK2lDBVIpJRWIaFI5K6SYhzAzj57z+biqp6wrnW+lcCpjS9yb\njxGUxTU7NtsdWVfMSTPPCeU6tK1RyuEcWCOH0VhEK/M4MluD0XLR6rZj//4D9WZLjp7xeuH6/ML1\ndAarMW0tnXmaUMoAjpgUQzTMs1j5OmdoWkvd3rLZW6qqJkVPmEf82EMZ7VXhLsNiKCUS4s12sy5P\n6toJJqw1NYqDq/iw2TLff6D/dqafZy7zyGnseT4fhRVzOtJPE2MMzEnYChFN0uCsmP7MOdJPnsep\n58frWQyLCmNmU1cc2o777ZZ3xpIQN76kFJ02GOMIC+5ZtoWCEyNLUiWrNWOkiIPQxXLOWG2omprV\nJG3hpStVVKiOGHwJoZ7ph5Hz9SpUPShdq3jipxQ5nV/Ee3qa2W06tpsN27YT17ssQSjjOMnC1Wh0\nghiKgCNKYAVocgnxThlRBicR0jxNI5+uRx7HKxERDaksyklFxmjYOsevbu/5dn9Lk4EYADGEaroW\nF6PQ1IL4eIQSphBCEHy/7AEysOkKnxyJElPleYcY8dPM7MXAbPGVUcB//v1f0W1aqrrin//5f/F4\nPOKfj9iuRTeOWF4L9SY7M6XIFASqS+XvlrxXVaT+cpvmV+oJrxO3vGe6EBTk661kxbwcBQqlX0kR\nQJHcB3Y3d+y6mq4TyGKJW1udT7VGOwXUAvWNIz4GjucTL88vYp0cQ9mjiPYgVxXvbu8Y+7FMv5nr\n0PNyPLKpK1EfW2FTubrCNVWxXIhlOW9wlQLvxUfIzwzDwPXSy3tQR3Fp7OQaSCozDQPT7Ikpl1jB\nbs3U/bePX6YjXxaVvB66iy+CnP6J/d5htCvBp46UDQqLMnA+PpJiZH/YS5JQ0pAtKRXfDJaLprg6\nvF2EkiAFok+E4QoUfrKSLtwZQ2NNsQQ1BDKfHx85Xs704Yq2HrQjYEogw0zWEaPl1FcgdqrWFk+T\nTIweHyR/07oGu68IfibjCNESdU3SGdtUomCde7RO+AgogzaOaD3GVKRk8UFjrWCJbSvxZMZ26LaC\nFDAknFPrxShJMiJZX6hX0skErG3QTvIv5aZP1BkaW7FvEiEGrtPAue247PYMD+8Zg8Axx37gOA5c\npokpxQKVJaYUxEsegbGmIDe5UZqzt7xMA1/6Cz+cjuyamk1Tse9atk3Ltm7oKgnf2DY1jXHUWknQ\ntdGoAsEZY0snFglzcdOzoIurY0xxdTfUhU44zxPTNDKOo6hNcykJSyeq1Gp7a8qirrIGQ0Vb17RF\ngZozK6NliYl7jZZLhOJjLrimA5QUdRWw1jClxNFPfDw/8zT1jCmS1OviTtbBmY2r+OZw4Nu7O+53\nB5yxhW0jlqtL7ihlesllUUzOBGPR1uBcZBzkOffX68oUi6XILjuIaZrEHM17mVaKsrBrW/7yt7+h\naSWj9L//4z/x6fMj6XyBxmIaRzCapJQ0UQU+ClH42kuq1dJ1/4wKvJSDUgVeKYmvWEpKkblYNsCb\nnem/69Ilxep8vhBTFuqgq4gxMo/TOqVVzmGLqjtoifuLUZaKQlWNWGeLq6dht/Orda28p4amqfEx\nMo0Tj8/P+O2G3X4r6VabDldXYlvhxTTMLGZvi35G67UZrZw0qHVdrVbdoonJhUwhCU2Lp/6fFUYu\n3XGW5WZmsWkWelleLG0truB4Shlisqhs0MbgfSrCDnmSSlVATcoVKTtQtuDisOQkSrLOskxN4vgW\nPc5Z2sqStSlBFI5N14lznnMEpfh8unAeHonnoaTBNFRNR/SeGDzR8CpIUohxjxHCv9wovRTyXNwc\nrUZrB6pC2RbTbYgpkHVajayUFvGCNk645AXaIGUmDyMZY03h3G+oXI1GnoO24CoLfiKniVC6U/n1\nmnS/JrcUWiARyTbNSuT1WREB52q21pD3e1xlicAwz3w5Hfn08szT+cycpDOcQ+B5HDhFz5gS5GI6\nRiIgnPLrPPF0PeOUodIa5zTbrmPbtGyaln3Tcr/b8+Hmhtu2ZVdVbJ2jc0JvdFphlCX6hM8TIWRi\nFrsAHyVXUbi5eR1VtdFrEZeJxGKsK1Sx4j2dhAFjC9WwcpWEs6a8Ml7ESkI6+nmeCTGSdVEwIgt3\n4UprqErcXOHTR2SCOceJz9OVj9cXjmEkqqWsLf9NOKW4bVr+8v1XfHN7z2GzxSjBtGWBH1jU0Urn\nolA2K/0tA20pjhdzWRulGCMhBtIoRWycJXN1HEf8PBHnIEW8jPnGGO73e949vF85+/35wjyOpPMV\ns99AXZGLh3ph2QnEtyw3WeAQtRbpBVVZn3l+Le0/08bmTPB+9eXO66nw7x9D33M6nTmfL7y72ZNR\njOPEOI3FiVCKZEqmUJ5jyS7NhQQhVgfW2jWI5HVpPeO9Z7PteEj3dE3LpR84ny+EFKk3LaauaDcb\ntFkEPl7YKKvPfyoHRxSVcQgr7Nc0kkVsrSlqceGRK2PQb9wQ/6wKudEShqxSXg5qtJLl3MP7B5yt\n8T6WzsiilC2qKIszjg8fvpWv4yRwNwZF8gNxlliqqFpirsnMRLQwXpTwlyV5KBOTFMKmbek2Gwaf\nZJS3DTd376nripAiL5eLfN9Kvl7KiRhmDBVNZdluNnRNV4Kd0+rxoVcCvyLnRvzNi9eHQrPZbdlb\nRyJzPL4Q4kzKkdPxiRCloGO1pM5YTUwepypcbalNTUoUmh6cL1dCODEMPdtWJMRdU4vHkbLY2tJZ\nRx09MYdyMShyFI8Q7wdmL37dTV3TNR0qK66XC9fzlcnP2NrRdS3bzQZXORKKd4cbvrl7x+g9TbeB\nnDn3PX/84Ue+e/7C58uJfpoZ8ZI6oyiLUs2irJuIjDFwOQf05SwxbkbTVhWHpuV+s+X9fs/7/Q0f\nbm95d7jhZrujrSpwCaMU6XphHCKhnwleXOuW7dtmu2XTddhsV6FMtfLIRVSU0uIBk9auaMHDZema\nC+Mkr0lF3osY6dJfiTljrMO5FrRliS3UJUrPOMcQPD4GTNZ8Hno+Xo88hZERUTqaJJ2rImNS5lDX\n/Opwx//5m7/km5tbNq4mzYGQRxDkPwAAIABJREFUw+sEYN16OOcgRYmCyQuTRqhsVokVb6K46FVV\n8fQQD6Ox24jHzTSRC52zLVQ3cYhURD/xm199zTRPXM8X/vD991zOV+ypR99YsjGkYqhVbMJeud8/\nq72vlrivXfgrU21xsVz/rAvrZTkA1CsMQ37b14vM/XK+8P3HH7jd77BaE72owI2TnVxGwif8ONEP\nk+zrnOWwP3BrRWymi6VzyrlYhMihnXPi/v6O5BPX85WPH3/g+x9/4sunL9zf3WG1I4bEFCYxBEvQ\nNg5XVaScuV57rlcRbx2PJ3yMYjo3KjKJrmvLEl2KubGOjBKVct8D+c/L/dCi0SlB8JBfu3BXVXIq\nmYoUxV4z5Uj0Xjr0XGGMwlYVSpsSegu2Emilah5QJLRR1LtvCL4n2w1aW9AVUTkWS/tsDNk6Apo5\nakxVSzxW5RhiZBp6iRibJ7qu5auH94QUiXMAJNEm+cDQ9zTOoe1bpoAtHiNq9cZ2xYYVFAYxgko5\nMS8RakGwSZ+kszLGCg9XG2IWmpdPoHyi1gbnqrIcEsaL956MdFmn05nTywnrZEO+P+xwXUelMil4\nxlE6iePxhWkaCWEi58R+u5XFYmVL51nR+YZOd0WOL0sywRyhq2pUhk2IWCNFb2sqtrri65s7TlNP\nH2au88ClMGKuw8h1mBnmmTllfM74nEgkYhYmw5hgiJ6rn3jpL/xwfGZb/8S+6zh0O262O242O3aN\nWPeanLFdS6daiAGVRE6jFLL8Mwal8uqbUtfNCoORq5JCFGUfAqt9aPBexmljBdbJcnhWhbFSVZUI\nh1ISGbl2xAQh8bNlYyRzHAfO84hLHZ+uF770V8YUCcgWUGfp/A1QoXjXbvhqe+C27XCFwrZ0uAt/\nfegFy5XpwVG7iqZrsdoQUlw70ZST7FIoocp5MQZTVNZSO0mkWvZFb1WEqw+Oihx2O/7md7+lqxr+\n7//6//BP333k5csLuqok/cq90n+FdCKbqrRU9DdV95WAkt826m8eqZAiCsVyiTpb2Sw/V23Ll8qc\nLxf+8Z/+mW3XMvRXcgo0TUXbNnRthwmKqtADN123cCvAyvs4T5Kb6b1Eu/X9deXi7/c7MSlrNI0V\nUWLT1Hz60qFj4uXLI37byTK52DtQ9jUxJ1zt2OgtddvgqurNTkbcDbfbLc46IJEXb/1y2MLS9P7p\naeSX6chRECPJz6gyAmcl+JO1rmyIxb5z6YCVA6WERiTJMIacJbfT6gpjNugN5BwJeWabBsLUo4rH\nckwZkwLoCqUqbKXwc2BOijxHNnWHrSuUUZyHKykGKLauu65j07TCbghyMxkjRXocBnonKi4Z1WXU\nVRQMdlmwWMn31NqULi8y+0CMs+C+KRFiAm3Rxoqc15h1naCUIUbFNEXIkVRFrANjpGuxlWOjNvhp\nxhcv6pAAE6lCBmuwRg6JEGemOdOPIruPYcZoyF0Za7PYCLvKstlKMlMuoo/Zh3X0NVnTGEda3NvQ\nNJXjUHd8dXPLnDxTDlz9xHnseRmuHI9nXi4XjteePkSGGBiiZ0qROSZCBp8lhSb5mXGaeLpegIxR\nhspVbOqW282Ou82O+82O+92Wm03Hvms4tB21ln1HZY3YtxZqpjEy2S2LGaMV1jiM0swqkAu8EkIQ\ny9sUxXe9qtFK/p0xuignNTEmbLGzTQlChtknok+vHagSCf5Lf+HH0wtV3PM4XjhPE0HnEvuW0RlU\nEkbPvqr45uaOrw43OISyGZKWhV/M+FG64vPlQsxizKS3W9pWbAi0UoShZxgHgpciZKyVwBZYlYSu\nHFDWViRXCf23FO5Xf/DSQZtM5QybruPr9x+YxxEfIi//459QlwFVVyjbrItMlkKe80pQWf5icUfM\nC64qL9P6Sx6C5RttSvjxfxx19gq5wPXa8y9/+CM3hz3jPFJXhv1+I/sOV0mUmpFGyDlHyhkfRbk5\nzRP9tQclfPr+Kpi7Uoq2a1GbLTpljIXaOR7u79huNux2W86nE/MwFkdMh6rEx55y36Cg7Tq6Mm50\nbSeahqKLsQXG0lqTYiDkZVkvSnRpRkpj+Ccev4wgqKRyzNOIS68/WM6pMEcshrbgSQnTatpuQ922\nWOeIUQJ8U8yScKNtWWpqlFU4A7dVA0k2wcH3VP0Bcz2Q2aBUR+0MMWj6/so8zFRdQpdtdAgjRkHj\nKnabHcTMPI6cekl1aZoGbaQg+nni+eko2KqVbrVrBU+VTbbGWjFdoizEtNKgLFXt2Gw67u/uuF57\njqcTl2EiozHGrZBQKqNmDKokex/XRZt1Bl2WM7vdnv3hjvqhwqAZp5lxnjide16OZ8mDtI66crx7\n/zXvv/4V09QT/ATRU1uN0VmEWkVgVbmKYZo4Xy9crlfpQuqGruvYtRsaW6FcMclfWQsZlxR11nTZ\nsqtq7rotX8XAeDvRjyOXYWQIgcs0chp6Ph+fee4vnKeZMUlBT4VhJvJvCEow8X70PPdn/vjJUGFp\nnWHf1Hy4ueHvf/OXfHVzy+12R1M3oCJKBcH8iw+99zNGKSqrwdl14WmsRiW5DpfsU2eseJwbJ7Fu\n5VcseKfWSvjxZFJ8PbjTWzjBKE59z3dfPmHnntkogn6FByi8YZsSW+f45uaWv/72W75990CaZ0IG\njEEpXRbsE+M4CjXV1jRdK524SE9XSm2MkWmeVhFOTOlVBYrAmW61HWCF6yi0t7ULTplUIAddjMD+\ny9//HT5E/te//JHrdSBXDtvW5HI4RfUaiF4MC1i0HGsRX/gn5XX62e9ZIBajkB2HLr7wBYpZWvp/\nmwM6TROPj4/86/ffYyrDN1+/F+aTkeXzbr9fLZNFbZyxCoZhIkweX+6rFIVE8PDunQimKkED/DTj\np4xWmqquqZuapm247Hf42VM1dcG67aoqV0qairowqoIXJhRIg2etRSuKXkaaDVcvEd1yPU7Fwrnv\nr3+ypv4yhbxso0MM2Cw/aIoRRSKGkWTFk0IrTTIKmzTOKvQiKU9ScI02mJJgk1MiRk+OCmVVyUpU\nqKzBbql3FW77gMoVfs4Mw0gTIso2pBQIUYllp5EF2bZrhXJWdczjSJi9sEMKmyEUdsSyVZ6mQM6a\n7bb4SyM0J1eJJV6IkXESqlpOqYyTefVZjrHIqbUmY0r350BLIZ9njyKh8kIxk028dRvxQOkhxQt9\nPwk+agy6MGnquntVCiJxbkM/EHPGWinWddNRWyPdfhA8MESPD7IsSqXzW0KO67L9V+X9zOvzCyWU\nuaIyEgaRyGKslCLe1YR2Q9gFQsrMITAGz2UaOE09x6HnPA1cJ88we65jz3kaucwjvhT4mCMhKWKO\nzESGGYY0g9Yc+4Ft1WKzYZ5CuWZ45eJmVaLRRlRO1CWmThnJowRZVNZVQ2UcVtsilik7iZyJJUl9\nmqYitikumlmVcAr1Vs8i70dTQW0ZVBLKZoF6xfJNYYV0w7aq+IuvvuLD7R2HbgPek7PCe4ktNNpQ\ntxuMdcIySgKnyfUByXvBV7Vm021om3atkL4I6IyRblHYTq+eQQCpqGBTgV8W2wyj9aqQHYeR2Xs2\nXcO3X3/gD18eCS8n7KYmdhXKGZJCoMgkHkoqL1uRJWzj7UG2VoZXaiJ5lfYbowsj6pXl9kpX/Pnv\nKUV8yJxOR6L/mg8PD7S1pWs72Y04Sy6sEGU0fpq4XnumYcIHX4JV5ECM5XUV249EDnnFsBd2kzK6\nTNBWDthpWn9GyU0NhJjQQZXnUvYmVgq2KZ328jKkJNYV2mjCPEvgsy8hM8V+4k89fjGJ/itXdTlN\nEzkFpumKyp7aVZhyExmtUERyLGGwMUAWKEbrDDkQg+Q7JjLKLmEBCq0sxlW4ak/lDCFk+unIZRjx\nMaNMhTaOeR7JSQq5sTXWNVIAjSMzg9JYJ5afiwDFORGqmM4yDAMpywJ14TG/Rs0J3avve+GohiCd\nkTNUTmLE+mEU32Ftiw+DLssZ8XzOpVooldAqAr5ANhqyIWaYxolhHFFGS/Zj1dBUDV0jIgSRM0eC\nLwsc79lsOipnULoSC4QUi81oJJNKZ6kEf60V1kl3UluLzqxBDijFMA4lc1BDVbjKwLK9ismQnVuZ\nCikVnnAWVssUPNe5F3XpLDz25/ORp8uZp+uFfp65ek/vi2VvFBgm5sycA1MUPUIInmkcCNMs4czG\nlOlIKGApJvwo0MkcM22ucZW8Rou+QWuNU2ZVH4eSeCQ+LBFf+OgxRLFaQOyIWewgWJZzJXuxrjBN\nzWApXiqgksCMFoVNYCI4NF3V0Lma2lXEBOPkiy+1L+ZdwhCZQmSaRryfaP1MqCpi5ejaRkyYmldh\nEArmENYimJOM66aYmOmkWXI2fYkZWxWy2pCNxOzNs2RZZjLb7Ya/+Zu/4nQdmC8neDlh7EHwvlw8\naZbYJfX6m9TnRdX5c28YEQYV98TySr71I88/K/z87GNvrT6ulyvTNNE2LXXB7q9Dv+bJaqR++Nkz\nDIPw97WmriuatoEsgd0pLSZYeg1BaepXGmBc7skCSQ3DQEq5TOiFgkwkBfl61toV616MtBa4ZD3A\nihBtGAeG61USsAoUZNyfLtm/UPiysA2cNWU01VijSDnT9yeGa6JrWrqm+CgbUyTQnpwbVNZYpSXz\nM/pC6h8YJ+GBCg5t0dZiXBD3uRiZvOX59Mz333/k0w8/UDmHq2oqVxcVWCInIeAb7cjZUDuPnzwh\nKbSrRHySIs5qnDW0TU3TNHx5emSaZ2bfU9c1MRmGMXC5nkvmJzw+PnK+XPDeU7c1+/2O3XbD89Oz\nnNrWgREfFowFa8UlcZAQ2CUtp2trUgr4ELBGnNYUhlRMOrLWJKUJIXIazxyfXyBnrFZUzrLbbtnt\nb3FOlrs5wTwFxnEWOCpHKgfONdSNI3lPKupXUznarsE5y+V85jJeGcaxqNGUOL41yxQiU4uzsgxW\nSaPFHEeKbxC2RU4ZHaHTlm23x91KKr12hkvfcx56TsPAy+XKTy8v/PD8xJeXF16Ggcs8M8ZApSxd\npTlsHJtGUSFpRxEtzAwvmKsujBKjNMY5MBZlKqytqKrFy6IEGqcoN1kpcFqpgvlKIRRBGyU0QvYP\nUdIGhQFVCota4gaNIamSW4rczDqBSWBjBp8YziMf//UH/vrmA6ndi4FZP3K5XrhcLsK6KGKScZ7J\nKWGN4u7mwH63QW23HHaSPam0keYmS4qWfsMGkX1hySZdBFQ5rR4fOQtdz77hrldVRdM0bDYb5jBT\ntR3d/sDz8cTwP/6RLz99oW1rtHMobVfKq3Rsy5MWzOw1REQBMv31/bUIZ2qsqfjZWMPyJcqTeENt\nWTDyt3qAaz/w9PTM509fuL/dcwozX56+MC/+3kqhC81yt91ye3MjGoamZbfbEWNkGMZCDxQvcKul\n+Vq6aB8CYZro+0F8Xs5nXo4vuKpiu9vx8PBQTLP0+uPmFIUskMKa4bmGhychRSglNg/99crlcsZP\nszh5qm4Vcf3bxy+j7CweEkKCE9qhNZZxnhiGkXnq6a8nurqha1u6dls651qKlbLivRKXzv41azJn\nIdzHlMnZk3zEOtn6z+PMd3/8jp9++pHL5UxVOaydyoLVrCNpyqD1TEo9lZOEkRRTMRXyeD+R48w3\n7x9oW8M0L6o6z+k6c2NvMUYXC91clmqG/W6L1pphHHAlrOB4PstiTWmyUoJDlpsHxFembiqqWhal\nxgrcoq1w1IW9YlFZpOFKa8HVUUQSSUmnuCjb5pg49QN6miCLyEGCOSxVXVM5swYMzH5kOp/xwwWj\nMnXtpHMloXLEakVbL97hWrxREBhIziRJnMllQS3irlQwyABRtvN+nuW5K4EZ0uiZxomYAsM4Ck8X\nzftux77q+Ob2HcM8MYRA72cuYw8xs6sa3m9atk5jik7BkIiAUYkYZCmZkyEqQ1KGEAx+nrgWZZ4E\n4go1UWlpNupK7AFiisTJg5ZFqXmjjpSO3DL7TJjjCgOojNj6lh5Ua+Hok8WTXyeFSVAlJVF5GU7H\nCx9/+kSFpmsabFOzc5aqaQhRFrG7IEEJWitqZ9h1DW0jSkZtnETP5UJJpGDVRYSSskCR1jqM06+5\nAJSlvNbl/wt7ZcnQVKy2GcZauk2HdTX/5e//jpgi/9d//X9JpyvaWuxtU6h8esXDl/l7JQIUk7ww\nB16eX/hv/+3/o24bPvz/zL3Xl1zZld75O/aacJkJW4bFpumWWj2akWZppDXz/6+ZedFoSVQbdrPJ\nqmIZIIE04a49Zh72uZFZZL0XAwuFQgLIjIy4d5+9v/2Zt2/45O3nWFcVEY253A+Xx7PO/HmXLgeS\nSPY/fLzjf/7DP/Gf/9N/5Pp6h3G2TMSxnC3SeDRNgzP2sti9iAm1EpjQC23VGYGElrCbaZroB/Fp\nmouQqm1a2S8YcwmesNaIonwxw5pDaUyLYKi4eUp2amCepAFqa0/jb0ApbGHV/Qi9B/ipBEE5YQBT\n8DetBM9NyzdbjNhTGAnzADHRrnZoLRJrpVIxVAqkkrqhlYTOKiWS+DlEQhL6EzkS40jf9bz79lvu\nHh7IZMIc0GaSCzclKicKvhA9OWnCHAs2/zRuj2MQGXMMDAHapMRgy3m8gpBkQggpCne3dBVaG9pW\ntvpCtNUM48DQ93gnAcMXyhpItxQDqqg0QXBNbawUfRwh6Es3vnh5iKWa/H9QipwVyllihlCk3P00\nitlSEMzUGSniVYxUlcU7zVy4w1M3MY8z3ghEEKaZSUEyGkKkdp7KVZdotVjUhSmmYg2wuN4lspLd\nQgxyMaOEvZRTYopi/mSUKARzDiU4IqC02Am0zQrjnIhPkOXdtAQix4hBJO06Z6HrxUzMUUKlWaLo\nKAycJCKsrJhKhqjWGm2km6TwwL134k+dk3TWhbGkndBMdbm3JF5Ok0s3e7FSLpi7uK2IapMsm8Uc\nAjoqvDJcVTUrX1M7ccHr+pG7/ZGYoG4kl9PWDSoGbF6SqRTWaLy31IUSabQhyNZSvidAkork8Mkl\nUT4V2bhS0rhM08wwDAz9KDmsRZDinagSrTaFzSRNhHUOqxTORX71q5/zuH/kH/7xt5zPA9E67GYn\nV6USyooEdi8VQFhdsl+RBf5xf+D3//p72rX4/7x6+Qasl2K4QFwoxOv9h0X8B5S8BWuOicfHA//8\nu3/lr/7q51xfX/H69RvGYZDDrfw9MU8TodnCDAEuNMwnrxOFtqaoZ9NlX7AoTl1x7txuJaLOe0/b\nNKLo1FIDQhATtb4foMRU6qL0dNZQO3tRJOecaZtaOnRjCVkxjBPjMP5oTf3JJPpGqXJxqMtFiaLE\nHEHjDWN/Zp4n+v7Mei2RSf0UyESIMwQxe9faiKrRiPgHBMvVqjAe5pmhnzg8Hrj/eMvxeMZVNdHF\n0oFHulMnKe7rFXVdE6aJobMYlVmtWqq6IiMmUQrLar1hzJbjmGm8Z3O1w3lLVpEP79/RjzPX1y+Y\n+kFUYWGWwqEiSkfOY8/peKLveq53W6yV6CgZOwM5BxIKpbL01jmCCWQjVrPzPEFGaEtaGDsLN0DC\nkWVioVA0U4I5CjUy9HIQybLUklVmmkfO3UludAWVs7S1l1Gz3aARNezpONAfB5xRWOfE/6GqCCFi\nlSaaUikVUqyUdMBLqn2cY+lEZHGklfB6j92ZYZSLdFWyD+uVUOmsFUFW07YiSy+YdQqJrCNB+QuO\nGWMUz5AwE3VknGUhSU54a1FOppW5KOxiTBJIkiFjSsfpSDgSM+du4BGFt4baO5rKY6wHbUmq3KQa\nSJl5HAlTLNmhT06HCyaqUZgonXiOkTROmJRZ1S2f3Vzx9volu9UW7SpM1sSkeDh0cBpY3EJVYVkI\npCbX/DTNBO8xNsihocSrvqqqC+ZsjSkhxlLD4qULF7vbw/HM7e0tH+/uC9Ml4bylbWrWq5bNqqGq\nKtrVis1qS1U35CyY8Ha75u3b13zxs8/5l6++Zjyc0DcTegroVKiV5b5f7v/F4S8kCR+ZxolpGNAG\nxr4nxaWgcTFQEzvcP9mP8sNivnTTOWf6vuf9+1v+8Z9+y2q14j/+b/8LTVXjtBaKYME75HoqnkvW\nlsZLaKZLUQ2h7H5KgfdeoB+lDbbg195XhSq5CBglVET86xN9NxEmKeaVczRNxWrVFvdJeW9SfrJZ\nWIRQMUb2xzMf7x54fPwLCl/WiO+GUZpIeaFSkOikrifMA8EbkalqwxwTh/MZnxRJW4z2shAp46KK\nYjObtHDNUR592SgrlFHkNNOd95xPjxz2BwlHcB5jdXFWlG5J5UQYR2YyOov0+Hg8cj6dQWt83VI1\nLVl5Hk+SY9l4x/HLb0gxsF411JXj5mqLMh5Xa0KMdMMR5yUJJ2a5kXzlqZyFFDkfD8whk7RcLIpM\njgGrFXUlad/eOLSRbspqwdWskWXL4gutiv9GKHh/zoLBajROg7IKVRlm7eQCk0RXAJwqSsEYGWeR\n05/OfVGpaqyG2hhaX6GtBa0YoyYO4lchaSeLUOYyHMgGfp4Zxo5pDKQo+HIiXXxNdps16/UKjRR2\nlSWEQyh3kX448rg/XkbeJUrPFle5RVhhymIzBFsMh1IxYXvqsARmdUVGPTHPmRgiIc0oLd16TjMp\niEgJpYnBMAfLMIqwyxjpfp19cmFcMOGFzgiU9yIITl02fZriS5IyW615VXs+3a25WTes2wZbtRg8\ny8GcWEKrbWF3CVyniqgpA1NUAjUSySmi9Ywxk7weJdPWWoXVJaEp5ctrqbVis72mbta8ePWWcRqY\nw4xSmaauaKqauoR8KK2JM+z7A+M00fcD2sgh+rPPP+O7798zdj3z4wk9J0wqrqPPC0Dp0osZEc5Z\nNps1b9++wTcV291O4DrK8k89+daEwq1+3pX/qSjo+cdijHz55ddcXV3x2aef8vrlNVVV47QpkyJy\n3SZ9YYssnjlL570cFLIbE5uEZQGbkvic7/d7pkkohaIgrmnb5rJ3USjO3YnDcc/xeKSuPPPckHOi\nqnxJNFogHYEhzcJoM7BqpXKu15sfrak/DbSyLDEE3iaSCj96evbCyWJPAyFmzn3PlA2+2ZARBxWt\npYNSaSaljhwHjPZYtyMlhbYakw1KeUgzKYykeWAaziR6EYNYUVFWzhGsYbaaoDRayXIoBHG5S1HU\ne806ESKMk/gvpLJsuvv4gZwCL66u+Lu//RtuXr5lvdlBDiL7rmrqppZoqNWBU9fJ4iNF+sOB4/FE\nN/X0FzFKIKcZpxWbtmGz8mjnsJjilCfjr1Uy22coNqQyuGqV0CoJtERAqYLZKcDI6xKUFr6vvCtg\nxFs7akmcH8tuIGvBgq01rLxmBoacMTrj5lk80BH5cGUsWIdxigXSiykwF0VpDBmtinJVLYHZIofP\nC5tClzTzJEVnWtwKh+lSyJy1tHVDUzd4/+SUJ0VdphITM7aIM2TEhcVrhyxeayIlV8y5NAVZXjul\nIOVQCrxg4GM5nYTWKfoFYyzWGpzRKE3RDVisXdJtUkGoS8aN1qgYsYiq8sY7Xq0aXqxbVrUc7M45\nvBERUlIULFmWcykKm2gxg8vIny+JWAusE4LI9Y1Bvh+KW/Aid3/WFTujpaloKmzVCOUtzkC6qBSd\nssW3PV280McxMs8JnTV1veJnX/yM77+/pX3c0253bOuWytoLjfCp3j5joGTxIm/alt3VFb72rNrV\nZaFodMnjvN5xfbXj9u7ugufzJ7DKpeA++3hK8PHunj/84WvevPkdVfW3NHUlTc2CBih9sWVQ5Tpa\nvFguZl1KfIeMEQsCmbiepoMlzWea5PCsq4YQZqraC+RLpu9PTNOA3HGp8Pyn8jyT0Hk1ZfkpZA2h\nfiqqymOdY/vnpB3gp6IfKnmxYmEsxJiZx8g0zjhnaJuG3XZHmGTcGoIYHSkTqBsjS6uyQAQxwMnh\nRH94j3ctu+taDIBCFumwXkMMeANei9/zXHDaxRdjUIp5kuSayteF352LtadwQRXia6Kdw9rigpcS\nU5wgZ9q6Yg6Zzz77OX/917+mqR0xSBxU3bRoa5mnSRgI5yPd+UB3PHB4vOf+4x2YO6b9nq7vGPoT\n1kAgo9JM161wxgjWW8ZGyNRXVyWrMoufzBJSjMI5wT8vftPlhl+wXp2z2K+qUhTKOCfTjkwPc8wk\nZcQiAHjsZ+5PcghZo6krgRvWbUNrDAkJf6gRAYfKmXM3cjqe6PqeyleS6tOsUEpgkhQD8zQxTAN9\nP9BUNet2zWazkU7WaAKyqO3DLJarasYZx7qVUOmcYIrh4qez3GTOV1TlRkgxiIXxPBGDTBFCkRMP\n+RgC1ggtr/IGFSfSNAjHOGXBwJVFa4FXrPVoJYEXWgv7om48bduIiVVZKpqlCy6caArOvasqXq1b\nXm+2bOoavShokxikGS17pGyLNESBtsKFl21d+bgt+DE8+/VydJRJIJPK/mbxlREBU7q4haLUJeFe\n4hANMQbmWeGMFBRh6tTUjcdVkSaKdYCvJ3yzhqQ5n3t8U/PpJ29Y1RVKZXKhZUouQCrJubIJV0ow\nd2MdxshPVXZfrnK8fHXNL3/xcw6nIw/7/SWc+LkY6DkFcSnmuYiKpinwzbff8n//P/8v19cbVqsa\nt11fzNI0go8vVF/KTue5DbIwxooyuECXqUw1kjNccbXbFQvcxKJKFStcMSQbxhHvDbsrERktKuME\nhU4r05RSQmNuqubijkiWQm/MXxD9MGiISuCUrjtzSoFpGKmNFU+ElSx8jJKxEBLW1lR1jbeedx9u\nmebAdnslLoUaEQSFnqQVpJnT4Z5pPAGJevMWlKPxM5++2TLNPe/vD6SFB5Yg5ETXRaZpRGvL/nQQ\nc6hLSIGM8npJ6lCSASgQQcQ4J1zxvuPLL79EkbjarXn96iXXN1tWm60UmDazWm+5KQnwcZ6Yxl6o\nS497Hvd7Hh8f2D/eczod6E97wtgxjoHedOQYmVJkDhJXNk0j1gHaSqanlkKqUkHLsxxIYJ4WQalQ\nsLSIE9LCsFgGpSjRWSlLQGvPAAAgAElEQVTLHiOLD4B0IVqTtC60NZiTIo2JIQwYPWCNovaW2im8\nke5YiNYe395Q15V0F5VHIylJcZyFo6vEerRyoj41Rb7tjKapxNelKQ6E3jvqqsJbx1z4vijQWZcI\nPRHQLNhwmIcL/99YI0V9nuj6TvjVWbr3yleFK2zZbbcM48DD/sDD455zNzLNHSHK66m1BeXIJUwC\nFLvdFqVeoI1gm+M4yo2JiH50zlRas/Wal5XjpmlY1zXaCGSjCyWXUq9VLkGFailSuXiyZ7lwWSh6\nSwEX6GiJehMIQDo9rYwc3EVVKnRbMRh7SvFRZekunyskCGNgIP7AvEpuinJBlb/rfc1nn39OiBFl\nLFVdS1FdOnCWieHJslfqryYrI5TFLOZXKecCKSWMV/zsi0+Z48Td/R3fvXvP8XS6TCB//ng+oUj/\n3HU933/3Pb/5zd9Te8f67/4Wk5JQCo0l5wUvl0IegzBLlmXnJWZNLTmlsnsiix+URmFrOcTV8pqj\nyvQyoDIMw8ThdOT7d++ZY3rySC/slso7rnYbtts1m81K3E1D5LA/cjwdpea4vyA/8qgh5MgcZ2Ic\nxTAqzTSVpGRU3hfPEnWhsGkjUIvSmnmaGcaRqhYVo0aJomse0ChCmBiHE+fjnShFlUMZyzyeubly\njPMVyjnGMYif9Sy2kikGhiBMhn7sZWFBFljCGKy2aGdKCnwuCkoLKuObWtRrMfHHb74BMm/fvBLZ\nfJTpQ2lxRvRVRaUkeZssfuW765EXrwbGaaI7nzgeDxIAfdzTnw+EqccQmaeBhw/vGYYRpUUR2jQt\nvqrLdOFQiEeLKnhsRpEIEm6QFCnNRaiQC8c9X4QPoIhJFH652LIatXRskieZFqphWebNoSwfyxd1\n/YxVGaNy8fIwOCtJ5hhH1paoNb7gkZiEShqrhOlSOY8zuhg+CZSyyOnxgtVWlaQIxVAWwSr/wOcm\nl4InFq+iElROA4KhqxSZR31JjUJpWVz5mrppWbWVBBcohT6cJFg5RnmPUyCEUdgu2qK0A+1ISRFj\ny3JiLqIPozVeG2plSCQ21nKjFBul8AjDph9GnLKYbLFmLruexdK0vIvPyRnLQpkFes+X/1siHmTB\nuHxY2GHPwWqtkvDfUyyFXBef76fF4QIPpZRQP4hSpLAuSg5pTsSUsHWNLxTaHJ/RMH/w3POfFGBp\nim5evAAy3tdcdgMFRlyvGz779DV//etfME5jSUeKP6Qe5uU1Kq9IWeaSM/McORxmfvvPv2O9WnG1\nu+LViyva2svztCJ4WkI4UrGeXT7vArEsr8vTqyQMLYnQc/iqImd1oRrGGJjGmaGfmabAOM50/cAw\nSYziouR01tDWNZvNWmqNE2PAOI2MJShkgdJ+7PHTFHIFcwqEOMkCJiesTqzaBud9oSPq8g066sox\nToOMvzFKNFNx/oNyyidRuaWA/N0wEcPA3B+ZhgNTGDgePtLUN/z8Z6959UnFfi+Lh9PxzP4gCro5\npPJ1hKqn0nJryILDFDl3iiI8MloKez1PzPVEcJ7b2w9U3tO0LV0/cD6fmaehGFH5S1rNcqNM5Xtz\nznN1dXPh3iYhPRPDzPl05PHuPV/94V/41z9+w+2Hj4Qws91suLq6YrNZU/kKaxJGiYe7KqbPKcMc\nRZSU0IRicZCSImZFjKLCUwpK0FhplyJpLrbDunRpMoeTVcmCLEOz0YakjUjVY2CYR1IIgkEX5oS3\nDu8iTR1ZrSo2q6qwQBwpDMJEShKTB1o402EW+N4U3F8rnMlYlQUmmSZxwCzilacDKTOOBaNWiqZt\nhcpV8jkNQJSbPCAh0cMY0K7CNSuqtqEfzpy6iY/3B47nkZwVTd2Qc2YYJN3cmEzdeup6RQiK9bq9\neJdnRN0acqK2lpWxWJW5cYZrBaY7M6vMIUM/J5pVxDcJYx1tC7UC7yqczhj0Bc+XKLlSsC/d89ND\nlfdDa/OsGGexqyiPjAQ8g7gTqrL0zn/yyZTS5Kxlof6D4rsYwyGL7Hmim0fQShhGlUaHjDXCUHuG\n+PzZQylo6oZf//rXzGU6EqVkESlNEzkH6trx7//u3/Lw8MD9/QOxExXlnzfli5dLeiLKIASAr7/+\nphAtDP/X//mfsDc75hyJ1l1wcW10MdZyhCBTVUrpQjF0zpXPFwsjLdDaptzTohM5nc4cD0fxxhkE\nWow5o4zl5uZVoVnPl7Qu70Sc9PLlK66utrRNdWmWnHO8evWKS7j8jzx+Ih65MJ+91lhvSVWDqUQy\nm5K4x+mkJCzBPCWqi1Anl1HHlNO6dAwxEUNEqyihAkSMSuDBGhkjvZqwKuDbiqvmNZ998pIUZ8Zp\n4nQceNyfeHw8sD+eOHd9yXCcC6Yoi7AYNDlwGYnmcuGPYeR8sjhtGfuBGCKrzYaPd/ciLCjsGGss\n1vni5ia83mkaUdrgfcX51IkcWIs4qK5rvK/YbCwhRFa7R9a714yzJN4YawjJ0o8yilaVKv4hFdM4\ncuo6Hh/3srDLCI6O4MhPeN8SOiEHWFzMfooASVGhyvJJMh7lgpqnqeRgRuqmFdGCdVJ0eYJgUkrE\nnBhCYk6RMQb6aWZ/7HBO1HLWaSpraJwjTwaS+MmEuUfrLMrfFIqZ04zWo1gAaFGwxmLZqo0qdgTq\nEnsmftJCbTTGUHtPWyLK5gjHvmN/6nh4PLHdHNluNmzWLX1xEMRUGCsUue40CrXP1VRXNe16RdXW\n+KrCugrvW3zlLmpIrRRGGWpr2ThLkyNtiugoUYBd39HfPbDvZpSvsb7BOEfV1HIguIq2qlnVLet2\nxauthC8bpWQaNSIke+rWlhZcHss6maXjXv7ggrNT2C9lEbv88Z/dtAr9g4Og4Mg5M8XIYRy4PR3o\nxoFNu+aLV29pFiFPXuaFp07/8jWWgybFEtQheyth95UpyVnWqw1NXRHmzOHYMU6R3/z9PzDN4dkB\n84zJshxy5Wss30OMmXe3d/zX//Y/yDnx7/721/zyr36GVxpfW2FnWUdKmTkUw7USFCFRlAPjMBaG\nrZDjbfnzYeiFNZahaRq894RicrZeN5zOZxKZuqmp60o+b2HGGG2pfUXbNkCm6ztx8s1ilWvtkzvi\njz1+okIuHhOu4FOVcySrSTlw2h/oug6tDTfX15i6lvE5Jun9VOGOx0JPSuJlvSR95FI4REWXRYWn\n5OBQWRY2tXesNhua1qJ1JoaZ7jxxOJzYX2/o+pFz33Hqeg6njvO5p+8GpnFmCsVutnQCGfHNyGNg\nRjOiiPPEqm05Ho48Ph6KyU7h/T5jOqglrQjpvFISMy8p4kK5kgQgwSdDBIynajY064ApvGtlLFlZ\nIo5I4Te7ijDODHPiNMyUjOqS7C7KsUVMYox0TClEQohP0VbGkJKD7C+qsgvchUaliM4ThoTJBosq\nMIzBakUu2ZcC3YipVCYR0kwcI3qUr2GcEd8aZ6isxpmAKotClcUwrdKy6Ek50Y+Rfjg9LaqniWmc\nCVHMiCStRyh24ySiq2HosdbQ1DW7dYvZ7XDWkrSmnyPHc8/j8XxhJHVdL37druLFzZb1amIaxCHP\nOyPQDQlfV1SNp64r6naF0hUZS06anCMKcRlsnee6aQhRYeeB2M08POzpU+IYIx8OZ7JxaOfRzmK8\n2BR466hdxbpp2W02/Idf/w3rVcXaO1wRkUUo0WplOadVMZGLJJ6685T1hfFRpHhcMHYlVgJPB8Jy\nOBSIiufHwxNHPsRAN418PB356u4DwzTyJmc+efGKpsCicqMu8E/5DH/CNokpitcQGWOXo6csSLUp\n3bDD6MybVy/57JO3/P4PfyCfe+Y5XJb6y0EmQ4DAPs9njJzhdOr4Zv6+qK8jzjl+/vlnggYsOLgg\n+uUae3qeuTBaUtktLGLBy58niRe01qIrTwxe/OudwlqZtKu6uhTynBLTLIW88hWZLPGMccZoMTbz\nxeJ2YfL82OMni3rTFD3iskjQkEPi4fGR2/fvsUYMqWrvZfmRMoVpV0Z9DcRSCGPBhFVxqVuCBYTD\nqpUFPDlVshW3DmMtznqMKjeA1dhNy/WmpaobYYaMEx8f9rx7f8uHDx+4fzhwOg8M40yc04XqJfdC\ngSISTBMMQ8/QnzmfTlSF+mbKBtpoc6EaaS0xYtY7Yk5FuZXlezSavu/RRm64cRrp+xGlHc63oASe\nEXlviYRShTmiHSFrIhrbrCBm4hQYp4H9oWOeBoyGm+sVq7YqwbFiyGWUTDTCatBSyBd+u9ZEpbHa\nUnmHb4QqZawu3HE5mIz1aO3IKl/sUUNMFyw1hVlYKyimCEMIpLOwKVxJeq+9pym0K1076koT54Hx\nsOfu/sA4DISYOPcj4yRuipLQIxe7zooYJ3ISg7HtVg5v6zzKGrI14DxRaQIajMTYjSGSu4FPXr/i\n1Ysbtttdec6BFAIxBU7dif1RGBQ6Z7wxVFbw/zmaor4V6qFSmVXlebFaE4JhOifup8C37z4yWcPs\nHP3CLhJ6EXEeIU7SUaeMt5bVY8OvfvkF7a7hk+0VOsluYiwJ91OKRBJWW+YYCFNPikG8x31FjMK0\ngMWIqrC+sohRpPqVm0yVw3Dp81Vm6e9RFP/8wDhNPA5nvjvc87v33+KdY71eExbaZVlIPi92y+Pi\nXcNTFOGyWJQXQrwhKZbM0zhwOpwgzVzv1ry4viIEsV6QA0qjitvlD9ksy5QiP1MSq+bf/+HrsgBP\nbDYbqtpLZOAs1z6oC8SjFE++KcA4ScB6ihFVIuRsUdZeBgByYRlZYjBUu+1l95dSFA+jVFKdlEEr\nGCcJe1ZZLBK8c7iS9PSnr9/zx08jCCo81hRT6Wo1ORtiUqzWG94UbLxthaJWVTU2WRLSUVjnqLUm\nE+RzxSKvVkoUo0UAIYK7jNLCUZ5TxZTMEsNIygpnhPOcQ7xwc2vvUUbjvSPGmcq94e3rG0KC0/HM\n4fHIfn+g70a6ceQ8TozzxDxHiFnyNqeOh/sPTOMnxNiQs7BbwizHmNjVyo+qH/HnTuTnqQT3lgt6\n8bugiA+UUrRtyzzPwNPWPwRFmgOKyNAHum4kpih468rS9SOPx45vv3vH6XBgu1nxq1/8Ff/2b37J\ndtOWnUIizhJjNseROUxFkVlCakuaufRySjrOC69aobXkEpQ6IOuqwutGK7yRkTylRLSRnESNmrSk\n6MRiTgVKtvpTYJxmTucBaxWV1+JTEsHVVxgrdDplB8w4MM4BUxarEpdlyCmgcsJbc2EDrJpKPLm1\npjWW9TAyjkJHvb66Yrfdsmlbdps1VmuOx0eGYeDcdZxPR7qhp+t7xnlit9lQ+5es6hZfNYRkyi6i\niHi0hEa3dU1cr5kmzSkGtBe3Sa0Nzjha49he32C85/50oCcTNYDBFBy/m2f++auv2fia9pcOM82i\nEF342LoIxRRgLbppsOoJWnFK7p3KVzgnfizjFC7JQNrokrwlXXoqylxyLt5FsaxOxA7Z6EzUkffH\nO759vOW+O7Kqas59T9d3bIyjclzIAcvjYqaVyzK5FMGqqrgsijMsDog5K07HM2N/5vHxEa00L15c\n8V/+8//Of/8f/8iXX33D6dw/ux/k8efN6/PFqEC2Hz7e8ff/8FtevHhB133Odt1w//GBcZwgi9Te\ne0vTVLy4uZGELEUJHRFYK8XIXL4nX/JeKQfdcqD4wraKMTL2YhMtTxJSzMypaCQKVGOdLT7sAk+O\n4yg5n39JwRKLIirGiE3C95SNf6aqZWlQecEcFxdMMZYXu9ZpmhjnGWOS0LXK8sIoUxal4n6oEGs6\npRy2qqjXAVc1hKg5Hk6EMLJbN6xqJ7hYWfqFecLi8EZztV1xtduQUcScOR2OnK42DP0NMSnGKbLv\nOm4/3rE/HBj7kZgS5Jn7+/cc9nes10Jp06Vw5Qyx4JU5C0NkmkQ9uVx9Swq7hASLxDqlzDgMGGOp\n66bwS8VlXGCTeLlWQxD8MqTMHBIPjwdu33/k9v0t8ziwXa14+eIVn376Ode7DXGe5eaNJSA2TbLs\nO50kpzKlUoQLHJSSqDnzQi/Tl68tHvHpsjy7dHGFJidhGYqcyr/VhpAzQcGMPG+QRW+IiansPKzV\nWCU9Wo6FZqkz9arCtxtARElGK0wRdKU4i0FXivL7EIhB+OsoOUBUjlTOcLVZcbVZsV1Lery1sod4\nPDxwOp85n890504SZcoIXddCizXGEqaZOSdiLDEKpTNcotjqqkaRmOuR1WbDm7evZUHsHH1KvHj1\nGqwV//ZpoE+RpE3J3JZYvG8+fOB6veGTF6+4dp5ai0I6KwlhSEq0DUoV3xotS1KywFiVc2J+ZiWa\nL8bAPM1kJcHgzhZfEwUCgCrGaeK83zPHZRHbUlWeISoeQsdtt+f2fKDPAU8m5Cgug7l4kVOCz/NC\nTHjiaGvFD673pVm5ID65mK9ZS/b+kiVqreP169dlWk/87vdfFRYIXC7EPwf6f/C7lBPnc8e797f8\ny7/+ntWqom0/L4eLwIdLiPrSmYcYiEX8Y4oP+cW3XZV8Ucq6NUunjRK0IJRIwR86NWoJ4yivyeXz\nXeAraWyW+MGY/oIKOXCRv/qCOcVSdLR1WFfJyZfEDD+nGedtOZ0Uh8OBc9dRNxazrcoo5oqJkRUK\nnnagKsS3u6JpVxjfoo3h3M08PH6kajVa3dDWNyWdPDEOA+OI+CY0NdvVGlfXZOB8PqPCROsMdf0J\n280Vyjgez2d++y//yrfffs/hcGCchFJ5Pj3wcH/LZl1hb65AGxYvGOEdazmcpiyG/lpuxMV2V/Be\nW7ba/pK+nVKirhvadoVW5nIIxrjAPVJkz72kH53OPd98/S3fffsdh/1jUWLCulnhjMfZhtq3+JLE\nohQoC8fjnvv7O0IQ7rVxQtGSFPmxBBFHQkiM48Q8jsVWIGF0Kk6MpfBnOWwWvbb4w8gCDq2wKTOD\neM1cutkilkhJ5PZRMyFGYHEOKCXxY9v1ilVb09QVlXfEMBHmgZzDhad/Puw5jD392TCtV6IsLVPR\n1HdYnXixW9PUFqMSYeoJYaDrex6Pe04n8YVGwXq1pq5r2qZmt9uyahpSThyPZyIO5RpxqSxwxXLD\nLvuRuqq5vr7COwtKDJGO48jVzQ1JSdTX+HjPOAxErUloTBY44+5w4o8f7vjm7p71Z5+yW61xlIIc\ngwSUxFDEdpGQwWojKUdawJIUSpRYFBfKfuhkMnSiLNVGFMPOS0cYp14oryHQrNdstytWm5o8a7rD\nxF1/4nHqSdagvUQapuLDD1wKlxzm+VKUyFn84p9TIrXsYeTfJSR3wNKud1iz4+WLF+IdY0vjg8B2\n79/dcuw6pnkujJ7lyy+YuUwlzzv2nKUw9sPI13/8lp9/8Rltu2LTbqUx1KY8H7lUtZImsut67u7u\nqKqK1WpF24pfinNL8Iw0CU45UFqo0UFsenNGnBSde8ZCWei/T6lMQl2MF7m+1qK+9fwF8cjJS5SU\nnHSSlVhYFVmREFm+4Nuaqqqpa09GM3eR1Wol8lWVS05mBO1QpkFZh3Ie316hlCNUPcrX0vkkxzAG\nzv3MMM34pmEcA4fD4eKL7euKum7E4raYOqU5EFOGBN7VZJ3Q2tL1A+N84n7/iDeKn33yBvvF5zJK\nIqk5VVWR00yYBpS1l622QkEuZiTLBa8AK513LPQk6Q4swYUiUpDxdsmfVEgAtdJgHT/g/jrv8L7G\n+nPh0spSBSIPj4/8z7//e97fvuNqt2O9ljSZVdvSrhqUysxFRLPdXbHbXbFabzDFhvd8Pl0SckJI\nTNPMeD4yj2cyxV7XOIlsS6qIdHh2Mwu3dzEIsnoxd1JieDUH0jzhrCVnSY/3pkJpK9ORtZebshsS\n49BhdI9zFms1zmqauqVerWjWOza7G8I8iqOkWoqbxhnFdtNK0UjlBponsjZUTV2837dsN+vCvS8h\nG76iqSpc5VFk5qkETZAlQ9Y6AaAuZmYX0ACtDXVVy3IrS6bnqqTXK+NY/eKXDH9QDHcfOaV4YZto\nrZlT4u505B++/AOvthu2TY0vodIhRvqu53g6kaLYDDtjCVoz65nJPHmJoBQhZYZx5vFxjzaSHRnm\nuaiC3cXQKcyBpq2YTuKv/+Ufv6Q5bLnrz/x/X/6eD8cTM4qsZfGK1mJad8HFJeztuV/48hzUsw5W\nF/8TCraeYihq5ARKPHz8UgBzJs0TN1cbfvb5W7744lO++uY7HveHcp0twRXynz/lrRdUBxACwMPj\nI99+9z1f//EbXl6/YLNaY8thHGMg54grgRmLodY8zzw+PjKOot62zl0OePFPkRSnEGKxudWFGvuU\nWLXQGi+L1Gc/5bXjoj7mR6imy+MnK+SXnzyjECYIoYzViOjEakVlfVm8ADnJxtc6Uk6FuwnZeJrN\nC6ra06435LSity2HfKAbA/080vUT85SY5jKeKCmEJMEyF3vOqm4uWFeIgXGYGKeJOSSmYWQcBdpJ\nZdk0h0lM9yuhtFVVdTkEhmEgTCPdqVCSSnIIZWySsIyyK1AKnZ3Il9OCoMvyVpMvIh4p4iX780Lp\nKmVi2dZjQHlSSgzjSIpRMgHLTdAPI+9vb+n7jrv1Pau2xfuKVduyWrdoLXQ/cmZ76Lg6dGw3W9n0\nJ8HLxcjJYWuhVeoUcAacNwKROQ9KE2IqHsyC58YYmefI4+NB/MZDFFpkadBTYQWRRZnqtEJ5i6+E\nH59K8UslYUgudlmmzjGVMd0wR4TaaBVGO5Q1aJvLayjYvTWJyjcYBWGOnE4nOfCMwZYAitZX0tfp\n4stjhUEhgRmLn7dkmYasUYX+KNf605KN8lyVMlgr/tLCC4cqy3OyznHdNHxxPNHPga8eHohKro2s\nhT56nEa+/nDLt/d33KxXrLc75hI9Nw6j+AJlMIjP/1I0rJPQgpwpmaNJwhGSWNtmRuZxxBpDXVWi\neVDy73a7Lb6uBAvWlrvzid9/vOV3t+/Zx0h8xnFWWr6PC1XuGXVksYe9/F0lhX6eA935XKyQ5VqZ\nJlkmVk5hPn/LqnkhxTUjVshhxlnNqxdX/N2/+zfMURhXx9O5eAypS90QqD9f3odLHSqNRZjFDvlx\nv6dyvgSkZxGe6aeEouX512VKDyEwzTO6JEjphRqtFVEJ6jCXPcSyyF346Evs23OoaYGcpZmTQ22J\ngtPmiSHzp4+fKLNT/eAJSxGXJy6hwumiLrRGURvNvODHOVM5CTiIWdwRSZFsPNubt6zamvV2C8qS\nqAiHmcP5wOPxwPHQARZjHHVTlxtKBDq+JO2Ia6KYREnmXuJ07jmdO+Yg4/PxeORwFCe+qnZs1y3r\ndQtZUoDWqxVt4ZHmKEk+09AjZk8GW8l4FEIkhhKVhtwARDGVUiiSWop3IuWC213i0zIQy1ZfipMs\nvXIxz9KXjNOcwjOsWHDrEBLnrkcpzTQHjscToEpEmBw2OZc9hrW0bcOqbTBG0TR1SQ/f0K5WYvdZ\n6G/KeTZX1zStpDvJUk3CZseSWToHwVAPp1Oxzx3K9ymMJGMX8ZDDGOGXW9dKuLCSqDa5RrJABKXr\nj4tvzzwzTBPnbmKxtzVW0dSeqnYC0ykldgfziLXiG21swBgPiEBDl7FYLGVFlOOdw1tfFl0lTlBr\n4fpvDXPIzOnpoGGZvi4wAyzdqcYJnVMpXEGanHO0dcUv3rxlionbxz0dSczNtFjZjiny4XTgqw/v\nebPb8Wa3ox8G+r4nxiT7Ja3xRgK+JeBXmpRMyaKcZyIT2gScr8tkFRjnCe8d2hjmEHDeiVnTZsOL\nm2uUtczK8s0//QPfPj7yfuiIxpEL6wIlDYZdPEF+hGXxA7peFt1C3w98/+495/OJoYRLT5NAdXXl\nWLWeFy92OOsv2HGIgVysMP7D//p3HA4n8fQ5ny+MGXjisT+B5k8d+fLrkmKfSIzTwLkzkGTn4n1V\nvPglJs5aewlgn+e5hHBrljzfcTkcE5By8asJF5vbJQZumUyeNBzxAqeEIJz15eDXi88Nf0GFXPin\nkTkGqsXeE8kD7M4d0zgVwUoxZfIO62WsDimhjLRui7cySqNMRbte0dRWjPNzQmklS87ccKU0bb2S\nwIi4bOPF0dBXLVZLtJU24i54OJ/YH04cjmeOx65kasqLjYL11Y4X1zt22zVtXTGWkIhV29LUNVbL\nSJqTyPPnMMH+cMkxXDihqnSPi/qx8pIcNEfJpRTDPg15Juv4RAlhGdlFFAOiyYTShygpFkYlVA7E\neWAeO1IU86YYxAPiWDX4qlj6FpyvqtyF57o8r2EcOZ9OGKMkNbzt2O9PF5OnlCLGaNqmpusGKl/h\nrb8o4ZYuxFcVZmUwzvHq5mVJhJ9EtjwMjONQXhOhn8YshWm1EsWkMQJN5ZSYZzkcxmliGCf6Etg8\nTYZ5ioVutxi0Jc7dQD+IO9089Bwe7nj/3de8vNlxfbVlva5xxcOlwaAQOGua5mLhWmC/4nFi1FMq\nfIypqPwyES3rj0sXpy/FXAvntGCughsvTAUFeG1YW8/6s0/BWX7/7nveDyfOKZC0RRchTyDz1Ydb\nXmy3fPrqFY2xbDZb1q2Ir3LRUlRVdSkwS97mMAyyT1HCP6rrWvYeY2aIgfEkae2H01H85p1YGjRt\nS7KWhynxL+/e8d1+T9KLh08mlkVrTGJI1vcdujQGzx+LodhSwKZJIIrf/OY33N7eMs4T7WrFbrth\nvW6Zwszp3HE8dVTXTuZNY2g3a7RVpJA4Hwc+ffOa7759x7v3t0Lvy4vE/0eKkHr6VWnDZr3m888+\n4d/89a9ZNy2Vq7CFyhpjZOgHxqJFWK7lBb7JcHmNz+czSktDtKpbsRKYRlIKYtyWZEo+n88scXq+\nWJJcjNZyFpdFLZDzMrX0/Znwl8RauXQnPI1cKSeGoed8OtH3A0ppnPekWGMKNxetOZ36y/JI8OWl\nM52p/QZrtWyVx5m+n9BGi4uY9XLxRhim6bKQnKaRKQRM5Vk4tDlmDscz7z/ece4GxkFocPMUUFqz\nXje8ffuK1y9u2KoEXqMAACAASURBVKxaDMIbH+uG7SbgrUcpddmoz2FmnEYglY8HoRa6Z5FupYg6\nY4kpMc4zMQ103cAwTgIjmeUiWixgZYGmVPF/YekwVBE2aLRKOCte4opEjBlUZoqyEBz7XlRjVjxM\nnLU4b0vAh3gvG22LYMfivZghnc49xopAJ2XhvTtnqeuK43nAWyvYrRW2RtM0NG0DWsyGqqa+JLJY\na9huVlxfbfGVFP0lfi+WTX5T1/hq8aEHVZauc+Hz9pMcBvvDUbj+g9ApJWRDrq/nrIHTQ8+H2w/8\n/g9f8d13ntVKzNrWqxXr9YrNZs2qRKf5yoPWpJCZ40CVHR5PpZ343VPYCkoDQUyUlIa8pFfpoogt\ngiqKf5AyTwCAKqrKrIjjjPeejXe83W44xZFhioTS8WbEFfP2fOSrjx/47PY9v3rxip2r0DrJJJdE\neSkKYoGrpmli6Ef6QSTnShswMtl2Xc/pdKTvThfHxqoOBU5qqa1niIn77sjv7h746vGB+2kklrAH\nCi015Ug/9tzvH1DWY1YbqurHF3SpTHzzNNGdOz58+MDt7a0sXqtKJjPvUTnx8Ljn/fsP7DYrnNKX\nRfKCs7vK8frNaz777BO+v33P/WFPP44XptQFXym/XZhUCjlQvbO0TcN2s6K2UsRBi8R+lMV+DKF0\nxcIAsqXTX7BucVSVvVZOGZ010zjB8wM150tM3MJoEsuHgdPpJErizCWcYqEgix5BNDM/9vjJ6IdP\nP+VjMl71nLszfT9gjLjKKR2Jx45+FkjmeDoRprEY4+vS0WS0STiXSanGq8R+fyQksL7B+vqJF5sk\nqksseSL9MLA/ntB6B9pgsjBlTp0UhTlKTJUuiytfObbbDZ998pbr7RpvLGGaaKqKC16tjVD/gvDc\nU4pMYaIfJNsvk4vBjmfJRFxOeZUgpEjWhnM3cjz13D08EnOkrjyrpmbd1oX8oS5FTwp34c0rBVkW\nq4ZE7Q1t46krSwjjZaQNc5ILDy245sWfXRfzfOGw21KQvfe0bY2ve2GwIGepsYa6bsWY/3jGqj3e\nGHzBZ6tKQnvrkjmptKJqxI/Ee0dVea52O16/ec3nn39K267wTop2Li3JMroqvXS2lLg/geemMHPu\nOm5vbzkczgzDVKAzc/HRHkeR64/DROiPfKwM3hvO3ZH94R4UVM7Tti2b7ZqXNze8ePGCFy9eIDGK\niRgnVk3NagVKW7Qt/G0lnuohJggzYC6duExH+jIeA0URKCwgypIfJeyjru8JOaLmmc9urvnYnziF\niViw0lQw8+M88c3DPf/0xz/yarVh7aToWWvIVnZMSgsUteDOKQlLbJ5mIjMR6aIf7u/ZPz4yhwln\n5EB2XsgETbtm3bbcno58e7jjN99/x3fdmXOO5OJFv8yHOSfO/Zl3d++x7Y7WOTZpI+ZlPF/G58u0\nusSmQYk+s6Lqvrm+Yd02hGni4WGP1YpffPEpxnvB1WMg5VCcEjWv3rzkF7/4OR8+3jH8fmYYxgvH\nfgG44OkMQF3OAnmU55STxETGKCyVaRLbY7kXnt7HpTAvxXkuQdc5Q5oSYQoYpajritV6fVlqjuOT\nXYQ2hhAi+8Oe77//nr7vscawWq3Z7LZYb0r4jeg0jP0LglZyKajPZ55URuVMku7P14Wv2sjyqHhO\ne1/TnTuOxyN9N0hgsDd4rzif1xgVUaGncp5VVWNdzRTKuJI1IQSJWGpr1uuWx/2Bd+/fE2LkarfF\nVxV9P5CyoqobmGYSAnZW1vLy9QvevHnJbrPGKC3mWeppIw8UKmMmJzmpAWyhnWmtmWbxKDHGsGpX\nOFuw35QJs6TznPqR7z8+cvd44jwEjAbnJf7rdBo4HY6Mw0DT1Lx4ecNmsy5MvjLGpwCI1WxdKd68\n3HF/t2UYb8lRxFBq4bIjO7mI5HjO8w8TyVVRARpthA9fUpe0FgjBGGEWPYVXCJZc+Yqqrp66+6IW\nMsbgC3zTNjWrlRhRScH3tG1HU7ViUVwWdVprsn1a3FFG3jnI0rnrex4e99y+/4ivPG9ev+T65gV1\n5bDFhEywSuma/u3f/Iz/8n/8e/YP9xxPJw7HE/vjmfOp8MW7M9M48v3373j37hZVbtzKO26ur3nx\nEnFyVBpnC+O68I1zlv2HmDBJItQyfIr5FHDpwvPlPVjoeBiD9Y6bpuJvm5rHaeQcI0PfIbt52XNo\nrTn0I//6zXf88tVbdlXN67UwTVKxYgXBWMW3RN6DGCN393ccTifGeQJUEQXJe7BZr1itWlzxBJpj\n5DCM/OH2jn9+d8s3hwNdDALNlIV0Wa+L13+cOA4nQruSJa26uKwUmOCpmIcgHWbbtvzir36BNZYQ\nA7/85S/55M0b6spzPOz549dfst8/XnyB4lyCSnJEWYNvV7x4eQ0ZwhT4eH/P4+OeSwjFc4hFPXdf\nVJfiOs+zeJtUFTnBkObSrTvw4sjpiqZDaWFXnc8SkReiuBNqK8ZxRhtqV2Orxdc/MBf822pp3GwJ\nTn/YP3I89RjjWK2Lal0rPt7dYfaGulrYOuoHNfP546cp5OTLSAPLcxOzVVPoUbqEATRtDVpkxClL\n0chpJ0owPwh3Ns2MQ+B87rC6wpvMy+sNla+ZI4Qsy4YYo3SeWlM5y26zASSH8/bugfMw0TQt0zRz\n6gbBo3KiqRyrquF6u+Pm5RWb7RpvBMJZUrHVs7gxlXLpyGcZvcrmX5tSFLW+FKcYxEZ3KPaW4zAx\nzIFumHh4PNGNMyHJDWCt0JtMhtFNhCBObahEzrLItJUV6p/W4l8RAirPfPrJS+YwYq2m60b6YWKY\n5iL+yZdis9C9Lo1M4cSCYL1znC/v40JUUEqJl3aRcBulsM5e0seXZZFggqYs3qTra5pa9gpNw9XV\njvO5E1HSFJnHWSYDJVFqttxE0snIMnpJc+nOHcfDiXGciue5w9kl21KV175CZU9Oie2m5ZM3rwhz\nYChp6KduYOgHuu7M+XySUbfvhbFUoLUwyzV07mb6+Y6qEquCxjkaXwk2mwT+WAzIZLkvdhILh2L5\ngVI/LOZKaHxamzKtVPzVqzfsh5G7vkNye2DJRZ1C5O505svbD7zcbnh9vZOIsFgiW8sbqRSS7F54\n285ZVm1DlfylAYEyXTUVxiiGfpSYw2FEVRVf33/km/0jxzgzP+VKsWxsSBKVaDVUVoIpTLGXUOhn\nXyeV+14KqtaGVdvy8y++YJxGjscDN9c37HY7nNFCByUzTVOBGNRlWtQodEmg98ry8uaK+Iuf81//\n2zXf+nd04/hs2QnPrurL60Kh+JHBWSshE1phY5lcUsldLWIpVfZCRhuaqmK7WUvwShLGTD9O5JQZ\n9CAkCA3Wi+2Ed9LUiOtQYpwnhkFYcGFZ/s4j0xTougHnLatVy6ppMFr/ZRXyxbd6UXWJvi+jtdDF\ncvHmUIoy5huW3G2Npqlrrq+vmaaZh4cHTqc98yjYeu0N6+2K3fYK5xyHcw9zZAoSq9Q0zaUYOOvY\n7XZMCb777T9zf+ho6rbg2D05TTij2W1WvH35ks/ffkrTeCDR9T0pzkzTQFfYH846fFUDS/SYWN0q\nKzL2jMKYjC1c1Jgy+8OJcZw5njoOJYx5mCNTzIQsvFyhPGkq52ibBqdkwVTVnqqSE9sUjLaqqrJR\n12K/GWdyCnzy5iVN41mvau4fDjzuzxzOnTgDzoE5pLIgLDa+zyhRFDJXIjHHLKLLcnMsVptLYdKl\n6xL/+HKD+UoMr1IsdMAnyMX7iqapePv2rfg1D3IThDkx2ZmlizNaYU2RLxuLtvrCMCLLkul8Ol+e\nc5gnTodHBmuLmEmYKK74YnjnqH0tOLE2Fyxdlw4tpsBYRE/DMNCdzxz2Jx7uDny4v+P240c+PNwD\nSFBBU7NpI97JUlQv6UdZ1LaLnzlQQpSfURQv9AkpMCGmIhGXVJjXmy2fXV3z+9t37EkELiAeETjH\nwB9u3/P25opfffqJsG2KAVq+KCWXQ0PM0m5KspTI8vXTclBJYzBNA33XcTqPjEkxOcM3j/d8HE7M\nxdJWk9F5+U6kEauN47qp+fTmmu1KaLzLgSX4fvozrrTRmqZpePPmDbcfPjBNE01TF3FMLl5DZake\n5VrkklZUlM9KAQnnDC9urnh5c8VmvWKYJqJwD/+kCj3ryOHiQ660KJR1mRxjEsg3xYhmhcaRsyzc\nrTFstxsJk7CaU98zzN8Vr/GZECecN7SpRhtF0zYylSvNOPZM08w0i1/NXH6NcWQYevp+YJ4zdRI7\nico7xDs+8WOPn6aQl+VAmgPEDE5oclplCSNGE6PifDrQdWequqVuWipfizkTCmeFXdG2FeO4oz8f\nOJ32hADrzTXWNaULmwgpiydK1+GqGqc0SYkQaXk7nfccjj1dN9G0NVoHWu/47M1r3rx8yYurK9ZN\nW0Ireqa+E8aL88w+MofEOEem2Evai5ZuRBlDQnHuxwvXtx96hl5c22JKxV9aEWKimwJziCWgOZcu\nVrOuV3gNeRqYFcxTBynRNmtWbV260FpyThccFoVRlsrJ4qiqKjarNcdzRz+OjNNM9/8z917NliTZ\ndebnKtSRV2RmZWZVdQHdBEHCOMSYzc+fv8CxIUHMDEmgWlSlvuqokC7mYXvEzWqANi9jVn2s70Nn\nXXFEhPv2vdf6VtvTdj3tpePx8cj53NFlnfzchggxiHY8qyES8t6FvPgoZPg4q9pTxtaKXnwQTK/S\npBSXk8jcZ7TW0TQ1h8MTl8uJshC5l7WDRLgx80qMVHiZqaO0zuHaovY4no5cujN9d+GIJ/mBVSVa\naFsUwmApRNIo0kKRnhZVtei7Q2b/6DyQrSt5XzfrDdzcEvJm93g48NOHj/zx53e0bSehITEyJU/0\nUDiDMyXWgs3SM5DNUeV0HtQv74m5QhTZWcv74wE/9hK+vdlQxsjb3RZ/OknLJJeSUYFXivdPD/y3\nn99xu9nyZrWmIhHDtAzInRNGyKouaepSipU4P6eZ/S7PVatEKkuqquHiEx8PR/7Hn/7A/XBmYMqH\ngblZP9MRE6VW/PDiJX//mx/4++9+gCESvcltvPn0LaTDEGerOhJVGNLSj/beZ8xGjh9U0q6axomn\n45G+GwjBMwyiIqkqOdVN08TQDXSXjuvrPd+8esnd4Uk08um5Pw+iCJnX9hCkMDscTxyORxwSdDEO\nnvP5zOPDA+MwMA07mqaiKuW60FohCBAyZ6Xgu2+/ZRpF7aRSZL1qaOoqU0bFPxL8SBhHpl6UWpfT\nkfOlZZxGcdUay2azA0xWb1likNPOXxT9cOZBBO9J2bwwmzhiiBjtsLUMDyOiER66Fj+OMggrCnEo\nZqVEWTrWTSUEMys0vhlcFLJQtCgrUEIIjEkzebh/OtIPPcfTCaMk+rCPE30X2G5qNusNq3oNaNp+\nJHho2zN914rrDHkd/TTR92Nu38RlEGKszXpRPc9bALlprStJygivuJ8YMiBq6MclnSRGiSorrGVV\nFTSVo6kKbK7OY4KmrlHZX2GMTNRj1gRrBABmtJb5gxejjTWa9apmv9+ilZEc0dOFh4cn4a+jCSmr\nHPpusSVf2pahlypiHLMWOcYlWm0uKcO8PSaIQYJE5CaKhJgX4mwAkh5pYhiH58FSjGg9LVWtyjCo\nWaYpShDpR0orDs6XM113oR86UjYyJR9wbsLYHm0sVV3hy5LJ2tym6ykHny3Uwj8X6ZdEDhalaKht\nVvQYK1rzVWzYnFdsN2u0Nkyjz7rmSZDJWmLLJO0+EY1U4nNyPVp60o+nA9Mk0sb9/moZfBeuoKkb\nvDWolNg3a1ZWoeuCyx/+wHA4MKW0VOVJJS7TyE/3d/znf/o9fP8dr9YrGqvycxbOtlEyJJbnoBc9\nv+TSTozTxDBOOUhbZImnfuQQPD+djxynCU8+Pcw9f+T82TjLbVPzN69f87tXr3m13tLrka4L9GN6\nFmx/NfydP0tZ3GUxHcaBJRxEqdzZ08SYOJ3P/Pjj73HWyUAyjBTOsVmveXl7KyjhShQnf/Nv/prL\nMPDTx48cj2fGcfqzvni+RJO0egXO1nI8X7je7UTSPM9zioLlBWdpWIgBHb9Kz2LOOXWSB0uO0UsR\nlXNRYxDSogx4xdehUqKpK6xzct2blKW10m4dh5GuvaCQ9Cyj/4IW8pA/uBDC4powSuOsxacJY5Sw\nM+oGlObS9hyPJ6ahYzQSKEGR0MqhtPycrUqu9tcQPXEaxGGXxDSC0lRVTVU1xCgGktFHDqcT7eXE\n0HUYrdisSqrC0o89ZWGpihrv4f7xTExHCmvpLmfGoccYqWJCkEp1HMYsURxBZR10WYrBJYdJVFW1\n8BhcWaC8Z4gwhpFz2+d2DVmalmcGhaEqbY7BkyFtmTX1MVcpw9gzxCBDGSUKDT9O1FWFyUOSrmvp\nuk6clDHiioJVVbNZr0ghclnV7FYNaE1Z1aK1DhP90NO1PY9PBx4fnjhnTX3XyfP1Xtox4zQKUjQm\npiiyS+lfy7QdhVinSbmFkYdPal5ER8ZJmOI+jCg1NxDmJBzNnHgzf4k7UxAFXdctOvQUJVJPo7GT\nz8Mj8Q8ELwv15D0JRVmN+GlkGHq6TnIgi8IJQ2PVUOVgXZ1bQ+PY03Yj49TlhdpAoQCLw4mjMs2E\nT5E/quzAXYacKMZx4P3797RtR9M0rNfbfPKQnvvVbotSiegnNmVJ1Ipqs+LHT595OJ5E/ZNbFkmB\nB76czujpZ765umK/XnFV14s5SGswyMlJhoWBMHkJHzlf5FqOEZ9PWVpZjFI8dS3vj0986vpMZNTw\nZ8d7pxTbquL7mxt++/Ibvllt0MOECuIYJX+S0rtQi6PxWSAg12zbtsKz+bOHQjZu7wP3d/d5wwNt\nEoV1GKUYNxvWq0YSnCqF0pa7xyde3l4zDZOgaWdR9vzIJ4KUFWbnS8vj4cS3bxJOyQKvc7EY86B8\nzMP2QMzOcrkPJYRdirHLWdYITYIU0EqUcrOHZPZdaKXl8ykq8TrEhDEwDAOn05lxOAvjZxhQzOHL\n5l+8P/CrGYICoqlOC2sBDa5wGZs6MHQX6rJgtVqxrkv2mxVt13M8XTg8XTCu4Or6Cq2FmDZ4obgV\n1tLUa6pmQyJgpkBsx4x9zfrO3Afr+4lhEIZ4Uxbc3tyw3qw5ticOhxP3j08cji1t2zEMg/TkUn7u\nOh/BtVqYFoU1qGzQWa3X3NzcoJX0iJu6oalr2q7jy5cvfPzyhcP5zOnSc+knsffmgamwyAWhWdcl\ndVXgCvm9Kc0qEgjTxOl4ZJgGtBXgjmAuRd4YUpSjetdJpJQ1bHc7UArnSuq6JkU5RRjluLm5lR5h\nlhSWZYF1hr7rOV8unE8tp2OLn8SBNvRSwU5+4ulwEAPV5SKY176j60fGQeYEYtWXk0IMiYjcxCFC\niJ5jTmW63ofcrglLBbTQFZV/Vggx0wXla8y+gBACXkulqZXBxZyik+R0NowTxjgmL0Am14lSKuTY\nOJECpoUDFHyku3T0Q0/bnmnbE2OIDGNkGhKa7ARWyMkoRojSapnZLETFTPhUUdqI4zjw+csXLueW\n/X6P9144MRoK57i92VGVjnHoGc8th7Mc+8sIjbWcppGYiXyiGtH4kDiNI8d+witDtdrglMDHtJKj\n+TRO9KP4E4ZBToDn9kLdNKw3W9abDVVhCSly37b8/OUL//zhA0NKRKWy7FUCXsgLVeUKXm43/O71\nG27qFbGf+HR8kOpdFyhTMQu5l7CIuRGnZGieUqQf+q8Co595LDEmtpstq6bkf/2Pf4e1soE3VbEk\nJTVVRd/3HA9H2rbn7u6e8/GJt69ecDqc8yxoyrC6udWS5cv5JHk4nvjp3Qe+e/sGX0e6c8v9/T0f\nP37g8eERUmKz2bDbb9ls11S5RdUNPe2l53JpZeGdRFSw365Z1RVKbTHGMo4d4yQzIKMNTS7K7u7v\neXx44Hg6st1uCSFwOp04HZ7wPuCspWlExVV+xSb/+vGrLOQmy+KM1lhF7peabEwxQu5rTzijsDqx\n3mzQlcWYGq0VrrBMPnA5H9FW41xB4WqUcaJHHyYOpw5XOJxbUxYT3l8k0RzRhs4wm7IssU1FsUiL\nFJUzPMXApe1IjIyTfL9SUFlLXcmArnBGKveywM6oy7ywlFXFerOlbwfIi5bP9MJxGiUlZPJc+kmg\nX0neE02iLC3rVcNm07BuatbZEl8V8ne6XuSL4zAQY8pmIpMrxiH3pp8TwJNSFFVFbYTtHqI4Hcdh\noiwsdVXQVDLwuXQd5/ZMTJGmqdls1qSUqKoaZ0uaer2kMZESzon7re06ufguJy59x+l84Xi+cDpe\n5Jg4TrRdxzj65b2PuUebUuDL54/cvbjmmxfXzKdq4PlILqkVJKWZMZ/PA6tEjKLZLbJDlQTjKLCz\neVgWoqSxaGWZAxbMaPJpQYaDgg+NWBtIacBowYi23YmuvzCObV55REEzZ+eorAaR5KAc+puDJea/\n7b0sUkmL4ePq6pq6WrFer5cWXIiJfhh4PJxynqli9AHvEzoqXl9d05O4jCNtksFnSrmlliL9OPGn\nDx94vdvw7Ytr2ZCUQhEECZwdwz7KwpysQRcF3TAwThPT0FOvGtoU+W8fP/H7+wce+p6g5kbOIsvG\nKCi14cVmzbdXN3x//RIXYZg6ulF078ZpCiuZZfIrsrQhKzRyr2JRpYjreVZJ5RFqkLCRqjTs80kF\nErWzmdvvskZbVhetHaAoypqr/TUKg/mn3/PTh4/5knq+dmbFHMDpdOHjpy8Mo6cp05Jyb63DlQXD\nMHLuO8bHwHm4sFoJzlchzBWFZrPe4KNAs25vrtjtryjLihgFWuenIAoibZaTQNe2XE4nzqeTeDbm\n/M6ba7l+tQg8qrLEuX99yf6VEoISWd0uJpI8vDKZvw0D49BzSJ4YR7ROFGVFYS1201DVBZe253A6\nMfU5qxMtmZpE+mEUotxqlaffFc76nLgeGbqW8/ks/bXdht12TZomisKigco56rKkLHq6MYhm14q4\nrigLVk3Dbr+RwZyzEkSRsjuzcIuLL6HE2juOIpeylnGSi7VZrQhoTv2Ej0JY00oStZuqYLOuxf5f\nVrnPLYvG4D2n44nj4YCfRrbbjagvlOJyueBzeO1sgZ4johY6W1lJf3sQuR7rFU0tqd3DcOF8vvD5\n/o5+GBZJYF3VVHUlJ4TGCdQqCfOmqkqcddni3TOMHUMYOZ9bDqczx8OJths4tS2H45muE0v+DMn3\nXvS17fmJ+7tPtOe3lIVb+v2ykM+JFcKbj18ZaxZ5HdL7d0bUzCCzgpiRDjorM5T3KMzz/qBYerEx\n97AjCtVPTFOUqi1J1RX8BElhnFkCrmOWms7PNUVBw0rVt4gKl0p/VuGUZcnbN2/xPubwgjyA9J5z\n3/Pp8xeUgvVmjQ6zgkjzcn+D14an85mPbcslRGKWgUZgDJ6fP37im92W7795gdnu0E6MYVMITCES\nUCgraVWFcyhrOR4OdKcTfuh47C58GUf+0x//xJ+ORy4xCdKCecwjw02nFRvn+Gaz5c3uihf1mtiL\nE9kjzB+dI/G+ThT9Oh92Dk+esdbAgoaY/97M6jZaij90fp9jXHJGEwgzqaiom8h2u+XFNNG3AzHA\nNHm+PD5KutesfMmPDGekazueHg+MOeF+nEamEHBFwWa7xY0DPgYikX4asZPJoRMNpWtyG1EzTAOu\nsFxfX9OsVsJpyXGVMS/M8poDfcj98iQnuK7viUkwt3XdZN18Qhs5+Rn7F1SRe60I1hHKCm8NIZtY\n5jDg2UF5Ph04HR9puxPX1y9Yr7doW+CsYbddsdmuOF9aurbndDygOGNchmCFQnrCXQ8pUpUN2+2G\ntrtwOh05Hp/Y73dsd2u+/+5bkp8Hi2LweHH7ks/3j/z86Z6Hx0f6rhNdrzHolPBDT5zgEgPjMMhm\norWYWuoGHyKHw4HDkyRpK2PYbDZsd3J87ceR4+XCub0QQsCoRGkNTd3gCkuYBi6nSH86SXxdykEN\ngA9e+vQKqtoRkqhhvny54+rqitvbW25ubpjZz1qLFLHreo7pTAyRrpPe6NPjPc5ZmqomxMgxm2PO\nbYc+XDgeO26ub9jvFc0q9+dylJ7Riq6LnP2J4/GY5aMWWzg2qzVVWfPi6pqkZLB2vrQ8Ph0Wpccs\n7xqGgYfHB8Jw5uPPf+DVq5esmjofxudlQ6ryGQiWdEbEftUzF9MLcmcmlc0osheEnBmqokJLt3hp\n3Mac17eoI8LIOPW5yMibhwhBMZl7j9KIAEU2tRTmqrLHT0LDnLNDlZrbZBVKiSa5KApevlwtQz8g\nOwQjT4cz//Bf/5Evd3esViuu9zuu9zuurve8rF/xzfaK4dvfcPnD72kvF6KKc/uZqOEw9vz+00d2\n/71B/9UPvFivKDUkLUA2Z40MYXO7qSwqQUNsdrjS8cenJ358eOKfH594DBPeKHTe5FReoE0KNNry\noml41azYa0u8dETvIQqywZaVGPnyhjZvdtPk86lYVPFL/qeX93gGSqHmWl0WuFHLCa6uS5zRlMYu\nUmIZiAoFU/reEwoJLv7bf/s7+mni/ecvvP/46as0obTMGcihKVOmIIZp4PHunsenJ1IS1Ox6vaJZ\n1VR1KcPgQuZeTdVQlyuGbuT3f/gj9w8PRGRuNAySItWUJdo4bAYGzmNiYxT762uSNgQ0h+OZLw8H\npnFkClNWUYnbdbPZsFmv/tU19dfpkTtHsdvT3L4klRUYu1Q+OkPwMYnoK8ah53Q+UdcNrihwMaGM\nBEgYa1mvasqioK69hCtEsVGfzwe0doJZNYaUhIuxWm344Ye/4ub2mseHOz59+sjjwx0GUYeUhaUp\nhT44Th6TPKWBat3w+vVrmrLKie5CzosxYLY7yGB4wV7KMK20BZtmwzAJDa3r5ch8PHf4IEYUYwxN\nVVIWhrqwbNfbXM0HisJKy0ZpSR0SYT3aWMahJ0yS5p1IGGvZ7rZYZxnGkcPxIIOUTKEz2qKsEZ24\nj/nfNM6WBHbUqQAAIABJREFUFIXFFQ6ToEmJXUoYWwIyjEHprHO/EKOnKCxNXWXWinDF20HMT9Yn\nymhwrqCsLE4rfAwMw0gIYK4tYSd/f1bFXFpN4W5FlULAaShzxJ33kwxUYw7TzoteDHMP9TlVCaWe\n1Q6INCwp9XV4kby3ShbiWcc9d2+XOi0m/KwSVDrzcCRoVySrkg8ZosR0xTS3ByJTfk3aGCzPhDsZ\n8soAW+WoP2OkqgeWalTub8Ol7bm7e+Lx4cT93QNNU7FaNex+3KDLgpbA2J6xMYgBzQv/Hi+I4PsI\nv1eGt9sdjbGYqkJFwRcfn458/PCJy+UiTJ+Un5tz1Fcb/nQ58+PhiYN/Nv7MHQgF6BSpneHVdsu/\n+/Y7/vb2G96sd2xMuSzAEUnBGqdIPwYJxsibZsgpO8L5fpbUzSlBM5pa5ZlWn8mOWkeGcaBuChEQ\nWLdY5lUmVXo/0XUDKWOWtdGs1g3ffvuav/9f/k7Iitk7sYw9Z09ElikWZcn1fsuqrri+uc6gqoQz\nhqoqcE7UZikk+ktHf+6Zxi+cjxc+fPpEO/a40jGsRyYvodJtTISMz57VV5JZIKfwqq7ZXV2hXYE7\nixktdNJu8iFQZgOUK34JIJsfv1JFrgnGEoxjyo5NlVQW2VtUWUAEq9eMpaNtW/q+R+szVRXR1mKc\nwyVLUYqqoGlkKtz3A+dLy9B3JAasqzDaMHkR6e+vtlxf3/Ly1Uv+ZA3v3/3Ehw8fGLtswNCKVWFZ\nNRXOFXQZCl9V0kcvnEEl6MeeGBNlUbLf77BaXGGlK/E+c4i3gWGa6IaJc9fz7sOnPJyUpBlrHU1d\ncb3fUhcGZxSbzVb6nTEIesA5rJEFEy1sBmMdXd/RtheG9pzNNZbNZiNozwTjMOJMIaRIJMHbe1lQ\nRd0hm1BRFvmrhKQxrqCoatZrySw01pIy07zrWvqhpyglxGC2AY3jxLkf8SGR9ERhPU1VCzbWmUyG\n7Gl7iceSdB6DDxFtDM5Z1uuV3JSKBaSUtMGHKSeka6KfFsPSokDQkjM5u4H11+EJSazOxK8P9ool\nCnCWw2mJy3uuztTSCkkEYi7eJVBZ53R6Jan08bnLC4nRj/RDT1U3WIGLLAs5KRLzX0oLqzI97zLk\nvFZns7Y7iLvyfJL/ZpQA0MoC21TEVQWlk0DgwZOmQPKBOHoOp44/dQP/tNoQLj23ux3OaU5PT3x6\n/5Hf/48fOTwd6IeRKUWSs+h1Q/nNDfcq8iV6hll/DV+9fwkL7KqKt1dX/O3bb/nN7pq9q1AhCaPd\naCKKfvKEOOZ5wcwmj7mlJe9xmFVFsDijn5UZssH0w8AwDhiTcpiJBIEM44jPaF9jDD4zd4ZxEC6o\n0WgnC/3Llzf8x//w7/nDH37i8fGwZN6C9MwVEHzIxcWItpbd1Z7NdiebjveSfpWv0TEIoXSaPF0/\ncng6cjyeuLQt+msTXMh5m0kUQ6Jak40qBDmZDONEPwozxhWOoiopM5OoKApSTKzXa/a7Pbvd9l9d\nU3+Vhbzret6/e8+JgjfrHWa7IaZACkl6doURRYCzrJqG/W7P58/3HE+f2e+vBbhUFoRg88CtwlgJ\nTBaErOZiesYp4KMXdkoONX46Hbi+3vPy5S3/5t/+O16//Zaffv4T//hf/oHPH97Tnk9YBcWsWlgC\nFAz//E//hNGGGBKXtuXt27d8991bOfasKpydwzDECBSizwqFnsPTk6g8lMaVNVonqrJkt9nw/dtX\nGBWZho6mKcS5mBclk8MNQJQkSimGYcSkgDMKVVdYbSlcQd3UcgzTBmcLopdFQhtN3/ecTo+8e/+e\ny+UsrQ0rzJPNZstuf0XhKoqyZLVas3RDY6DvO+K6xsctD09PtF3Lw9MjHz9/Ysg9R1fURKXkRDT5\nXO3LxjeOA36c8DFIRadkHlLXtYDAVhsJQ0Ys0Z++PJH8HSkE6qZkt9+z2Wx5OD5xPl9kBhK8uPus\noSjLbIiSYZDBZDszXy2SUiyAHPKD0hmXIEqK2ahktF0Gq2RpGlFkhCm3LojibwgxPo/q8t+Z8cwx\nztEGGpD3WrTCWT2VJDXpGXOAfLcCq1VWZii8jqQgg+UQpO8+hh76AT30KGcBReh6dEg4ZfD9wCVG\n+i93/O/vP3G133N7fcWL6z3d6cTT5zvGs7RBUpRltrM6L4yRdl3SFwaPwOJAETRiv1fQGMs32z3f\nX93yZrvFEZnGjvlco5MlKb0oqGYshUJwCXVZiPw0Bsa+Ew9CECXHjBGIWd4X8+aXjCbpwBBGQl7E\nnx4PGYInpiCbo+lcaUl5qDhHtW33K6qq4vvv3/LpyxfO52M+a+jl8+iHiS9fHvg//8s/8nB/x37b\nUNiC0jrJQHAalTTGOXa7Ldo4QlIMg+fFi5Gu6ziez5ltLxLJaRg49C3OGtarFa54FiZ0fcfD4xP3\nT0dO5zPd0OOjX0xQm/WG/W5LU6/YrFY0qxVV+RdUkasY8ePI2LdCPptGRgLGSV4iaSJqtQQLhODZ\nX11xvgwcTi3q0uFKR9001JWnqSOrRrCgGkXhFL4woBImJApb5DSUgJ8mDk8nGYAMgdW65Ifvvme/\navjw7ifevfuZu8+fF2azscUCalIaCifs7u1uy9XVnqquSCnR9gPjOGG0pPH0w8Dl0qKN5dL1nC4n\nxjBmVrFlt1uz28jX9X6LJjIOZV6sdeazZKJhkCP7OE2kGHNFr6jLClU1C52wdMUvdLIi5ZSgjq4X\nCaVEocnHbq1U8av1CqPN4uwj8RWR0VFWBUprQkqMwZNUIqTAMA7yemzJ1dUVrigIKdKdL5I36Sem\nIcgQsq7yKaMUuuB6k288aWL7ECUqLWvNk5ZKc7PboJ3l8dTy6V4W8mEcJYRWJZQRjo2EG5ds1iuu\nr69YbVcokJPYOMiQedZI57aGUQYTVT7Ci909aumBK/V8ayxGEkE9LpK4MMeQ5QEmSqHShEo+f0kb\nRTH/TVGlpD8bhM4f2fx35iixupY5wTQ9h4/IxiJ8GS7SakoJkvey6VuVe8XC/D9MEhT8+PjEhw81\nKkYYPSYm6fGn7A7VmqlwjIVhNOC1EJFiHiSLsSVQaM3eFaxiRJ0vHD5+YtCGupDnqx3EYaIdRy7d\nQFAWUzTLa1PpGcg2V9IpSuJ813ZcLhfKUOBzYMM8xLTG0vcd7z98pGuFEHi5dIvRR3rYjai9mgan\n5TRLmhkzBhy8+eYVtzfX/PTzO2ZHb8pnyxgjl7blH//r/8PPf/qJzbpmv93w6vqam+0Ga2G1bths\nNiL1HX12dPvFpbrOw02tZKOS3NMi4yhyIEmKC2SramqulKFerRjGQYxESWIuRdJciDPbWFlX+v5f\nXVN/HflhAp0yq8GATxGPZEzq5CEElHM5bk3kYFVdM0yJ892BMXi0sdQrT1V2rOuecT1KwnYh4RDG\nJAo0yYJShskH1JDwAYKPnA4XxmHk5nrFi5sNP3z3ihfXDa9f7fn97//Il7t7TucWjVkWirIsWNUN\nTVXnXM+Cosj88HbKA0mNn0a6TlQhdbOSgYXRrFa1JJkXJS9eXLPbNKzqirosUClhtZaEM3LVaqz0\nFdOUc0yDuMaCUOrKcobfz9pqI66xHMA79EJ08xnupZRmv99TlqXIpbTi+vqKshI+zDBMi3lillLa\nLLHURhbyZlXjo0z0JUk8ZeZ4SdMIQsE3FWM/iJs0xAXXm1IULnnd0DQNiZQlmeIqVPmIHYxQday1\nFM1K5Ki9wIj6yQsqdmlZkHuYkvCUkswDrHEZWzwyeU8/DIxhwkcJMjEZwCVxbiqHPCisdljtcKZY\nSJLSHxYOiYoeH5PEioV8wjA5f1ZpDBPOJLRsC1kSAYsuNf8uvqripSLM/wlpO9jccnJeVAqCQ57z\nLLPZapgW1Q4AVlw/83sds2Gt7TrarufpcKByjsoVwopHFGNRK0JVEFcVvnJEq7JKZdGAolOkVImN\n0dyUJXtjsOPI8e6eWNWoVRSj1eAZcyuxnyZstWbt8kL+taQUmM1BPiX8NHE+nzmeTlS+pO97mmaF\nQkKHldKM48TheCL6gMvO5mGU61vclh6toClLCiOqHJsHokopool88+qW25trtDbEFJZkKnK7J/jA\np893XC4t63XNw/0Tw7klvLxlt1sJRyef3mKCKUhEpcmfV1VXzwmtMaByaLyxgraevF+QF9poKaQ2\nOac3K2JCkIIzTMLpMUgR8LWy588fv85CHqUXXTgZLupC0I9WS7WujEEXNTOfQ3ktie6PR9q+Zwry\noR3OIyoFSmvZNg2vX92y32+oa9mJnTYYI/1cT0ArqKsSYyQooOuO/OmP73n/08C3r274zfdv+fu/\n+zf89fdv+PGPP/HHn99xOFxkELHbcX11zaqsJT3EGsZBtNHH84l+zAuM1iS8EAz9hBonNisxB9mi\nWBaPpinQOpGCJ/hRZgQhZd2z9JAlBAK0tlSNzXI6uXELKzyXFKPof70nxkTX9dl0EIleFpqZM7Ku\nSlbrFXPqyTiN0oqwMnQbZgdciDKrUMipxonhJUUoC5eHqA4/eeF7pxE/eVZNyW634ZuXL9m8fZ1h\n+va5GsuLb98PPD485uGXJMoUzkrlrpUAg7xnHDwPD30mXVr2uw11VUqWYlXiCoHuRxJlkTnia2kL\nDcNE27ZMY8849QzTxLlrOfctl74jKQkHmHnornAYa6lMSWVLGltSOYfNQRZp1tB4xelyoet7Jh+o\nCkddVaiqRBmL1cJ/1zqJeSyJEzLlBV1aDM9M6V+GArNUqsF7/DihkrQbk5XPP8291bxJLr4clcMz\nYsA5S0JyUucg8IhU8vNGWLpC2gXWkpzFNyVhVTBZlSFToDMjRXKoYGsdt1XFN6uGb6/27MoC5Sea\npqauRfV09+kLbdsRk6Jq1ktb8OvXl7567Ql53uM0cTqfOByeGIaSy+XMdrOlcOXiBE0JNus12/Wa\npq5pmgayZLPvWqnG12u2641EBVp5fdnFhC41L29vuL4SoF6IEtYA2dxnZEi/3Wz5zQ/f8eL2ij/8\n8488PElS0m//5rcYnRgGmTFVVUVZ11RNKUVkWWELye1VebDtp4lhHOk6+ZmUA2LKsqBqGupmBZhl\ntkOSOUCfXdMpqV8QFyXH818+fiWMrWYcJvTpwnDp0KVICrXKhowgsjRjLClnU57PA09PF/pBTB4h\ng99VUkw6MHbC/3g8nNjua7brlRz1kDe1MApTOiJaHHgqUJgRH450lwfeDV+Y+nsur95we/uSv/72\nFbdXWz7dPfB0PNEPHXd3nzmaIgORCgHrTBP3T2fabpA0G5elkc2auihpypq6rqirUvqxCXQKqDCJ\nI0/JwGeuylISK7L3gSlMuVeusgRLJIR+nOQiNUZiqMZBcLUoxmmCBFVZCDYzD5+0kVaJnnuXSVE4\nYYv3/cDheBYNbS986jKHSKzWDVsnbRABFGmqsqYuSzRJnGyjVPubzZqbqyv2u/2yiAOL1MxPnpiE\nJvfwdC+s50Kg+zOrPcRA17bCh/by/XP/+Gq7kZAPLXFzZVVRFC4vbBP9MPDu3bts1x8YJ1FGxBDw\nMTJ6z5iH3oFIUkn8AYVBO2FMW2UptKXQjjIvBM4+m06MMVyOF86nM33bslk1rJuGuq4AUMZgizIb\njERPjlILgz8tcz/1i3tiXuCUEjVRirIIjMOwaKXl3pFBeAqyscz2o7k1EEKgLIpcDExfhRAnFokd\nETwoq8EWpNLiS8NktYRXIGufYCLAkHAJvtnu+d3NDX9zc8O+qaiMnFCcLYTH0/eM04AymtKVVE1D\nkTn15MWIr17H/NznomKcpqWvHoPExcVcCMzV6DhOKOT0SoxUdYVratRmjTWCTi6cGOfm1CwSpPz7\nnNU0Vcm6qWVRDGHxEcybqnOW25sdv/2rbyk1vHv3gXefvvDtlzvevn7Ji1evKDODx1ibQ1lkMZZ3\nejY65Q9UiQdCGSdwOWOISTOOHlQv3PdkF1xJGCfGXvJtrbEYV6CtbKd/vvHPj18p6k0TxsDY9viu\npwyShEE+6gzjxNPTkc1mm9OqLdMkwx5nLTpGmKR9EJPGe8WQJoZp4tJ3nId62blMI1eP0XJU9yGR\nkGQRFVtMujD6I6d+IvozU3+COHJz84rXt3u265qPd/d8uX/ieGw59yPBn4lRU9UrQoxyUhgE9lNa\nS+Uc5WbFuikzgzhXt+k5HT4FtYD+Rf4kWmmfe5uCteyRNC7hhAid7cw4jJSZ4ic3RFxgSyGETI0j\nY3WLRRVAItPlQuacyEV3aVsOhyOn00miqRKMRYnW0KxkgGqNW6RhZVFSOEv0ok5pM8mxLiusFWhQ\nnwZQgygTstN0HMQENIy9AMGc4DnXKxkqgfT1KQuMSoxeLVFyJIUpbWaTa2zO1iyKYgEQTX3P08MD\nx7ME+Io8UFQuPgoXfDZLhSTHWAzoaDExYqwh6UTA04Y+t1Oy67jIEXTaMLQ9/bllbDuGoaVrJVA3\nxkBZVmy2e1arHUpn3naW0s0qmawZhT/rlc/D2VnREfP7Nh/7tdLLKYb4L3GwID8joLEccpCZ+vMf\nSAj/xuOZUkARBWKnhNcSs2IHNXtWIwZwWnNVr3i9v+HNzStKp8R5rRTJR8ZhwOiJumlQSmOLUob6\nzi1Iia9eYo55i/kvSLdFCoVyCf8GMt9dNkRj5Bqvq5q6EhxsaaxY162T2VLGHCtgzlJNM70zq05W\nTcV2s6btB0IS05eQL3OOaBLF2NXVltL8QIrw8PgI2uDKkvVmI4VM9r38Ai2bsq8gB2DMRZkPaZGr\n+mmUHnmKsumVFS4PeZ01+BCyqiyCkyJX5fbKci//2eNXcnZqOe2EiPJRhi5KggK89zw8PPFf/uH/\n5ne/+x1v374loSnKks12RVEWQuu7tIQwMUxiN04pSfJNPzJGMYQoFFVRAhJyao3wXXwM+KllvNwR\nxycKPaJLIPU8PX7meDjw9s33fP/9X/PN6ze8uL3h1A58+HjH+49f+PTlicPjmVPXMfnI8XiW3mtK\nDLnv6KeJ3W4jkj8n1n9n1DLAmGV4Ju+0KE0IibG/cGlbaZEMg9xQKjEME18eHnh8OhATuS1VUjUV\n17sd+92O7WqN99IXnYNdRW9rhHk8yCJurRVta9vSTzI8ca7gzZvXUrGNE0opNps1u/1eFAE5Q5Dc\n6nTWst1sxIHW1ZzPZ8Zx5PPnz9zd3WPzUNVZR13VMjh1BZMaMaZi1dSs12v5b9YyDjnw93gSmV7+\nSlrSVrQR23VKiilFhk6Gt9ZofJhkHjCOVGVJTOCKUiRww0g/jvhxlPUszE5Dg82bwrpsWG8bms2K\nsioJKXE6XzidLpy7lkvf4i95M4gRFRIWTaEU/enI4+mAUZBCZNWseDEFbl68oSqKfIsl8UBM5bKJ\nzotvIi5URGF1x2VwtoC3Ym6d5LxGiUnLrPj5S1wwS6tNNMcFISexL9+THzHPJ9IkwoI0FkRfkJwm\nZf5R0nnIiaIyDpc0NmiscssmXBelEACTJNsPQ58Bah6fLPEr01Yuexdr+uSnrP2X53p1dS2+iqZi\ns17jrFmokkqLU/v1m9e8urmmqSpBuxIxWRxQZLhZys9FYu2ydDD36ItCEpCudlvuH58YxucA5fSV\nIeh4vjBMgRcvXnC1vybGxO2LHVoF+mlgmjzOCNseZKM1uReus47eB59luwN9L3z7vuto25b2cmac\nBBAmfCWFc5rtdstqJQobkpgnJ5+FG6bEub8gaNbXWYZo4WcsO7PW2KJgu92KO04ptLG8eHHDfr/F\nOk0ME8MwcekmHk8tXa70ZmmgZAAmjucOpZ+wWlNXJau6yjrQgFUeFVpM7FFqWvgOMcoF/uXLBzEX\n9C3X1y+o6jVvXgoqs1lVKH7mfOmJIbBqCtphZJpkEHk8d/TjxMPxhLVGzD6V43q/ZbfZoK1lDIF2\nHMSQ4ZMA5oeB4/kszA8tm1BZ5krYFYwhgha7b1PXbLcbtrsN66qmdG6pQpYPN2N0nbPLYGkyz+qH\nonS40mYHvFnSvKV+1BSulAo7SNpRjDI8leFhz/39A23bfVXlywCnqioqJLTBqrn68mKvTOJsRIka\nQytFihaIFM6wWdcL5TCkyOly5tx1tP2BycfFXm2NEWddIVRChaZqKqqmZp9y9ZOgnyTL8+HpSYZS\n2mY+RsIZTVMW0oaqLK50YppKiU25ws8+gLHn2F449a2wNjITZIwJ7bTEzY0jMVu7nS0XVEL2EjEn\nSGltmOP/YgqMg8wzilIKDrk/pOVUluUCkopfVd7xq2o8/wCzqSXmwaHOJ1ARC/ySBy5Fj+RMxjl1\nppMWi3JavpS4OQutcT7hTxf+cPdPnP7HT/y437Pdrtnvtlzt91ztd2zWK6qyQKFJWFKSU2XUBqPi\n8pl+/Rq/zu9USrFaNYTgs6nOEaPAxfqhQypywVzH/LolIcgsATV+8stnsFTXGZcdc/B5zC7pZiWE\nz1lBNKdiEaHvRn788Sf86Hlxfc2rFy+5vb7meDpD8oQwEb3QIxVJ7hujMw+mxOXA68lPHI9CMLxc\nOpllTRPTJKfeOcvWuUJSrJSiLN2CKOj6lsmL1LaqSlQu6v61x6/TI9fZcm20pLPkvlxMCpShrGpe\nvHxJ0zTMAc2rZoVSEWMgRZH9bKbIertm9CFPwmXIdTmLrHGYAg+HsySQ9MJabmpHVWgMEZMCENB4\nLCb340UzfGmPmdUsLYEXL16z2V1xvWvQeo+KA/cPRw7HjkvnCSkjbSMMk2fwHj1Iqo1zmrLQebBW\nURQS+9UNA+fLJe/YPV03ZMrifMQUvklTyxClqGo22y3TKAqd9VoS322+Gf04LfREpbWEKji36M9F\nU6szuF8uPKXnQZ70RMv8HAUspIhBhjtzmnjbtTK86TseHx7pesETiLEGbCHzDWstdSNOV6H6ia1e\n1CGi2NC5dxyCWP6VnquqXHX4kE8qURDBOQdzHgjOxwMx0eQhrLXMaUyiLvG03UrmCXkhr6oSiDit\naMpCYGUKASyiiQqs1djSkpQ4U09ty7G78NSdufRddgeOIrHsxa2I0QRSjh+ck5Pk8eeB44nIMHiO\nRzGnXF1d4ZyDjOu9ubmh+65n/bSh67uMm5jldnwlYWRZEPNfkoU/zkM8/YsWzPJFko01b6b0I6l3\nqMqCdlmBA0UEN3r844nP90e+DBN/KEs26zW7rUhwb25vuL66Yrfb0lQFZWFwWqFdgXG/bP3kHuPy\nXsTc9pht8PMcQl5HzNddv/gjpmlakpYE66HkRMBcMIjiI6bnQfo8m5kPJlpLss+cQBTn9yN/WCHA\n08OJ6COPDwcOhxNPt09stjXb7Yq6KjICWAq5ohCDjzFmGSCb7FGYJpHKxkxNNVbjioa6qmjqRhzr\nLj+P4BezVEri6pyxCCYPY/9nj19lIZ+MkvACo0hZ6iT5i3khr2tev/kmS+vUAmRXSlxx3suHEybP\ny6stdSNBsZFE13U8HY7cPxw4nlounRhWjueew8mw3zXcbCu2lcKoTK+LMvjCiOQPaUHSTwOf7z4S\nomjQXxOxVnHVWL75u99x/3jk3Yc7fvzTB7qBDFkyhCRuQ2MtzmhS9FzantOlk+cZoaxKxpDofZCI\nqK7H+0BRyACvKkuqsma9WrPbrnPFJoD96CdSkt5njJ7gU3Y/mpxEYpaFepYRaqWleln0VnJ8H4cx\nV4+RoirkZiqspOFMnr6fOJ3OXC4Xzucz5/NJQiCmSSquNNuuI9oorBf5ZdNUOGfYbNYyGDLCB9c6\n93nTcxBFSpGhl2GxbB4il+zHgSlETFGyyYA1q00mTYIr5LShkuQ+amNyOr20gIbJUxhDbR0rV+QT\nUyQRmaZBCoLB48Mk6UewIBEUCsqKoqyoy4pNVfMi7rlMI5e25dReOLZnWditZtKGolGUWRPMos5Q\ni2pi+b8a/BQ4n4+8f/9ODF7GsNvtKFyF0fDb3/6WN2+/5XQ68eXujo8fP/H+3TuOx0PGQMvq9fUC\nLp9qrsq9X66DmbmztHOSfP+cZE9IMHkYJxgngYwpS2EM5RTQl57wcGQ6npn6iZO6cH/3uIDgyrpi\nu93Kgr7fcnuz59XtNd99/x2FFTqgXhYoWYDnPnIIYfma20qzBl7r58BsVziKwuLDxMwJnwe+KrfI\nUt4gAEkhCsLWiTEKwiAbu6q65vb2lrIs5H2IM/NeZemypSwbqmpFP4z8X//tv/MP/9fIq1e3/Mf/\n8O/53V//IC1SZ7NXQ06k/dBzymERVVFKm6SpWa83TMHntqbJ/PkNpSvyZhYZx4GhFwbMMAxcLpnB\npHXO0U3iPE3/ktcOv9JCvv7d9/T9BFXFaHLKRprnBNKxUiLvIKmAj7IYaxTWFLhCiHvBeYyCvm05\n9E8YZ7HOcb3dUmhLZR1f1IFLO0hVN8Hh1BF9oKsMJjq0qlBKbmpNQKeEUY6oNDEpQpy4XJ54etTU\ntaIqZLCldGS7ril/84brqx0/fbjj3cd73n9+5NJPTPnGCaMiRU+Kni+fHzgdLrm3XeMKh3WWq6sb\n7Au5GAubNb5WLpS6LqUVMN+QuXKYB0TzEMQoTUJCHJSS6nj0U7545YgqCTxeBoDhWVVhtKEsKlbN\nSqBdzhLTCCFJ9JRTVLXD2jXbbZNpihJYEaK47E6nMzEF5o7jqhHW+eHwmKVgovKZpomu7yVtaBhy\nwDUMo/Qdg48Z5hSeB1B5SbTWUBiD0wZNom5q1tsN69UKa8RirUAGhUkS2qUKS8RpQsWc9G41VVHn\nnuZXg7i5U5F/xlonm0MOELZBNopKWdZFxVW94mbacbpcaM9nUp73NFWdNfEyoIyJrBOOi+QupSgt\nql4+lxngpJVgbpumpFmvubq+5uWrV+x2O4ZOosGGcViGpcsA9blrQUrZRDZJ1JukbMVlAV/aGvnf\nlZ8w3qJHT+hzWwZDjUIdWtLTCRcCBph0yr3nWQ4nvgUJLG/59Kliv93w5dULytWe125FVTxLUJeB\nLs+Obz6kAAAgAElEQVSyOhkMhnzKyjOB5InJ5GtK5ROqQ2vJHHXOgtWLB8AotSzkSoHLhMuYoK5r\nKYBiZBwlvOTFzQ1N0wj+9usUVCVaoO12xV/91Xe8eHHFzz/9xKdPH3lxc8V207BqSjZ1LQow72nb\nucg5044TZQ4db5pGTEDWUqs6J/xIi02liPcjRmfnsxcXeAjSApKWi0UrJeiPssyL/l9Qa8W+eYXt\nBqKyjMbgksLN8rvc2xQJdUIluQHOlwvTMGGtEYZH4SiMyZN9T3tpCSTKqmK32dIUBWFdM/gBP3m6\nOGVzhEyRu8FQOUWpLA4B1Ns4gQ4YM6JJeXGEMAXO54n7u8hmvWez2WELiytyCMDVJqeSiwzt8/2B\n02Vg8jCNEZ8gRMW5HWi7CWsGinNPVUu/XRgTZTYwZG2zcxgjd+eSpJQrraHvRVqXB5tK5dAJQBmV\nj2GKmGPUZLg5SUWaF5OFtZwzHcu8OSqUKB+ioEeNUVRVQVWKKsBZ4UgUrsi8FHGxHo6HHOzgc+9b\nE0OgbVtCURBswGhZxE/ns2Qv9j0xRrSxhBgYh+fIvBhjbnIp0NIiaKqCWBYo57AKUiwFH6xt7isi\nn1lKxDBnPUqfVCPcC5TKdnmp5t3cemKOnXt+pMTy8yFILKFJuS2oDcaVkuJeJqxP0ouNCZcdtnOV\np5ixpVmWlp5jwVbrNeU0SQpR1qwrhRzBncjbVqua8+mUB2lfafjUjP16/rf5Nv/aPLJsIOmr751b\nUzEBAT159DhBJ+5oFxLOBdLpQjy3aB+w+Xgf0tdD17Dom4dx5GzEaGes43TuuR4DziXM80HwuSUE\n+TMSuaFa9qM8S1EzEGuGaWm8FypgjEE0/vlajrnBLVV9XN53nfNA51PJOApQqyjcIs/1IX3Vnkpy\n2kWCwt+8eklTWF5cbXn96pbX37zkarNhs2pyd8BTFU5SmEDcnFmaOEfa6aymKXLbSGud71u5TyWu\nULKKfaaCzqod0pxS5PLz++U1uqyp/9/L7v//j2G7gdUapwsmZfFJYeeebFL4AD6CyhLPEOHu/pEv\nX+4Y+4HddsXN1Y6XtzfS78w358PjIxxPDN3IdtUI7nazou1EUzx6ccL5EGgnjdOexihWtqDJwPbE\ngDIdyva4HK2UkmLsWz5+OhHjtxSlow4rTg8twQcKW3B985KXN7f88P13/PjjH3j/6Y7HY8vTZeTS\nTYxjrsqSJkZNGCP91HI4XTg8HahKS1FYyqJgtxLTQ+l07nGL+kMrUfUcDmfB92Zly3x/Kq2wTlgO\nhZNIvLIs2O32mUdeoJTLyhbZBNzCt9BLq2TyEypPkKzRFKtGhm9FSVE4tDKQpDqYQsikw3JJnR86\ngWRNfsxzB7lJhmEQ+lyePZD7+HME3jTl4dCpZRhGuSGzdM1qTVM37LYrtqtGmPGrhrppclizLMrC\nhpcwAh+lWgx5QDv3SlWWcTlnlwGvDMWiyPby5z6NE+MoQ+i+77Lk2DzH0nnPkDnsYZoWBUmcB2e5\nWOZfWcSssey2O8qiJGYokkIvi1LwI4pADDKgG/oL59NJ3LKZD66Ye+/6+e8tapi0KJggn0q+TjqL\ncamIdUwoH9DDJAqhELC9DA7UpUdPnjh5nNFEZfPGIMdC6S2rfAyIKC3BEEXhMo3Q52o4yWlxLtiy\nisRkE8zXrZ/5eVlriNEuGvSYAqfLifP5xLqqqYs80AzylVLIXzHnpmoShja3Lvuupxs6xinQXwac\nVlL1Tv4Xqp8QAw+PD7x79zO/efOKv3r7lv/t3/8tN9c7+f6cDjZNYtgyq5XMhKqKuqoZ8jC8a3vm\nrRYvLRddltivZgHGaEEx5A0oxpgLmmH5PSgJOldKYf6SEoIulw6tc3+zH9FW+sPT1C260Sm7E0mQ\nVKJuNuz2kU+fPnG8tBhr2O227K8k+MD7iC1L2q6TNoyVoVa9WoEuqOszp/NZNM0hStSXMnhlmWiY\nbMKYEUPLNJ3QYUAzofAYDSRNCBOHx48iKRwDVdXgXIlSga49YuwASfP29Q3bbcP905E/vf/Cx7tD\nlvT9kj09X8z9GCjKkrpes1k3bJo6w79UjjbTxJBAa7R2bHZ7XFHhijOHw0HSgjKUarWqxfFYOApr\nqaqaslgRQpKWSmaXD8OAnyYKY/MNRk6OLzGqWhJvADwyoe/ajhildz4ME1MQk40wMXLodDZHKGsw\nOKYgZqDoAymKUsUoxfV+D1pTOGHmmAzbf3kb6fse72d8qlS6fpqAJGCyBD4K6TKkRBVEUmkGTafM\nwiKZEQA+KwVk0CUadJu1/SHzKy6XC8fjUU4nZblsLkuQthElxhw+IRt8wgHkRSdFCW9QGXqWX0Be\nsKTK6gep+rRWWONoGtkU5fVnCSL5BBATGuiHidPxSTjZ+ejNvFkwr0Fpua7+Z0nr80MtlXya/ye2\ndx9ZJUWRoCRRW0OvoA+RMcQ8Y1GkICEXy+/KvWXrJOv0ar9jt93hXLEEx7C8HXmjCqKxVooFPzs/\n/3lRlxZgfo5J4X2kDyN3Xx7QQfPNy2/y+5bygNfLzChXu1pbjHXPc4MgOZmCxVhze33D3eORSz/k\nNzFjilNiHEa+fLrnP/0f/5m7b1/zux++Z7tbUVZSfSsUWFGGyVoji3jTrIXQGSLPhrCUTWx1BrvJ\ndTW3gea2V4yy+fZDz+ks7mERLbicfPUXpiN3tiAkxegjvutI6JzX2QtsJkWZ9uZe7hxbljKxLsSA\nRC5qrCup6gbQ6KKgzvbZOb9y1k8TA84IVF97D4jyZQqWNinSpAg4CmXAe6xOuDQAEy4ljIaEYexP\nhGiIsWC727FerdFVhQ896v9l7r26JDmTNL3nky4iIiMzS6ABzLBnuWe5Sx7y8IbL//87hmfVdDeA\nUqlCuPoEL8zcM4HG7C0mcKq7kJXICuFun9lrrzAV5wJ9G2jiDft9T4yBvmv4W/PI6TQyzVmd8Vb+\n+LoUE65013aihrOOJQu9yRoxBTJGPD2iXaXlQTI110KeE23X0vWNpgpJatL5NDAMk0wkCDa7zAtL\nmuli3MRFq08yCCxhVVE4p0Qq0q3Py8LlOnC5jkwpMU6zmHgVSSMSPC9K51UKtSb5+cqdN0YghaBK\nyaZt2PWvpkqlGtKul3dEl5erMGZZFlYbVGuUuUFV3vIiUIZaklqMdDKawLLCPXi/dV4pSUd3Op0E\n37xe8d4LO+d6FXxzDS4wRo3T5JfVYiGFSRhYNga8UkRX5oU83rI0iv67092GToJv6ITGVKiZWhKl\nZqZh4Hp9YRgH8rYkNq8//S0V8c1DGgcQXFxgojd/KP+tUfpfKdSciQV6oDPQGSPRc7lgigibHBCc\noyhctHmoGLEDFrqtMK2CWr6+PVhWWuBqRWyt2ZadXjtUsaYw237DWUff9jRNoJaFUhyX68JlmOja\nKNcIbIt32UOYLfg5BA99t7GkvBd+/4f37/np81e+Pj6Lbz3rYFHIBa7DxC+fvwlzZpnY3e74xx++\n426/x63Zoxo+4hyEoPyvKqSEZZlZ0kKtRbB9pQO/TUBax7W1kCuCKn4uzq0ftXzu1fxd8PX6+EMK\n+e39PafrwOkykOczS87EGCkl0QRJL1kXVfOSNl+OWgs+BKyJxKbHhZaCpSAS6l0I7G4OVAsvXx/4\n+ukzn37+zPNFchbbriM2DdE58FkMfrJhNo4hVZpgaVyDzTsJk6jQ1ITxSdixtpJMIS0zD9++MU0D\ny+0R9/49sY34AN7B+TJhQ8PHj+95//6eH77/yLu//Mz/91/+ha8PJ6a5UK0HE8B4nKlgJPTBWEcF\npkV8XGqVpPDbEFRgIyNj10ZuDjvu7m50HEcmGOWZVu1YX57OfP70jccn8UE3XhSfFfGrOXQdtzc3\nHA57PXj8hsmJT4VhuQ6guKix4jWdamUphWGWDfs8TRT1/lgdDZ01dE3gsJeFZN/1NHHd1IuHSNc0\ntE0jEn7tquSil4LQBPGnEcxdLU2rPPdpnhinUcKlS1FyiIiujPPbiL520E7NrdZMz1V1t2L1Nzc3\nKgOfuV6vv4oeu7u7Y7/f0zRRu8OkcJHAKrkkyXE1jmL8tgx+y1Nel4Og2PAaCaj49SunWmTxhowp\nM3m5sExXUpo361x+VRzf4M7mjYJU/91uf88bwyWzGmNpF68FllpE7IQTuDNXaiqYApgi3Pu2pRih\nd87zGkxRNBNV8OsYvLCLdflONVpos5pGFV3cCfOqlkzTyXJQGjeJ7BM9Q+C7D3/i7v6etGSGy4WS\nFl4uA9Y7mhgQm2GjzDPFpa3DeUcbA846uDOkedHJsPDhw3tuj0eC+0W95nU6KRIYblzAxZZfvj3y\n5fkbycH/W/8v4j/9L0Tk52Mtc8ragS/bc1/3I04X5eY3B9qrAdoKPbL9vmlafAh0KbMUnXjXyel3\nDmz4gwr58/lMrYYmBM7TwDwbMsJ4OJ8u1Jzodz373R7nHKcXCWQOseHw/h2lSpTU8+nCNCec+wZY\nSpUi4IKjCx4XPO8/3tONe5Xmy00TQuTG3xD9ies0MWme3jIXEglbLbOLLFW6dGMmqDPBJooppDqx\nZIMhYYwYZQ3jTN/v2PU9xfQYDOM8UnOmbwP/2//6D9wd9/zt56/8y9++8PQyM86rVL5QsmEcZn7+\n6bNghkUwXmNFlvxyHrg/3nB7I57E1grbxzlLsI41aSZnpdMh6svYBLq+4ToGifpSZVguMrovOTGM\nA+ezJwaLDzsJrY5RRkxdPK4RYSE0OB/odzswApWMo/Dgl1mtR4twgpsQaINs3IMPr0ULVsoNaVkk\n/cWrp0QDUDdTNe+Ez1xLJqfViF87HSpY1MRfeMYUtnG+qAFbtZZ5GMhFlqDVaJapDbjGczzektQa\n+JdffuFZud211I3d8/XbV7yPuE1hKkUpLTOFLJ4104QNLaHp2O8Wgi8qVnn1DqkrpKz3wq87aaM/\nW9kNxVDKwnQ5MV5O1JRk1yXesr+CU94+3sIrKzZu3xwar9+IMkjWxaX4uyTnmEpmvI5chkl0GkW8\nxo2BGD3VOpwuGFOSyDjxTBF/oMO+J3qLpa69xbaAFabLwpIXgmLTxshn7tXBUt5rSwiZ3W5HKpXT\nZcLaiOuORGfwDhbjoFisj9SiJgMGas4UdRSzyoAqqTBeL6RZoEFrC20jpmlLltW6THEAlhgjP3z/\nPcsyMo5nnh5feHoSKCZvi1ZpkrOqfmstG9+7ZCnWzouDo3vTjVedHlZxl0wlqqkw0myFGDc3h1KE\nev2rZfebxx9SyK/DIBzuKlhfKRlTxAZymieGyxkJI1bLSAPGi2PY4XCQLnFZhNt8GQVbrFBrlgvC\nW46HHbuuIXYtxQbm5ZXO1rQNbdMSvOMmJ5acmMeFvCRKTgjJb6GiPul5gHKl2qv4UtRMThOzWcAI\nzCPLiYmSZ/CV0IpCziI+L30T+NP7O5oQ6NuWX7488/B44eUsmaLBSjc/TRptlkWeXjGMkwSzWidc\n2TZOkruomNmK4XofyCUxjgOnl2eGceJ6ufLycuJ8uTCnhJndBkkYU9l1DU0rXtJN0xKiGIJVrLzO\nCsY6HR9loLTG0oSMsZBzQ+5blrQnrSHBiLVt8AGHWLKu+ZVbR6oLRkBcA2N8HSVr1r9HLIzzih/m\nJIVA/bmNs2qOpF708htZ2FmhRpacyVXSamYtzsGujBVPiIKVx6bBO8t+vxMXQU2uWRZxUZx1ketc\nUdGR0j6dKO5ssPS7HutbsJGUVtUmcmC9Eemsj98WXPm0AQzBB3rvaPeB5+cT0TnM6nH8phi/VUb+\nz762FozffwhMU4qoQrOHXK2YdmlMoS9yH9p1s27FZ8WayGQScy5k7bBjdByPO5pGpmujOLGxcg07\nZWIIdLfuC9yG3b/9X+GuyVLdLAXr1eK5WpZcmSbwRhhmVMGdvVoMWFNwVfzWJchCmrmsLpFNE9nv\new6HHddxJpWidlfyvbIPmtn3Pbf7HUteMDjF7F8FRmWdpuTK5u12e/XqeQvR/cobR6e07V6wBmuE\ngKAmPbrslX3Zb90k18cfIwiaF6YiF/kK+K9qxhdrmaeJx3liWSaaRlR4MQhdyFrJkTQYnpYn5qXI\nCVot1sqFZKiaWF04HHZqe1vJlc0zxDrL7e2NSGK9ZbwMpFkKxFIr12lmGCeWaWbJI6acoT5h7Cgj\nqgpxxiExTjJS5bxQ60Q2C+0uY4KnjZGSMuMwEHzgw92BD/e3/OnDM//y10/8j7/8wjxXmsbRdpGB\njDFe8guceDDLaV05X0fmZcHr8kRuBo/R17Xb74DK6fTCX//6F15eTgzXgWWZX4uIrNwJwdN1LX3f\ncf/ujg/376XTdwLtjPMiAp1aMdYr5i1dhjGyMyg5YYvgf+1OgnatQhjwSgM0euN651XYpRFWytCo\nRmwYQE2fdNQutWx8fLTrEYaSQD5rMS2lUJZErhXnwybqwcKc8obrn85npnHCVLEBFitR8VHf9R1t\n0/Pdx++4v3/HksRhMi1J7FVfnkm5aBpRu7EOJMdTitTucEMunnEqzHNiGEUF65zfZOObiesbKET+\n9ZUeaI2liYH7m4Y/3XVcrxP/7b9/UqBBvfVqfVM8fv14K+VfBUHr11esfyv0uhMx2hnPy0LRRbDQ\n9kSzYIxwtZ345WJrperOCUC42JJa1bWB401P13pRYpO3EAnvHcllmtSQ0kwpi/jOR6HXJWUXifpY\nwk6u14Hed7jWsCieXhSGEKGbxSOZnc4aYnB4J7bY3kHMhuAN3lp824MLFDPSL5Wb4w23tzfYy8Co\nLp01GzKFaZn461//yn/4d//EDz/+wDBP7Lo93kWczSpuswg7WKEinS4MlWqFDeYUzlu7ackCeFXc\nrjCdGH4FbUTEQ2jdQYQQ8ebf2LLTrNvyUkm14F2hkl/lt8YQgqNrW3a7DjDawcHXr1857PfEELm/\nu+P5+cQ0CvXLOja70ZIL5/PMMIg5lKTPlE3OLQeDowkSRxZVfeisJbjA7f7AcX/DNA7YmnDc4eoH\nhutn5vEJaxaCSViToM6UsXApM8s0cllGQjtzGSr7vmffd+z6VrjSw5W8LOy6yL//8/d8fH/H0/OJ\nx6cXHp5eeHmZSQVCjBzaG6hwTSMvLxemObLf7TjeHKilchlmzqdHkjIZgneS3j1NnM5nljlRndwk\nu11P33XiBREDbduw6zsOu44mRKZl4vRyZhhGWapRFaZRAy4nXPMmNrRNJDbi82yoEqxgrPLeZRaU\nhaQVab6O7eOyejJXmqYRQode6HXrw1aYvwCvdEAJLHYQ6tY9rou0snY0xhBixFphlIzTSMkLNc14\nU+ijw1Wvua4DLyeRTHdtQ9c29F1HVix7jRADuUljbDg0Df2ul5R3u3LEla7oHW3fE+KOSuDh4YGH\nxyeeXx6hiimUc8r1Z6XaFswaCMor7G0Q5W/TdVgXmebMOC8agiDfX0XK+4ZBLo/fwi2/14W/nQy2\ngwTBX5ciTKRoYElZD3M9BNQQzlaDw1KLYS4JcoKaFXYRb6HgDc5JYLacFVlhJbsdwNYKG2tVfH77\n9o3PX76Ko+jDN/b7nTRlqmmwFIwRYWCmakiIvB+lQjayu5mTdO+UDEVok9GrTUawGBzZRIgV3+5p\n+h0xSTdund2W5FRJWvr05YHrsHAZrjhn2fUNf/7xA7kUxmlgGuctiWst2Ebxb2NlL9M0kdJ1GEUT\n1sX3ytBxTlSyQgtO24HrjFz3drs3/g1h5G3bM4wTk1IMO2tog9jY7rqO6ORU3e06XS7JBTdPM+fT\nwKhjyn63gwpzu0ARzDLEQNd3jMPEOM4Mw6KFzhKC2zDdaRwZvcU7cM5shdx7J5zptiWGQHBV4t18\ni2UvznsJ5uWEKRPBZKxdqOlMKuK5fZ4LYYm03SQhj9UqgyBRs8AvfXD0XUvbdPSt0JKMs8zpkdN5\nYp5H0hLxDvrWc75OzFPlqhFs1oq5U8GQK6IwG0dZqtWCsYHQiBLOOpGNH/Y7jode8y2jioDEiXI1\nIypVppdxFl+Vy3BlnpMwZpzEyTXNasSlTAZrN7fDGBs6fT2NCodA8dNp0i0+MiUoYOy9XMTOWh0f\npUusCKZvcVTE/raWqhe33dSTG39Fpc65ZIWnMssybxBSGzxL0zC2jUIGspeRJagXUoDSxnJK23OL\nUYp80zREL548Mrq/KvWcczgZvPWAS9Q6k/NIKV67slemwjq+rz/rbZp8rUVfS+Ipz3z6+sDD02nN\nHALeFuDf7cn/1Xvv92CYlb5YgEUplBN1M0MrFYqprIRQgwh8aqnYotbNu552v6ONVvJrnbCt1u5U\n9qhZcXY5mFcKnveScvX16wPfvj1ineX55Vk8ilQotcEutbwyTEyBKnawa5h1kT8QCmqGmi1LMUwZ\nhlxxo9CJLZZcI747cLh7T8JhhyvTOCrfXtll1XAeJqalUmriy9dv/PWvP7FvnLozFpYlM4wj1+vA\n6SLeTs7KdblST2MIEhHnHGlZcM5vOoYYoy7hLeM8bztAr+6oqw1yzZn0r3y0f0gh3+1vGNMTc74S\nnaVrAjdtyziP7O6OhPAO52GNeVs0zNdQiUFGjiUtOO+4vZWuteTKNA6isDzsuDYT5uXMkhK7plfz\npsjpdGEYLnrDa+7ilHmuA9SCs1ZjyyK7PnKz6/Cup2sC1gemfM+UDONkceYqFpq2YOtMTZm0GKiN\nJIRkQ5ozgxnFC9knvMt4W5iXiZot1rTsugOhaemPO2zw/PzzI89PF6bhyuEg9qq1Cnd9nmdeTmcZ\n770oA3MWtsA4CstFnO8kzFi4qVmxPcXrrAQTT/NCWuwWett0HaFpaHc9j89PDPPMvBShGirEQ1kd\n2KToW1NFFeuFErnre97dveP2eCsxVn2PNa987bXjEROsZRv/21aDk43T8VGir2KVQI6UEo+PT+Sc\niepBHX3YsPaV1payHGhLWjSoVp7bvr+hJOm2i5WgDaM3W0p585Sel5lpHDfOfGgi+8OBNkTpBDVG\nz6o3tnMeh8HkCimx5AtLqizjGUuibTy5iCpx/fuqMozqBnuYraCCmD9N88xLmXlarvzLz7/w6eER\nKYNKrbPabb9hrMBrgVek5DfQzauCcX2P0RkB/V3WrnwqhWmS6bAYQ7FWA5ENvlZMUavdUul2LXff\nfeAf/pd/JE1X3t0did5KwVT0pmbJZa21ELx8LnbNpvUB6zzPLydeXs7EGLleLqTbPb1v8SXirBf0\nucqEYAGvfHsAdI+zqinFmM+DleefamWaC3lZsEAITjJOuwN3H7/HhIbzywuX84VqA8ssjqqr0jwV\n2O/2zPPCp0+fObSOu+OBrusksLvC6XTmv/z3/7qJ8qx5PTidc9zf3hFDpKREEyKHmwP39/fYtlF1\nJxLcnQv4gKFgjcBSFt7Yavz944+BVirifVELHz985HgQAxmRJXv13K0bTc2YWTwHQmTX7/jp5194\nenwk58Kd+mVbCyWJi9g8T3hnxKUsSVSUjwHnAl2bFYaRD3ItAFFX62I833N3u+fudsdx12FzltEx\neD68/8Dt8Y7L3Qe8WfBmxNgz1+efGc4v1LkQ/EIwF8r8DeyOVCLL6Iix0jQV2xgIi/g1Z1lgosXm\nf/9Pt/z4/cjXL898/fKZlEXIcn97j0YbYZwR2t14JeVKjA1tJwGvxiAe4V3H9Trw8nLidDpxvRae\nnp759MnhvVKnlJfedy03hx19J53BSoXq+x4XIv1lYBz01ziqm5t2R6XIDQ74GNgd9tzdS0rQXn2V\nV2rbfr9biRnUyuZMtywT8zQxKOyyJgWlnOm6jmmaeHx65OvDA85ajocDdze3BO9VCTez2+84HG5o\nupbYtqSSiSFsMEIMAbsyNBRnl0PlNXyiaJFYtQtZYT6vxlxOWQPVyE4ihkAIypnXopiSdOO7rsf6\nSJcqBU9FlLNvD571Gn8Lb2CgWsOSkrBCcqEah7GvvHQ1CdhsFn4rAHpbzN9axa7fu35t3U3J1Ftf\nrQh0byEY+hthFmp2Vu0WVmeB2LT86buP/N//5/+Bo7DrIrfHnYBjJlCN7jJqJqfX9Bx5l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ms0IlK897IZMylGQxwVP0Mx2XSjDQxEDb7/jHP/8jP/74HVkNx1YjNFM1dm/7R56fQIVe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\npfLk1EJeqVQqT04t5JVKpfLk/AsNeRHC6zkzzAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fac306356d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "transformer = tools.SimpleTransformer() # This is simply to add back the bias, re-shuffle the color channels to RGB, and so on...\n",
+    "image_index = 0 # First image in the batch.\n",
+    "plt.figure()\n",
+    "plt.imshow(transformer.deprocess(copy(solver.net.blobs['data'].data[image_index, ...])))\n",
+    "gtlist = solver.net.blobs['label'].data[image_index, ...].astype(np.int)\n",
+    "plt.title('GT: {}'.format(classes[np.where(gtlist)]))\n",
+    "plt.axis('off');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* NOTE: we are readin the image from the data layer, so the resolution is lower than the original PASCAL image."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Train a net.\n",
+    "\n",
+    "* Let's train the net. First, though, we need some way to measure the accuracy. Hamming distance is commonly used in multilabel problems. We also need a simple test loop. Let's write that down. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def hamming_distance(gt, est):\n",
+    "    return sum([1 for (g, e) in zip(gt, est) if g == e]) / float(len(gt))\n",
+    "\n",
+    "def check_accuracy(net, num_batches, batch_size = 128):\n",
+    "    acc = 0.0\n",
+    "    for t in range(num_batches):\n",
+    "        net.forward()\n",
+    "        gts = net.blobs['label'].data\n",
+    "        ests = net.blobs['score'].data > 0\n",
+    "        for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n",
+    "            acc += hamming_distance(gt, est)\n",
+    "    return acc / (num_batches * batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Alright, now let's train for a while"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "itt:100 accuracy:0.9526\n",
+      "itt:200 accuracy:0.9563\n",
+      "itt:300 accuracy:0.9582\n",
+      "itt:400 accuracy:0.9586\n",
+      "itt:500 accuracy:0.9597\n",
+      "itt:600 accuracy:0.9591\n"
+     ]
+    }
+   ],
+   "source": [
+    "for itt in range(6):\n",
+    "    solver.step(100)\n",
+    "    print 'itt:{:3d}'.format((itt + 1) * 100), 'accuracy:{0:.4f}'.format(check_accuracy(solver.test_nets[0], 50))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Great, the accuracy is increasing, and it seems to converge rather quickly. It may seem strange that it starts off so high but it is because the ground truth is sparse. There are 20 classes in PASCAL, and usually only one or two is present. So predicting all zeros yields rather high accuracy. Let's check to make sure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline accuracy:0.9238\n"
+     ]
+    }
+   ],
+   "source": [
+    "def check_baseline_accuracy(net, num_batches, batch_size = 128):\n",
+    "    acc = 0.0\n",
+    "    for t in range(num_batches):\n",
+    "        net.forward()\n",
+    "        gts = net.blobs['label'].data\n",
+    "        ests = np.zeros((batch_size, len(gts)))\n",
+    "        for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n",
+    "            acc += hamming_distance(gt, est)\n",
+    "    return acc / (num_batches * batch_size)\n",
+    "\n",
+    "print 'Baseline accuracy:{0:.4f}'.format(check_baseline_accuracy(solver.test_nets[0], 5823/128))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "### 6. Look at some prediction results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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oaWdkMB8GUkqEcSRME+Nmk/2wAgHT5AIwnJ4wbDdIENP29nt0VhgHZLJ3ZRoN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OO8yWu6zq7RYpL+o9HK2ny/UivVbRNDELHNYhGiNK2V2hdVFcPD7aY6722SeDWM2XeD2Xnt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\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KJteAqh6jvRi8GYUhYJhlDTdYUinxIQopk6pI+XWbVqVU0ztoTWcdO+OEQ84aJ+qtqXmH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wInswnXFye8fnnBD2+ueXY+Y9YMjZSz68nIKMmTrglLXWC5jQFamsI5ciuB6GHfdqzWO+4eNrz/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\nndiSd5M5/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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fabdc077450>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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SvKcDPnh0NgrylEdRhKqwVGW/KrVSGq02wCtOTKq59VCGgTs5MJfCVDJD9Gyc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\nEBi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EhlEArk0dqVxB4TTGASbTLmteWketE9MxovNAVTiev3jF88++ZLFa8u2vf8vXv/41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RRY1llCiUUucAYVRI6pFEQVhIuCQJTODTBai46fyuf7iqvNIrfkrImpNACdp+RzKMTyE/I0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\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sqYRGZJ4mcs5Y55jOI8pqEhnnGlpjcArGZWFcpLdPq0Y8JUYRo6hzUpTaudJJiewSAomC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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fabdc193450>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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M928NahTbCL6x5f1zgjiH05owKLGLjGPAWsGIgEKMkewctTGoMaiUgR0MKoJSeEyRwn0V\nuEqwxpKzIQkkzUQUW8jpQzn1lRB5XWfu3V1Re4/mQIwjqomUIkh5gN5ZRCyqk7WSE1njzeCKSAbJ\n5DyNtkYwJgETqQOqTC+foigpZy42W55eXvHuszMeP32CkURbCWGMpFhGT+c6dt3AEAKLpiGR6ANk\ntSQVxDp8A+1qRbNcka3HuAoTDSle03UbOrcmxpEbX1fWDCJY56lFyktWTAQa33J6cMJw/w1mvqKt\nK2ZNw7Nnj3n48BH5nci8rVnowN0aDluPuIouWXZXG67P1lycb7kIgTErISuL2QxnLSRo61npfVFp\nfctyfojJwm59ySff/Cy37zzANwt++mf/X0KKqAl88s1P8N2f+xz37t3n7Xff5vnTM3LM/Kpf9b08\nOTvn537+C1xdXrO92jFzFnJiuZhzcnjMvbv3OTq6Rd3MGcPI5dUFZ2cPOTt7TEI5vzrnervlu77r\ndW7fPmIMAbVwcX1Jn3tu2xOeX1zz7OwCV1uYiMJlqCqHax29q9hcj3RDJDxP6E7BZZwRclREwVcO\nGcBEwVWeqnIMYeALX3ybw9WcuqpZHLbgWmKMdFcD1mygitSN57t/1ef47He/yf03bvH46UP0yQU5\nWIZhRK3QDi3LZUtwIK7HAc3MM5vX+MrTX+x48vyKXi8Ju4AVz2xW0c5qfO0Z8kicyFGzkrOiolgn\n5Cy0Vc1rd2/RbTuuL3ecP+sQQyHiMUG2SGWx3pMkozmRQyp9KShxVOpZBqNYm3E1VPMGL55xPZBi\n6ROqyiZCa+CgElwtuMoio2JFAAsKQdNkNJV+mTGlk6HF+h4U20WMr7C1KcaYsfiqpvaek4MVD26d\ncOf0gPX1JU/OntHOFow2ILmjS9MsAiUNI0mLMa0GkoWUlBBGQk7kDNZYVAovxJAx1Kix4MrMHAxG\nCjnfDAzWO1ztsc6BAZXSP40p96iSijUtpgwUlBlTThkriaoWFgtPHg1hTITdiAJGDEYsMQW8s1Mb\nKjkKMUTSZoMgaBayGKyx0zFCShlMsfybeYWzBk0J6wSyojFRO4NEwaTvICKPKbBctKzmLSlFQhxJ\nOaDoiwdoRGCysEQAEQwGYw2oQLE7poYHRBHMdLyZGmPahZJSZAhbrq7O2FxfEsaOEAasySRbRmNj\nLCrCbugJGsHArh/IScjBI+2MGDI5JrwxhDGw2WwIIbKYtVijeDF48WjObHaX1PUK6yqcEYwxLywu\nQciayTkiWWlczcnymMp4VBMxlIEkpowxlhAtl1EZCHg/0OgVmR2bs47Hu4HOCGbZ4jFUxnPsK0Sh\ndjWr2SGtN7T1jLqaMVsegTr6zZbbp2+wWtwiZCWMxeJpW8982dI0Lc40LJo5s/s189mS1177FEOA\ntn6bVVNzvDrk9ultxJeO1VQNIWS22y3OX2Kc5eHDL/Ps6SOshW23Y3O1pkycPbsh8ezsKZt+RzcO\nhKgYLJW1VBYKdxiccXifOT5YcnhywPqq59E7F2weXZLWETOCqRT18kLOKIO6wTmHqKAxEntld91j\nENo2IcZQ+ZqcLcM2IEZpZpbZvOb+g9u89tprnJ4eM1+sqKslgudLb3+ZPgVCr2g0GBzWuiI9AGNI\nnF9e0w0j3nm8MbSLFquOftzRh56QI1EysTxyXsyujQGnkATrDM4ajlYzFnXNYr7k6dklm6sOY0C1\nvOvWWiyWlIWYMk3dYiZDoWodtjbYyiAmEUclAqgipvyNXWTbJ9pWWB7PyqBXO3xv3o+5UcdoEpOt\njqhQiHKa/aqgavCmxpsKoxZntBhYKJJzSTEpwjgOhBRxVcWtk9vkqFytr3n36RkOAWtJ3pFjQhMY\nZ/EI2QjeQIhFuvAINY6cajbdyBgNMWcw/qU6ZWQicUGoKk/lp5k3RaoVFKZ3RjWRKRKOTEQrmBfc\nYq3gvKDJEmNEx0zVVCSJk/VoyAqSKQQ9vYeaoUSgmtLGKKqZVCYNiCkWuas984XHe8PQJcIQkVHI\nKFLG5A/FKyHyMQSgxXkL0aC5WOMyEV1J5VOmclBeOKOmWLTGIpN0krU8gPIA84tBQJBJgnkfqoEY\nd4zdNY7I4ayhnxfd2hrBWjNZ/pl+HEiawQjDGCAJHku9qJEUSM4yq2tqV0GGNEayT9S1p5nNseJQ\nFa43FzTNCutrGmMx1mCsw4pHxJBTaUwBKuuYNwvqqmXbbbkYz0iqGO+pvIfsuAw7zmIim0A9blCF\nbh3YJUuqHH7RUNmaytXM6xrJcLg85lOvf5bN+pLa19TNgll7hDMNOgZWB7cxtqHfrRm6AQus5jNi\nSlxcXkJ2WAy3bt3l1u37zOa3mM+ecrBoMbePefDgNe7cf0AklPbKyhgym8011jjaxYyri2dsrq+4\n/+B1wpgwanDGsu0D2zjy5bcfcr6+BBEWiwXGGKwKi6bF6Mg4JjKJqjYcHM+5+/oJzWzH9cWOZxNJ\n1FRlcB0GbgS4QMLk0g1zUBxCJQZJSr8bSClTNRZxggbwYlgeLKhqS1V7jo8POT464eDghFt3HjCf\nr0AjQ9jw5PyCGDLjEMlJcdaV2V9Wuj7SddfklGm9ozUNTVNhjDD0G7qhI6QArpB4SkXCMGKLLGTA\nmELEwziwOFiwXDraZeZyu4PNiPUG5Ub/VeyNpqqO2aJl1lZU3pBMRjzghBBGhi4SkqIiiIJkJfeR\nLhrW0XGwhGQE6w3OWzQWghNrsNlitMxwjZ36oUoh9clyd+KwOKyWQVQAkzOikTQO9N0OZSBqoqpr\nKtcg3hKCYvMZNitGhGzNNFso7SLGgLMkb4lJ0QQuK0sDmhNGDNddRkMsEpSYMsOZ9GiZuKLyReYZ\nxoDmXIh84o+sStZikXMj04q8sLaNSpGRNEGCOCZSn7BO0ZwnopbSr3Muvj6lGKBiyTm94DPRm98o\nztmp/ct91sua1YHn+nLH0IEdigGnWb9qMOsrIfKUhCEoIQpHyxXOeWIM1PX8xdRKJ6eKTg6KYs0W\nq7lMgwRNuTTw1BConax0M3WqaUqVM+SIJ3A4q2jv3eXe6SHz6m0uN9cMcSCmTEyJIYyEGAg5kAW8\nr/ACy6bhzbunhDGBCgfLY1ZHhzRtAySWs5amchhJXFxes931DN2GTXeB8zUiDoxQVS2u8WXgIWMN\ntG3DGGq63uC9hzCSrKFdzBhDR0yJbtcV2UM8azw1jspA5XNx7iqEfqRtK1zOdJsttW+ofcPJ8S1W\n8xXOViwWx1T1grqeM2sanG/Z7Hbsukti2FF7j7WHPHr8hGdPn3F6cMD3fvev5eDgFoeHd8E0HKwO\neP3+fez9O9y6+zrN8pC3Hr9N3ba0VU0eEtYa0Ii3cLCaU3nDG69/jjvHOyr7Zd5994znV5cEk9mM\niSFBCoGxv6K2jqZtuHV0h43rWI9rNnmDm9UwsySbcU5ovGFRG9rjmtM7t6ibhp//hS8RQwbjECPk\nEEl9JMfM8eqYw1tLdrsvE1SJMcOodJtzlvWM7/7M6xwdHaIGLq+vmM1mNG3LanGMcZblbMPJyZJP\nf/o1XGN5+PSCzWaNGME7Q84GUUsaDX2IOCLOZsY1OCzVzCP2xn8j+KoikolaLFfnHcYYUhYUIaE8\nv7zi+HiJqQyb6wtCDogpFnaWTCQRQsD7CmMtvm2o24oHr93mwf1Tnpy9x9nVBde7HSKeFJRxCLiq\nQqJCUEyEFOG6izx6foUOiWwUay0xFunEeYuNgomKZC11FUtKGU0ZtAyYYzdgvaFqWkwufi+rghUI\n/Y7rNfj2hNligWJ4+613yZky9KaEy5kUE0aLTGbF4I2FG3J0hjFmYkhojAQimiMqGUhF7zYGNQ5I\nRIEy3SnyhzMWTcrY9+ScMCI4Yym0HckEROxUmAIriuNTs2EII/SR3GV2u8DYJcw4EGIkxlRksqgU\nG7pY4GIMRtyLYA1yxhiDvjh/4SxnPOOYyZrxc0ODw89rUrCsZc2gkZQ/nMlfCZE7ryyXc05Pb7Oa\nHdG2M6wxGGMnKV8nTd+gmomxTIELgU/TIIq+pUohb3nfUi+HywtCFxGcrTD1ipNbnyClkWEcUJ3z\n7Pw9rjeXWGcRZ+nDyFvvPSSMqURqRKhcxapZ8fq9T9I0C5yb0c4O2O7WhNRTV8JivqJtZhjrGeIj\n1G5QgaSRbrjG+5aqnmHEoBlS6kgpkVMmaaIPO677Cyrf0G8u6c6f046BWopccHjUctUJXQpUbcus\nnaFRee/5M/o+oGJp3QxfN6yWB9R1S+UqjlZHgGO5WJVBpK5fPGdTLRBx1D5ysFzw2c98Et/Cu++9\ng4uwaCuOj5ZUtaXrtzx99h4ihqHf4CtHysqzy+fEywvO1s8RlFnVcLI85mB+QDtbglQsV4fMFisO\njk7ZmS2z2SXtYkEXR667LWfPL8l5YDVruXN8yuWzS/p1T+xhtw3028Q4JKxLjIPS7SJPnl2w3nWY\n2uMXNXbhMI2lXnnSBnIUSDdTZEtImavzDWqhbjxpiIXIyTS+YjFfMV8cYmwNkpm3C2rf4G2Ft+We\n+92acRipfcNyseCgHxjzWGYQYpFUokbGMLDbDdw+XfLgzgHz1nC163h+fk7Xd0RNtLOKbRoRKzhb\n9NKMIpKovOCcK9N4hYt1h3cjKcKd2wfMm5Hzsy3ZGNKYCCGRcqB2gnc1GhLdtmO73XG4PMBai3dX\ndDERU2YMlAEuJFKcHIpOUQdXCZyAcYJxhtQrWRXvhFntEBLXfSLGgDJFmGCw3uJqx2zRUNUOYkJz\niW7BGZJmRs30mgjGUxmP5ESSLWNKpAh97IsvLGdiGEDLTNwgpDANGCJkmXxfMTLmTA7KblCSCOLK\nczTGo2qBkckEhpu+lyPjMACKM57a1lPgxBS9QiH2RH5hoZusZHWM14Y8FsMpxkzMuQyEKU4Wt5nI\nuUSlSO3wtadtavrrTBpikYZzGTqKzCKQM6IJSYaxj2w2gawUhjaKnYPphNh9B2nkR4cLjo8OOTo8\nprJzrHVlRNYb7waUVOmFqFPKGCvlAUkJ6blx3hYFRBA7jao3oUbT3/KgAFNjnaNqV6QUafoea1qa\nqubiqsE5g1hLF0aGYWToBoZth4rSuIq2nlM3Sw6P7tDMSlTG1W5N1/fUvoQY1vUBYmva2Q5xBjFC\n1+1IeSDnHqhRzeSUSHEoTlgtU8gxdfTjBTnVDLtzwu4Knwacz9jaMm9bcILuirWmeMYMZz30Q8I7\ncFocKb6uOT46paka5u0KxFA3S+p6Xhw8N4vAjAcM3jesVoc8eO01rocrHp29S9NWHKwWHB2uCHHg\n+eUzzHpdOl8eQTKjRvrNFd0Y6FLHbrspPgIVTg9uYa1js91R1TOqqqFpF/SbUKxl7+n6K9bXV2y2\nW+oKZm3Dvdu3yV3ker1j3I1064F+MxJCwvSJfhfZbgKXV9d0IaCNI1lh0IAitIcNCWHYBDTk4o9w\njkzi4mxNFwaqlUVjcdCJwOHJkjt3bnF65w7OCCIZMZmjwyNmbYsV6DYXdNeXaEw0vuZwdUDQzLOL\niyKtYJn5hixKGJU8DDgOWCwWHJ9YxqeBdDGy7Tpc5Zgta9bPR0xlcPVE5CFhVPHeMJucXjFmrrYd\nTixtU3FyOqdqOi42W4RJ1kglOsKKBaeEIXB1tcZWcPv0GO8qmqphTB0GsCghRoggakgGxCrZZUZJ\niC9U5oJDu5E4WYmVK1rQbhgK+WoqBpUVrAHriyRljSlWf12kUOctGQvOk52jCxl2PY6AqROaBvo+\nMsShkJeUsMqcKX0laJExbiQoV/p0jBGCkmMmpjIrslYhS4lOkUw2nigZMVocnCqklAgxgIKVInVm\nUjEIERyWLEKewi5FBdUpCmVn0d5iJllMRcgTcYtM7rubLxh865gdVBysGs7Hgb6MH0Wzn2YJN1p+\nTiMkQxozY58xFYjLYDN4QUss8Ydy6ish8s98+jO0zRKxji706CT2O1NhrCsjpyo5y4sitjgDbsKi\nlJc8zVDkhUkfZ/rdi2BEY6aBwRfNyyaq2rM0gthM3Tp22zVXmy1DH3hw+z79rqO/3iG1sGzniIMv\nPXmX875n1s4RSTw/ewgpMavLf+ISUxPjSO1ajGaidqgXvIPaKSlsGFWQqozaxjiMqUpUhUk40xGG\nNZgeOxfQ6YBsAAAgAElEQVQu1gHJSqtCbbRINEZ4enaFsyMqjiwG4yvECmNOXFxfY4ylcXPak5a6\nbqjruujApjiTyyzl/UFOxGN1TkyWmChWoqlBPV0fOD9/RNPOaGctY7ejrj11W+OscHS44thaLneX\nPA07dusNTx6/w8nqFCOWt999m9u3brNc3kKNY4yJ3eTwvNqdsxs2OGdo5zPq+QrbzLn/+gP66w3r\nyyu6qy0aRjQpMUb6vqfrHLay+JkjMLLbbcgmMosti+WsDP4aiYMioTjmnBf6bU/XD1RbUwb1yjOr\na+7dPuZz3/WAz3zyM6zmS5wzxDxy5+5rzOYrcsps1teEvudwPmM+a6jnLbZtePzknMvza7x47tw+\nol5ZwjiQxp6u2/L46TP88piDoxVvOos8fITBMA6gFzusg3YmtLUQeiGOZYbZLmucN6yvtmW6LY55\nPUeaFkKEqoQYVtZiXUW3G9EciXEkhYh2iXQVudquyyChyq4bCUOAUZGYqYynaiwjStKIGHAO6kao\ntfQZuzX048iu61hUDc44KuMm/xUwhfqpFsu935VIrZACh4s5zbKmWrQgLb7yuNpweXHJdQ40LnFw\n2jAEZYgdKZc+a52hqRuGPtPvIkMYSxhfyhgBgwdgDJk8BIyCr6tJay7vNwZyNJh6Th9LiGHdNFhj\niFOUkKqQVYmayMTJIVocuEq5n7JkUso9ZsXeyGcSwRbnZzIZQ4lBRyjHASqGauY5OKm4d6dl9/ya\n7ipTsu6UGYIxFiOQtcyWQvAYYNYYpEpklwlJCaHMgNW8/H9R3scrIfLD5QFZHSGUAPkYAikmKjej\nMsXho2LJuWhCMSXEQlJFJv3QiMEYVzzL2InEzfsWOUxRMO8PAKplBBVKkL2qUPklyzmEMBLjBdeb\nK4x3JJTZYs6sbSErm6Fj8/Y7zNsLVos5x4cNtY/MlnMWyxneV+Sc6fo1xBFrDN619P1ViXRJHYmM\nSCTGEubk3BxrZxhjsYZiVYtlOxj6BJdDwlkBI2wlsekj63XH1brD+0RVVTROEWdo6oqj1QHeeZbt\ngtX8kMrPUDX0/YBIR1LBWV8sG2PwxkzPRclqCEHJGZy12Moymy1YLo8x5hoRQ0qRMYbitA2ZfozY\nIeMrT1NVzJqWsOsZ+h3X23Osg2G4BE4wrjh7+6Gj221RTViBytqpLWCz3fHovfc4nM85PD3l1q27\nnJ3t2A4DVhzOe3xdHMWr1YoYYNePiLVYY7EYmKy2uq1YLmaM64ExDCXaKQQUsAgP7p2wOlgQwsgn\n3njAm2+8zunpAdZMoWFuTjtbUlUNcQwcrA7xToh55J333uPRo+d84d13ef58w+66x0vktfv3OD5e\n4p3S1jXr7Ybd2LMbEquZYb5wHB62XF8rfYhYL3hvWM1mPLh3QtcNXK93XF/v6IcSz77ZBpbzFYvZ\nktlsxnrTcXnVI+KpKvC1wS0d68s1ZKUyjj4FIKOSGFORClQhhhIFkcZUAg2MkHImaSEXY4W68hwe\nz2i9Y8sOf+EwW5litYuM0viaFMbie5q0/JwyQz9gjcfYsg7DegsWYi5yUQwjfT+QU2Y+m3E0XzB2\nGzQq81nLoinBA95ZDg8XPHt6yeP+nBRTCQ9OJfzYqyCm3BMi1K3nzv0ln/rkPSpf8eTxFefPN6wv\nd3RdmJyVJapKRCYjMU+eisITSctzKNFuJWLuRiLx3tPULSkpXixWhJiLf0GT4nLhErjhl0lasab0\nv045P+sYh0lJMCXUVF7EzpiyJiBb+qEEfjSVsjquWW8i62cdqYtITLivklLlFcWRt8RA8SprYEwB\nRAlpxGk9kW+xFjOZTCKpQr4JLyxhgnIzomFeeM5fRKwILyzy91G2l5jSTEoZwePcDCMe50p86XXf\nMeaErRxVW9HtOnbbHf0Q2TXXkFacHNxh3jYcLJeslgc478sLkgLkiLVgrS+anEZy7kk6lOmeq6dZ\nQ/ti4LHGUvmaZJSoPdtxpM+JhaupXIt1Naq7EtKEKX9zYrWomTcty9mCo8NjnK2Zzw45ObyNdRVW\nKm4WOmgYibHMZpwtxKqUGNkYQomVNZa2bsArdVPjqxo/dsSYGMeRYQx454snHkuKGaVsm89WEIXt\n5Zp+2DGMNbNZg6/KCjjVxHp9Rd/3nJ6ekC9G4nYkxDLV3W63eGDZ1LSzOav5Ic6XZ2Wc4GpHVi3R\nNWLJsSwGsd4BBpIQUtF+DYa6qokmFsVuClO1UpzLr71+m9M7R1ycr7l77z537txndXBICAMxZsaU\neX55heiaFEeMZGbzGWOoGcbHPHt2ydtvPaYPkTGURTZdPxDijKatODpZkEzgutuxue6obC4ymfdE\njQwxY53ijeDxNK5FGktOSogjm+3IbhcZ+siydVjjSDGyvtqyve6waqh9WWBTWU9TWXIsi2nyOOK9\noa09IdzMTQXjDfRlhmtciexQcqGsVKK+nHU0i5q2dYRdwDUWYwwhJtLkpGt8RciZrOXZ5qlPpZRQ\nEYx3YMFVNc57xFmyRsa+Z+y21PWMg+UBd+8e8+V3fx6jhsPlgoPFauoHjpPTYyr7Lrv1juuLforw\nKBZ5SglzM/oj+MqyPGz55Gdvc7hacHQ85913nvLwrUi/3r0gce/c+7P6KcqNyRmaXw5ZfaGPlPMb\nMVjrMEapKoPzhip5tteBtMs3UYcvAkomI75ILckwbJXzbc/Y50kWlfelYcrMWKybBqfMOAS665F5\nYwjXke4ykIeIUeVGKf4gXgmRo4a6qqi8BS26nLEQiWU5742vUzJiEmJL+JmmQuLGyLTgZ4oVRVAt\ny1d/EXdzEyNQnmzWUlAlp+K0Kda4cnx0h3Z5xN/70ucJOTPGwNXVJd1uxziMGFtR1YnloeHOvUOM\nWio/Zz47xZhikdf1ijA8I2lAg2CkQkxCKSkCyuCTEVuBlFCqGBUxNVV9yPU2sBkvWI8bfJU4OZzx\nxskp89USf/Gc0WSMrxm2A5Wz3D055d6t2xwdnNJUS5p2Rdsc0NRLxhAx1jFfLIhJGUOkG3c451Dv\nJ8tDyDEwjjusJOaN53CxKA5mgc3umvXVhpRLp91uO2ZNy2zWslo1DCnRjT2brmM1P+Jgfoun+hDF\nIsbziTc+QdWsyBn6bsvZ0yfEGPj1v+Ef4+/8zE9x1V0xxoEwZGyrVAcL6go0RzbrHd31wO6qJ0pm\nfggDHbEfSX2m60b6EMtMZgiYJEhOjGNkDJnUKeNmJIaMFYtSnIirg0Nuv3abuw9uUc8PODy+w2J5\nysHhMV3fc355wcP33uXxk2ds1mvQwOc+eY/j4xMwDaMWRVWMxRe1jpwS7zx+xOX1OYeHLXfvLaha\nS508l88v0dzQzCou18UaVzsikiBa+quRL3dP8S3YGhbLOefPnrN+1pEjXMmG8XoAiYxhhJBxCeqq\nwiQlpoHXX7+LauD5szNSH2jbisNFy+W6JxqD8R5TGzRbcg7gy2pFkzIEw7DriSOQF4QUGUVxC4dr\nLNYZQiySgQCVd/TRElNZbWkQ1BpsVeHbCltXqAVft8xnM6qZ5fnzK/rdBkLEL1uO7t7j3icf8OjZ\nQ0wInKyO+NSb91kt59R1RVXNqEQYtz0XT7fEcUTU4q0Uh+gUsaaaiUnpBuiGwJ2Z4Vd/3x1O7mTq\nqufs4QWhT1jrmLXtFE75gdBkKYusQDBa5JkbMhcgxIQZRqrKYytHM3NY2xDGDf0ulkU7L4xH82L9\nCjljopB76PuRNAJ5ijOX4kMTI1hfnrGxIB521yNf/PuXvDd3qFMCilGPMeDch2dDfjVRK7bG+6Zo\nVJuMsxV1PSsjEje5VBIpBVIcgICzDc7VJX9ClheSyc3iIc0liuWFFa5fOeq93ywgYnGupp0r280V\ncQgsFkc4X1GHgaP5I/rNJXncEcZQdGxr6cZAPWY2feTsqmPm54UUMYRUVnCFcUvXXwMjvsqMOaA5\nwi7R9cWp59yWuq2KNkZAjJCyoRtBbIWrGppmQVt57pzc5eTodrGawhWSLMeLOW5lWM5a7t27y9Hq\nmFm7wpiGys9wvsEYS1U5jPNY26BEbFTQktIgo4wxFO98GAnDQOUqlu0Bw+KEbrjGWUflKlLbFmut\n8pycwGLWMGsrjKlZ1i1ZhLOrM2Io8sh6syXmRNPOyeLZdQPpekC54umzZ5xfPqO6cGyGDWOOpDQt\n1nAV89mMpmnZbjY8e/iQbrdl1jb4WUWXeuK03qCqXbFsnGCcZQgj/W6gEsvYBUJI5LYqizYAbxxW\nEjnCdrvj+fkF2WUePzwnxsyTJ884Pj1hvdlyfnXB2flTtrstQ98Rw8h685yjw0PqZs7Tiyu23a6E\nq44DOSesEVIK+GrGYlHTDT1pCml99vSarg8085bnVwNJRnCZxWrJnaPbLNsZu27D9fWG9XkgZqHb\nZjQLFk8eFFdZDg4XdENPU3vunBxSVzXD0HO9XnP/zhHz2Yzw4AGP33pKHEeMV1Ky7MahDLa7nu02\nMAyZWV1NS/0zJgl1VeG9UM0drhYwmTEnfO1oZ3VxOuZc2lU9jTjUKL0kqD1SeVxdkYgYDFVdE4fM\n5qrD9oJVZdZacpVBOh4+/DKXV895+uwaZ6FtB2aLGavVHNHM1dVzQt/hRJB8Y9xN1ixKXTsOT1bs\ntjvEQtSxrKTOido5To9PuXXas1w8QoObIlEoDtpJVimSy004aAmzFLmx9G/I/kbGLast4xAJVjB1\nIeSMIraEG0oJpSNLKodmw7DdlgCDFMmJyRovMfrGlGga48B6g3X2hS8g6ZSTxYN4Q+1LzH/M30lE\n7iqcq1AEX9VY46jrGdZOGhZFq0opknLAmIgzBu8ciJs6/rTY4IbQjU6Ozxdrtt43zidtnKmRRAxi\nKypr6Icdxlrm82VZgTn03Dm6g+RyzfeePSWjJAwpKVmLk+X8co1ZNaQ59KFjHDv6/pq+ew90g5jM\nkBPbbktKiX4w9N2WthHqusP6QyyJmHtiSvRjZLsbEFs0PG89ta2ofUtdz8uKvWrOcjawmC1YtAsW\n8xWr1SFtu6DyLUYqjHHTojbFTsmBrHVTQFC8cduQc+mY1tgXL3bj5yxnh4TQ410ZEq1UyBzqqqGd\nzRFncAacCJia2reIsazanqu4K7ObHOmHjvXmmifPz+i3A7vtwNhnHj5+xKa/hKeRdb8maCht6MoK\nRVXDrg/sLnc8fvgYNHN4vGJ1suLtR+8yxowYi1WhaWuaWUV2hqvNjl3fk6gYdyNpDGVhmQjiDRrK\ne5BDZH215tGjZ6y7LU+enDH2I1frNcv/n7n3+JEsy9L8flc9acpVqNQlunrI7gEGA3BBgn88V1wN\nCJLD6Z7qyqrKygzl0sSTV3Jxn3lEVtdwQYLINiCQkR7ubu5m7517znc+cbvlYX9gfzrQ9Scy2zUS\nfaQbeurqNnP+Q+JwOOKcxU5zvua0IuQxkZQSj48nnI/MY+TpcaAfPGXjGGxA1h5TQaFryqqirEqG\n+cQ4Oo77KTcFczZsKoymLgw3lxf85tdf8fD0SN0UfPv1K0pTMU8Tx8OeV69es93uMEpz3f7E08Mj\np+EIQmP6ntR1PC1eICElrJdEl/BTRNiYaXeVRBa5U48pMbuAKgztqiLYwDRNuBgIZC8aU0iCipi2\nRhZ5wnPeEqNDJI2dLdYL0IGmESiTmS3Emfu7D7x7/5HoPEUhKUvDw/5AVWoqownOMk8T1jmq2jBb\nxzz7xTdJZB1BLbE++5cIFSBGtJBsVytWTcnhReDm5gfCfMTaM1HCZ9MvzoZV2WYgM+Jy7cj6lVxB\n0sLxl1IhYiS4wDymTC0UiarRlLUmWoubPM5+gm5FSrjZ5wYzpcx+WeAcIUXe2wgQWiL1UshdVrMS\nEzbEPNUbUMXiFxPC366p/79V6/+Hh9ZFVnFKyW53CUmipEbrjKWy4G0x5eJ8Po0FMkMoSgHqMwk+\nz0yV80d+hrAIeMZr5KfPIRqMqakbT1Ntlq9WfP3qN1xsr1ivf+L+cGI8PTJNM4WCttSUKjEenhCb\nS4QMHE53nPqe4+me7vgTN9eXFKag7zqOp4EYcmH2dsQHQ1NbqioSomNyE/M8MPQ9fX/ExYmhPxDd\nyDhNdPWGcX3JenXByxeJ9Sa7HrbFNUZtMnbpNFFKTKmW7XdW3ilZoLREKUkM+TWJWUpICokUPLIo\n0UphqgaSJImETzNVpXBzhiXMyrBpt6xWF8zB5z1AiAhZMPQW62aUlpRaU1X5xvfe8rS/5+7xwPHx\nyPGppz8Gjv0eUQZCM3GaTiQRqEqBiHlMfjgMPB0nfD9jZ0ezabl5fc3N6yvuj3f0dsJ6j508F5st\nFxc7rE/MFrow42JknhzezuhKUbYVSijC5Ije4pxlfHT89ENBdajoh47tas1se+zTxPu7ew7dCecC\nZVlQlhWmqHk8HvEPB7TMTYG1jnmccbPPl5NLCB847TtS8Nw/PjGMHmshBoHsPeo4oSqNjAnrBV7N\nvP94yy2Svus4HUamMQt+SInCSOoy8eJly9///Tf8z//T/8gf//w9Pjhurq9ZNbsslkmB1WpH27QY\npdiWO3768S+8+/ATZVmxaloqU/N4OzLLhCwidp5JThCnhDtYkvYkZXAh4EOJTILZQmUKzFoTLVhr\ncc4zElFGo7SiaBTFZkMSEtf32ejMe+ZxxMeMHQfh8dFRt4ayaZAu4qaJaXYIqQgRbu8c/+l/+8+M\nf/crfv3NV6w3W1z8QDf1XL/aICQcn7qspF2ogfvjnnkOlJWhLPNkXpctX3/xNS4WTJ3g62++oD8E\n9k/Ts/HWM2tLSETKplQpZZ8bRO7awzOpcGkKlUQrhY+ZnpyImBI2Fw03r7acHo483XU8PqZsFkZc\n1sB54UmSkNfCeS8Vs2hOKY1Ui8thyjsAZIaxUhSZguki82xR5APhb9bU/89V+f/Fw3mLEQItNZVp\ncN4RokeERWW1qPLKoiYZA3jUAqvkdlMuI5BYnMP+9i8nPqvmCZaR6fN/FxRFngS0KhcKY+5qR+9x\nXpIW4r6SUBlFXUjWdcmmvaBeOuWxu2eae2a/Z/YTh+5EiNllz7mI0Zp107BerWiqDSFqQgKtBLUq\nAEvC0g/7zNEOjkYbqlKzXa3ZrS5p2iusD8xupiyu0GaHoCAxZMqgBSECQiW0znzTlPIOgES2zwwe\nnyI6hYXGlcA7Ju9x80zdbpi94zSMNFXLZv2KuloRolvgl4lxGhAiH6ZCzVRtwUqtKYuGx322Fagq\nibUzXTdwe7vn8fGeh7s9wUrmMCNE4mkfUEZwc3XB1WbDx4+PdL1j353w1pNGjxgD2hhu7x449ieG\nfszcX6kA6PsZ657ytOUCm6omRaiMQQBlVbDZrVBCcD99hBligGFK3L4/UI2WelPwdBjwSVC2itWm\nRhWCjx/v6LqZrutRyjBPM6SE0RJtDFJKmrpAi8Ua1RSE6On7wKkb6YZIiMt2RoosvS4N28s1Xjhc\ntKQYOJ06oiNbnIaE0BqjNZu2RqbE4XCi6y1vP97zn/7P/51h6Gmbkl1oWa2+Yr26Qsozq8LR9weC\nkmyvLmk2LUjJx/s7fv/99xQ/vCP2DmtdxseXJsmHzLAQQTCfLPcuZetUGyhUgS7lc0PgbMLOM8Wm\nRO/W6G2LHyy2G5mGCV3ozDCLEZlitmQ2hquLDVEmhtGBDQilqFYaHwTJBaYxcvvuiWS/5+O7e7TW\n3D48cJon6rpCFwmRPHGe8UIgUMhCQXbSQMrEzasLXr65QcqW01PH/rFjHt0iesqTuw8OHxb4QgQg\n4Bfp/FkmH1P8VFPS+f3LWHkI5D3LukEp2FzU/PbvX/L2j5q5Txy7kSQMSWokLN5MMZvWJYGI5KId\nIkktz4nIwqeUDyNZ6GyONlhEWHD3cGbk/RsSBOVtt8kGUkKTVB5HfQzL6BAQqNy5i5KY3AIZSNLn\nx+RCnTvTDM/jkFhGpJ/VbfHXL0CGWpTO3HWRMoNDL45qIQasnTFaZm/gqDCFQohsk7pabyiqhpjg\n1O2xtmeae6wLHLoTs3Psj11mwpgaqfOI5mOgmwY43lO7kbIo81ZdSZQUOOfRQlJUJauyYbe6Yt1e\nos2GlB6ZbUSKQDAeYxRSGaQsAYP3AqMSUiq00kAWT7nIsgC0i4+zQ5HQJGbvCd4TiRQ0uRsJCaVK\n6mbLdn1FTIGxPzLEJyoEzlp8CJhCUjcNbX1BVWwXC1BLSNnreRwt3lmGoc/7AVNTGkWUgWkYubq5\n4PrikhcXlzw+ddhDzzhNeBsQU0RPYEjYo+fplFkzgvSsdJ4mSz/PFIXGSMWqzQyXoqgoiyorGgtF\n9JZ2XSGcx1pPmATTfmIOEV1oDoce5z2rWPB6+xKpMhrrnMvqzzQvcB4ErygitG3NbrNhHAakyK/3\nsTsxTJ55Dswu915SJbSU6EJSNwW7TYuNM/0YGcYJOwbcFHEuIWTu+rTSlEVJWmh3h2PPj+8+MgWL\nUZLryy2rdc2q7XA+L82r0uL9xNA/ZVteLShUy2q3ZRaR5v4d1Voj93naNVIt4GSm++nKUNUahcAP\nHucjUSZiHVGlpmkrhqPBzy7DMLVBNgahJWGyhHFeln5nUV4iheyLrjEYaZiCZxw9MiSKSmOUJrhc\nVJML+Mnh7QNPTye0MUxuxgdHSJHZ2VxgQyKIBCEL4MrS0LYlTdNwcbWjXa+Y5sDbtx/48Ye3PN4f\nsq3GQjl20RNihieyHmXh0Iu0sN9YFJ3puWbEmPDBE3z2ZpFowpmuWZa8evWC052lKDvqOqKUIqqE\nEhBcxM/5d8PJhZghsttlyCpWtYhrs3p/wdylWD6YC3muZ5LFiOpfPX6RQn6GqxfmJYUpkdrQDwPW\nWUxMaF3mJaM0Wc56Vnwu3NW0YL3ymUJ0fqS/brx//sQ/+6uA7Mn2fBgQAz4OjPMT1u1Z14Z5LOjw\niFIyRcvgLbIqUFWBd4nhNDBPPbOdmJ2gn3pcdCSRKOsi33TritPhSAgdUh/4uP+RuqzZrnZcX7ym\nLmouNi94ePpASom6atm1V+zWL6jLHS4a5jmx3w/c+4G67tltr3n14oZCt5AU3tt84xhDUdR5zI3Z\n6N5ah50tzjnGqUfgKZRg6I4Yo9jsdiA8SkvqOi+eQ4g4H1DSUFWrvLU3DXcfP/J4f0tZtVTFmrbd\nUJkN1o+ENHLqIvNk6U4d8zgSvMMYxeXVCqWzlez98XGZNi7QosY5GKaJfhwxQiOjwDvQpcYHzxws\nUi6rrsXawAVPkJG61BSFotSG0hRsN5esVluEVDw83HMaT2wvt0Qb6XuHTI5oHXPv6PYDYfbE4NEm\nET1kD/wM9wUyKyKGhWLnPc55tpsdb159Qdcdn319Dt2RGBaFoMqTp3MeqQuKsqBdF9S1poiCGBz7\nxz1uTlibGEdPUWX1sZCasctTkp0d4/xEN030k6MqFHb2FGXD0+M/k2JithPb9Zaq0hQFNG3JNHim\nLvDalIx4Up1odobiwSC7gBI6o4wiIWvN+rJidVVRNoppdAyDZfR5Ua8Kw+Zmw3Q84WdLLDVFo4HI\n9PhEOA2IkKjrimkxlDJKklwOiCFJ+oeOMQUm5yiUQoZEnD191+WivCiCnQ2k5Clag3Uwjo6HuwPM\nQJSgTTaPEomkIuuLkhc3O66vXtI0O0KUPHaP/Nd//mf+r//8L/z04x1+NqBMLuTBZ0/8xSE1Ekgi\nZIuPhamycOQyHCIE3jqm7AWC0hofA/d3PVUl2W43VOYSKe5RUrPdbJjkRFAOIQNKVLjRc3rICQvB\nZz3MmTUXrEMpk4X9MYvegg8ItwgfU1avJ7l06/+qIc2PXwZasS7ztpUHkVDSoFSJKRIpZVqWOs99\nAoTSnLtvFqph3jB/Op0+UYo+/ffn9fxsbfv5C/HZSlRkz/AYIsElCl1ys7tk2xq265a7p3ue+gOr\nekNbrjkdD4zDjLOB7nCgKkpW1ZayiDx2Hm89EU1/cuAG0pyYbWIOHhv3BOepjGEcLli3W4yp2G5e\nYYzBB4eWBVebL2jbayKG28ePHE57xnlkGHqeTo88nW6Z5294ffMtqzZ7qnifO0KZYhZlSI02Ohei\n8zQjQ5YDJ4tLE30/sx+eiFFTFYZVUxGiZn88sD901LWhKgtMUSClpZ9PDOOB7W6FkgaBzh4e88Q8\nTkSvuLt74uHhgcvLHY+Pe4SUXFysefniJQjJu9sPrFYNKQT2pyOTtYQIJI1IuSi324rrL27o7cD7\nh4+s6hZnA8NpIsnMw04agkw4kW+Cx8cj+8eeurknackwDARrWZWZNujJN2+MjjQ7hn1Ep4ZUaqJN\ndIcJnyLj6AkBQgBnszFZlptLovc83j0x91OGBROEmOi6npSy2ZSQaVHOGqTUKFWgZEm0imny9IfI\nPEisdcSQUErgo6MfPVM3IkMONGjXDbPPjIy7u3tKnZNmYrJI9KJQDGxXG64uL7i+vsh0wdnSn078\ny+9PHMY9tw8PZLmGJkUBUWWLggSmKilXLevdmouLhkIpvA08Ho5oCbXQrCgZLjf4lBhUglIgZKDw\nPjMyEEgNyeUAkJgkSWZb3Xl2BFkSC4HQuVuPIQPotTY4lgPDFJnKmALOTUAWjQkrMnxWADFjQeWq\n4IvvrrhYF7S1IYSZH396T3c4YvuODx+e6E8WZyPe+SW1R2C9Iy7vEfFss7Ww3VKuAdmMWKDOjpQp\nP69UgqoxFJXBDhZrIx/eHvhf/5f/g+1G8I//4YqkIn/44ZEPdzOztawu6sznHyach+RzcIVSarlO\nshI1CI9IAh88LmTYTaCQQmfuuEwkmT4xYv7q8Qu5H1piUMSgQBWL1ZVEqwISSJHyRpdcjqVQS6HO\nsMbZ1fAs8Dk/nsvyz2r1GWaJnxX+zwn/+f+XYZC0qNyqouHy4iWFecFmfUXbrklv/8jF+pK2XmFn\nyzRZvPNYN1GWFbooKbQiECjdioRiGsdMw4qOOQR6OzDMpxyAUFXUhWGeO7RqAMPlxUti8KQgKMsN\nCEp6KD0AACAASURBVE0/9bx9/yO3Dx/o+iNzmJjmiVO/R4TEqtmxajcolR3UlFDLRZDHdGNKCpMP\nqZgCUtdMs+d4PNENe6ybSUJxPPRs2hatrun3B8bRkkLk5YsLVqs1xhTM3nI4PpJEoK4bClORksR7\nx2wnpnlktoH+1NMdelamZrNa0TY1L19c89UXbxZnuYQXjslZurGnLCVXFxuEF1SiIE0JPwbKusLr\nSD2VrNqWqZ+ZThNKSLyISCkwWudIQJ+YvMsYpE/Ms8NbByEHJGQBGEgRUaR8+IwzvixxY2DsLIfj\nQBQR53I3JIWkMCoX3BgXZ0IYxom+G7JiMoJf2BT5svWkxcuaRevgZk9/HAlTYpwsp37COSDJnBpF\nIqSUlcwuECdHaTTlao1AEpNjngasDdl+IA5okcUzdVWwbVeIlAM43OwI3hH9xN3jgbvjI/f7Jw7H\nkdl6oshhFhmTBVNqkDJ70gcotMqCoqLKBVZIalPQbio6NzLOEyl4hIsoH3IBF0t3u9yTkuz7E1Km\nzEkXkTr7rigZKYzEKMUcBT5lcRGyyPdhzLBWTJHgItGdhYG5jicFqlQ0m5pmqygMRD/x40/vuNMG\nLRzdocOOHjdHYpTEGPAhOxQujPHsi7L4qJzpETHl/YBYcHgldMaok0ApRbMqKdsyG3bZQHcc+OP3\nb/nv//GGm8uGJCfUj5Fgs1tiURkkWX3qB/9zKrQ4a2ByQI4EUnTE4AjBo2QWKQiV/Vwkn37Ov378\nQoKgmRQhJomURd7kxoBEoXSDlCyYd+6+pRQLfU7kv7N4CYu48M7P2Dg8S6ryS5QBmPRJBCAW3ujZ\naF6caTGwRM3l56nLNWVZsV63XF7OrNob7DyzXq0oyoL98QFSvnCjSswiohVUdcGb9VcICkKQ3N/f\n0nUH7DwyTD3H4Ug3HrjZ7ah0QWkK7NwzhJFxjlxdfoVRCjdlxkM3HOmnjh/+8j2P+zt8mqnXFdZb\nvB1QKfDm1ZfE9IKqqKjKJkNVMvNUldIooTDaEIzH+omqqJnnI49PHzjub5Fa07QXTNMJmRxlofjT\nD+/oTkdKI2mqv4c445zjw92PFLrmxfVXbLaXSxZpwoeR2U3MdqLve/xs8YPn/u09u8s1u6sdFxcX\nXF9ek2KgO7Yc545+7pl8x9X1ii/VFRvVsCpaPr7f80//8heO/UhSgc26YV23YHNAAeRlUiTRFjWF\nVjgVCNuGdltTNgXh8ZD3KjZhnSWFLHHWZEUlUeB9IjjJNIE7TKT2hCxyAZLkIIKmqHh42jPMljn4\nzM9/lnqLxQUvsV5VRAIuOFTMHZ5MEeEj/VNH93RCKo2LOdxBG5Gtj1NkGOe8q1EKIWGaHdY5xsmS\nCpEFPAsl9zR5xo8D27bl6mLNZr3iV998y7ptcHYiWocIMdNrpxMPDw+8/fjAcW8Zp5moIlHkRaRQ\nEq0Vk5uZnizd0GOkQgu5YMaBZAxKS4paUzSCNEykoyBGlb3Ga0kUCRtmkpSLH0tJFBGrEkHDlBJl\nStRKURaJ9UpTlYaPPw1ZZ8ES6LAUKusC3jr8mLFp63w+iJNClIBMzMHidUFZRaS1/PiuQ0XNqxdr\nop8QweMnUGVWsFprnz29MyydC6lI8tl9MoiEWBwzkRohiud+0RQF1aqiXpVMp4mgswNjN06cZsu+\nV4zDiYePI8MhUq4qpFJoA0VrOO09IcUMo6REiuRYuExjyTUpBSSeJHMkZEr58EJlj37535B2/iKF\n3OgKKcySspEQIqEUaKXz9lyIZXNM7qg/a7w/FeTFiP0MuPPJrOZfnVnPJ2z+n0/4+Cfy/3m3oZSm\nadYYY4jJo5Uias16dcE3X32bl5Vjx+OpZ5rGvEB0Dr0oHjerlpeXX9PUNUYari8lm80O70fu7t8h\ndU5tKQpFXddcbC+oCsVkPUJaxrknaUOMkdn37A8jH+7v+PH2R8Z5ACk42BnvPEpCW3tMIWjbgrKo\nKVSdF6xKLa9TVpBprTFRU8bM9123a7775rcM19fs90/cPz6RokOrhlKXFDJxsWm5ub7iyzdfU5Ql\n+8Oe8S8Tsi7QWhPchIPc2doJIwUKyY9//oHD/kBZVnz95ReUrSGKyN3DAxcXl1xdXvG7puW//OGf\neDqdKGWTbYaVZhJ5eXYYhrwzUZGi0RgjebjfU+qKb7/5jqePR7p5JJaJ169v2G3WgODHDx+ZwkQM\nnnVTQ1XnQGIXOQwZz1bC5AgzkygqwRffXVFvah5P+8U8KcNRZVkgkUzWPRuTEjMXPbvy+UzrlJqq\nLNFaZ29xFoEH5I5bfuYDJCRakXURpaKQmbUhoyfFhJCJQmsCOtutTtnVUGlNUxaM3UQKEW0UTV1x\nfXXFd99+w7fffsdusyb6LLAKznM6Hvj9Tz8yTy5zxm2mjaol3DuRltCOEYJABpV97XHPJntaCmSI\n9HrEaE1lSpL1TMOwpCOB3BSYQmdmy1IcHZnXTUzIlAg2IISmbjWvX27wMdKdJlYXCjNlgyytNbgM\nU5EEcQ6EccbP7rkWOO+oGoMUif4wYmRAXzS8vNoR+gGJ4PK6xN1XaDOBmDDGIIn46Eg6WyTEEPLy\nkDPcujR1nIWG5LrAUkx9pO9n0kfPeq7ZbvNr6OZE3ZacDiN2GnOY+95ie0dwEfdiRV2U3FytGO9m\nnEjZYkQlTCkp6ypDUT6QQqQqSyppCCky9I4YJcicFKVEFlb9rccvs+xELtiPQYhlLiOfNlIonj1T\nFlD3c0nts/Xj4rVyTgX66887Qy9nBsvnX/9XP8wzAway74GUVY7uih5ImCRo6jVXly94ODww7i3H\nIRfx4LP6VFoPMdCU2ewrLSncVV2jg2SaMyZeGoNNhrR0IEKkbHErPEo7+uERyoayqNCl5nh35McP\nP/LUHUkxZCvOkKmLVWmomxIpAzHlrECjzWJcJJ5/QSFyApI2ChOz2VBdtVS14ViUdN1IdzoiSdnM\naHvFy5sTxmhevHjN1dVrEjBODqXzQRCC43Q6oHVPDGFhxDjcOPNwe4+bLavVmvVuS1SBce6Z5pEU\noSwbVuuSsvgBJQxGaOqiIQHTOCOkyiG/ISBjtk9IQXA8jqxqQbmqFow1F4lxGim0xhiTvV2CIBEo\nN9XiIZOtZ8PJ0z8MKGVQAaSGcl2yvmyo1iV90BSlRmi1iEAk0WerBuuzL370ibgUqehzMZBaIsLS\n8WW+WOYRi78ahJ/XNwlEXDBuCD7l7xvzVFfoHAyeoiS4RJIRnQRayUxfSylDIlqhdabLHbuOtmnZ\nrbcMw4SNFjA4G3E2EH1EhLNCUpJ8Fqv4yeN8TpA3RizhCHnDJpREKpN588FjlKYyBQrFNHuCzQ2C\n9NkLBykwi2o4hnwwERPSp5xcIRKxUpRiS1HmBiCm7P0SQmZhxXSm6yWi9bhxyqIrKUFl+9hmbVit\ny8zlcIJS1bx58YZa7CF5Li9rPop9VkYudUIJ0FLjpUIuwc9CLIHMIvvJx/NaTpylJ3n/cPYqTzYx\ndtlUrRIV0T+fVXifYV+PyoVXZwZZpQ2agn4Ycjg2OdlMG4kpBNqA99nfJZEdXaVWKBKTXLAGKTFF\n1h/g/g1h5CkGpFSYogJV5R8jLV0L4vnvn26CJXPv2SjrXMg/s67l0yLzTEU81/WFFJUxqfTzYv78\nOZ99/9xFSYTUxOiztWdR09Qrbh/v6YaBfppQWqGKgpQ8wmc+cV0VpOSYbUeMikrV+DDR93uCn0kh\n0+iQnmHq6IYDKo2oQqBM4tgPCHFF3dSs6h02/Jm7/S3Op2yUmSRGl6xWmt2m4vrygoSnH/asm4tF\n5SoXEWvi3GtIJdFIdMgua9l2oOZwyPLxrjux2dRsNxtevnxNYRRGl2x219TNFcM0gCxYrbfIJLDz\nxP7pfsGBAzEmQoj0x46xG9HaUDUNaMloe0Y7ZR9pqQEDy4JbKYOSglXd4KznNBzZ7dZUZY1Smqoq\niCJ3Q9PksPOR/jQwPo45FcbBn7oDRmvauubm1UvWTUtZa1bNisPxiPOWly9vGB4n9rcdc5/FO0or\nVps1stCgBU1bUa1rUJpxskzDhJ0czuWF3TzZDIjHgIiLulim3Dk7zzAEiionAWWnvjN4p5b5mOwp\nFPyy+wEXwdvsApoW5kwhBNPi95EC+DkQfCRID34RjcT89ePY85ef/sL+8Uj33a/57/7udxwPJ/rT\nwOPTnmnO0nCRElpAIqezu3Fm7h1uDqhSUlQKsxACUoyZ/VRKiqJAmwIvE6XSlLqgNiUDIdu6ioW6\nG7PIRUtJitmawCNy8bIRFSPJe/roGZ527F633Lwouf2Ylc8AxmTZewoR4RPRWdw4MI8TSWlMXdCs\nKy5vWrbXa2JMtFXJbrXhzasvuWxbvO/RRWSaLV0/ZbO2GElLHupiqYJSi3kYAmTurn08KzCX+0Ys\nqUSLPEgmiQgKPygexkVcJ7I//9X1it1NjcXRdxlFaGuRc4m94N2fT9g+QhQEApXRaCMgOkhqGday\njwwhH4qIpaHQOZwiWYe3/4YKeVllBoRWBVFohDCLN7d6Jr2fLQ9izKKWXFjVZ9330nUvCMnnbJQz\nnv45z/zTI332/dNnnftfURPPzBjy5lrJkkKvKUyNkobkfXZg1Dk+6vrFDYUueHw6ElOB0QPeR3Sn\nECJCtAiRGTlLNgjjbHk4PFKaa1IIjG6gMhVCeJyfeDq947B/wo0Z78y2r5K2kry5ueDF9SVNs8Ha\nmbv7D6yqK+S6oBTq/Jt84vSmBMisnl1YJkPf88cf/8T7+/eoUlM1NUJp+mni8XAihI6nzrPZWR4e\nbnn37o94f+TNyy959fpLdDIcDo+MQ0dTNxTGsGpXXF/foExBs25BJOZ5Zp4slSooigqpC8Ypcep7\nvLds1muuL65QSLZFQ3CRviy5vr4hllnibFTJNO2xk10MlLK9qCk0LnmklHgveP/uDqkihRG0bUtR\nGqq6pO96hIF6UzHsZ4TNLw9SoFRJVVaIKNhcbbDBczyccLPDLyk61rl8HUZIS2JuDnZIpOSJacmX\nVQLlE1qovBx12ZBMLiHQxuiFChwZuyHrGKTElDqH6wrB5OdMgUuRebQInTnLUeVkGikFIeS4NRcC\nH+7uuRcHvHWEeWIaJ/ZPR+4eHjmcDhRVyeX1NYXp6buZsbM460g2W6MmAYWsKLXC+5xhWxhN0xTY\neeZgJygrymaFKTR1U6OKidk6fAqZ3SEXjYWNWfUbI8KYrKScPd47Ah4XJfcfjnSTIxTQ9f2ichT4\nMFKVhrIwROkZHvPvSZIknxfVq23N+rJmtSuxU2SaZ96+u6XQv+fXX16xqhsOxyfs6Akx0wVDis/C\noexyGMgs7bz4lCyZmZ8KC4hFlroUhHOD56xFKsHuRYmRG0yh2ewUv/2HG1Y7zV9+/EBRRXoF05T4\n4fsPeBeYpg4fArkMK7zP1ghaqQzzxHxwuNnlnaEg212n/JNar7KuwP9bKuRFjdYVShWQPhXxTzCJ\nWDbHmQaUCxJAXIr7p2KdUlpyPrO39nMB/lwF9bwq/lS44edQDJw79Z/7LuS4OIGUBVo11OWGdbNh\n26wILKwJo7m+vEEKRT+MPB0PzwwSoySCAMFi/YyPlhAcCkHwgWG2DDYxz5Zj1/HqxjDbiRSf6IYA\naWbdGqwTkCKFzgnvV5cveHH9hhglp27PPI84PxOCX8bB81IkfdqSLweIkCoXJASn4Uhve9ASU1bY\nELh9emDf5dzRcHtLWZV03ROnwwNtrdBas2p3KFEyW7dc3NkvvKorttsdSUBZlTRNjVCXTFVLtJ6i\nqAHNOPWkEGnrhtdvXnN98WpJeyp4enzExYhzmT+exBlvDc8ahBAiwgWkkZiiIKaUFXwye2fHxXK1\nrkpKU+JmR91UbC/XHD6c0CYn23/55Wu++uo19argdNxTbmsOpx4/WfxoiT5jmkrkQOAsZ1gSY4QA\nuczWy/Iq+AxlpJB78egFySWiiLnbSnkfJAT4mBOiTGmoViUECXFJiRSRKPP1L6RAKZHTb8geIdoo\nlMlJNqN1KCLDnBfObVPibMHTEaSRFLpAI5hmi/GZXhtwOUUMQYwesfBTQwzLog+0zh71IURmAi7F\n3ExUJdWqysOJze+LigqpFCl4UiBDMmThS/I5X9MnT0KwfzxSp4BqC4L3SCkotGazKdmtWyQKG/Y5\nDIXcmbJMQNoYhJT4EOmHDG16H/nTD+94sa1Q0fDw4chwmgkuIqReYM7lno8xTxwCYvTkUzXbxz4f\nxpyJEHmnIcUi7kIRQraSvbguWTdrjDQkPITIeJrYvxvAwnpV0BSa+/uB7jSTUrYCWDSkz7tBYtYm\npEWc5ENYjIUznJie0YXEErH6Nx+/0LJzhVQ1QpRLcnjmU+ZxZlkKJRYYJX3qKmPKF8OSBnQu1DHm\n9yPmFFcSoM/0xc8+98w/Z+GJxvgpeOKTMjQ+d/Pnj5MSURikrGnrHTe7FwwvDhy7AykF1m3DbntB\njJKqOXF3/46UAhebHVJoop0ZTnswubNLwWdescj6ymGWHPaep8eR6+0l4zgxxAkfNE0tefVyxfGY\nPUakUhRNxWpzQ7t+Q3/oUXLM+KlIQCCl8OwQmX/j87b4ExMoW7CanPamIjECquA0Tdzt32Lnnv1+\nz9PTHh9GCiVZVSUxrhboqaAqN6zXLj9f8gglMYVhtV4x26y43O12vKm/wM2OD+/fUxYNkKGZuqxY\nr17z2+/+nqbecewH7vsBK/ec5on7h0dmkbMhVcrRX4XKB//RdZmiJj27dkfwOcR4vQibwNNUmu1m\nS9u2nE5HNps1WMlb/ZGy0lxdb/mHf/gd3/7mNcrA27c/MkTPYd+RbCTZHPOltKAqTIY0UiKGfLin\nlLLBeRTIQI6Pi4l58gQRMVLnVPlzJxgiHgdJoKTIgQ+ZPE/TlhBVpogimK1FxSxvl1JkMoDMikOl\nFVVdglZZKKI1Rhmqpma72/D1F1/QnTpkpel/8PTzjI/kJJtCUtUlQc8ElSdelxzRZ3+aqM/KxmwV\nZ4wmKUArPPlnNZVhc7UGqTjenRDOZ5qr1Mwh97iyKJEhs2dETM/MMe8j3alDN5p6Uy0aEsWqrfny\nzSXXlxuCT9zdd9lxUOqFrZYVlaQcUOxPM3d3PXWjiQbuHg7cPTxhO8O7Hx457Se8jYsP/mfT+ZJ2\nH1N6Ns+Si9ArRg/psyZQZO1AWIp7DncHbQq2FzUvrloIind/PvD2T4/M08Sf/8uR1a7m+lXD668a\nJucYRocyNbOPGXIjEHzOH046EpzNmZ8p5owAQc4jPh8oC5QcBfj/Rk39RQq51CUImV3Glhbrk30k\neZRK+QX/5HmwQCHx3IEvYankN0ssm27II9S5IOc3Rf3s+c9L1PzUS1r2MnLlf1uCoEUu9onMazdF\ny8Xui+dp4nC8JYSJVdOgiyrbt9oZmQTOR47diboowTvmyRNdpKg0X77csF1v0MbgIwzzzGwHEImy\nWLOqNwihSWhS0tg5IDc1T/s9/TAgOfDw8J7alFRmzbrdoLUghBnnJ7Qql4s/vxYpw4AIITAqu066\n6HHBo1AkD/0wchomrB24u3ti6A5oGakrRV0bVs2K3eaS3WbLZndBTIn7+/f03YFxPOL8RF2XSCm5\nvNyQCNRNw/XlK9r1FQLJZntN26wgwbop+btf/xZlNG17CcA8D/T7R6QPGKkRWiNiRAYoQl4SxRns\nlA399VZSX5fcvGlpdEOlGzavrnl4eOJ0OHJzteXlzQ11VfJQaLSu0KGmMjWqFay3Let2w+s332Eq\nyakfeff9HzjcH9hVK8rNjml23B06tFRgABGJXhLytJ6d90jopCmVYpwmhnFC6Cx31zJf337Jd6zr\nElNmjrm3S1Kkj8zdzOWuZbtpadqa29t7jt1AVJp+nJawDIXSIIwgakHvOmQQ2JBTkD4+PvLHnz4i\nVcPptOftx49MbqYoDK0p6ceeoZuySlYLLAkXIQmd7xEpiYJc2IJjnAWFKTNOnlUpCA1FXfHioqWq\nC8anEzIKks2sEGfzoS5jZsfIJLFaQ5iJKQcUhznie0dsErISvLxZ883XN7z5coM2JQ/3I3EMYBMy\nZuW2LjSr1YpdvaVSkigiZVGiZVz+QD8P2Elwd98zDYLoswlfZFF/p2VyT5nhFkRezhoEUp1FQZ88\nTfKdH5aasdhdCXBu5uGuoypWGCk47ffYx5FptgyzY1NUVFtJdRVpbwp2ClYryV/+64R9sJDiuZUk\nJEWIbmm2RKaDlgplBG50nF1r7TwTXUSmv13KfyGJfnk+45Yu8vznM/w7fsKwQwjLSXm2rWX5eE4W\nks+UQvVsa3v+bvL5RD4/0jOEAvKzYp8+PfeyKD0TXnLBzxdyJfMEoZRk3a5wbqSsKrIb457des+m\nXWPtRDd1RDeRoqcwGhcDq2rDF69es11v0aZk9oG3dx+oKoOSK7bra3bbS0iKrp8o9MyqCVRV5tZO\nbsbaicPxgbZquNpq2qamrku0zris8y7/jIh8XZKIIUNGWRjh6McTD4ePTPMpm2JFT0iJ2VlO3ZGx\n79iuK9p2RUyOlFgcAVu0LrHW8vRwCyTKsqaoS6qqyk6KugYRqaqG3e4FVb3Jcv95pirbHAWmS1yY\nccExuplpHOi7AzJGbDfg+inj4AuXe12WrLYbgoPj00ByD8g6d7LrTcOXL77i5uIVosmiEg188803\nfPn6C7TSuDlSNi3YkqI0SJOvrfdv3/Pr3/0KIWqG0wgucrXdcvXlFU1TsT8e0X/6C8dTx+BmbPIE\nS+7IRRZq5HVmohQabx0yZkM4rSVKgXMRXUhUIbMlqcoMlNJohCxIQuCD5Xq349uvv+Dy8pIPF+/4\n8HDHw6kDCWPviHMezJG5k7beYoxCF4vgaOp4f/8RRMK7mX7uscEhF1FOUoLVpmVtGqZ3HZGAjx6Z\nKdOYAlBpceDLHHsIpOjAaJzwKCEoK8HuZk1Tao7v94ReED0El7HnlBJ+thQqZ+o+T9oL1BJcYB5m\nxtPIqiq4uFzx5VdXvHhR83Q/crw7Mj5MpDEHn5ebghevLrl6uWO1axl8j42WL16vmaesb/Ah8LDv\nCYPn7bs94yiIUZIW/31EgBiIy2I+W3JI1PInN3ACJXUmRSybLCEy/JQEhGWi9zZw+/FAjHn5ezwM\njHZidh7roBsc+8NM+RSYZr/UnCxIzLa5GcZKMRBSfM4/zY6wYpnAJLqtcw5okozjDClm/cPfePxC\nhXyRFgPiuaSnHD2Vcqp0PJP2Y8S7HDiaKXTmuYPOW+O0wCc+d59nAHKp5J9DMJ/44+cD5Oc8UlCL\ncvTnzJYzRJEnAUMlFUVhqMoK6yakMmhpqMsLgvW0bcM4dfz0/s883P+U6X51zWThcvuCL178iqZu\nMxsgRUY7k2L2Wr66fMVmdYX3cDp+QMmWVauIAvp5pJtOzNPAOHacuj3r5orL8oL1eotWBlCEGDOj\nI2a0LZMc8sLJ+olpPvF4+Mj72z9zON3i44xaVHdCssAk2RCoadZ0w4muH5nGwM3la+bJIXzP8fjA\n7uKSy+sXFHWN0QUiwTiMxOgoypLV6hIhJN4O2MGxbvP3LJvIYXiiOzzwsL+j3x+w40ilC4bDwHDs\n0REqrdhsG168vOLNr74meLh9d08MHi8tq6qmLVrevPmKb7/9LadxoD/2GCH57le/5psvvyXZxE9/\n+kBdrbCrhKk1Kgqsc/z+n/6Jr755xe5yy/s/v6MqNV9/85rf/fbfYYzkw+1HQnTc3d/z1J042Il5\n9ESfL6gkJZDycs9lap+SJgehlAKlAjZGirakqjXDEjihlaFqCsqyJSXB6eh5cXXJb777Fa9efcnN\nzZbVTxXu+z/iAxAkk7MZyw6ZSkiIyEJjqjLLzaNlf3zAuoGiUCATLnnmfiR4cCJxvb2gqQveq7fE\nlBeQQih0kShqEJoskEmKFCXT6LDCEZqCWuew56qE6+s1u/WK7nbg/qcj/WHO/HalgCzACkTODqVS\nSmTUkLKD4DhMCH1ifXNN1ZQ0u5qqKekPd3z403tOHwfiFFk1NW++veHf/ftf8fKLS/rxxB++tzjr\n+eo3a+7uNI+PPeNsubvtGfcT794+YcIKgSaGbEYGgRgtMYZnd0MpcxEXLGZVSyEXMouDMpa+0JyX\nWgTZAfLh9sQ4BEpTQh+YfA5JTkHy9DhjY2CcNYd9yOrUGcIMEr0Y2S7PKZa9nVh47Ckv06OPrJqG\n0lTEJJiGGUFe8P+txy9SyH2cFt8PAUJnQD9mPqxcbowQwzMfO6djL4kefKLVZX/gsLzAaaHsnG0t\nPzFcPu+2P7Fe5HMnHuOnaSCnB32Oq3/OYc+jV0JAKjBmg1RtxvmEwqiWpqpIKfLw8I5b+Y51XaFX\nmsvtGqlattuX7LbXeJdIQaGU4Gb3krrM6e9tfUVZ7NAKNpuIUIZ+3HMae/phZpwsdZmXiBcXF3z7\nzW9YtxeURY3SxfI6ZjzN+YibbfatEJkSeTo+8XS44+nwwP74wOP+hPdQVmuUqvF+pB8m7DzQlAXD\nMDMO2XckycRPbz8wdoHdaoNWic1ux+XNa5SpSCEuy6sCO08IJUlC4Z3DO5tVbDl0kThPxOOAfzoy\n3j3Rd0dccCQkSZvczbeW7a7ii29f8+VvvkHUBucDpik5PZ0QInFxs850Q9NQNWs2ly8pRMHYnfjV\nd/9AW9Xs7++QIvB0e8vD3SN1JamqPBl0pwN//v0f0EXB7U93fPvrr7ioL6h0zdPTA6fHE2XS/Or1\nNxyGgZ9u7zjann4amK2lbg1aCZKIdHbCRo/XMAsLUVMWkvVVTpAHQRxVphvOFutAa0dRFKzbhraq\naKuaq901Qgb6aeL97SPz/IQRiubFJXiy8VkYwOT7RWIotcqOelFydfUCqRKH7ilz4IOHKHHec//x\nEbrAaT/iHSipcwK9KTBGYwqFnSPeRlLy2Q/ESFwMiFJTVg2tLPj661+hZUkYDfb0B8bTPT5EUPzy\nnAAAIABJREFUCmVQOmP63vnnxfP5HhMoUvQ5CHryuCny4cMBXb/nuzc3fHzb8/iux3dZHXt5seE/\n/g//nt/949fUa8n33/8BqSLz6Lh/NxKAsjQ4b+iOjuODZZ6yaZkkJ+1oNCkFnLfPsCqIRb3Ks095\nWqi7JLFM4dk9NLtnL3RokSeilKBdrVm3a4Z0YjplnBspsQ7iPjBPM9Fnaua09/g5ex+J551VOusd\nM/spxiwyWwzjwrxHKYMQCucsCvmZS/rPH79IIZ9tz1kUpM5GDWJJrUn5jXfOZp9embmeSiwKuWXh\nlOv2ueCeFZ753+LibnbuwM/w+6cue9lILy/iGRf/XFz0/JnLEvS5sC9fLWWGEDTZNlYgMNpQFQbr\nRoriSF21ENco6SirmlV7xXZ7w6q9ZhztEgCbuNq9YtXs8D5RmjVa1kgpqKsZFyZm1+Ocx82e6AVF\n838z917fkVxXuufvuLDpkPCFchTl2t25M73m/v9v8zQ9PdPdaqklisWy8EgX/ph5OJGJIqV+pmKx\nVhWBRCIyMmOffb79mTQ6BAYRuz+dxwBpZXDEwU07tHS9pe8j/q6Uw7meIAUueAY30A4DdoAkLTla\nnAOawUbsfBgcVdXwtNqM1reOQEfXeZqqoV8uefvmJXk5I00nIDX9UDN0HcPQUjcVUmkms5ODMERL\nwdC34D2utyRCMS/niDOBlpLO9iid8FFck6UpV1eXzI5yXr5+zZtvfknjBqq2JgTF5asrpA9MJhnK\nQFHEAOp8coT2gb6tmc+Wo5dIwnRUfrZFxsn5nCHukznJpmjt0cpzdnrEN69f8erqNbNyQVvXTLMJ\n/uiUxdER26ZCK8E63bLdVXS2Y76ckWUpfe/43e+/A/oovEoF2giU1igTcfJIlxY4G0bL34E0F2SF\nYnE0J02zmLxjJG3XUdcteMmsKJETwaTMaaoeKDDZCQ+7e5q+3xN68ENgEA7belCRS21dJAAoKUiN\nwe4GunUDPSgfja10okhSHTNAjUFai9UOoyJe7ERg8JY2eOrgUS6aYuVlwdWbK979/jPKKDQepWKx\nRoHtoyhOjP7b+0444KO9cm+pty2rVY25XWN3ji/vHnl6jGHfi+UR3/7yl/wf/+c/Mz02bKsHrCdi\n9srwdN8zPcpjHKGt6CpLXzmkV6P3yxiyzIiJO3eASwIx41cS3Qhd9AIZm0VxqDWDb2NXrlQMoAwu\nioOCj35LumNwdpznxdcnAlgrCbUcKXbgx53JWHb2veWhUdxbjUg/amiCwFqLFZHJEfYwwd+SsrPv\nGpTSY5xYLMaeyOschp7Bdgx9T5ZkSJlA+DrWbZTij7h13MjF7/kwfkhCiJ4MYQ+HPOPq8YjF/zDJ\nHov8IYWIvwxo3f+cIJrqSCExIg5OkGOaYBDgMxAObXKm0znGDODbKG7Jp+T5gjRbEMSAcz0ES5LM\nsKmj7x1aFaPiNRrYG60iIyWGkqLQZDojkUkcqvZ97CaQ0XDJe/qhZ1Pv6LohprrYmiSRGKOYlAva\nvmbX7pCbFSbJmU6OODk5Y1Ntsd4ShMe5EKPnWKOUoneW3llS2TH0DWkKaf4bkrQYt7DQ1DWb9QNN\ns2bXVCRpwXxxTugHvI1hFkPf4axHIcnyCeVswal6gdKSqtoipUa4QJoaLq4uSArN8fkFpycvqfsO\n1g9Uu46T0wvoHUaDyT3TScG0KDBJjpzOsKkeZySSLC85uziPDJY8p6mf+HL/wGAtx0cLJtMpk3JC\nmZb83d/9lpPTFwRhEDhyk7CcHjE/mlO1O9IssDut2NY7mr5jfnxElpU09cB3339EyoZMSiaTqDQV\nIg6zrPNxV7P3yvdgrSc3mrzMKCfROrgbBtq+4v7xnvv7R2znOJrOyXKDMh7vLUmScXp6Sv+xoV87\nbB/FObjIPqrWsVvvhmEs4pJEKNIso+0bfGhJpCaI6DmepIIkU9FmQCVkeEQWPcPrtqfqW1rv2Q0d\nSkQztk1VMZlNOXtxQjnNMGmE5eLIK6CEGgeHCi09DHsyQUzJAYm1lu2modjlZNuU1actjx82bLYD\nFsf51QX/+D/+iX/4p//Bw/ozn6+/sN1ZdJqS5Zb7mx1FCWhBV1v6eiD0Ywd+mLGN9+1YSEV4nokp\nxJgQFEeacdamkIBRcbcc7BC7cSEJCpy1I8UXttt4j4neRxWyEDgEJol+M3jFEIM6EahRWORGmuPY\nMO6ruggHsZIkWl34kbIYFyP53MT+lePnYa1IhVYJWqXRBlVoRu0x/dDQdhXOWlJjxkxJMfI95TPu\nLaKSUsiveOHOHt684P3hpnl2PXy2woUfX5OvmvV9Y/9VJx5/hRAjv30/MA1ivPgjvQoIIvK087zk\n9OQSa6cQLEpJElOi9IRASpoVBN/jbYN3Di0TkiJBKDOyHIbovy2iB83x8ojHp0fauqLIJ1yeX3F1\n+YbZbIYxEhf6aN5kYyDwMCb0tF2D8z1zNSdPS4xO0eopWrJKOD85ZTJdkGcGhybN4g1pvUfaga4b\nHfkCeCFAC3Q6ZTqfxqIpYh5ocI6Hh3vev/8jq80t3nsmkzlG5IS+RwOz+YRiWpIVU6QyICP334Ue\nLxW77Y7t/RN9VaO1JJuk0aDfZEzKJUlqqaoW13m6umWou4hpf/OGk+UJRhu0MnTB03UV7eCYTJcU\n5ZS33/wKgeDh8Y5V80CXKrZVxSAc5dGMy8srLk4vePHqG+aLM6QxLM7mNNWWelejjGawLScvT3DW\nc3t/y/vP71ltV1TrgWprqdsOnCU3KafzJdkkofeWz18e6Icxu1FYVAraROpgXqQoo3h4WlHmJVmW\nIVWgriJTJlUplyeXaCO4efxEOS1wLvDpyzVt3SN8QAnPbFqiQvSCKcs8ime9wPnA0PYkSnN1fkGf\nNNxViuZzZMKEJFDMogdKmmkSkXJ6fsykyGmGHR8+f2HTRO1D30mCyZkuS5LCkJYJ02zCZJGRF4qm\nCtghuocGxAGqkEqMrKkw3nSx6cI6ml1DX7eEIWNoXQzlsLGLv3rzgt/+42+YzmY8rG7YbXs+fd4i\nlcekEhi4u3nEOku1bWm2LUMT/W+U3KvEiYPFQ0H86iZnT7MIIGS0G5bqQGPGxxoz+Bg0E8QIuxDd\nWLt2wA4BHaIiVipDluWcnR6hleLhYUNouggTI8ZB696aYzwFsVemx1nhweFwD7kQA2fCHpr6WzLN\niiHL+8K8P3UZk3JEjHtTOvo4KKUIYkys9uMqKvb4t+BgKTLiTc7bA+k/CE84FPrn4r0nFu3fxgMs\nc2CzcPhePJ7hligS2E+048/Fz6YYF9foNZymBdPJKdaVeB+Tf7VMUCqFIKJ4QhjAgfCRT68ShIh+\nE4SAMZqymMdhoWsp85ST5RFXFy+5vHjLyckVeT4jCB078b5nsBbn4yKoZfRU1iYd7W01RhukUEzz\nKdmLNxGTz3KQsF7f0jU1wQXK0jDNE6ZZwuPTLg7WtCRPJUZ7QhggDPjQU9VrPn/8xIf3f+bTlx+4\ne7rGWUeZTRiqjuVsxvHxCUk5BR2VmMFajMnw3lPXFfd3d3y++cLmcc2q2lBMS0ymmR0dsVweY3SK\nUillWlKYjJ0GmUKemwgrhLiAeu9iapJO2DUV3dCPFLoYoTaZ5Vy9OadTFu4CXb3j5OyUqxdXTCdz\nkjxHpQkmzdCJIisKymmH9Q7nLRO/pG9agtTs6poffvjA7e2K7bana7toZSsEfrDgIkdaqsjdjxqi\n6BeifAzi7fqB1XqH9rCY7ri7f2CzfkLpBOE0RZpRV1Xk+gfITcKua7i7fqB3PUkaF/lXFy8xUlLV\nG3adZfCelIzz4zP6ukUFeHV+yU7uaL60yJFSqEtFNtfITOGEo3cDdVMjQ8SXJZo8yUlUyXwy5c2L\nC/7nb3/N0XLB0WLJYrrk9TevWd2u+PjuBu9jWqVEoLRiGFzUToyN0J65EWEKT992rG7X4C1i0LRV\nP95DkmKek89Strs1q6cHtpsVzluKIiFRYBRUdUvT9HRtz9BGFe7eolYcZmNxN8CIL+/vZk+U40ez\nvRgsIfxY6qVEqgiz4CPnPBbjfXXweNcTvEeO9rtR96KiMCw47BB3yy6Awo/FeISXwt6LJ56rPNSW\nWENifyhGOEUcFiLr/6bohzFWzY8XcUS5UUKS6gRFTKs3OuZnBgS9teMF0KP4Jb646Csixi3kSEsc\n8So/JlmLkbUpeM74/AkUzteOZ38JRYXDUvC1n8uPjhChGvCjbWwOmWAYDIOtGWwX/U2QhDBE6TEB\nhEIZM26dVNyduAEhJFmakyYlaVKw292xmE6YFSWvX37D2ekrptMTpEoZbBzkWOewNr7RRZbRZh1y\nnxikU7TSaCXQ0rCYLpmXLymyJUIq6m7L99//Ht8NpCohn2hOFyWLosBbS9W2SAnTUiOFpWl22KGl\nabestzv+83f/D+vVI5vdms1ux25XYcQjDC3pr3/JWX5OUk5ph5a2WTEMDXk6xfWe9cMT158/cX1z\nw2pXsa53pNOcYlJweX7JbDpnGGxc7IMgVYokhaAVaSrZbnbstjXdsUX6FqRC6Ixtc8t2s0EgOD+7\nZFLkIC0n50es6g1Vs2PoW46Ojjk+OsW5QF3VoBKmSYJSCUlmUCalaxvsGMu12zb0XaQh3n154v37\nj1S7Dt/bkW8dqEbaoMoNykTM1RELefTMYYSjWupdQyI1VdOwWq+o1jvOTi8pJxOMNtzd34LyLE7m\nTPIJtg3YMfR5Mp/w9sVr/uHXf49Snpv793z3+RP9ziK94ez4jC5rGNqO08UJYidRMoEsRauUdJ6S\nTgUYFxfYINnVW4auJU71JPPJgsl0wunxkl//4i3//M//k77rMabgaHHBt7/+NU+3T9x8vouReHvo\nMVUMLtC1/UGaLsfmx4dIhcV6Vndbmk3LtJwQrERriVYBoQM9DY9Ptzyt7ujaHXmqSI3Ceh89/W2I\nXjRdiLYlXo5NYoRfFQJCTB2KAMroCgqHgunHmhQ5hn4ks0UUYD+IFCMDLIzmVkG6Az1aSHPoAYMP\nVLuK4KN9gPWj3/mBUx2Rh7g7EKPIjLh7Gc877GHaESvflyI/ipn+2vHzBEsMHqQjqAHp+yhBHj/g\nqYmp7oONUuGwL/Zi70ImseOQUEiHJOKQwAF2iWqucejpPeO9NT7P+EDEj4rxc4Hm8AZ9bZm7N+b6\na9j5j742PlZJTdA5/VDRdTV184CeniGlYOi6iBKOyTFpUqBUEgM0vAAVO3YlCwISYzIuL95QZAXO\nOk6XF0zKJakp8ULivcWoQKKTPYqPUprpZEIZSkySRK8JKZBKUBQTBAXTyZQim43bt4TLk5cMTUtp\nFHWzJleK3GjOljPuHx3bqkJSYnvHruppe8/t7S2r9Zq7+08Uac7Lyxecvbjk/Zd3bHcrQu6RuQFj\naIaBz9c/cP/4mbpfMbSCdt1R3W0RGfSD5f7LPYMNzI+WvH39DVqXbFY77m7XCARPT3fc39/TDB12\naKnaiqLryG4KZKI5Wp4jpGK13fKHd3/i6eEWIzXewtXlC9JU0XcQvEaSQtB4q+j7QNe2fP/9eybT\nOf/wv/8zRqcjc2FgGDrapmG3q/i3f/+/+eMf/8C7dz/w6csNXT9G5Dl1GKr1ViCaBuGGWPS9x8s9\nX9ugEWw3FRBVniF4mqamzQxpniITiRWWqm+omh3lJOfs+Iw3r97QNi2p0eyqivl8yTevf8Xp6RVD\nv2OzfuBkOqevH1ltW5KThI6GumkYhhj2/Lh5QpYGYyQ6i3Q7EcAowWI+5eX5BYkyfP/9D+RGMZ1O\nePXqkvnsiJPTJQGD6zqk8wSnef2Lb7m7vuff//U/IHQxcNgLijLBh4GmjZ4pXkTfIoJFsM/LBOFi\n4ZpOJyipGdqB7baibiqqesNkZsizjOlkzqJsebxfsXrY0WwDwqUYKQmyHf1Eo896CA4pZHQbHRp6\nN0T+uIgF1ARBqjSWwBDCGH0n447COzo8vRVxWAtoISIbbBzW7gkXQsTdxyiqxvaOyo216lBnwqEI\nh7G5lIeN/1iY5T7oglF4FA7zPUI4NKF7zvlPj59n2Nn3aGNQWkW1FzqyUoREoEFGXwsp4igz4s+R\n2L93JItvVJyei3Fl3cv99z+z9557hltCtN0cqYlSyLHLHwv2/mK5Z0XpASf/aQMevg58jscBTg8H\nnAbnBgZXM9gt61006PHBoZOEJJmQpUdAdLQTAlwYQERBidzjfIlgVp4gg8BZS57NMSpDCBP7i688\nstSgsC5aqSZJgpJxyLX3c0BCkuQxNMFMSHQ23lyC49kScfWKs+WMqt5ESMh5nDNxh5Rpggg4Ar0f\n+PDlA/TQNwOLyYLzyxdMFnO2XcW2X+F8DcGzWq/Rnz/ztB6ouye6ocbajs9fblnfb7GNQyZxW3u2\nPGG73pFKzdAPVLs1213NZrumLHLatqFta+7vV7Rtg5KC06VitX5CGsVmt0MZzWq34vv373i8uydT\nCZN0ynw2wfmM6y+3XH+5ZfW4JvjA7e1tDP8detarp5Fz36FkEge0duD65jNfvnzk8/U1Hz7/mU+3\nn7ldP1J7yyACBIcU/iAi897Rth1h6EdYxhOkQBl5YFbtb9B9d2gHF5OIRKCuGyZSslzMkWGgLDIW\nxZTz40tccAxDQ9cNzGbHvHz1S44WRwQ3J0szFstzlPiOvnlHU6+pmy1t3/Dly2fu7u+pmobB9aAU\nSiecXZwRaBhsRVP1bDYNmfE4G2dQkuicaaQmTQrKyTGhd0gkUnmOTo+4eHnBxdUZ1x9u2K4bvPck\nuaYIGX0X7Y3DmFYvkPgxrYkxSSnPEt68fcF8MaFrW37373+mtz3r7ZqiNGw2W/q2Y1pkbGUssHlu\n4u52AMvINglx92N93PEqNfqaezeqOQNGCBL0aF0bK7Dk2aYj+vY4goyQiBznYdFYy4+j2j18Ewut\njDQZvIsMmL3v0wHeEYyztK8g2xFuO/zP/nH7UQL7BlMgR73Ms4fSj4+fp5DbHqlBCo0Q0eTJhzHV\ne+RrKjVOisPeE9iPux2LdUNcKaUkuP0gNHaxQki0Tggj8d8Hi3V2vHGiYiqqMEes+xk+j0dgnCr/\nFD75SSXnr3fnz9/zeN9jXc1gKwa3o+86nPM435MVBdPJOVk6G2GggeAH2r6OA0ldElfzmPKTptMx\neXvAmBIhEva7CqXGoZKWsbvqe3o3oKQiMYYsSbAimknZ4DAmQQtNoouRpyrQSpMlCceLBfJ4zmA7\nnlYrbu/uQXZM5wvyWcHN/QPOBZq+48/v/owZFPNswTf/8L9x/volKk/o7n5gOs2x7QSFZLfZ0jY/\noNIHJrOMNDcYM8fZzzEsQwj6pqM0BafLcxIfLVXXT09UjWWz3bCr1vRdwTA4dtstXz7fUdcNaWJI\nVUqaJnjhsHe3mFRT9w1397esHzdkKuX+4YbH1TFFn3Nze8PN9Q1VVTOdFjw+3tLutgx9R5GnGBNX\nxoNKOHju76/57t0fePf+BzbthlWzofYDTgucivQ6Lb+ixAbGIN04JPYRKI3WA+NzRltVEUuQiF1r\n20Zfe0FFkeWcHp9QZgajFalKyNICnWquXrxECs10uuT49AqjI9NpMlmQFVPqqmG9emBb7+j7CucH\nPn78xOPtlqbpaasaYRXFNKWclEhpaHae1f2Gp7CmLEqKbIKQnsSYaNs7nquUGqkVUgSE9mRZzvLs\nmKvXV6zu1qyfKgYHuUrI0pSycLQuEPoeGEVDAgYXacJGSsos5fXrS65eH1O3FZ8+32CdZb3eMZ+X\ndG2LCIHT0yV9148whOLhfsXjraOruj2achgkxj8BFxzO7wthjORLpB6fI9rTSinHti++V86P0KyS\nB2qgkDGlJ4Qo+4/zNjF21Xv9ix9nZgE5Llr7E/PPDfb4d6zY8ePy3JHvS82eTRdT0eQo1/8b6si9\n78cVR6NEBiFiu1LoWJSFHoU/X/mtCBGL8tDSdx14QZqUIHykMprkGVoZ3zBr+9j9uQGlNIlOGR0y\nvppj/mUy9U/jlA74+Vd1+y+dE8dtFAJEDHro7YZ2WNO0K6pqyyRfEkKgbhu6YcCYOSfL2FXUTU3b\nVTTthjI/QkpNYvLDcyudkeYC8CTJJMJJY9HYUzgJ4JTHqhiASwjR29mHaJfpPUM3kJiE1KSkaYoS\nAms72m7Dpn6irqNzozGau6cn/vPP3+HcwOnJCfP5OddfOqr1jqGtCO2ab16+5tWrV3zz7W9x2nO3\nuebL7Z8wxnF19YIsmfH48Mhmt2Xot0hzTpGfcTw/5vVFRyYyqt0OkQTapufz9SdOZ6fkecZ2u2Fb\nbUF6ZvOEx/sHHu7W3N2u+fLxC03TkhiN8j56kGjP02ZDXuQILckSDbOCRGj6ruXT5w+kqcGFnkBP\nCB0SQ55oVIDb+yd+8b/+F7/49jcU5RQtU0IIGK+RKlAWhovzOdt3a5qmo266KHRREBIFqUQ6gXQx\n4Wo0/WFwnuBi961lzNgUwUdnRS+QQmOkwVmodi3egVEpoMjyktl8CsHRdh2P6xXzxZKjoyvKoiDL\nSpK0BBWo64rHxwee1tdoE3j7+or3n79gHXR9zc2XNZuHirrqqO9XqFyzkZ4//pfg7dtzTo6OUK2n\nzHNOThZcXJ7j6JEqMC1zpuUEIVq++9O/obRlOpszG4tQXky4eHHJH//tO7yLLpDWOiSQak2uFIyG\nUJOyoO4NIarmSZBx11QWTOc5OnOUk5yut+x2PdPJEeqF4PR4ycn5KRdXV+x2FUWe8O5P7/jd//sd\n99crgvPsQyCUjJ2+szFr1Y/3uiBSI7U09MHhQzRiM4kZi3gM0w4u0gFDCAwhOhImxGZTi+hJ40Y1\naMT83diQxTohx2ASMVYc72NebvSX+rqOjPDMfgUKezYLPy7oMpri/Xe9488TLCGjwU5vu2hrGhQ+\nkrMPg0bvRitZ4REyXlQpJCIolIpq0DhBDvGm8J7IAIlpH4yDCe9HKa5UaGXGLv+wkTmwUPZHbNT3\ng0v40RXd75vDM5a+f8h4KkBMwt41a24f33N3/z1N/YhiINWWrut4XD0CEQOf5HdkaTaKaLb4YMmS\ncuTb7mlKsWMwRo7YmY6/VOzPKU6/lRAkKiCMQAlz6D7CmPWnhEcGQaITMpOi9BjtZTuq6gnnOpwf\naDrLarPiw+dPPG3WCAuhu2dzu2PzuMN5R5YaJpM5y+M5+bTAZBpra5xrUMKR5BMyM0WGhL6/pakb\n0iwyJWSQuDbQr1uykPDi7a+429zT7K7Z7J4okpJFWJKmKXXbsKt31HXN7d0DXTsgU0E2NaADUkg6\n71FJwmy+YAgORBQ95UnCLC/Jk5REatqmpWkamrYjzwrMOPztuo4hWJDQNg11VTMMFpXEG0iphJOT\nc3zoKYuMu7s192oTKW4mGh0Fr8gTgxwCrnUMXYTetBZkarzZEdhuoN93Yd7H4ec4I8pUhpCwqbes\n1lvU51uC0pydz5jPC/I04+HxAes9p6fnke2gBIlWSKWp3Za6XjH0O8oy42j6hjQrSD58pK0/EUIM\nNRnaDoOgLDLm8wlZosA5tNB88/YNV1evuLy84uhoyc3tB+pmzWJekqYFCEU7NPjBoZJoL7DbbQjS\n8/Lbt1y++i9Wj2vub3YMrUWE0WLDu4gASkmeZSil8NbTNF2kWZqM+WJJlk+jXkAptpuKh9sngtUc\nzY/xroXBY4Jmkk6YT0qu5WfEEKCPu+uAx7poqCUlWDfgxpSfgEALhRYxzzZ6NQkSETNKnQi4IDBK\njUPOvbWV3zsVj7ecOBA0Yp0Zs3uJZl6j2H5sQp+FQPsktH2nvefOCSJXQhpJlumo3m329tNjbytj\nXRJ/S14rATcG9loOHfJ+AzMqO60dkCq+gEP4AyBEFBKN1s7P0+L9cFOOnZAQCKkiT12GQwTanujz\nNVtmv6fZ1/PngWj8+znp5Xm1HBmHP+nM4/P0tmG1u+fjzZ+5v/+I9B1HZUHfdezqHav1Cq0TjH4g\nNR85WhwhhMe5Ni428qsPQojmPntq0/4ExtEr+4Dp/W5AiQgxJTqNWF+IIhIlFKhAohIynUYaIiMF\nbGipmw1axU6i6zu+3Fxz//hA3/ekJGwftzy1D9SWmKFZ5izmE8ppjjLQDBU29GglKPMCIXKcV9R1\nExWKDhblnGlWIoNg/biietqRJZrXL97QWItWTxijafuOfhhiKIALVLuO1WrHelOhjCQtU7JpAkZA\nkFjpUMYwm81BSXb1jm29w2jNyXLJYjrHNgPbqqHvOxKTUZ5OEAS6rqWrB1zfIpRgvVnx+PjAWRtd\nJLU0SGU4Ob0kTQ3TvOTDhzueVhWDDVR9TzvEQdpsEsOha9FSDx5jJGmuI22291gvMELjrMQFgaVA\n6wR0GuGzooBgGdZrqrbGP3ga22M5I8hTtE6x9RaTGlw4ieKt4NAqdvUKEL5DhJ4yX3By9ILJdIZE\nUm0a7q43RBMsS54lnJ4ecfbqjKTMKLKEIsv51S9/za9+9XecX1whhKZpa7ztmRRzgpA0fc+u3gGa\nLPM469lu13gRuPzmLa+/fc3j9QPruxrb2dEvvB9TgAJaalJt0FJhe4frbTR40wZjMqwT1G0MhajX\nW+6/3PN0t+Hqakmea7ZPTyQi7q4TmREGgesCwj7fE24PW4kQh5zBs/cb1EKjZRSL+TAyzGQU3SFi\n42OcjndT8FgE2hObTCmjyyr+MKzcs+/GYhLhzXH46V14bv4Od6g84OZ7/jjE51apopgaXBtwrf0K\nKQgH1elfsOXG4+fxWrEtSmZjZJscKVmxaltnGWzPMHQoL8dCPtIHDybwo1GRHHtRORa58PzChRzT\ncET0PpFCjSwY2BfnEBhz+saFQv6Yeij2UM2++90vpXB40P55BAIh4zCjaR55evrA9e33uKFlmqfk\nRUbT1+yaLb3vSXRCP1Q8PH5EK8/RYsnx0SlgMGZC7OwdRphxgBtfF1+dwv5LjOU8BMe5QYt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AppPEqMVhBOIr0noiQRGzL2CKWx+oxGprSxpM8kzkYoJNo6/NCRm5aq1yg9I05mSB3T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Rqq5o10uu1plFbVnWDt+fxMeaIux5fOT5acdi6bDOCBSQIiF6Ygrs9s+8f/839vsH8WTPiW++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\n5DygCKyaFus8cU4M/UQKMM49MSWstTzcv+eHj/+Gj+9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3ixQXIuegiOFpNfSg6XvIowRf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CzKWYN5VyeC4pUdigk+rM1HkDUY6evohkolcTztOZxUEJHygZxnWhF82NBqZJ4L//A//pH/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\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fOvaPHbcvr3jzxVd88eU3fHp3x/F44Nju+XD3HlJEBUncDCmcQ7XkVTYoowmDZwqJqirl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QRoHTx2viYcc9bsnwALtFTLPDcgXWDDzxfRuPdNNY9LGjgH33vA1LonVU/xbr4/ZgIzMmpcj9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\n77ryEsbvRQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fabd65950d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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RUhR5HkR/N1fDvdeyzMZoKG9Y6eFXIzU2a2zgwvU+0lf4lgm8cS2uHLk5clpT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sKGrNP7NRNngryQnFUskJBgBmP45HY+8EfH700hDkNjgCee04PIamev1NkpPjFv+DTNf4Ne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3D8/iD/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\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ykiV5fQ0B0VZy3cEVyfWOV9tKJvxvE1nzQi9BKZPiN+d58DgPBVqbsXDuKcdyFQvhmwf0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0MjzjTa98IED2+/DLYg+hCwZXJmnfS2Pvj3MXQBOKSSlAgBwPhoqKL0O0fh8oHLO95fNOq5xkDE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\nROkMnut9BfMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fabd63b3790>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ovFuqR0uKWI4aVShFKRqzYvP3jt1+alWWGmYXN7OkYSFzsCWPZlIRafnqRYTt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\nGErFecga8zl95UV6xmPX7tRDrFt08eJ51TB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5dNPxE08+yrwZEsjzcOoR55z+78Pw/+Vi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jhWge/XVZv3I4163ajnDrCeQNF2AqwBeJiVjp2eGXVqWcA+gcNDnz7dCsSPdP41h+PwgWN4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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fabdc913990>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test_net = solver.test_nets[0]\n",
+    "for image_index in range(5):\n",
+    "    plt.figure()\n",
+    "    plt.imshow(transformer.deprocess(copy(test_net.blobs['data'].data[image_index, ...])))\n",
+    "    gtlist = test_net.blobs['label'].data[image_index, ...].astype(np.int)\n",
+    "    estlist = test_net.blobs['score'].data[image_index, ...] > 0\n",
+    "    plt.title('GT: {} \\n EST: {}'.format(classes[np.where(gtlist)], classes[np.where(estlist)]))\n",
+    "    plt.axis('off')"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Multilabel classification on PASCAL VOC using a Python data layer.",
+  "example_name": "Multilabel Classification with Python Data Layer",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.11"
+  },
+  "priority": 5
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/pycaffe/layers/pascal_multilabel_datalayers.py b/examples/pycaffe/layers/pascal_multilabel_datalayers.py
new file mode 100644
index 00000000..9420cb32
--- /dev/null
+++ b/examples/pycaffe/layers/pascal_multilabel_datalayers.py
@@ -0,0 +1,216 @@
+# imports
+import json
+import time
+import pickle
+import scipy.misc
+import skimage.io
+import caffe
+
+import numpy as np
+import os.path as osp
+
+from xml.dom import minidom
+from random import shuffle
+from threading import Thread
+from PIL import Image
+
+from tools import SimpleTransformer
+
+
+class PascalMultilabelDataLayerSync(caffe.Layer):
+
+    """
+    This is a simple synchronous datalayer for training a multilabel model on
+    PASCAL.
+    """
+
+    def setup(self, bottom, top):
+
+        self.top_names = ['data', 'label']
+
+        # === Read input parameters ===
+
+        # params is a python dictionary with layer parameters.
+        params = eval(self.param_str)
+
+        # Check the parameters for validity.
+        check_params(params)
+
+        # store input as class variables
+        self.batch_size = params['batch_size']
+
+        # Create a batch loader to load the images.
+        self.batch_loader = BatchLoader(params, None)
+
+        # === reshape tops ===
+        # since we use a fixed input image size, we can shape the data layer
+        # once. Else, we'd have to do it in the reshape call.
+        top[0].reshape(
+            self.batch_size, 3, params['im_shape'][0], params['im_shape'][1])
+        # Note the 20 channels (because PASCAL has 20 classes.)
+        top[1].reshape(self.batch_size, 20)
+
+        print_info("PascalMultilabelDataLayerSync", params)
+
+    def forward(self, bottom, top):
+        """
+        Load data.
+        """
+        for itt in range(self.batch_size):
+            # Use the batch loader to load the next image.
+            im, multilabel = self.batch_loader.load_next_image()
+
+            # Add directly to the caffe data layer
+            top[0].data[itt, ...] = im
+            top[1].data[itt, ...] = multilabel
+
+    def reshape(self, bottom, top):
+        """
+        There is no need to reshape the data, since the input is of fixed size
+        (rows and columns)
+        """
+        pass
+
+    def backward(self, top, propagate_down, bottom):
+        """
+        These layers does not back propagate
+        """
+        pass
+
+
+class BatchLoader(object):
+
+    """
+    This class abstracts away the loading of images.
+    Images can either be loaded singly, or in a batch. The latter is used for
+    the asyncronous data layer to preload batches while other processing is
+    performed.
+    """
+
+    def __init__(self, params, result):
+        self.result = result
+        self.batch_size = params['batch_size']
+        self.pascal_root = params['pascal_root']
+        self.im_shape = params['im_shape']
+        # get list of image indexes.
+        list_file = params['split'] + '.txt'
+        self.indexlist = [line.rstrip('\n') for line in open(
+            osp.join(self.pascal_root, 'ImageSets/Main', list_file))]
+        self._cur = 0  # current image
+        # this class does some simple data-manipulations
+        self.transformer = SimpleTransformer()
+
+        print "BatchLoader initialized with {} images".format(
+            len(self.indexlist))
+
+    def load_next_image(self):
+        """
+        Load the next image in a batch.
+        """
+        # Did we finish an epoch?
+        if self._cur == len(self.indexlist):
+            self._cur = 0
+            shuffle(self.indexlist)
+
+        # Load an image
+        index = self.indexlist[self._cur]  # Get the image index
+        image_file_name = index + '.jpg'
+        im = np.asarray(Image.open(
+            osp.join(self.pascal_root, 'JPEGImages', image_file_name)))
+        im = scipy.misc.imresize(im, self.im_shape)  # resize
+
+        # do a simple horizontal flip as data augmentation
+        flip = np.random.choice(2)*2-1
+        im = im[:, ::flip, :]
+
+        # Load and prepare ground truth
+        multilabel = np.zeros(20).astype(np.float32)
+        anns = load_pascal_annotation(index, self.pascal_root)
+        for label in anns['gt_classes']:
+            # in the multilabel problem we don't care how MANY instances
+            # there are of each class. Only if they are present.
+            # The "-1" is b/c we are not interested in the background
+            # class.
+            multilabel[label - 1] = 1
+
+        self._cur += 1
+        return self.transformer.preprocess(im), multilabel
+
+
+def load_pascal_annotation(index, pascal_root):
+    """
+    This code is borrowed from Ross Girshick's FAST-RCNN code
+    (https://github.com/rbgirshick/fast-rcnn).
+    It parses the PASCAL .xml metadata files.
+    See publication for further details: (http://arxiv.org/abs/1504.08083).
+
+    Thanks Ross!
+
+    """
+    classes = ('__background__',  # always index 0
+               'aeroplane', 'bicycle', 'bird', 'boat',
+               'bottle', 'bus', 'car', 'cat', 'chair',
+                         'cow', 'diningtable', 'dog', 'horse',
+                         'motorbike', 'person', 'pottedplant',
+                         'sheep', 'sofa', 'train', 'tvmonitor')
+    class_to_ind = dict(zip(classes, xrange(21)))
+
+    filename = osp.join(pascal_root, 'Annotations', index + '.xml')
+    # print 'Loading: {}'.format(filename)
+
+    def get_data_from_tag(node, tag):
+        return node.getElementsByTagName(tag)[0].childNodes[0].data
+
+    with open(filename) as f:
+        data = minidom.parseString(f.read())
+
+    objs = data.getElementsByTagName('object')
+    num_objs = len(objs)
+
+    boxes = np.zeros((num_objs, 4), dtype=np.uint16)
+    gt_classes = np.zeros((num_objs), dtype=np.int32)
+    overlaps = np.zeros((num_objs, 21), dtype=np.float32)
+
+    # Load object bounding boxes into a data frame.
+    for ix, obj in enumerate(objs):
+        # Make pixel indexes 0-based
+        x1 = float(get_data_from_tag(obj, 'xmin')) - 1
+        y1 = float(get_data_from_tag(obj, 'ymin')) - 1
+        x2 = float(get_data_from_tag(obj, 'xmax')) - 1
+        y2 = float(get_data_from_tag(obj, 'ymax')) - 1
+        cls = class_to_ind[
+            str(get_data_from_tag(obj, "name")).lower().strip()]
+        boxes[ix, :] = [x1, y1, x2, y2]
+        gt_classes[ix] = cls
+        overlaps[ix, cls] = 1.0
+
+    overlaps = scipy.sparse.csr_matrix(overlaps)
+
+    return {'boxes': boxes,
+            'gt_classes': gt_classes,
+            'gt_overlaps': overlaps,
+            'flipped': False,
+            'index': index}
+
+
+def check_params(params):
+    """
+    A utility function to check the parameters for the data layers.
+    """
+    assert 'split' in params.keys(
+    ), 'Params must include split (train, val, or test).'
+
+    required = ['batch_size', 'pascal_root', 'im_shape']
+    for r in required:
+        assert r in params.keys(), 'Params must include {}'.format(r)
+
+
+def print_info(name, params):
+    """
+    Output some info regarding the class
+    """
+    print "{} initialized for split: {}, with bs: {}, im_shape: {}.".format(
+        name,
+        params['split'],
+        params['batch_size'],
+        params['im_shape'])
diff --git a/examples/pycaffe/tools.py b/examples/pycaffe/tools.py
new file mode 100644
index 00000000..7f6c2d83
--- /dev/null
+++ b/examples/pycaffe/tools.py
@@ -0,0 +1,121 @@
+import numpy as np
+
+
+class SimpleTransformer:
+
+    """
+    SimpleTransformer is a simple class for preprocessing and deprocessing
+    images for caffe.
+    """
+
+    def __init__(self, mean=[128, 128, 128]):
+        self.mean = np.array(mean, dtype=np.float32)
+        self.scale = 1.0
+
+    def set_mean(self, mean):
+        """
+        Set the mean to subtract for centering the data.
+        """
+        self.mean = mean
+
+    def set_scale(self, scale):
+        """
+        Set the data scaling.
+        """
+        self.scale = scale
+
+    def preprocess(self, im):
+        """
+        preprocess() emulate the pre-processing occurring in the vgg16 caffe
+        prototxt.
+        """
+
+        im = np.float32(im)
+        im = im[:, :, ::-1]  # change to BGR
+        im -= self.mean
+        im *= self.scale
+        im = im.transpose((2, 0, 1))
+
+        return im
+
+    def deprocess(self, im):
+        """
+        inverse of preprocess()
+        """
+        im = im.transpose(1, 2, 0)
+        im /= self.scale
+        im += self.mean
+        im = im[:, :, ::-1]  # change to RGB
+
+        return np.uint8(im)
+
+
+class CaffeSolver:
+
+    """
+    Caffesolver is a class for creating a solver.prototxt file. It sets default
+    values and can export a solver parameter file.
+    Note that all parameters are stored as strings. Strings variables are
+    stored as strings in strings.
+    """
+
+    def __init__(self, testnet_prototxt_path="testnet.prototxt",
+                 trainnet_prototxt_path="trainnet.prototxt", debug=False):
+
+        self.sp = {}
+
+        # critical:
+        self.sp['base_lr'] = '0.001'
+        self.sp['momentum'] = '0.9'
+
+        # speed:
+        self.sp['test_iter'] = '100'
+        self.sp['test_interval'] = '250'
+
+        # looks:
+        self.sp['display'] = '25'
+        self.sp['snapshot'] = '2500'
+        self.sp['snapshot_prefix'] = '"snapshot"'  # string within a string!
+
+        # learning rate policy
+        self.sp['lr_policy'] = '"fixed"'
+
+        # important, but rare:
+        self.sp['gamma'] = '0.1'
+        self.sp['weight_decay'] = '0.0005'
+        self.sp['train_net'] = '"' + trainnet_prototxt_path + '"'
+        self.sp['test_net'] = '"' + testnet_prototxt_path + '"'
+
+        # pretty much never change these.
+        self.sp['max_iter'] = '100000'
+        self.sp['test_initialization'] = 'false'
+        self.sp['average_loss'] = '25'  # this has to do with the display.
+        self.sp['iter_size'] = '1'  # this is for accumulating gradients
+
+        if (debug):
+            self.sp['max_iter'] = '12'
+            self.sp['test_iter'] = '1'
+            self.sp['test_interval'] = '4'
+            self.sp['display'] = '1'
+
+    def add_from_file(self, filepath):
+        """
+        Reads a caffe solver prototxt file and updates the Caffesolver
+        instance parameters.
+        """
+        with open(filepath, 'r') as f:
+            for line in f:
+                if line[0] == '#':
+                    continue
+                splitLine = line.split(':')
+                self.sp[splitLine[0].strip()] = splitLine[1].strip()
+
+    def write(self, filepath):
+        """
+        Export solver parameters to INPUT "filepath". Sorted alphabetically.
+        """
+        f = open(filepath, 'w')
+        for key, value in sorted(self.sp.items()):
+            if not(type(value) is str):
+                raise TypeError('All solver parameters must be strings')
+            f.write('%s: %s\n' % (key, value))
diff --git a/examples/siamese/create_mnist_siamese.sh b/examples/siamese/create_mnist_siamese.sh
index 43ad6b18..03adce54 100755
--- a/examples/siamese/create_mnist_siamese.sh
+++ b/examples/siamese/create_mnist_siamese.sh
@@ -1,5 +1,6 @@
 #!/usr/bin/env sh
 # This script converts the mnist data into leveldb format.
+set -e
 
 EXAMPLES=./build/examples/siamese
 DATA=./data/mnist
diff --git a/examples/siamese/mnist_siamese.prototxt b/examples/siamese/mnist_siamese.prototxt
index 332731bd..5d783ba0 100644
--- a/examples/siamese/mnist_siamese.prototxt
+++ b/examples/siamese/mnist_siamese.prototxt
@@ -1,10 +1,11 @@
 name: "mnist_siamese"
-input: "data"
-input_shape {
-  dim: 10000
-  dim: 1
-  dim: 28
-  dim: 28
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param {
+    shape: { dim: 10000 dim: 1 dim: 28 dim: 28 }
+  }
 }
 layer {
   name: "conv1"
diff --git a/examples/siamese/train_mnist_siamese.sh b/examples/siamese/train_mnist_siamese.sh
index 84a30a8a..e01ac2ce 100755
--- a/examples/siamese/train_mnist_siamese.sh
+++ b/examples/siamese/train_mnist_siamese.sh
@@ -1,5 +1,6 @@
 #!/usr/bin/env sh
+set -e
 
 TOOLS=./build/tools
 
-$TOOLS/caffe train --solver=examples/siamese/mnist_siamese_solver.prototxt
+$TOOLS/caffe train --solver=examples/siamese/mnist_siamese_solver.prototxt $@
diff --git a/examples/web_demo/readme.md b/examples/web_demo/readme.md
index fe74b9ef..e50c4f10 100644
--- a/examples/web_demo/readme.md
+++ b/examples/web_demo/readme.md
@@ -11,7 +11,7 @@ priority: 10
 ## Requirements
 
 The demo server requires Python with some dependencies.
-To make sure you have the dependencies, please run `pip install -r examples/web_demo/requirements.txt`, and also make sure that you've compiled the Python Caffe interface and that it is on your `PYTHONPATH` (see [installation instructions](/installation.html)).
+To make sure you have the dependencies, please run `pip install -r examples/web_demo/requirements.txt`, and also make sure that you've compiled the Python Caffe interface and that it is on your `PYTHONPATH` (see [installation instructions](http://caffe.berkeleyvision.org/installation.html)).
 
 Make sure that you have obtained the Reference CaffeNet Model and the ImageNet Auxiliary Data:
 
diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index af360ac2..2f59471c 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -220,6 +220,7 @@ class Blob {
   void set_cpu_data(Dtype* data);
   const int* gpu_shape() const;
   const Dtype* gpu_data() const;
+  void set_gpu_data(Dtype* data);
   const Dtype* cpu_diff() const;
   const Dtype* gpu_diff() const;
   Dtype* mutable_cpu_data();
diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 6b902a42..4904d1d8 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -153,11 +153,19 @@ class Caffe {
   static void SetDevice(const int device_id);
   // Prints the current GPU status.
   static void DeviceQuery();
-  // Parallel training info
+  // Check if specified device is available
+  static bool CheckDevice(const int device_id);
+  // Search from start_id to the highest possible device ordinal,
+  // return the ordinal of the first available device.
+  static int FindDevice(const int start_id = 0);
+  // Parallel training
   inline static int solver_count() { return Get().solver_count_; }
   inline static void set_solver_count(int val) { Get().solver_count_ = val; }
-  inline static bool root_solver() { return Get().root_solver_; }
-  inline static void set_root_solver(bool val) { Get().root_solver_ = val; }
+  inline static int solver_rank() { return Get().solver_rank_; }
+  inline static void set_solver_rank(int val) { Get().solver_rank_ = val; }
+  inline static bool multiprocess() { return Get().multiprocess_; }
+  inline static void set_multiprocess(bool val) { Get().multiprocess_ = val; }
+  inline static bool root_solver() { return Get().solver_rank_ == 0; }
 
  protected:
 #ifndef CPU_ONLY
@@ -167,8 +175,11 @@ class Caffe {
   shared_ptr<RNG> random_generator_;
 
   Brew mode_;
+
+  // Parallel training
   int solver_count_;
-  bool root_solver_;
+  int solver_rank_;
+  bool multiprocess_;
 
  private:
   // The private constructor to avoid duplicate instantiation.
diff --git a/include/caffe/data_reader.hpp b/include/caffe/data_reader.hpp
deleted file mode 100644
index 8ed5542c..00000000
--- a/include/caffe/data_reader.hpp
+++ /dev/null
@@ -1,82 +0,0 @@
-#ifndef CAFFE_DATA_READER_HPP_
-#define CAFFE_DATA_READER_HPP_
-
-#include <map>
-#include <string>
-#include <vector>
-
-#include "caffe/common.hpp"
-#include "caffe/internal_thread.hpp"
-#include "caffe/util/blocking_queue.hpp"
-#include "caffe/util/db.hpp"
-
-namespace caffe {
-
-/**
- * @brief Reads data from a source to queues available to data layers.
- * A single reading thread is created per source, even if multiple solvers
- * are running in parallel, e.g. for multi-GPU training. This makes sure
- * databases are read sequentially, and that each solver accesses a different
- * subset of the database. Data is distributed to solvers in a round-robin
- * way to keep parallel training deterministic.
- */
-class DataReader {
- public:
-  explicit DataReader(const LayerParameter& param);
-  ~DataReader();
-
-  inline BlockingQueue<Datum*>& free() const {
-    return queue_pair_->free_;
-  }
-  inline BlockingQueue<Datum*>& full() const {
-    return queue_pair_->full_;
-  }
-
- protected:
-  // Queue pairs are shared between a body and its readers
-  class QueuePair {
-   public:
-    explicit QueuePair(int size);
-    ~QueuePair();
-
-    BlockingQueue<Datum*> free_;
-    BlockingQueue<Datum*> full_;
-
-  DISABLE_COPY_AND_ASSIGN(QueuePair);
-  };
-
-  // A single body is created per source
-  class Body : public InternalThread {
-   public:
-    explicit Body(const LayerParameter& param);
-    virtual ~Body();
-
-   protected:
-    void InternalThreadEntry();
-    void read_one(db::Cursor* cursor, QueuePair* qp);
-
-    const LayerParameter param_;
-    BlockingQueue<shared_ptr<QueuePair> > new_queue_pairs_;
-
-    friend class DataReader;
-
-  DISABLE_COPY_AND_ASSIGN(Body);
-  };
-
-  // A source is uniquely identified by its layer name + path, in case
-  // the same database is read from two different locations in the net.
-  static inline string source_key(const LayerParameter& param) {
-    return param.name() + ":" + param.data_param().source();
-  }
-
-  const shared_ptr<QueuePair> queue_pair_;
-  shared_ptr<Body> body_;
-
-  static map<const string, boost::weak_ptr<DataReader::Body> > bodies_;
-
-DISABLE_COPY_AND_ASSIGN(DataReader);
-};
-
-}  // namespace caffe
-
-#endif  // CAFFE_DATA_READER_HPP_
diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp
index dad9ad46..bb92ded7 100644
--- a/include/caffe/filler.hpp
+++ b/include/caffe/filler.hpp
@@ -250,10 +250,10 @@ class BilinearFiller : public Filler<Dtype> {
     CHECK_EQ(blob->width(), blob->height()) << "Filter must be square";
     Dtype* data = blob->mutable_cpu_data();
     int f = ceil(blob->width() / 2.);
-    float c = (2 * f - 1 - f % 2) / (2. * f);
+    Dtype c = (blob->width() - 1) / (2. * f);
     for (int i = 0; i < blob->count(); ++i) {
-      float x = i % blob->width();
-      float y = (i / blob->width()) % blob->height();
+      Dtype x = i % blob->width();
+      Dtype y = (i / blob->width()) % blob->height();
       data[i] = (1 - fabs(x / f - c)) * (1 - fabs(y / f - c));
     }
     CHECK_EQ(this->filler_param_.sparse(), -1)
diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp
index 6a8c5a02..0ba67665 100644
--- a/include/caffe/internal_thread.hpp
+++ b/include/caffe/internal_thread.hpp
@@ -42,8 +42,8 @@ class InternalThread {
   bool must_stop();
 
  private:
-  void entry(int device, Caffe::Brew mode, int rand_seed, int solver_count,
-      bool root_solver);
+  void entry(int device, Caffe::Brew mode, int rand_seed,
+      int solver_count, int solver_rank, bool multiprocess);
 
   shared_ptr<boost::thread> thread_;
 };
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index 10f353f9..30dbfd53 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -38,7 +38,7 @@ class Layer {
    * layer.
    */
   explicit Layer(const LayerParameter& param)
-    : layer_param_(param), is_shared_(false) {
+    : layer_param_(param) {
       // Set phase and copy blobs (if there are any).
       phase_ = param.phase();
       if (layer_param_.blobs_size() > 0) {
@@ -66,7 +66,6 @@ class Layer {
    */
   void SetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-    InitMutex();
     CheckBlobCounts(bottom, top);
     LayerSetUp(bottom, top);
     Reshape(bottom, top);
@@ -92,30 +91,6 @@ class Layer {
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
 
-  /**
-   * @brief Whether a layer should be shared by multiple nets during data
-   *        parallelism. By default, all layers except for data layers should
-   *        not be shared. data layers should be shared to ensure each worker
-   *        solver access data sequentially during data parallelism.
-   */
-  virtual inline bool ShareInParallel() const { return false; }
-
-  /** @brief Return whether this layer is actually shared by other nets.
-   *         If ShareInParallel() is true and using more than one GPU and the
-   *         net has TRAIN phase, then this function is expected return true.
-   */
-  inline bool IsShared() const { return is_shared_; }
-
-  /** @brief Set whether this layer is actually shared by other nets
-   *         If ShareInParallel() is true and using more than one GPU and the
-   *         net has TRAIN phase, then is_shared should be set true.
-   */
-  inline void SetShared(bool is_shared) {
-    CHECK(ShareInParallel() || !is_shared)
-        << type() << "Layer does not support sharing.";
-    is_shared_ = is_shared;
-  }
-
   /**
    * @brief Adjust the shapes of top blobs and internal buffers to accommodate
    *        the shapes of the bottom blobs.
@@ -428,19 +403,6 @@ class Layer {
   }
 
  private:
-  /** Whether this layer is actually shared by other nets*/
-  bool is_shared_;
-
-  /** The mutex for sequential forward if this layer is shared */
-  shared_ptr<boost::mutex> forward_mutex_;
-
-  /** Initialize forward_mutex_ */
-  void InitMutex();
-  /** Lock forward_mutex_ if this layer is shared */
-  void Lock();
-  /** Unlock forward_mutex_ if this layer is shared */
-  void Unlock();
-
   DISABLE_COPY_AND_ASSIGN(Layer);
 };  // class Layer
 
@@ -450,8 +412,6 @@ class Layer {
 template <typename Dtype>
 inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
-  // Lock during forward to ensure sequential forward
-  Lock();
   Dtype loss = 0;
   Reshape(bottom, top);
   switch (Caffe::mode()) {
@@ -482,7 +442,6 @@ inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
   default:
     LOG(FATAL) << "Unknown caffe mode.";
   }
-  Unlock();
   return loss;
 }
 
diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp
index f385afcc..2369c132 100644
--- a/include/caffe/layer_factory.hpp
+++ b/include/caffe/layer_factory.hpp
@@ -1,6 +1,6 @@
 /**
  * @brief A layer factory that allows one to register layers.
- * During runtime, registered layers could be called by passing a LayerParameter
+ * During runtime, registered layers can be called by passing a LayerParameter
  * protobuffer to the CreateLayer function:
  *
  *     LayerRegistry<Dtype>::CreateLayer(param);
diff --git a/include/caffe/layers/accuracy_layer.hpp b/include/caffe/layers/accuracy_layer.hpp
index fe2adb93..dd2247b9 100644
--- a/include/caffe/layers/accuracy_layer.hpp
+++ b/include/caffe/layers/accuracy_layer.hpp
@@ -39,7 +39,7 @@ class AccuracyLayer : public Layer<Dtype> {
   // If there are two top blobs, then the second blob will contain
   // accuracies per class.
   virtual inline int MinTopBlobs() const { return 1; }
-  virtual inline int MaxTopBlos() const { return 2; }
+  virtual inline int MaxTopBlobs() const { return 2; }
 
  protected:
   /**
@@ -68,6 +68,8 @@ class AccuracyLayer : public Layer<Dtype> {
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
 
 
   /// @brief Not implemented -- AccuracyLayer cannot be used as a loss.
@@ -77,6 +79,8 @@ class AccuracyLayer : public Layer<Dtype> {
       if (propagate_down[i]) { NOT_IMPLEMENTED; }
     }
   }
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   int label_axis_, outer_num_, inner_num_;
 
diff --git a/include/caffe/layers/base_data_layer.hpp b/include/caffe/layers/base_data_layer.hpp
index 2c49b731..c8b6998c 100644
--- a/include/caffe/layers/base_data_layer.hpp
+++ b/include/caffe/layers/base_data_layer.hpp
@@ -26,8 +26,6 @@ class BaseDataLayer : public Layer<Dtype> {
   // This method may not be overridden except by the BasePrefetchingDataLayer.
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
-  // Data layers should be shared by multiple solvers in parallel
-  virtual inline bool ShareInParallel() const { return true; }
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
   // Data layers have no bottoms, so reshaping is trivial.
@@ -67,16 +65,14 @@ class BasePrefetchingDataLayer :
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  // Prefetches batches (asynchronously if to GPU memory)
-  static const int PREFETCH_COUNT = 3;
-
  protected:
   virtual void InternalThreadEntry();
   virtual void load_batch(Batch<Dtype>* batch) = 0;
 
-  Batch<Dtype> prefetch_[PREFETCH_COUNT];
+  vector<shared_ptr<Batch<Dtype> > > prefetch_;
   BlockingQueue<Batch<Dtype>*> prefetch_free_;
   BlockingQueue<Batch<Dtype>*> prefetch_full_;
+  Batch<Dtype>* prefetch_current_;
 
   Blob<Dtype> transformed_data_;
 };
diff --git a/include/caffe/layers/batch_norm_layer.hpp b/include/caffe/layers/batch_norm_layer.hpp
index 9b2d5126..43f7b28b 100644
--- a/include/caffe/layers/batch_norm_layer.hpp
+++ b/include/caffe/layers/batch_norm_layer.hpp
@@ -13,25 +13,22 @@ namespace caffe {
  * @brief Normalizes the input to have 0-mean and/or unit (1) variance across
  *        the batch.
  *
- * This layer computes Batch Normalization described in [1].  For
- * each channel in the data (i.e. axis 1), it subtracts the mean and divides
- * by the variance, where both statistics are computed across both spatial
- * dimensions and across the different examples in the batch.
+ * This layer computes Batch Normalization as described in [1]. For each channel
+ * in the data (i.e. axis 1), it subtracts the mean and divides by the variance,
+ * where both statistics are computed across both spatial dimensions and across
+ * the different examples in the batch.
  *
- * By default, during training time, the network is computing global mean/
- * variance statistics via a running average, which is then used at test
- * time to allow deterministic outputs for each input.  You can manually
- * toggle whether the network is accumulating or using the statistics via the
- * use_global_stats option.  IMPORTANT: for this feature to work, you MUST
- * set the learning rate to zero for all three parameter blobs, i.e.,
- * param {lr_mult: 0} three times in the layer definition.
+ * By default, during training time, the network is computing global
+ * mean/variance statistics via a running average, which is then used at test
+ * time to allow deterministic outputs for each input. You can manually toggle
+ * whether the network is accumulating or using the statistics via the
+ * use_global_stats option. For reference, these statistics are kept in the
+ * layer's three blobs: (0) mean, (1) variance, and (2) moving average factor.
  *
  * Note that the original paper also included a per-channel learned bias and
- * scaling factor.  It is possible (though a bit cumbersome) to implement
- * this in caffe using a single-channel DummyDataLayer filled with zeros,
- * followed by a Convolution layer with output the same size as the current.
- * This produces a channel-specific value that can be added or multiplied by
- * the BatchNorm layer's output.
+ * scaling factor. To implement this in Caffe, define a `ScaleLayer` configured
+ * with `bias_term: true` after each `BatchNormLayer` to handle both the bias
+ * and scaling factor.
  *
  * [1] S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network
  *     Training by Reducing Internal Covariate Shift." arXiv preprint
diff --git a/include/caffe/layers/bias_layer.hpp b/include/caffe/layers/bias_layer.hpp
index eedc3aaa..9639c9cd 100644
--- a/include/caffe/layers/bias_layer.hpp
+++ b/include/caffe/layers/bias_layer.hpp
@@ -10,13 +10,13 @@
 namespace caffe {
 
 /**
- * @brief Computes a sum of two input Blobs, with the shape of the
- *        latter Blob "broadcast" to match the shape of the former.
- *        Equivalent to tiling the latter Blob, then computing the elementwise
- *        sum.
+ * @brief Computes a sum of two input Blobs, with the shape of the latter Blob
+ *        "broadcast" to match the shape of the former. Equivalent to tiling
+ *        the latter Blob, then computing the elementwise sum.
  *
  * The second input may be omitted, in which case it's learned as a parameter
- * of the layer.
+ * of the layer. Note: in case bias and scaling are desired, both operations can
+ * be handled by `ScaleLayer` configured with `bias_term: true`.
  */
 template <typename Dtype>
 class BiasLayer : public Layer<Dtype> {
diff --git a/include/caffe/layers/crop_layer.hpp b/include/caffe/layers/crop_layer.hpp
index bab29071..5219fa5c 100644
--- a/include/caffe/layers/crop_layer.hpp
+++ b/include/caffe/layers/crop_layer.hpp
@@ -11,8 +11,8 @@
 namespace caffe {
 
 /**
- * @brief Takes a Blob and crop it along either the width or height dimension,
- *        outputting a cropped Blob.
+ * @brief Takes a Blob and crop it, to the shape specified by the second input
+ *  Blob, across all dimensions after the specified axis.
  *
  * TODO(dox): thorough documentation for Forward, Backward, and proto params.
  */
@@ -41,9 +41,38 @@ class CropLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
-  int crop_h_, crop_w_;
-};
+  Blob<int> offsets;
+  Blob<int> src_strides_;
+  Blob<int> dest_strides_;
+
+ private:
+  // Recursive copy function.
+  void crop_copy(const vector<Blob<Dtype>*>& bottom,
+               const vector<Blob<Dtype>*>& top,
+               const int* offsets,
+               vector<int> indices,
+               int cur_dim,
+               const Dtype* src_data,
+               Dtype* dest_data,
+               bool is_forward);
 
+  // Recursive copy function: this is similar to crop_copy() but loops over all
+  // but the last two dimensions to allow for ND cropping while still relying on
+  // a CUDA kernel for the innermost two dimensions for performance reasons.  An
+  // alterantive implementation could rely on the kernel more by passing
+  // offsets, but this is problematic because of its variable length.
+  // Since in the standard (N,C,W,H) case N,C are usually not cropped a speedup
+  // could be achieved by not looping the application of the copy_kernel around
+  // these dimensions.
+  void crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
+                const vector<Blob<Dtype>*>& top,
+                const vector<int>& offsets,
+                vector<int> indices,
+                int cur_dim,
+                const Dtype* src_data,
+                Dtype* dest_data,
+                bool is_forward);
+};
 }  // namespace caffe
 
 #endif  // CAFFE_CROP_LAYER_HPP_
diff --git a/include/caffe/layers/cudnn_relu_layer.hpp b/include/caffe/layers/cudnn_relu_layer.hpp
index e01f568a..a1cb29e7 100644
--- a/include/caffe/layers/cudnn_relu_layer.hpp
+++ b/include/caffe/layers/cudnn_relu_layer.hpp
@@ -37,6 +37,7 @@ class CuDNNReLULayer : public ReLULayer<Dtype> {
   cudnnHandle_t             handle_;
   cudnnTensorDescriptor_t bottom_desc_;
   cudnnTensorDescriptor_t top_desc_;
+  cudnnActivationDescriptor_t activ_desc_;
 };
 #endif
 
diff --git a/include/caffe/layers/cudnn_sigmoid_layer.hpp b/include/caffe/layers/cudnn_sigmoid_layer.hpp
index 9c597958..7b3486f8 100644
--- a/include/caffe/layers/cudnn_sigmoid_layer.hpp
+++ b/include/caffe/layers/cudnn_sigmoid_layer.hpp
@@ -37,6 +37,7 @@ class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
   cudnnHandle_t             handle_;
   cudnnTensorDescriptor_t bottom_desc_;
   cudnnTensorDescriptor_t top_desc_;
+  cudnnActivationDescriptor_t activ_desc_;
 };
 #endif
 
diff --git a/include/caffe/layers/cudnn_tanh_layer.hpp b/include/caffe/layers/cudnn_tanh_layer.hpp
index c0f0053f..59e758d7 100644
--- a/include/caffe/layers/cudnn_tanh_layer.hpp
+++ b/include/caffe/layers/cudnn_tanh_layer.hpp
@@ -37,6 +37,7 @@ class CuDNNTanHLayer : public TanHLayer<Dtype> {
   cudnnHandle_t             handle_;
   cudnnTensorDescriptor_t bottom_desc_;
   cudnnTensorDescriptor_t top_desc_;
+  cudnnActivationDescriptor_t activ_desc_;
 };
 #endif
 
diff --git a/include/caffe/layers/data_layer.hpp b/include/caffe/layers/data_layer.hpp
index 6c361791..667a4ae4 100644
--- a/include/caffe/layers/data_layer.hpp
+++ b/include/caffe/layers/data_layer.hpp
@@ -4,7 +4,6 @@
 #include <vector>
 
 #include "caffe/blob.hpp"
-#include "caffe/data_reader.hpp"
 #include "caffe/data_transformer.hpp"
 #include "caffe/internal_thread.hpp"
 #include "caffe/layer.hpp"
@@ -21,17 +20,19 @@ class DataLayer : public BasePrefetchingDataLayer<Dtype> {
   virtual ~DataLayer();
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
-  // DataLayer uses DataReader instead for sharing for parallelism
-  virtual inline bool ShareInParallel() const { return false; }
   virtual inline const char* type() const { return "Data"; }
   virtual inline int ExactNumBottomBlobs() const { return 0; }
   virtual inline int MinTopBlobs() const { return 1; }
   virtual inline int MaxTopBlobs() const { return 2; }
 
  protected:
+  void Next();
+  bool Skip();
   virtual void load_batch(Batch<Dtype>* batch);
 
-  DataReader reader_;
+  shared_ptr<db::DB> db_;
+  shared_ptr<db::Cursor> cursor_;
+  uint64_t offset_;
 };
 
 }  // namespace caffe
diff --git a/include/caffe/layers/dummy_data_layer.hpp b/include/caffe/layers/dummy_data_layer.hpp
index 4180f1d0..13a63d47 100644
--- a/include/caffe/layers/dummy_data_layer.hpp
+++ b/include/caffe/layers/dummy_data_layer.hpp
@@ -22,8 +22,6 @@ class DummyDataLayer : public Layer<Dtype> {
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
-  // Data layers should be shared by multiple solvers in parallel
-  virtual inline bool ShareInParallel() const { return true; }
   // Data layers have no bottoms, so reshaping is trivial.
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
diff --git a/include/caffe/layers/hdf5_data_layer.hpp b/include/caffe/layers/hdf5_data_layer.hpp
index b04cf8e1..601b36c6 100644
--- a/include/caffe/layers/hdf5_data_layer.hpp
+++ b/include/caffe/layers/hdf5_data_layer.hpp
@@ -23,12 +23,10 @@ template <typename Dtype>
 class HDF5DataLayer : public Layer<Dtype> {
  public:
   explicit HDF5DataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
+      : Layer<Dtype>(param), offset_() {}
   virtual ~HDF5DataLayer();
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
-  // Data layers should be shared by multiple solvers in parallel
-  virtual inline bool ShareInParallel() const { return true; }
   // Data layers have no bottoms, so reshaping is trivial.
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
@@ -38,6 +36,9 @@ class HDF5DataLayer : public Layer<Dtype> {
   virtual inline int MinTopBlobs() const { return 1; }
 
  protected:
+  void Next();
+  bool Skip();
+
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
@@ -55,6 +56,7 @@ class HDF5DataLayer : public Layer<Dtype> {
   std::vector<shared_ptr<Blob<Dtype> > > hdf_blobs_;
   std::vector<unsigned int> data_permutation_;
   std::vector<unsigned int> file_permutation_;
+  uint64_t offset_;
 };
 
 }  // namespace caffe
diff --git a/include/caffe/layers/hdf5_output_layer.hpp b/include/caffe/layers/hdf5_output_layer.hpp
index 487d08fc..061e279d 100644
--- a/include/caffe/layers/hdf5_output_layer.hpp
+++ b/include/caffe/layers/hdf5_output_layer.hpp
@@ -28,8 +28,6 @@ class HDF5OutputLayer : public Layer<Dtype> {
   virtual ~HDF5OutputLayer();
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
-  // Data layers should be shared by multiple solvers in parallel
-  virtual inline bool ShareInParallel() const { return true; }
   // Data layers have no bottoms, so reshaping is trivial.
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
diff --git a/include/caffe/layers/infogain_loss_layer.hpp b/include/caffe/layers/infogain_loss_layer.hpp
index 633f339a..3b3caa27 100644
--- a/include/caffe/layers/infogain_loss_layer.hpp
+++ b/include/caffe/layers/infogain_loss_layer.hpp
@@ -8,24 +8,26 @@
 #include "caffe/proto/caffe.pb.h"
 
 #include "caffe/layers/loss_layer.hpp"
+#include "caffe/layers/softmax_layer.hpp"
 
 namespace caffe {
 
 /**
- * @brief A generalization of MultinomialLogisticLossLayer that takes an
+ * @brief A generalization of SoftmaxWithLossLayer that takes an
  *        "information gain" (infogain) matrix specifying the "value" of all label
  *        pairs.
  *
- * Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the
+ * Equivalent to the SoftmaxWithLossLayer if the infogain matrix is the
  * identity.
  *
  * @param bottom input Blob vector (length 2-3)
  *   -# @f$ (N \times C \times H \times W) @f$
- *      the predictions @f$ \hat{p} @f$, a Blob with values in
- *      @f$ [0, 1] @f$ indicating the predicted probability of each of the
- *      @f$ K = CHW @f$ classes.  Each prediction vector @f$ \hat{p}_n @f$
- *      should sum to 1 as in a probability distribution: @f$
- *      \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$.
+ *      the predictions @f$ x @f$, a Blob with values in
+ *      @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
+ *      the @f$ K = CHW @f$ classes. This layer maps these scores to a
+ *      probability distribution over classes using the softmax function
+ *      @f$ \hat{p}_{nk} = \exp(x_{nk}) /
+ *      \left[\sum_{k'} \exp(x_{nk'})\right] @f$ (see SoftmaxLayer).
  *   -# @f$ (N \times 1 \times 1 \times 1) @f$
  *      the labels @f$ l @f$, an integer-valued Blob with values
  *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
@@ -34,7 +36,7 @@ namespace caffe {
  *      (\b optional) the infogain matrix @f$ H @f$.  This must be provided as
  *      the third bottom blob input if not provided as the infogain_mat in the
  *      InfogainLossParameter. If @f$ H = I @f$, this layer is equivalent to the
- *      MultinomialLogisticLossLayer.
+ *      SoftmaxWithLossLayer.
  * @param top output Blob vector (length 1)
  *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
  *      the computed infogain multinomial logistic loss: @f$ E =
@@ -60,6 +62,12 @@ class InfogainLossLayer : public LossLayer<Dtype> {
   virtual inline int MinBottomBlobs() const { return 2; }
   virtual inline int MaxBottomBlobs() const { return 3; }
 
+  // InfogainLossLayer computes softmax prob internally.
+  // optional second "top" outputs the softmax prob
+  virtual inline int ExactNumTopBlobs() const { return -1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline int MaxTopBlobs() const { return 2; }
+
   virtual inline const char* type() const { return "InfogainLoss"; }
 
  protected:
@@ -91,8 +99,8 @@ class InfogainLossLayer : public LossLayer<Dtype> {
    *      infogain matrix, if provided as bottom[2])
    * @param bottom input Blob vector (length 2-3)
    *   -# @f$ (N \times C \times H \times W) @f$
-   *      the predictions @f$ \hat{p} @f$; Backward computes diff
-   *      @f$ \frac{\partial E}{\partial \hat{p}} @f$
+   *      the predictions @f$ x @f$; Backward computes diff
+   *      @f$ \frac{\partial E}{\partial x} @f$
    *   -# @f$ (N \times 1 \times 1 \times 1) @f$
    *      the labels -- ignored as we can't compute their error gradients
    *   -# @f$ (1 \times 1 \times K \times K) @f$
@@ -102,7 +110,35 @@ class InfogainLossLayer : public LossLayer<Dtype> {
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
+  /// Read the normalization mode parameter and compute the normalizer based
+  /// on the blob size.  If normalization_mode is VALID, the count of valid
+  /// outputs will be read from valid_count, unless it is -1 in which case
+  /// all outputs are assumed to be valid.
+  virtual Dtype get_normalizer(
+      LossParameter_NormalizationMode normalization_mode, int valid_count);
+  /// fill sum_rows_H_ according to matrix H
+  virtual void sum_rows_of_H(const Blob<Dtype>* H);
+
+  /// The internal SoftmaxLayer used to map predictions to a distribution.
+  shared_ptr<Layer<Dtype> > softmax_layer_;
+  /// prob stores the output probability predictions from the SoftmaxLayer.
+  Blob<Dtype> prob_;
+  /// bottom vector holder used in call to the underlying SoftmaxLayer::Forward
+  vector<Blob<Dtype>*> softmax_bottom_vec_;
+  /// top vector holder used in call to the underlying SoftmaxLayer::Forward
+  vector<Blob<Dtype>*> softmax_top_vec_;
+
   Blob<Dtype> infogain_;
+  Blob<Dtype> sum_rows_H_;  // cache the row sums of H.
+
+  /// Whether to ignore instances with a certain label.
+  bool has_ignore_label_;
+  /// The label indicating that an instance should be ignored.
+  int ignore_label_;
+  /// How to normalize the output loss.
+  LossParameter_NormalizationMode normalization_;
+
+  int infogain_axis_, outer_num_, inner_num_, num_labels_;
 };
 
 }  // namespace caffe
diff --git a/include/caffe/layers/inner_product_layer.hpp b/include/caffe/layers/inner_product_layer.hpp
index 250576a4..18d0d619 100644
--- a/include/caffe/layers/inner_product_layer.hpp
+++ b/include/caffe/layers/inner_product_layer.hpp
@@ -44,6 +44,7 @@ class InnerProductLayer : public Layer<Dtype> {
   int N_;
   bool bias_term_;
   Blob<Dtype> bias_multiplier_;
+  bool transpose_;  ///< if true, assume transposed weights
 };
 
 }  // namespace caffe
diff --git a/include/caffe/layers/input_layer.hpp b/include/caffe/layers/input_layer.hpp
new file mode 100644
index 00000000..0ffdc724
--- /dev/null
+++ b/include/caffe/layers/input_layer.hpp
@@ -0,0 +1,42 @@
+#ifndef CAFFE_INPUT_LAYER_HPP_
+#define CAFFE_INPUT_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net by assigning tops directly.
+ *
+ * This data layer is a container that merely holds the data assigned to it;
+ * forward, backward, and reshape are all no-ops.
+ */
+template <typename Dtype>
+class InputLayer : public Layer<Dtype> {
+ public:
+  explicit InputLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  // Data layers have no bottoms, so reshaping is trivial.
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+
+  virtual inline const char* type() const { return "Input"; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_INPUT_LAYER_HPP_
diff --git a/include/caffe/layers/lstm_layer.hpp b/include/caffe/layers/lstm_layer.hpp
new file mode 100644
index 00000000..a0e67c9d
--- /dev/null
+++ b/include/caffe/layers/lstm_layer.hpp
@@ -0,0 +1,154 @@
+#ifndef CAFFE_LSTM_LAYER_HPP_
+#define CAFFE_LSTM_LAYER_HPP_
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/recurrent_layer.hpp"
+#include "caffe/net.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+template <typename Dtype> class RecurrentLayer;
+
+/**
+ * @brief Processes sequential inputs using a "Long Short-Term Memory" (LSTM)
+ *        [1] style recurrent neural network (RNN). Implemented by unrolling
+ *        the LSTM computation through time.
+ *
+ * The specific architecture used in this implementation is as described in
+ * "Learning to Execute" [2], reproduced below:
+ *     i_t := \sigmoid[ W_{hi} * h_{t-1} + W_{xi} * x_t + b_i ]
+ *     f_t := \sigmoid[ W_{hf} * h_{t-1} + W_{xf} * x_t + b_f ]
+ *     o_t := \sigmoid[ W_{ho} * h_{t-1} + W_{xo} * x_t + b_o ]
+ *     g_t :=    \tanh[ W_{hg} * h_{t-1} + W_{xg} * x_t + b_g ]
+ *     c_t := (f_t .* c_{t-1}) + (i_t .* g_t)
+ *     h_t := o_t .* \tanh[c_t]
+ * In the implementation, the i, f, o, and g computations are performed as a
+ * single inner product.
+ *
+ * Notably, this implementation lacks the "diagonal" gates, as used in the
+ * LSTM architectures described by Alex Graves [3] and others.
+ *
+ * [1] Hochreiter, Sepp, and Schmidhuber, Jürgen. "Long short-term memory."
+ *     Neural Computation 9, no. 8 (1997): 1735-1780.
+ *
+ * [2] Zaremba, Wojciech, and Sutskever, Ilya. "Learning to execute."
+ *     arXiv preprint arXiv:1410.4615 (2014).
+ *
+ * [3] Graves, Alex. "Generating sequences with recurrent neural networks."
+ *     arXiv preprint arXiv:1308.0850 (2013).
+ */
+template <typename Dtype>
+class LSTMLayer : public RecurrentLayer<Dtype> {
+ public:
+  explicit LSTMLayer(const LayerParameter& param)
+      : RecurrentLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "LSTM"; }
+
+ protected:
+  virtual void FillUnrolledNet(NetParameter* net_param) const;
+  virtual void RecurrentInputBlobNames(vector<string>* names) const;
+  virtual void RecurrentOutputBlobNames(vector<string>* names) const;
+  virtual void RecurrentInputShapes(vector<BlobShape>* shapes) const;
+  virtual void OutputBlobNames(vector<string>* names) const;
+};
+
+/**
+ * @brief A helper for LSTMLayer: computes a single timestep of the
+ *        non-linearity of the LSTM, producing the updated cell and hidden
+ *        states.
+ */
+template <typename Dtype>
+class LSTMUnitLayer : public Layer<Dtype> {
+ public:
+  explicit LSTMUnitLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "LSTMUnit"; }
+  virtual inline int ExactNumBottomBlobs() const { return 3; }
+  virtual inline int ExactNumTopBlobs() const { return 2; }
+
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    // Can't propagate to sequence continuation indicators.
+    return bottom_index != 2;
+  }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 3)
+   *   -# @f$ (1 \times N \times D) @f$
+   *      the previous timestep cell state @f$ c_{t-1} @f$
+   *   -# @f$ (1 \times N \times 4D) @f$
+   *      the "gate inputs" @f$ [i_t', f_t', o_t', g_t'] @f$
+   *   -# @f$ (1 \times N) @f$
+   *      the sequence continuation indicators  @f$ \delta_t @f$
+   * @param top output Blob vector (length 2)
+   *   -# @f$ (1 \times N \times D) @f$
+   *      the updated cell state @f$ c_t @f$, computed as:
+   *          i_t := \sigmoid[i_t']
+   *          f_t := \sigmoid[f_t']
+   *          o_t := \sigmoid[o_t']
+   *          g_t := \tanh[g_t']
+   *          c_t := cont_t * (f_t .* c_{t-1}) + (i_t .* g_t)
+   *   -# @f$ (1 \times N \times D) @f$
+   *      the updated hidden state @f$ h_t @f$, computed as:
+   *          h_t := o_t .* \tanh[c_t]
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the LSTMUnit inputs.
+   *
+   * @param top output Blob vector (length 2), providing the error gradient with
+   *        respect to the outputs
+   *   -# @f$ (1 \times N \times D) @f$:
+   *      containing error gradients @f$ \frac{\partial E}{\partial c_t} @f$
+   *      with respect to the updated cell state @f$ c_t @f$
+   *   -# @f$ (1 \times N \times D) @f$:
+   *      containing error gradients @f$ \frac{\partial E}{\partial h_t} @f$
+   *      with respect to the updated cell state @f$ h_t @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 3), into which the error gradients
+   *        with respect to the LSTMUnit inputs @f$ c_{t-1} @f$ and the gate
+   *        inputs are computed.  Computatation of the error gradients w.r.t.
+   *        the sequence indicators is not implemented.
+   *   -# @f$ (1 \times N \times D) @f$
+   *      the error gradient w.r.t. the previous timestep cell state
+   *      @f$ c_{t-1} @f$
+   *   -# @f$ (1 \times N \times 4D) @f$
+   *      the error gradient w.r.t. the "gate inputs"
+   *      @f$ [
+   *          \frac{\partial E}{\partial i_t}
+   *          \frac{\partial E}{\partial f_t}
+   *          \frac{\partial E}{\partial o_t}
+   *          \frac{\partial E}{\partial g_t}
+   *          ] @f$
+   *   -# @f$ (1 \times 1 \times N) @f$
+   *      the gradient w.r.t. the sequence continuation indicators
+   *      @f$ \delta_t @f$ is currently not computed.
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// @brief The hidden and output dimension.
+  int hidden_dim_;
+  Blob<Dtype> X_acts_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_LSTM_LAYER_HPP_
diff --git a/include/caffe/layers/parameter_layer.hpp b/include/caffe/layers/parameter_layer.hpp
new file mode 100644
index 00000000..188b92ac
--- /dev/null
+++ b/include/caffe/layers/parameter_layer.hpp
@@ -0,0 +1,45 @@
+#ifndef CAFFE_PARAMETER_LAYER_HPP_
+#define CAFFE_PARAMETER_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+class ParameterLayer : public Layer<Dtype> {
+ public:
+  explicit ParameterLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+    if (this->blobs_.size() > 0) {
+      LOG(INFO) << "Skipping parameter initialization";
+    } else {
+      this->blobs_.resize(1);
+      this->blobs_[0].reset(new Blob<Dtype>());
+      this->blobs_[0]->Reshape(this->layer_param_.parameter_param().shape());
+    }
+    top[0]->Reshape(this->layer_param_.parameter_param().shape());
+  }
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) { }
+  virtual inline const char* type() const { return "Parameter"; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+    top[0]->ShareData(*(this->blobs_[0]));
+    top[0]->ShareDiff(*(this->blobs_[0]));
+  }
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom)
+  { }
+};
+
+}  // namespace caffe
+
+#endif
diff --git a/include/caffe/layers/python_layer.hpp b/include/caffe/layers/python_layer.hpp
index b839d526..1407d921 100644
--- a/include/caffe/layers/python_layer.hpp
+++ b/include/caffe/layers/python_layer.hpp
@@ -21,11 +21,12 @@ class PythonLayer : public Layer<Dtype> {
     // Disallow PythonLayer in MultiGPU training stage, due to GIL issues
     // Details: https://github.com/BVLC/caffe/issues/2936
     if (this->phase_ == TRAIN && Caffe::solver_count() > 1
-        && !ShareInParallel()) {
-      LOG(FATAL) << "PythonLayer is not implemented in Multi-GPU training";
+        && !Caffe::multiprocess()) {
+      LOG(FATAL) << "PythonLayer does not support CLI Multi-GPU, use train.py";
     }
     self_.attr("param_str") = bp::str(
         this->layer_param_.python_param().param_str());
+    self_.attr("phase") = static_cast<int>(this->phase_);
     self_.attr("setup")(bottom, top);
   }
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
@@ -33,10 +34,6 @@ class PythonLayer : public Layer<Dtype> {
     self_.attr("reshape")(bottom, top);
   }
 
-  virtual inline bool ShareInParallel() const {
-    return this->layer_param_.python_param().share_in_parallel();
-  }
-
   virtual inline const char* type() const { return "Python"; }
 
  protected:
diff --git a/include/caffe/layers/recurrent_layer.hpp b/include/caffe/layers/recurrent_layer.hpp
new file mode 100644
index 00000000..ca17371b
--- /dev/null
+++ b/include/caffe/layers/recurrent_layer.hpp
@@ -0,0 +1,187 @@
+#ifndef CAFFE_RECURRENT_LAYER_HPP_
+#define CAFFE_RECURRENT_LAYER_HPP_
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/net.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/format.hpp"
+
+namespace caffe {
+
+template <typename Dtype> class RecurrentLayer;
+
+/**
+ * @brief An abstract class for implementing recurrent behavior inside of an
+ *        unrolled network.  This Layer type cannot be instantiated -- instead,
+ *        you should use one of its implementations which defines the recurrent
+ *        architecture, such as RNNLayer or LSTMLayer.
+ */
+template <typename Dtype>
+class RecurrentLayer : public Layer<Dtype> {
+ public:
+  explicit RecurrentLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reset();
+
+  virtual inline const char* type() const { return "Recurrent"; }
+  virtual inline int MinBottomBlobs() const {
+    int min_bottoms = 2;
+    if (this->layer_param_.recurrent_param().expose_hidden()) {
+      vector<string> inputs;
+      this->RecurrentInputBlobNames(&inputs);
+      min_bottoms += inputs.size();
+    }
+    return min_bottoms;
+  }
+  virtual inline int MaxBottomBlobs() const { return MinBottomBlobs() + 1; }
+  virtual inline int ExactNumTopBlobs() const {
+    int num_tops = 1;
+    if (this->layer_param_.recurrent_param().expose_hidden()) {
+      vector<string> outputs;
+      this->RecurrentOutputBlobNames(&outputs);
+      num_tops += outputs.size();
+    }
+    return num_tops;
+  }
+
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    // Can't propagate to sequence continuation indicators.
+    return bottom_index != 1;
+  }
+
+ protected:
+  /**
+   * @brief Fills net_param with the recurrent network architecture.  Subclasses
+   *        should define this -- see RNNLayer and LSTMLayer for examples.
+   */
+  virtual void FillUnrolledNet(NetParameter* net_param) const = 0;
+
+  /**
+   * @brief Fills names with the names of the 0th timestep recurrent input
+   *        Blob&s.  Subclasses should define this -- see RNNLayer and LSTMLayer
+   *        for examples.
+   */
+  virtual void RecurrentInputBlobNames(vector<string>* names) const = 0;
+
+  /**
+   * @brief Fills shapes with the shapes of the recurrent input Blob&s.
+   *        Subclasses should define this -- see RNNLayer and LSTMLayer
+   *        for examples.
+   */
+  virtual void RecurrentInputShapes(vector<BlobShape>* shapes) const = 0;
+
+  /**
+   * @brief Fills names with the names of the Tth timestep recurrent output
+   *        Blob&s.  Subclasses should define this -- see RNNLayer and LSTMLayer
+   *        for examples.
+   */
+  virtual void RecurrentOutputBlobNames(vector<string>* names) const = 0;
+
+  /**
+   * @brief Fills names with the names of the output blobs, concatenated across
+   *        all timesteps.  Should return a name for each top Blob.
+   *        Subclasses should define this -- see RNNLayer and LSTMLayer for
+   *        examples.
+   */
+  virtual void OutputBlobNames(vector<string>* names) const = 0;
+
+  /**
+   * @param bottom input Blob vector (length 2-3)
+   *
+   *   -# @f$ (T \times N \times ...) @f$
+   *      the time-varying input @f$ x @f$.  After the first two axes, whose
+   *      dimensions must correspond to the number of timesteps @f$ T @f$ and
+   *      the number of independent streams @f$ N @f$, respectively, its
+   *      dimensions may be arbitrary.  Note that the ordering of dimensions --
+   *      @f$ (T \times N \times ...) @f$, rather than
+   *      @f$ (N \times T \times ...) @f$ -- means that the @f$ N @f$
+   *      independent input streams must be "interleaved".
+   *
+   *   -# @f$ (T \times N) @f$
+   *      the sequence continuation indicators @f$ \delta @f$.
+   *      These inputs should be binary (0 or 1) indicators, where
+   *      @f$ \delta_{t,n} = 0 @f$ means that timestep @f$ t @f$ of stream
+   *      @f$ n @f$ is the beginning of a new sequence, and hence the previous
+   *      hidden state @f$ h_{t-1} @f$ is multiplied by @f$ \delta_t = 0 @f$
+   *      and has no effect on the cell's output at timestep @f$ t @f$, and
+   *      a value of @f$ \delta_{t,n} = 1 @f$ means that timestep @f$ t @f$ of
+   *      stream @f$ n @f$ is a continuation from the previous timestep
+   *      @f$ t-1 @f$, and the previous hidden state @f$ h_{t-1} @f$ affects the
+   *      updated hidden state and output.
+   *
+   *   -# @f$ (N \times ...) @f$ (optional)
+   *      the static (non-time-varying) input @f$ x_{static} @f$.
+   *      After the first axis, whose dimension must be the number of
+   *      independent streams, its dimensions may be arbitrary.
+   *      This is mathematically equivalent to using a time-varying input of
+   *      @f$ x'_t = [x_t; x_{static}] @f$ -- i.e., tiling the static input
+   *      across the @f$ T @f$ timesteps and concatenating with the time-varying
+   *      input.  Note that if this input is used, all timesteps in a single
+   *      batch within a particular one of the @f$ N @f$ streams must share the
+   *      same static input, even if the sequence continuation indicators
+   *      suggest that difference sequences are ending and beginning within a
+   *      single batch.  This may require padding and/or truncation for uniform
+   *      length.
+   *
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (T \times N \times D) @f$
+   *      the time-varying output @f$ y @f$, where @f$ D @f$ is
+   *      <code>recurrent_param.num_output()</code>.
+   *      Refer to documentation for particular RecurrentLayer implementations
+   *      (such as RNNLayer and LSTMLayer) for the definition of @f$ y @f$.
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// @brief A Net to implement the Recurrent functionality.
+  shared_ptr<Net<Dtype> > unrolled_net_;
+
+  /// @brief The number of independent streams to process simultaneously.
+  int N_;
+
+  /**
+   * @brief The number of timesteps in the layer's input, and the number of
+   *        timesteps over which to backpropagate through time.
+   */
+  int T_;
+
+  /// @brief Whether the layer has a "static" input copied across all timesteps.
+  bool static_input_;
+
+  /**
+   * @brief The last layer to run in the network. (Any later layers are losses
+   *        added to force the recurrent net to do backprop.)
+   */
+  int last_layer_index_;
+
+  /**
+   * @brief Whether the layer's hidden state at the first and last timesteps
+   *        are layer inputs and outputs, respectively.
+   */
+  bool expose_hidden_;
+
+  vector<Blob<Dtype>* > recur_input_blobs_;
+  vector<Blob<Dtype>* > recur_output_blobs_;
+  vector<Blob<Dtype>* > output_blobs_;
+  Blob<Dtype>* x_input_blob_;
+  Blob<Dtype>* x_static_input_blob_;
+  Blob<Dtype>* cont_input_blob_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_RECURRENT_LAYER_HPP_
diff --git a/include/caffe/layers/rnn_layer.hpp b/include/caffe/layers/rnn_layer.hpp
new file mode 100644
index 00000000..6dce238a
--- /dev/null
+++ b/include/caffe/layers/rnn_layer.hpp
@@ -0,0 +1,47 @@
+#ifndef CAFFE_RNN_LAYER_HPP_
+#define CAFFE_RNN_LAYER_HPP_
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/recurrent_layer.hpp"
+#include "caffe/net.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+template <typename Dtype> class RecurrentLayer;
+
+/**
+ * @brief Processes time-varying inputs using a simple recurrent neural network
+ *        (RNN). Implemented as a network unrolling the RNN computation in time.
+ *
+ * Given time-varying inputs @f$ x_t @f$, computes hidden state @f$
+ *     h_t := \tanh[ W_{hh} h_{t_1} + W_{xh} x_t + b_h ]
+ * @f$, and outputs @f$
+ *     o_t := \tanh[ W_{ho} h_t + b_o ]
+ * @f$.
+ */
+template <typename Dtype>
+class RNNLayer : public RecurrentLayer<Dtype> {
+ public:
+  explicit RNNLayer(const LayerParameter& param)
+      : RecurrentLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "RNN"; }
+
+ protected:
+  virtual void FillUnrolledNet(NetParameter* net_param) const;
+  virtual void RecurrentInputBlobNames(vector<string>* names) const;
+  virtual void RecurrentOutputBlobNames(vector<string>* names) const;
+  virtual void RecurrentInputShapes(vector<BlobShape>* shapes) const;
+  virtual void OutputBlobNames(vector<string>* names) const;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_RNN_LAYER_HPP_
diff --git a/include/caffe/layers/scale_layer.hpp b/include/caffe/layers/scale_layer.hpp
index 924df2e5..45b714d4 100644
--- a/include/caffe/layers/scale_layer.hpp
+++ b/include/caffe/layers/scale_layer.hpp
@@ -12,13 +12,15 @@
 namespace caffe {
 
 /**
- * @brief Computes a product of two input Blobs, with the shape of the
- *        latter Blob "broadcast" to match the shape of the former.
+ * @brief Computes the elementwise product of two input Blobs, with the shape of
+ *        the latter Blob "broadcast" to match the shape of the former.
  *        Equivalent to tiling the latter Blob, then computing the elementwise
- *        product.
+ *        product. Note: for efficiency and convenience, this layer can
+ *        additionally perform a "broadcast" sum too when `bias_term: true`
+ *        is set.
  *
- * The second input may be omitted, in which case it's learned as a parameter
- * of the layer.
+ * The latter, scale input may be omitted, in which case it's learned as
+ * parameter of the layer (as is the bias, if it is included).
  */
 template <typename Dtype>
 class ScaleLayer: public Layer<Dtype> {
diff --git a/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp b/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp
index 598dca5f..3d925244 100644
--- a/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp
+++ b/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp
@@ -59,6 +59,8 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
   /// @copydoc SigmoidCrossEntropyLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the sigmoid cross-entropy loss error gradient w.r.t. the
@@ -95,6 +97,13 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
+  /// Read the normalization mode parameter and compute the normalizer based
+  /// on the blob size.  If normalization_mode is VALID, the count of valid
+  /// outputs will be read from valid_count, unless it is -1 in which case
+  /// all outputs are assumed to be valid.
+  virtual Dtype get_normalizer(
+      LossParameter_NormalizationMode normalization_mode, int valid_count);
+
   /// The internal SigmoidLayer used to map predictions to probabilities.
   shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
   /// sigmoid_output stores the output of the SigmoidLayer.
@@ -103,6 +112,15 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
   vector<Blob<Dtype>*> sigmoid_bottom_vec_;
   /// top vector holder to call the underlying SigmoidLayer::Forward
   vector<Blob<Dtype>*> sigmoid_top_vec_;
+
+  /// Whether to ignore instances with a certain label.
+  bool has_ignore_label_;
+  /// The label indicating that an instance should be ignored.
+  int ignore_label_;
+  /// How to normalize the loss.
+  LossParameter_NormalizationMode normalization_;
+  Dtype normalizer_;
+  int outer_num_, inner_num_;
 };
 
 }  // namespace caffe
diff --git a/include/caffe/layers/window_data_layer.hpp b/include/caffe/layers/window_data_layer.hpp
index 35f41b80..b9b66b7c 100644
--- a/include/caffe/layers/window_data_layer.hpp
+++ b/include/caffe/layers/window_data_layer.hpp
@@ -16,7 +16,8 @@ namespace caffe {
 
 /**
  * @brief Provides data to the Net from windows of images files, specified
- *        by a window data file.
+ *        by a window data file. This layer is *DEPRECATED* and only kept for
+ *        archival purposes for use by the original R-CNN.
  *
  * TODO(dox): thorough documentation for Forward and proto params.
  */
diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 543133e2..d3c9306e 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -23,20 +23,25 @@ namespace caffe {
 template <typename Dtype>
 class Net {
  public:
-  explicit Net(const NetParameter& param, const Net* root_net = NULL);
+  explicit Net(const NetParameter& param);
   explicit Net(const string& param_file, Phase phase,
-      const Net* root_net = NULL);
+      const int level = 0, const vector<string>* stages = NULL);
   virtual ~Net() {}
 
   /// @brief Initialize a network with a NetParameter.
   void Init(const NetParameter& param);
 
   /**
-   * @brief Run Forward with the input Blob%s already fed separately.
+   * @brief Run Forward and return the result.
    *
-   * You can get the input blobs using input_blobs().
    */
-  const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL);
+  const vector<Blob<Dtype>*>& Forward(Dtype* loss = NULL);
+  /// @brief DEPRECATED; use Forward() instead.
+  const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL) {
+    LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: ForwardPrefilled() "
+        << "will be removed in a future version. Use Forward().";
+    return Forward(loss);
+  }
 
   /**
    * The From and To variants of Forward and Backward operate on the
@@ -49,14 +54,9 @@ class Net {
   Dtype ForwardFromTo(int start, int end);
   Dtype ForwardFrom(int start);
   Dtype ForwardTo(int end);
-  /// @brief Run forward using a set of bottom blobs, and return the result.
+  /// @brief DEPRECATED; set input blobs then use Forward() instead.
   const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom,
       Dtype* loss = NULL);
-  /**
-   * @brief Run forward using a serialized BlobProtoVector and return the
-   *        result as a serialized BlobProtoVector
-   */
-  string Forward(const string& input_blob_protos, Dtype* loss = NULL);
 
   /**
    * @brief Zeroes out the diffs of all net parameters.
@@ -82,9 +82,9 @@ class Net {
    */
   void Reshape();
 
-  Dtype ForwardBackward(const vector<Blob<Dtype>* > & bottom) {
+  Dtype ForwardBackward() {
     Dtype loss;
-    Forward(bottom, &loss);
+    Forward(&loss);
     Backward();
     return loss;
   }
@@ -227,9 +227,34 @@ class Net {
   static bool StateMeetsRule(const NetState& state, const NetStateRule& rule,
       const string& layer_name);
 
+  // Invoked at specific points during an iteration
+  class Callback {
+   protected:
+    virtual void run(int layer) = 0;
+
+    template <typename T>
+    friend class Net;
+  };
+  const vector<Callback*>& before_forward() const { return before_forward_; }
+  void add_before_forward(Callback* value) {
+    before_forward_.push_back(value);
+  }
+  const vector<Callback*>& after_forward() const { return after_forward_; }
+  void add_after_forward(Callback* value) {
+    after_forward_.push_back(value);
+  }
+  const vector<Callback*>& before_backward() const { return before_backward_; }
+  void add_before_backward(Callback* value) {
+    before_backward_.push_back(value);
+  }
+  const vector<Callback*>& after_backward() const { return after_backward_; }
+  void add_after_backward(Callback* value) {
+    after_backward_.push_back(value);
+  }
+
  protected:
   // Helpers for Init.
-  /// @brief Append a new input or top blob to the net.
+  /// @brief Append a new top blob to the net.
   void AppendTop(const NetParameter& param, const int layer_id,
                  const int top_id, set<string>* available_blobs,
                  map<string, int>* blob_name_to_idx);
@@ -241,8 +266,6 @@ class Net {
   void AppendParam(const NetParameter& param, const int layer_id,
                    const int param_id);
 
-  /// @brief Helper for displaying debug info in Forward about input Blobs.
-  void InputDebugInfo(const int layer_id);
   /// @brief Helper for displaying debug info in Forward.
   void ForwardDebugInfo(const int layer_id);
   /// @brief Helper for displaying debug info in Backward.
@@ -307,9 +330,13 @@ class Net {
   size_t memory_used_;
   /// Whether to compute and display debug info for the net.
   bool debug_info_;
-  /// The root net that actually holds the shared layers in data parallelism
-  const Net* const root_net_;
-  DISABLE_COPY_AND_ASSIGN(Net);
+  // Callbacks
+  vector<Callback*> before_forward_;
+  vector<Callback*> after_forward_;
+  vector<Callback*> before_backward_;
+  vector<Callback*> after_backward_;
+
+DISABLE_COPY_AND_ASSIGN(Net);
 };
 
 
diff --git a/include/caffe/parallel.hpp b/include/caffe/parallel.hpp
index 85fc2b55..64bb48e6 100644
--- a/include/caffe/parallel.hpp
+++ b/include/caffe/parallel.hpp
@@ -1,8 +1,11 @@
 #ifndef CAFFE_PARALLEL_HPP_
 #define CAFFE_PARALLEL_HPP_
 
-#include <boost/date_time/posix_time/posix_time.hpp>
+#ifdef USE_NCCL
 
+#include <boost/thread.hpp>
+
+#include <string>
 #include <vector>
 
 #include "caffe/blob.hpp"
@@ -13,6 +16,7 @@
 #include "caffe/solver.hpp"
 #include "caffe/syncedmem.hpp"
 #include "caffe/util/blocking_queue.hpp"
+#include "caffe/util/nccl.hpp"
 
 namespace caffe {
 
@@ -51,7 +55,7 @@ class GPUParams : public Params<Dtype> {
   GPUParams(shared_ptr<Solver<Dtype> > root_solver, int device);
   virtual ~GPUParams();
 
-  void configure(Solver<Dtype>* solver) const;
+  void Configure(Solver<Dtype>* solver) const;
 
  protected:
   using Params<Dtype>::size_;
@@ -59,55 +63,55 @@ class GPUParams : public Params<Dtype> {
   using Params<Dtype>::diff_;
 };
 
-class DevicePair {
- public:
-  DevicePair(int parent, int device)
-      : parent_(parent),
-        device_(device) {
-  }
-  inline int parent() {
-    return parent_;
-  }
-  inline int device() {
-    return device_;
-  }
-
-  // Group GPUs in pairs, by proximity depending on machine's topology
-  static void compute(const vector<int> devices, vector<DevicePair>* pairs);
-
- protected:
-  int parent_;
-  int device_;
-};
-
-// Synchronous data parallelism using map-reduce between local GPUs.
 template<typename Dtype>
-class P2PSync : public GPUParams<Dtype>, public Solver<Dtype>::Callback,
-    public InternalThread {
+class NCCL : public GPUParams<Dtype>,
+             public Solver<Dtype>::Callback,
+             public Net<Dtype>::Callback {
  public:
-  explicit P2PSync(shared_ptr<Solver<Dtype> > root_solver,
-                   P2PSync<Dtype>* parent, const SolverParameter& param);
-  virtual ~P2PSync();
-
-  inline const shared_ptr<Solver<Dtype> >& solver() const {
-    return solver_;
-  }
-
-  void run(const vector<int>& gpus);
+  /**
+   * Single process version.
+   */
+  explicit NCCL(shared_ptr<Solver<Dtype> > solver);
+  /**
+   * In multi-process settings, first create a NCCL id (new_uid), then
+   * pass it to each process to create connected instances.
+   */
+  NCCL(shared_ptr<Solver<Dtype> > solver, const string& uid);
+  ~NCCL();
+
+  boost::barrier* barrier();
+  void set_barrier(boost::barrier* value);
+
+  /**
+   * In single process settings, create instances without uids and
+   * call this to connect them.
+   */
+  static void InitSingleProcess(vector<NCCL<Dtype>*>* nccls);
+
+  static string new_uid();
+
+  /**
+   * Broadcast weights from rank 0 other solvers.
+   */
+  void Broadcast();
+
+  /**
+   * Single process multi-GPU.
+   */
+  void Run(const vector<int>& gpus, const char* restore);
 
  protected:
-  void on_start();
+  void Init();
+  void on_start() {}
+  void run(int layer);  // Net callback
   void on_gradients_ready();
 
-  void InternalThreadEntry();
+  ncclComm_t comm_;
+  cudaStream_t stream_;
 
-  P2PSync<Dtype>* parent_;
-  vector<P2PSync<Dtype>*> children_;
-  BlockingQueue<P2PSync<Dtype>*> queue_;
-  const int initial_iter_;
-  Dtype* parent_grads_;
   shared_ptr<Solver<Dtype> > solver_;
-
+  // Should not be necessary, https://github.com/NVIDIA/nccl/issues/37
+  boost::barrier* barrier_;
   using Params<Dtype>::size_;
   using Params<Dtype>::data_;
   using Params<Dtype>::diff_;
@@ -115,4 +119,5 @@ class P2PSync : public GPUParams<Dtype>, public Solver<Dtype>::Callback,
 
 }  // namespace caffe
 
-#endif
+#endif  // USE_NCCL
+#endif  // header
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 38259eda..a28d8cb8 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -6,13 +6,14 @@
 
 #include "caffe/net.hpp"
 #include "caffe/solver_factory.hpp"
+#include "caffe/util/benchmark.hpp"
 
 namespace caffe {
 
 /**
   * @brief Enumeration of actions that a client of the Solver may request by
   * implementing the Solver's action request function, which a
-  * a client may optionally provide in order to request early termination
+  * client may optionally provide in order to request early termination
   * or saving a snapshot without exiting. In the executable caffe, this
   * mechanism is used to allow the snapshot to be saved when stopping
   * execution with a SIGINT (Ctrl-C).
@@ -40,9 +41,8 @@ typedef boost::function<SolverAction::Enum()> ActionCallback;
 template <typename Dtype>
 class Solver {
  public:
-  explicit Solver(const SolverParameter& param,
-      const Solver* root_solver = NULL);
-  explicit Solver(const string& param_file, const Solver* root_solver = NULL);
+  explicit Solver(const SolverParameter& param);
+  explicit Solver(const string& param_file);
   void Init(const SolverParameter& param);
   void InitTrainNet();
   void InitTestNets();
@@ -72,7 +72,7 @@ class Solver {
   inline const vector<shared_ptr<Net<Dtype> > >& test_nets() {
     return test_nets_;
   }
-  int iter() { return iter_; }
+  int iter() const { return iter_; }
 
   // Invoked at specific points during an iteration
   class Callback {
@@ -118,10 +118,6 @@ class Solver {
   vector<Dtype> losses_;
   Dtype smoothed_loss_;
 
-  // The root solver that holds root nets (actually containing shared layers)
-  // in data parallelism
-  const Solver* const root_solver_;
-
   // A function that can be set by a client of the Solver to provide indication
   // that it wants a snapshot saved and/or to exit early.
   ActionCallback action_request_function_;
@@ -129,31 +125,11 @@ class Solver {
   // True iff a request to stop early was received.
   bool requested_early_exit_;
 
-  DISABLE_COPY_AND_ASSIGN(Solver);
-};
+  // Timing information, handy to tune e.g. nbr of GPUs
+  Timer iteration_timer_;
+  float iterations_last_;
 
-/**
- * @brief Solver that only computes gradients, used as worker
- *        for multi-GPU training.
- */
-template <typename Dtype>
-class WorkerSolver : public Solver<Dtype> {
- public:
-  explicit WorkerSolver(const SolverParameter& param,
-      const Solver<Dtype>* root_solver = NULL)
-      : Solver<Dtype>(param, root_solver) {}
-
- protected:
-  void ApplyUpdate() {}
-  void SnapshotSolverState(const string& model_filename) {
-    LOG(FATAL) << "Should not be called on worker solver.";
-  }
-  void RestoreSolverStateFromBinaryProto(const string& state_file) {
-    LOG(FATAL) << "Should not be called on worker solver.";
-  }
-  void RestoreSolverStateFromHDF5(const string& state_file) {
-    LOG(FATAL) << "Should not be called on worker solver.";
-  }
+  DISABLE_COPY_AND_ASSIGN(Solver);
 };
 
 }  // namespace caffe
diff --git a/include/caffe/solver_factory.hpp b/include/caffe/solver_factory.hpp
index cfff721a..a5b16073 100644
--- a/include/caffe/solver_factory.hpp
+++ b/include/caffe/solver_factory.hpp
@@ -15,7 +15,7 @@
  * and its type is its C++ class name, but without the "Solver" at the end
  * ("MyAwesomeSolver" -> "MyAwesome").
  *
- * If the solver is going to be created simply by its constructor, in your c++
+ * If the solver is going to be created simply by its constructor, in your C++
  * file, add the following line:
  *
  *    REGISTER_SOLVER_CLASS(MyAwesome);
diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp
index 38ee4664..317ce29a 100644
--- a/include/caffe/syncedmem.hpp
+++ b/include/caffe/syncedmem.hpp
@@ -3,6 +3,10 @@
 
 #include <cstdlib>
 
+#ifdef USE_MKL
+  #include "mkl.h"
+#endif
+
 #include "caffe/common.hpp"
 
 namespace caffe {
@@ -20,7 +24,11 @@ inline void CaffeMallocHost(void** ptr, size_t size, bool* use_cuda) {
     return;
   }
 #endif
+#ifdef USE_MKL
+  *ptr = mkl_malloc(size ? size:1, 64);
+#else
   *ptr = malloc(size);
+#endif
   *use_cuda = false;
   CHECK(*ptr) << "host allocation of size " << size << " failed";
 }
@@ -32,7 +40,11 @@ inline void CaffeFreeHost(void* ptr, bool use_cuda) {
     return;
   }
 #endif
+#ifdef USE_MKL
+  mkl_free(ptr);
+#else
   free(ptr);
+#endif
 }
 
 
@@ -44,14 +56,8 @@ inline void CaffeFreeHost(void* ptr, bool use_cuda) {
  */
 class SyncedMemory {
  public:
-  SyncedMemory()
-      : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(0), head_(UNINITIALIZED),
-        own_cpu_data_(false), cpu_malloc_use_cuda_(false), own_gpu_data_(false),
-        gpu_device_(-1) {}
-  explicit SyncedMemory(size_t size)
-      : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(size), head_(UNINITIALIZED),
-        own_cpu_data_(false), cpu_malloc_use_cuda_(false), own_gpu_data_(false),
-        gpu_device_(-1) {}
+  SyncedMemory();
+  explicit SyncedMemory(size_t size);
   ~SyncedMemory();
   const void* cpu_data();
   void set_cpu_data(void* data);
@@ -68,6 +74,8 @@ class SyncedMemory {
 #endif
 
  private:
+  void check_device();
+
   void to_cpu();
   void to_gpu();
   void* cpu_ptr_;
@@ -77,7 +85,7 @@ class SyncedMemory {
   bool own_cpu_data_;
   bool cpu_malloc_use_cuda_;
   bool own_gpu_data_;
-  int gpu_device_;
+  int device_;
 
   DISABLE_COPY_AND_ASSIGN(SyncedMemory);
 };  // class SyncedMemory
diff --git a/include/caffe/test/test_caffe_main.hpp b/include/caffe/test/test_caffe_main.hpp
index fc156091..294f7e50 100644
--- a/include/caffe/test/test_caffe_main.hpp
+++ b/include/caffe/test/test_caffe_main.hpp
@@ -18,9 +18,8 @@ using std::endl;
   #include "caffe_config.h"
 #else
   #define CUDA_TEST_DEVICE -1
-  #define CMAKE_SOURCE_DIR "src/"
   #define EXAMPLES_SOURCE_DIR "examples/"
-  #define CMAKE_EXT ""
+  #define ABS_TEST_DATA_DIR "src/caffe/test/test_data"
 #endif
 
 int main(int argc, char** argv);
diff --git a/include/caffe/util/cudnn.hpp b/include/caffe/util/cudnn.hpp
index 8a7e17c6..498cfe38 100644
--- a/include/caffe/util/cudnn.hpp
+++ b/include/caffe/util/cudnn.hpp
@@ -41,6 +41,10 @@ inline const char* cudnnGetErrorString(cudnnStatus_t status) {
       return "CUDNN_STATUS_NOT_SUPPORTED";
     case CUDNN_STATUS_LICENSE_ERROR:
       return "CUDNN_STATUS_LICENSE_ERROR";
+#if CUDNN_VERSION_MIN(6, 0, 0)
+    case CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING:
+      return "CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING";
+#endif
   }
   return "Unknown cudnn status";
 }
@@ -91,8 +95,13 @@ template <typename Dtype>
 inline void createFilterDesc(cudnnFilterDescriptor_t* desc,
     int n, int c, int h, int w) {
   CUDNN_CHECK(cudnnCreateFilterDescriptor(desc));
+#if CUDNN_VERSION_MIN(5, 0, 0)
   CUDNN_CHECK(cudnnSetFilter4dDescriptor(*desc, dataType<Dtype>::type,
-      n, c, h, w));
+      CUDNN_TENSOR_NCHW, n, c, h, w));
+#else
+  CUDNN_CHECK(cudnnSetFilter4dDescriptor_v4(*desc, dataType<Dtype>::type,
+      CUDNN_TENSOR_NCHW, n, c, h, w));
+#endif
 }
 
 template <typename Dtype>
@@ -104,8 +113,14 @@ template <typename Dtype>
 inline void setConvolutionDesc(cudnnConvolutionDescriptor_t* conv,
     cudnnTensorDescriptor_t bottom, cudnnFilterDescriptor_t filter,
     int pad_h, int pad_w, int stride_h, int stride_w) {
+#if CUDNN_VERSION_MIN(6, 0, 0)
   CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
+      pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION,
+      dataType<Dtype>::type));
+#else
+    CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
       pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION));
+#endif
 }
 
 template <typename Dtype>
@@ -123,8 +138,21 @@ inline void createPoolingDesc(cudnnPoolingDescriptor_t* pool_desc,
     LOG(FATAL) << "Unknown pooling method.";
   }
   CUDNN_CHECK(cudnnCreatePoolingDescriptor(pool_desc));
-  CUDNN_CHECK(cudnnSetPooling2dDescriptor(*pool_desc, *mode, h, w,
-        pad_h, pad_w, stride_h, stride_w));
+#if CUDNN_VERSION_MIN(5, 0, 0)
+  CUDNN_CHECK(cudnnSetPooling2dDescriptor(*pool_desc, *mode,
+        CUDNN_PROPAGATE_NAN, h, w, pad_h, pad_w, stride_h, stride_w));
+#else
+  CUDNN_CHECK(cudnnSetPooling2dDescriptor_v4(*pool_desc, *mode,
+        CUDNN_PROPAGATE_NAN, h, w, pad_h, pad_w, stride_h, stride_w));
+#endif
+}
+
+template <typename Dtype>
+inline void createActivationDescriptor(cudnnActivationDescriptor_t* activ_desc,
+    cudnnActivationMode_t mode) {
+  CUDNN_CHECK(cudnnCreateActivationDescriptor(activ_desc));
+  CUDNN_CHECK(cudnnSetActivationDescriptor(*activ_desc, mode,
+                                           CUDNN_PROPAGATE_NAN, Dtype(0)));
 }
 
 }  // namespace cudnn
diff --git a/include/caffe/util/db_leveldb.hpp b/include/caffe/util/db_leveldb.hpp
index e9fa0d32..4cdb6db9 100644
--- a/include/caffe/util/db_leveldb.hpp
+++ b/include/caffe/util/db_leveldb.hpp
@@ -14,7 +14,10 @@ namespace caffe { namespace db {
 class LevelDBCursor : public Cursor {
  public:
   explicit LevelDBCursor(leveldb::Iterator* iter)
-    : iter_(iter) { SeekToFirst(); }
+    : iter_(iter) {
+    SeekToFirst();
+    CHECK(iter_->status().ok()) << iter_->status().ToString();
+  }
   ~LevelDBCursor() { delete iter_; }
   virtual void SeekToFirst() { iter_->SeekToFirst(); }
   virtual void Next() { iter_->Next(); }
diff --git a/include/caffe/util/db_lmdb.hpp b/include/caffe/util/db_lmdb.hpp
index 4e1568ac..ee370322 100644
--- a/include/caffe/util/db_lmdb.hpp
+++ b/include/caffe/util/db_lmdb.hpp
@@ -3,6 +3,7 @@
 #define CAFFE_UTIL_DB_LMDB_HPP
 
 #include <string>
+#include <vector>
 
 #include "lmdb.h"
 
@@ -54,14 +55,16 @@ class LMDBCursor : public Cursor {
 
 class LMDBTransaction : public Transaction {
  public:
-  explicit LMDBTransaction(MDB_dbi* mdb_dbi, MDB_txn* mdb_txn)
-    : mdb_dbi_(mdb_dbi), mdb_txn_(mdb_txn) { }
+  explicit LMDBTransaction(MDB_env* mdb_env)
+    : mdb_env_(mdb_env) { }
   virtual void Put(const string& key, const string& value);
-  virtual void Commit() { MDB_CHECK(mdb_txn_commit(mdb_txn_)); }
+  virtual void Commit();
 
  private:
-  MDB_dbi* mdb_dbi_;
-  MDB_txn* mdb_txn_;
+  MDB_env* mdb_env_;
+  vector<string> keys, values;
+
+  void DoubleMapSize();
 
   DISABLE_COPY_AND_ASSIGN(LMDBTransaction);
 };
diff --git a/include/caffe/util/hdf5.hpp b/include/caffe/util/hdf5.hpp
index ce568c5e..71549c1c 100644
--- a/include/caffe/util/hdf5.hpp
+++ b/include/caffe/util/hdf5.hpp
@@ -13,12 +13,12 @@ namespace caffe {
 template <typename Dtype>
 void hdf5_load_nd_dataset_helper(
     hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
-    Blob<Dtype>* blob);
+    Blob<Dtype>* blob, bool reshape);
 
 template <typename Dtype>
 void hdf5_load_nd_dataset(
     hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
-    Blob<Dtype>* blob);
+    Blob<Dtype>* blob, bool reshape = false);
 
 template <typename Dtype>
 void hdf5_save_nd_dataset(
diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 6f6d3fee..e549120a 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -52,6 +52,9 @@ void caffe_scal(const int N, const Dtype alpha, Dtype *X);
 template <typename Dtype>
 void caffe_sqr(const int N, const Dtype* a, Dtype* y);
 
+template <typename Dtype>
+void caffe_sqrt(const int N, const Dtype* a, Dtype* y);
+
 template <typename Dtype>
 void caffe_add(const int N, const Dtype* a, const Dtype* b, Dtype* y);
 
@@ -128,16 +131,16 @@ inline int8_t caffe_sign(Dtype val) {
   }
 
 // output is 1 for the positives, 0 for zero, and -1 for the negatives
-DEFINE_CAFFE_CPU_UNARY_FUNC(sign, y[i] = caffe_sign<Dtype>(x[i]));
+DEFINE_CAFFE_CPU_UNARY_FUNC(sign, y[i] = caffe_sign<Dtype>(x[i]))
 
 // This returns a nonzero value if the input has its sign bit set.
 // The name sngbit is meant to avoid conflicts with std::signbit in the macro.
 // The extra parens are needed because CUDA < 6.5 defines signbit as a macro,
 // and we don't want that to expand here when CUDA headers are also included.
 DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, \
-    y[i] = static_cast<bool>((std::signbit)(x[i])));
+    y[i] = static_cast<bool>((std::signbit)(x[i])))
 
-DEFINE_CAFFE_CPU_UNARY_FUNC(fabs, y[i] = std::fabs(x[i]));
+DEFINE_CAFFE_CPU_UNARY_FUNC(fabs, y[i] = std::fabs(x[i]))
 
 template <typename Dtype>
 void caffe_cpu_scale(const int n, const Dtype alpha, const Dtype *x, Dtype* y);
@@ -185,6 +188,11 @@ void caffe_gpu_add_scalar(const int N, const Dtype alpha, Dtype *X);
 template <typename Dtype>
 void caffe_gpu_scal(const int N, const Dtype alpha, Dtype *X);
 
+#ifndef CPU_ONLY
+template <typename Dtype>
+void caffe_gpu_scal(const int N, const Dtype alpha, Dtype* X, cudaStream_t str);
+#endif
+
 template <typename Dtype>
 void caffe_gpu_add(const int N, const Dtype* a, const Dtype* b, Dtype* y);
 
@@ -209,6 +217,9 @@ void caffe_gpu_log(const int n, const Dtype* a, Dtype* y);
 template <typename Dtype>
 void caffe_gpu_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
 
+template <typename Dtype>
+void caffe_gpu_sqrt(const int n, const Dtype* a, Dtype* y);
+
 // caffe_gpu_rng_uniform with two arguments generates integers in the range
 // [0, UINT_MAX].
 void caffe_gpu_rng_uniform(const int n, unsigned int* r);
diff --git a/include/caffe/util/mkl_alternate.hpp b/include/caffe/util/mkl_alternate.hpp
index 3355b665..8c2294c7 100644
--- a/include/caffe/util/mkl_alternate.hpp
+++ b/include/caffe/util/mkl_alternate.hpp
@@ -7,9 +7,14 @@
 
 #else  // If use MKL, simply include the MKL header
 
+#ifdef USE_ACCELERATE
+#include <Accelerate/Accelerate.h>
+#else
 extern "C" {
 #include <cblas.h>
 }
+#endif  // USE_ACCELERATE
+
 #include <math.h>
 
 // Functions that caffe uses but are not present if MKL is not linked.
@@ -31,10 +36,11 @@ extern "C" {
     v##name<double>(n, a, y); \
   }
 
-DEFINE_VSL_UNARY_FUNC(Sqr, y[i] = a[i] * a[i]);
-DEFINE_VSL_UNARY_FUNC(Exp, y[i] = exp(a[i]));
-DEFINE_VSL_UNARY_FUNC(Ln, y[i] = log(a[i]));
-DEFINE_VSL_UNARY_FUNC(Abs, y[i] = fabs(a[i]));
+DEFINE_VSL_UNARY_FUNC(Sqr, y[i] = a[i] * a[i])
+DEFINE_VSL_UNARY_FUNC(Sqrt, y[i] = sqrt(a[i]))
+DEFINE_VSL_UNARY_FUNC(Exp, y[i] = exp(a[i]))
+DEFINE_VSL_UNARY_FUNC(Ln, y[i] = log(a[i]))
+DEFINE_VSL_UNARY_FUNC(Abs, y[i] = fabs(a[i]))
 
 // A simple way to define the vsl unary functions with singular parameter b.
 // The operation should be in the form e.g. y[i] = pow(a[i], b)
@@ -53,7 +59,7 @@ DEFINE_VSL_UNARY_FUNC(Abs, y[i] = fabs(a[i]));
     v##name<double>(n, a, b, y); \
   }
 
-DEFINE_VSL_UNARY_FUNC_WITH_PARAM(Powx, y[i] = pow(a[i], b));
+DEFINE_VSL_UNARY_FUNC_WITH_PARAM(Powx, y[i] = pow(a[i], b))
 
 // A simple way to define the vsl binary functions. The operation should
 // be in the form e.g. y[i] = a[i] + b[i]
@@ -72,10 +78,10 @@ DEFINE_VSL_UNARY_FUNC_WITH_PARAM(Powx, y[i] = pow(a[i], b));
     v##name<double>(n, a, b, y); \
   }
 
-DEFINE_VSL_BINARY_FUNC(Add, y[i] = a[i] + b[i]);
-DEFINE_VSL_BINARY_FUNC(Sub, y[i] = a[i] - b[i]);
-DEFINE_VSL_BINARY_FUNC(Mul, y[i] = a[i] * b[i]);
-DEFINE_VSL_BINARY_FUNC(Div, y[i] = a[i] / b[i]);
+DEFINE_VSL_BINARY_FUNC(Add, y[i] = a[i] + b[i])
+DEFINE_VSL_BINARY_FUNC(Sub, y[i] = a[i] - b[i])
+DEFINE_VSL_BINARY_FUNC(Mul, y[i] = a[i] * b[i])
+DEFINE_VSL_BINARY_FUNC(Div, y[i] = a[i] / b[i])
 
 // In addition, MKL comes with an additional function axpby that is not present
 // in standard blas. We will simply use a two-step (inefficient, of course) way
diff --git a/include/caffe/util/nccl.hpp b/include/caffe/util/nccl.hpp
new file mode 100644
index 00000000..e01fb745
--- /dev/null
+++ b/include/caffe/util/nccl.hpp
@@ -0,0 +1,37 @@
+#ifndef CAFFE_UTIL_NCCL_H_
+#define CAFFE_UTIL_NCCL_H_
+#ifdef USE_NCCL
+
+#include <nccl.h>
+
+#include "caffe/common.hpp"
+
+#define NCCL_CHECK(condition) \
+{ \
+  ncclResult_t result = condition; \
+  CHECK_EQ(result, ncclSuccess) << " " \
+    << ncclGetErrorString(result); \
+}
+
+namespace caffe {
+
+namespace nccl {
+
+template <typename Dtype> class dataType;
+
+template<> class dataType<float> {
+ public:
+  static const ncclDataType_t type = ncclFloat;
+};
+template<> class dataType<double> {
+ public:
+  static const ncclDataType_t type = ncclDouble;
+};
+
+}  // namespace nccl
+
+}  // namespace caffe
+
+#endif  // end USE_NCCL
+
+#endif  // CAFFE_UTIL_NCCL_H_
diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp
index c94bb3ca..b145822a 100644
--- a/include/caffe/util/upgrade_proto.hpp
+++ b/include/caffe/util/upgrade_proto.hpp
@@ -59,6 +59,18 @@ bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param,
 
 const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type);
 
+// Return true iff the Net contains input fields.
+bool NetNeedsInputUpgrade(const NetParameter& net_param);
+
+// Perform all necessary transformations to upgrade input fields into layers.
+void UpgradeNetInput(NetParameter* net_param);
+
+// Return true iff the Net contains batch norm layers with manual local LRs.
+bool NetNeedsBatchNormUpgrade(const NetParameter& net_param);
+
+// Perform all necessary transformations to upgrade batch norm layers.
+void UpgradeNetBatchNorm(NetParameter* net_param);
+
 // Return true iff the solver contains any old solver_type specified as enums
 bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param);
 
diff --git a/matlab/+caffe/Net.m b/matlab/+caffe/Net.m
index e6295bba..bb99ec89 100644
--- a/matlab/+caffe/Net.m
+++ b/matlab/+caffe/Net.m
@@ -68,6 +68,11 @@
       self.layer_names = self.attributes.layer_names;
       self.blob_names = self.attributes.blob_names;
     end
+    function delete (self)
+      if ~isempty(self.hNet_self)
+        caffe_('delete_net', self.hNet_self);
+      end
+    end
     function layer = layers(self, layer_name)
       CHECK(ischar(layer_name), 'layer_name must be a string');
       layer = self.layer_vec(self.name2layer_index(layer_name));
diff --git a/matlab/+caffe/Solver.m b/matlab/+caffe/Solver.m
index f8bdc4e2..2d3c98b2 100644
--- a/matlab/+caffe/Solver.m
+++ b/matlab/+caffe/Solver.m
@@ -36,6 +36,9 @@
         self.test_nets(n) = caffe.Net(self.attributes.hNet_test_nets(n));
       end
     end
+    function delete (self)
+      caffe_('delete_solver', self.hSolver_self);
+    end
     function iter = iter(self)
       iter = caffe_('solver_get_iter', self.hSolver_self);
     end
diff --git a/matlab/+caffe/private/caffe_.cpp b/matlab/+caffe/private/caffe_.cpp
index 1b1b2bff..a32bd5e5 100644
--- a/matlab/+caffe/private/caffe_.cpp
+++ b/matlab/+caffe/private/caffe_.cpp
@@ -44,7 +44,7 @@ void mxCHECK_FILE_EXIST(const char* file) {
 // The pointers to caffe::Solver and caffe::Net instances
 static vector<shared_ptr<Solver<float> > > solvers_;
 static vector<shared_ptr<Net<float> > > nets_;
-// init_key is generated at the beginning and everytime you call reset
+// init_key is generated at the beginning and every time you call reset
 static double init_key = static_cast<double>(caffe_rng_rand());
 
 /** -----------------------------------------------------------------
@@ -197,6 +197,17 @@ static void get_solver(MEX_ARGS) {
   mxFree(solver_file);
 }
 
+// Usage: caffe_('delete_solver', hSolver)
+static void delete_solver(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('delete_solver', hSolver)");
+  Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+  solvers_.erase(std::remove_if(solvers_.begin(), solvers_.end(),
+      [solver] (const shared_ptr< Solver<float> > &solverPtr) {
+      return solverPtr.get() == solver;
+  }), solvers_.end());
+}
+
 // Usage: caffe_('solver_get_attr', hSolver)
 static void solver_get_attr(MEX_ARGS) {
   mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
@@ -271,6 +282,17 @@ static void get_net(MEX_ARGS) {
   mxFree(phase_name);
 }
 
+// Usage: caffe_('delete_solver', hSolver)
+static void delete_net(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('delete_solver', hNet)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  nets_.erase(std::remove_if(nets_.begin(), nets_.end(),
+      [net] (const shared_ptr< Net<float> > &netPtr) {
+      return netPtr.get() == net;
+  }), nets_.end());
+}
+
 // Usage: caffe_('net_get_attr', hNet)
 static void net_get_attr(MEX_ARGS) {
   mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
@@ -522,12 +544,14 @@ struct handler_registry {
 static handler_registry handlers[] = {
   // Public API functions
   { "get_solver",         get_solver      },
+  { "delete_solver",      delete_solver   },
   { "solver_get_attr",    solver_get_attr },
   { "solver_get_iter",    solver_get_iter },
   { "solver_restore",     solver_restore  },
   { "solver_solve",       solver_solve    },
   { "solver_step",        solver_step     },
   { "get_net",            get_net         },
+  { "delete_net",         delete_net      },
   { "net_get_attr",       net_get_attr    },
   { "net_forward",        net_forward     },
   { "net_backward",       net_backward    },
diff --git a/matlab/CMakeLists.txt b/matlab/CMakeLists.txt
index f420df8d..987730d9 100644
--- a/matlab/CMakeLists.txt
+++ b/matlab/CMakeLists.txt
@@ -20,7 +20,7 @@ if(NOT BUILD_SHARED_LIBS AND build_using MATCHES Matlab)
   message(FATAL_ERROR "Matlab MEX interface (with default mex options file) can only be built if caffe is compiled as shared library. Please enable 'BUILD_SHARED_LIBS' in CMake. Aternativelly you can switch to Octave compiler.")
 endif()
 
-# helper function to set proper mex file extention
+# helper function to set proper mex file extension
 function(caffe_fetch_and_set_proper_mexext mexfile_variable)
   execute_process(COMMAND ${Matlab_mexext} OUTPUT_STRIP_TRAILING_WHITESPACE RESULT_VARIABLE res OUTPUT_VARIABLE ext)
   if(res MATCHES 0)
diff --git a/matlab/demo/classification_demo.m b/matlab/demo/classification_demo.m
index 2b603329..435c0778 100644
--- a/matlab/demo/classification_demo.m
+++ b/matlab/demo/classification_demo.m
@@ -8,7 +8,7 @@
 %
 % ****************************************************************************
 % For detailed documentation and usage on Caffe's Matlab interface, please
-% refer to Caffe Interface Tutorial at
+% refer to the Caffe Interface Tutorial at
 % http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab
 % ****************************************************************************
 %
@@ -24,6 +24,7 @@
 %  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64
 %  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
 % Or the equivalent based on where things are installed on your system
+% and what versions are installed.
 %
 % Usage:
 %  im = imread('../../examples/images/cat.jpg');
@@ -39,7 +40,7 @@
 % Data coming in from matlab needs to be in the order
 %   [width, height, channels, images]
 % where width is the fastest dimension.
-% Here is the rough matlab for putting image data into the correct
+% Here is the rough matlab code for putting image data into the correct
 % format in W x H x C with BGR channels:
 %   % permute channels from RGB to BGR
 %   im_data = im(:, :, [3, 2, 1]);
@@ -54,7 +55,7 @@
 
 % If you have multiple images, cat them with cat(4, ...)
 
-% Add caffe/matlab to you Matlab search PATH to use matcaffe
+% Add caffe/matlab to your Matlab search PATH in order to use matcaffe
 if exist('../+caffe', 'dir')
   addpath('..');
 else
diff --git a/models/bvlc_alexnet/deploy.prototxt b/models/bvlc_alexnet/deploy.prototxt
index ff10daa9..45b2b0e3 100644
--- a/models/bvlc_alexnet/deploy.prototxt
+++ b/models/bvlc_alexnet/deploy.prototxt
@@ -1,10 +1,9 @@
 name: "AlexNet"
-input: "data"
-input_shape {
-  dim: 10
-  dim: 3
-  dim: 227
-  dim: 227
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
 }
 layer {
   name: "conv1"
diff --git a/models/bvlc_alexnet/readme.md b/models/bvlc_alexnet/readme.md
index 008d690f..a83e3d4e 100644
--- a/models/bvlc_alexnet/readme.md
+++ b/models/bvlc_alexnet/readme.md
@@ -1,5 +1,5 @@
 ---
-name: BVLC AlexNet Model
+name: BAIR/BVLC AlexNet Model
 caffemodel: bvlc_alexnet.caffemodel
 caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel
 license: unrestricted
diff --git a/models/bvlc_googlenet/deploy.prototxt b/models/bvlc_googlenet/deploy.prototxt
index 1f90ee21..50b54a9f 100644
--- a/models/bvlc_googlenet/deploy.prototxt
+++ b/models/bvlc_googlenet/deploy.prototxt
@@ -1,10 +1,9 @@
 name: "GoogleNet"
-input: "data"
-input_shape {
-  dim: 10
-  dim: 3
-  dim: 224
-  dim: 224
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 224 dim: 224 } }
 }
 layer {
   name: "conv1/7x7_s2"
diff --git a/models/bvlc_googlenet/readme.md b/models/bvlc_googlenet/readme.md
index 061b6d74..ef04db62 100644
--- a/models/bvlc_googlenet/readme.md
+++ b/models/bvlc_googlenet/readme.md
@@ -1,5 +1,5 @@
 ---
-name: BVLC GoogleNet Model
+name: BAIR/BVLC GoogleNet Model
 caffemodel: bvlc_googlenet.caffemodel
 caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel
 license: unrestricted
diff --git a/models/bvlc_googlenet/train_val.prototxt b/models/bvlc_googlenet/train_val.prototxt
old mode 100644
new mode 100755
index 5dee3abe..5fe367f2
--- a/models/bvlc_googlenet/train_val.prototxt
+++ b/models/bvlc_googlenet/train_val.prototxt
@@ -1692,7 +1692,7 @@ layer {
   type: "SoftmaxWithLoss"
   bottom: "loss2/classifier"
   bottom: "label"
-  top: "loss2/loss1"
+  top: "loss2/loss2"
   loss_weight: 0.3
 }
 layer {
diff --git a/models/bvlc_reference_caffenet/deploy.prototxt b/models/bvlc_reference_caffenet/deploy.prototxt
index 127f1e26..907116ef 100644
--- a/models/bvlc_reference_caffenet/deploy.prototxt
+++ b/models/bvlc_reference_caffenet/deploy.prototxt
@@ -1,10 +1,9 @@
 name: "CaffeNet"
-input: "data"
-input_shape {
-  dim: 10
-  dim: 3
-  dim: 227
-  dim: 227
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
 }
 layer {
   name: "conv1"
diff --git a/models/bvlc_reference_caffenet/readme.md b/models/bvlc_reference_caffenet/readme.md
index 671e47a5..5352e536 100644
--- a/models/bvlc_reference_caffenet/readme.md
+++ b/models/bvlc_reference_caffenet/readme.md
@@ -1,5 +1,5 @@
 ---
-name: BVLC CaffeNet Model
+name: BAIR/BVLC CaffeNet Model
 caffemodel: bvlc_reference_caffenet.caffemodel
 caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_reference_caffenet.caffemodel
 license: unrestricted
diff --git a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
index ae1df967..e330a770 100644
--- a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
+++ b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
@@ -1,10 +1,9 @@
 name: "R-CNN-ilsvrc13"
-input: "data"
-input_shape {
-  dim: 10
-  dim: 3
-  dim: 227
-  dim: 227
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
 }
 layer {
   name: "conv1"
diff --git a/models/bvlc_reference_rcnn_ilsvrc13/readme.md b/models/bvlc_reference_rcnn_ilsvrc13/readme.md
index 9a11a24d..12543b2b 100644
--- a/models/bvlc_reference_rcnn_ilsvrc13/readme.md
+++ b/models/bvlc_reference_rcnn_ilsvrc13/readme.md
@@ -1,5 +1,5 @@
 ---
-name: BVLC Reference RCNN ILSVRC13 Model
+name: BAIR/BVLC Reference RCNN ILSVRC13 Model
 caffemodel: bvlc_reference_rcnn_ilsvrc13.caffemodel
 caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_reference_rcnn_ilsvrc13.caffemodel
 license: unrestricted
diff --git a/models/finetune_flickr_style/deploy.prototxt b/models/finetune_flickr_style/deploy.prototxt
index 0f07e47a..b8f99c74 100644
--- a/models/finetune_flickr_style/deploy.prototxt
+++ b/models/finetune_flickr_style/deploy.prototxt
@@ -1,10 +1,9 @@
 name: "FlickrStyleCaffeNet"
-input: "data"
-input_shape {
-  dim: 10
-  dim: 3
-  dim: 227
-  dim: 227
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
 }
 layer {
   name: "conv1"
diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index a2264140..c53299d2 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -3,13 +3,13 @@ if(NOT HAVE_PYTHON)
   return()
 endif()
 
-include_directories(${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR} ${Boost_INCLUDE_DIRS})
 file(GLOB_RECURSE python_srcs ${PROJECT_SOURCE_DIR}/python/*.cpp)
 
 add_library(pycaffe SHARED ${python_srcs})
-target_link_libraries(pycaffe ${Caffe_LINK} ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
-set_target_properties(pycaffe PROPERTIES PREFIX "" OUTPUT_NAME "_caffe")
 caffe_default_properties(pycaffe)
+set_target_properties(pycaffe PROPERTIES PREFIX "" OUTPUT_NAME "_caffe")
+target_include_directories(pycaffe PUBLIC ${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR})
+target_link_libraries(pycaffe PUBLIC ${Caffe_LINK} ${PYTHON_LIBRARIES})
 
 if(UNIX OR APPLE)
     set(__linkname "${PROJECT_SOURCE_DIR}/python/caffe/_caffe.so")
@@ -22,13 +22,19 @@ if(UNIX OR APPLE)
 endif()
 
 # ---[ Install
-file(GLOB files1 *.py requirements.txt)
-install(FILES ${files1} DESTINATION python)
-
-file(GLOB files2 caffe/*.py)
-install(FILES  ${files2} DESTINATION python/caffe)
+# scripts
+file(GLOB python_files *.py requirements.txt)
+install(FILES ${python_files} DESTINATION python)
+
+# module
+install(DIRECTORY caffe
+    DESTINATION python
+    FILES_MATCHING
+    PATTERN "*.py"
+    PATTERN "ilsvrc_2012_mean.npy"
+    PATTERN "test" EXCLUDE
+    )
+
+# _caffe.so
 install(TARGETS pycaffe  DESTINATION python/caffe)
-install(DIRECTORY caffe/imagenet caffe/proto caffe/test DESTINATION python/caffe)
-
-
 
diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index e2881b89..776945ee 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,5 +1,5 @@
-from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver
-from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list
+from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver, NCCL, Timer
+from ._caffe import init_log, log, set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed, solver_count, set_solver_count, solver_rank, set_solver_rank, set_multiprocess, has_nccl
 from ._caffe import __version__
 from .proto.caffe_pb2 import TRAIN, TEST
 from .classifier import Classifier
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 12a57455..72659a4f 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -26,6 +26,19 @@
 #define PyArray_SetBaseObject(arr, x) (PyArray_BASE(arr) = (x))
 #endif
 
+/* Fix to avoid registration warnings in pycaffe (#3960) */
+#define BP_REGISTER_SHARED_PTR_TO_PYTHON(PTR) do { \
+  const boost::python::type_info info = \
+    boost::python::type_id<shared_ptr<PTR > >(); \
+  const boost::python::converter::registration* reg = \
+    boost::python::converter::registry::query(info); \
+  if (reg == NULL) { \
+    bp::register_ptr_to_python<shared_ptr<PTR > >(); \
+  } else if ((*reg).m_to_python == NULL) { \
+    bp::register_ptr_to_python<shared_ptr<PTR > >(); \
+  } \
+} while (0)
+
 namespace bp = boost::python;
 
 namespace caffe {
@@ -38,6 +51,25 @@ const int NPY_DTYPE = NPY_FLOAT32;
 void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
 void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
 
+void InitLog() {
+  ::google::InitGoogleLogging("");
+  ::google::InstallFailureSignalHandler();
+}
+void InitLogLevel(int level) {
+  FLAGS_minloglevel = level;
+  InitLog();
+}
+void InitLogLevelPipe(int level, bool stderr) {
+  FLAGS_minloglevel = level;
+  FLAGS_logtostderr = stderr;
+  InitLog();
+}
+void Log(const string& s) {
+  LOG(INFO) << s;
+}
+
+void set_random_seed(unsigned int seed) { Caffe::set_random_seed(seed); }
+
 // For convenience, check that input files can be opened, and raise an
 // exception that boost will send to Python if not (caffe could still crash
 // later if the input files are disturbed before they are actually used, but
@@ -73,19 +105,42 @@ void CheckContiguousArray(PyArrayObject* arr, string name,
   }
 }
 
-// Net constructor for passing phase as int
-shared_ptr<Net<Dtype> > Net_Init(
-    string param_file, int phase) {
-  CheckFile(param_file);
+// Net constructor
+shared_ptr<Net<Dtype> > Net_Init(string network_file, int phase,
+    const int level, const bp::object& stages,
+    const bp::object& weights) {
+  CheckFile(network_file);
+
+  // Convert stages from list to vector
+  vector<string> stages_vector;
+  if (!stages.is_none()) {
+    for (int i = 0; i < len(stages); i++) {
+      stages_vector.push_back(bp::extract<string>(stages[i]));
+    }
+  }
+
+  // Initialize net
+  shared_ptr<Net<Dtype> > net(new Net<Dtype>(network_file,
+        static_cast<Phase>(phase), level, &stages_vector));
+
+  // Load weights
+  if (!weights.is_none()) {
+    std::string weights_file_str = bp::extract<std::string>(weights);
+    CheckFile(weights_file_str);
+    net->CopyTrainedLayersFrom(weights_file_str);
+  }
 
-  shared_ptr<Net<Dtype> > net(new Net<Dtype>(param_file,
-      static_cast<Phase>(phase)));
   return net;
 }
 
-// Net construct-and-load convenience constructor
+// Legacy Net construct-and-load convenience constructor
 shared_ptr<Net<Dtype> > Net_Init_Load(
     string param_file, string pretrained_param_file, int phase) {
+  LOG(WARNING) << "DEPRECATION WARNING - deprecated use of Python interface";
+  LOG(WARNING) << "Use this instead (with the named \"weights\""
+    << " parameter):";
+  LOG(WARNING) << "Net('" << param_file << "', " << phase
+    << ", weights='" << pretrained_param_file << "')";
   CheckFile(param_file);
   CheckFile(pretrained_param_file);
 
@@ -101,6 +156,14 @@ void Net_Save(const Net<Dtype>& net, string filename) {
   WriteProtoToBinaryFile(net_param, filename.c_str());
 }
 
+void Net_SaveHDF5(const Net<Dtype>& net, string filename) {
+  net.ToHDF5(filename);
+}
+
+void Net_LoadHDF5(Net<Dtype>* net, string filename) {
+  net->CopyTrainedLayersFromHDF5(filename.c_str());
+}
+
 void Net_SetInputArrays(Net<Dtype>* net, bp::object data_obj,
     bp::object labels_obj) {
   // check that this network has an input MemoryDataLayer
@@ -207,6 +270,112 @@ bp::object BlobVec_add_blob(bp::tuple args, bp::dict kwargs) {
   return bp::object();
 }
 
+template<typename Dtype>
+class SolverCallback: public Solver<Dtype>::Callback {
+ protected:
+  bp::object on_start_, on_gradients_ready_;
+
+ public:
+  SolverCallback(bp::object on_start, bp::object on_gradients_ready)
+    : on_start_(on_start), on_gradients_ready_(on_gradients_ready) { }
+  virtual void on_gradients_ready() {
+    on_gradients_ready_();
+  }
+  virtual void on_start() {
+    on_start_();
+  }
+};
+template<typename Dtype>
+void Solver_add_callback(Solver<Dtype> * solver, bp::object on_start,
+  bp::object on_gradients_ready) {
+  solver->add_callback(new SolverCallback<Dtype>(on_start, on_gradients_ready));
+}
+
+// Seems boost cannot call the base method directly
+void Solver_add_nccl(Solver<Dtype>* solver
+#ifdef USE_NCCL
+  , NCCL<Dtype>* nccl
+#endif
+) {
+#ifdef USE_NCCL
+  solver->add_callback(nccl);
+#endif
+}
+
+void share_weights(Solver<Dtype>* solver, Net<Dtype>* net) {
+  net->ShareTrainedLayersWith(solver->net().get());
+}
+
+template<typename Dtype>
+class NetCallback: public Net<Dtype>::Callback {
+ public:
+  explicit NetCallback(bp::object run) : run_(run) {}
+
+ protected:
+  virtual void run(int layer) {
+    run_(layer);
+  }
+  bp::object run_;
+};
+void Net_before_forward(Net<Dtype>* net, bp::object run) {
+  net->add_before_forward(new NetCallback<Dtype>(run));
+}
+void Net_after_forward(Net<Dtype>* net, bp::object run) {
+  net->add_after_forward(new NetCallback<Dtype>(run));
+}
+void Net_before_backward(Net<Dtype>* net, bp::object run) {
+  net->add_before_backward(new NetCallback<Dtype>(run));
+}
+void Net_after_backward(Net<Dtype>* net, bp::object run) {
+  net->add_after_backward(new NetCallback<Dtype>(run));
+}
+
+void Net_add_nccl(Net<Dtype>* net
+#ifdef USE_NCCL
+  , NCCL<Dtype>* nccl
+#endif
+) {
+#ifdef USE_NCCL
+  net->add_after_backward(nccl);
+#endif
+}
+#ifndef USE_NCCL
+template<typename Dtype>
+class NCCL {
+ public:
+  NCCL(shared_ptr<Solver<Dtype> > solver, const string& uid) {}
+};
+#endif
+
+bool HasNCCL() {
+#ifdef USE_NCCL
+  return true;
+#else
+  return false;
+#endif
+}
+
+#ifdef USE_NCCL
+bp::object NCCL_New_Uid() {
+  std::string uid = NCCL<Dtype>::new_uid();
+#if PY_MAJOR_VERSION >= 3
+  // Convert std::string to bytes so that Python does not
+  // try to decode the string using the current locale.
+
+  // Since boost 1.53 boost.python will convert str and bytes
+  // to std::string but will convert std::string to str. Here we
+  // force a bytes object to be returned. When this object
+  // is passed back to the NCCL constructor boost.python will
+  // correctly convert the bytes to std::string automatically
+  PyObject* py_uid = PyBytes_FromString(uid.c_str());
+  return bp::object(bp::handle<>(py_uid));
+#else
+  // automatic conversion is correct for python 2.
+  return bp::object(uid);
+#endif
+}
+#endif
+
 BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);
 
 BOOST_PYTHON_MODULE(_caffe) {
@@ -216,19 +385,36 @@ BOOST_PYTHON_MODULE(_caffe) {
   bp::scope().attr("__version__") = AS_STRING(CAFFE_VERSION);
 
   // Caffe utility functions
+  bp::def("init_log", &InitLog);
+  bp::def("init_log", &InitLogLevel);
+  bp::def("init_log", &InitLogLevelPipe);
+  bp::def("log", &Log);
+  bp::def("has_nccl", &HasNCCL);
   bp::def("set_mode_cpu", &set_mode_cpu);
   bp::def("set_mode_gpu", &set_mode_gpu);
+  bp::def("set_random_seed", &set_random_seed);
   bp::def("set_device", &Caffe::SetDevice);
+  bp::def("solver_count", &Caffe::solver_count);
+  bp::def("set_solver_count", &Caffe::set_solver_count);
+  bp::def("solver_rank", &Caffe::solver_rank);
+  bp::def("set_solver_rank", &Caffe::set_solver_rank);
+  bp::def("set_multiprocess", &Caffe::set_multiprocess);
 
   bp::def("layer_type_list", &LayerRegistry<Dtype>::LayerTypeList);
 
   bp::class_<Net<Dtype>, shared_ptr<Net<Dtype> >, boost::noncopyable >("Net",
     bp::no_init)
-    .def("__init__", bp::make_constructor(&Net_Init))
+    // Constructor
+    .def("__init__", bp::make_constructor(&Net_Init,
+          bp::default_call_policies(), (bp::arg("network_file"), "phase",
+            bp::arg("level")=0, bp::arg("stages")=bp::object(),
+            bp::arg("weights")=bp::object())))
+    // Legacy constructor
     .def("__init__", bp::make_constructor(&Net_Init_Load))
     .def("_forward", &Net<Dtype>::ForwardFromTo)
     .def("_backward", &Net<Dtype>::BackwardFromTo)
     .def("reshape", &Net<Dtype>::Reshape)
+    .def("clear_param_diffs", &Net<Dtype>::ClearParamDiffs)
     // The cast is to select a particular overload.
     .def("copy_from", static_cast<void (Net<Dtype>::*)(const string)>(
         &Net<Dtype>::CopyTrainedLayersFrom))
@@ -254,7 +440,15 @@ BOOST_PYTHON_MODULE(_caffe) {
         bp::return_value_policy<bp::copy_const_reference>()))
     .def("_set_input_arrays", &Net_SetInputArrays,
         bp::with_custodian_and_ward<1, 2, bp::with_custodian_and_ward<1, 3> >())
-    .def("save", &Net_Save);
+    .def("save", &Net_Save)
+    .def("save_hdf5", &Net_SaveHDF5)
+    .def("load_hdf5", &Net_LoadHDF5)
+    .def("before_forward", &Net_before_forward)
+    .def("after_forward", &Net_after_forward)
+    .def("before_backward", &Net_before_backward)
+    .def("after_backward", &Net_after_backward)
+    .def("after_backward", &Net_add_nccl);
+  BP_REGISTER_SHARED_PTR_TO_PYTHON(Net<Dtype>);
 
   bp::class_<Blob<Dtype>, shared_ptr<Blob<Dtype> >, boost::noncopyable>(
     "Blob", bp::no_init)
@@ -270,10 +464,19 @@ BOOST_PYTHON_MODULE(_caffe) {
     .add_property("count",    static_cast<int (Blob<Dtype>::*)() const>(
         &Blob<Dtype>::count))
     .def("reshape",           bp::raw_function(&Blob_Reshape))
+#ifndef CPU_ONLY
+    .add_property("_gpu_data_ptr",
+        reinterpret_cast<uintptr_t (Blob<Dtype>::*)()>(
+          &Blob<Dtype>::mutable_gpu_data))
+    .add_property("_gpu_diff_ptr",
+        reinterpret_cast<uintptr_t (Blob<Dtype>::*)()>(
+          &Blob<Dtype>::mutable_gpu_diff))
+#endif
     .add_property("data",     bp::make_function(&Blob<Dtype>::mutable_cpu_data,
           NdarrayCallPolicies()))
     .add_property("diff",     bp::make_function(&Blob<Dtype>::mutable_cpu_diff,
           NdarrayCallPolicies()));
+  BP_REGISTER_SHARED_PTR_TO_PYTHON(Blob<Dtype>);
 
   bp::class_<Layer<Dtype>, shared_ptr<PythonLayer<Dtype> >,
     boost::noncopyable>("Layer", bp::init<const LayerParameter&>())
@@ -282,8 +485,12 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def("setup", &Layer<Dtype>::LayerSetUp)
     .def("reshape", &Layer<Dtype>::Reshape)
     .add_property("type", bp::make_function(&Layer<Dtype>::type));
-  bp::register_ptr_to_python<shared_ptr<Layer<Dtype> > >();
+  BP_REGISTER_SHARED_PTR_TO_PYTHON(Layer<Dtype>);
 
+  bp::class_<SolverParameter>("SolverParameter", bp::no_init)
+    .add_property("max_iter", &SolverParameter::max_iter)
+    .add_property("display", &SolverParameter::display)
+    .add_property("layer_wise_reduce", &SolverParameter::layer_wise_reduce);
   bp::class_<LayerParameter>("LayerParameter", bp::no_init);
 
   bp::class_<Solver<Dtype>, shared_ptr<Solver<Dtype> >, boost::noncopyable>(
@@ -292,11 +499,17 @@ BOOST_PYTHON_MODULE(_caffe) {
     .add_property("test_nets", bp::make_function(&Solver<Dtype>::test_nets,
           bp::return_internal_reference<>()))
     .add_property("iter", &Solver<Dtype>::iter)
+    .def("add_callback", &Solver_add_callback<Dtype>)
+    .def("add_callback", &Solver_add_nccl)
     .def("solve", static_cast<void (Solver<Dtype>::*)(const char*)>(
           &Solver<Dtype>::Solve), SolveOverloads())
     .def("step", &Solver<Dtype>::Step)
     .def("restore", &Solver<Dtype>::Restore)
-    .def("snapshot", &Solver<Dtype>::Snapshot);
+    .def("snapshot", &Solver<Dtype>::Snapshot)
+    .def("share_weights", &share_weights)
+    .add_property("param", bp::make_function(&Solver<Dtype>::param,
+              bp::return_value_policy<bp::copy_const_reference>()));
+  BP_REGISTER_SHARED_PTR_TO_PYTHON(Solver<Dtype>);
 
   bp::class_<SGDSolver<Dtype>, bp::bases<Solver<Dtype> >,
     shared_ptr<SGDSolver<Dtype> >, boost::noncopyable>(
@@ -339,6 +552,24 @@ BOOST_PYTHON_MODULE(_caffe) {
   bp::class_<vector<bool> >("BoolVec")
     .def(bp::vector_indexing_suite<vector<bool> >());
 
+  bp::class_<NCCL<Dtype>, shared_ptr<NCCL<Dtype> >,
+    boost::noncopyable>("NCCL",
+                        bp::init<shared_ptr<Solver<Dtype> >, const string&>())
+#ifdef USE_NCCL
+    .def("new_uid", NCCL_New_Uid).staticmethod("new_uid")
+    .def("bcast", &NCCL<Dtype>::Broadcast)
+#endif
+    /* NOLINT_NEXT_LINE(whitespace/semicolon) */
+  ;
+  BP_REGISTER_SHARED_PTR_TO_PYTHON(NCCL<Dtype>);
+
+  bp::class_<Timer, shared_ptr<Timer>, boost::noncopyable>(
+    "Timer", bp::init<>())
+    .def("start", &Timer::Start)
+    .def("stop", &Timer::Stop)
+    .add_property("ms", &Timer::MilliSeconds);
+  BP_REGISTER_SHARED_PTR_TO_PYTHON(Timer);
+
   // boost python expects a void (missing) return value, while import_array
   // returns NULL for python3. import_array1() forces a void return value.
   import_array1();
diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index 537193db..983760a7 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -79,6 +79,7 @@ def predict(self, inputs, oversample=True):
                 -self.crop_dims / 2.0,
                 self.crop_dims / 2.0
             ])
+            crop = crop.astype(int)
             input_ = input_[:, crop[0]:crop[2], crop[1]:crop[3], :]
 
         # Classify
@@ -91,7 +92,7 @@ def predict(self, inputs, oversample=True):
 
         # For oversampling, average predictions across crops.
         if oversample:
-            predictions = predictions.reshape((len(predictions) / 10, 10, -1))
+            predictions = predictions.reshape((len(predictions) // 10, 10, -1))
             predictions = predictions.mean(1)
 
         return predictions
diff --git a/python/caffe/coord_map.py b/python/caffe/coord_map.py
new file mode 100644
index 00000000..a3413cfa
--- /dev/null
+++ b/python/caffe/coord_map.py
@@ -0,0 +1,185 @@
+"""
+Determine spatial relationships between layers to relate their coordinates.
+Coordinates are mapped from input-to-output (forward), but can
+be mapped output-to-input (backward) by the inverse mapping too.
+This helps crop and align feature maps among other uses.
+"""
+
+from __future__ import division
+import numpy as np
+from caffe import layers as L
+
+PASS_THROUGH_LAYERS = ['AbsVal', 'BatchNorm', 'Bias', 'BNLL', 'Dropout',
+                       'Eltwise', 'ELU', 'Log', 'LRN', 'Exp', 'MVN', 'Power',
+                       'ReLU', 'PReLU', 'Scale', 'Sigmoid', 'Split', 'TanH',
+                       'Threshold']
+
+
+def conv_params(fn):
+    """
+    Extract the spatial parameters that determine the coordinate mapping:
+    kernel size, stride, padding, and dilation.
+
+    Implementation detail: Convolution, Deconvolution, and Im2col layers
+    define these in the convolution_param message, while Pooling has its
+    own fields in pooling_param. This method deals with these details to
+    extract canonical parameters.
+    """
+    params = fn.params.get('convolution_param', fn.params)
+    axis = params.get('axis', 1)
+    ks = np.array(params['kernel_size'], ndmin=1)
+    dilation = np.array(params.get('dilation', 1), ndmin=1)
+    assert len({'pad_h', 'pad_w', 'kernel_h', 'kernel_w', 'stride_h',
+                'stride_w'} & set(fn.params)) == 0, \
+        'cropping does not support legacy _h/_w params'
+    return (axis, np.array(params.get('stride', 1), ndmin=1),
+            (ks - 1) * dilation + 1,
+            np.array(params.get('pad', 0), ndmin=1))
+
+
+def crop_params(fn):
+    """
+    Extract the crop layer parameters with defaults.
+    """
+    params = fn.params.get('crop_param', fn.params)
+    axis = params.get('axis', 2)  # default to spatial crop for N, C, H, W
+    offset = np.array(params.get('offset', 0), ndmin=1)
+    return (axis, offset)
+
+
+class UndefinedMapException(Exception):
+    """
+    Exception raised for layers that do not have a defined coordinate mapping.
+    """
+    pass
+
+
+def coord_map(fn):
+    """
+    Define the coordinate mapping by its
+    - axis
+    - scale: output coord[i * scale] <- input_coord[i]
+    - shift: output coord[i] <- output_coord[i + shift]
+    s.t. the identity mapping, as for pointwise layers like ReLu, is defined by
+    (None, 1, 0) since it is independent of axis and does not transform coords.
+    """
+    if fn.type_name in ['Convolution', 'Pooling', 'Im2col']:
+        axis, stride, ks, pad = conv_params(fn)
+        return axis, 1 / stride, (pad - (ks - 1) / 2) / stride
+    elif fn.type_name == 'Deconvolution':
+        axis, stride, ks, pad = conv_params(fn)
+        return axis, stride, (ks - 1) / 2 - pad
+    elif fn.type_name in PASS_THROUGH_LAYERS:
+        return None, 1, 0
+    elif fn.type_name == 'Crop':
+        axis, offset = crop_params(fn)
+        axis -= 1  # -1 for last non-coordinate dim.
+        return axis, 1, - offset
+    else:
+        raise UndefinedMapException
+
+
+class AxisMismatchException(Exception):
+    """
+    Exception raised for mappings with incompatible axes.
+    """
+    pass
+
+
+def compose(base_map, next_map):
+    """
+    Compose a base coord map with scale a1, shift b1 with a further coord map
+    with scale a2, shift b2. The scales multiply and the further shift, b2,
+    is scaled by base coord scale a1.
+    """
+    ax1, a1, b1 = base_map
+    ax2, a2, b2 = next_map
+    if ax1 is None:
+        ax = ax2
+    elif ax2 is None or ax1 == ax2:
+        ax = ax1
+    else:
+        raise AxisMismatchException
+    return ax, a1 * a2, a1 * b2 + b1
+
+
+def inverse(coord_map):
+    """
+    Invert a coord map by de-scaling and un-shifting;
+    this gives the backward mapping for the gradient.
+    """
+    ax, a, b = coord_map
+    return ax, 1 / a, -b / a
+
+
+def coord_map_from_to(top_from, top_to):
+    """
+    Determine the coordinate mapping betweeen a top (from) and a top (to).
+    Walk the graph to find a common ancestor while composing the coord maps for
+    from and to until they meet. As a last step the from map is inverted.
+    """
+    # We need to find a common ancestor of top_from and top_to.
+    # We'll assume that all ancestors are equivalent here (otherwise the graph
+    # is an inconsistent state (which we could improve this to check for)).
+    # For now use a brute-force algorithm.
+
+    def collect_bottoms(top):
+        """
+        Collect the bottoms to walk for the coordinate mapping.
+        The general rule is that all the bottoms of a layer can be mapped, as
+        most layers have the same coordinate mapping for each bottom.
+        Crop layer is a notable exception. Only the first/cropped bottom is
+        mappable; the second/dimensions bottom is excluded from the walk.
+        """
+        bottoms = top.fn.inputs
+        if top.fn.type_name == 'Crop':
+            bottoms = bottoms[:1]
+        return bottoms
+
+    # walk back from top_from, keeping the coord map as we go
+    from_maps = {top_from: (None, 1, 0)}
+    frontier = {top_from}
+    while frontier:
+        top = frontier.pop()
+        try:
+            bottoms = collect_bottoms(top)
+            for bottom in bottoms:
+                from_maps[bottom] = compose(from_maps[top], coord_map(top.fn))
+                frontier.add(bottom)
+        except UndefinedMapException:
+            pass
+
+    # now walk back from top_to until we hit a common blob
+    to_maps = {top_to: (None, 1, 0)}
+    frontier = {top_to}
+    while frontier:
+        top = frontier.pop()
+        if top in from_maps:
+            return compose(to_maps[top], inverse(from_maps[top]))
+        try:
+            bottoms = collect_bottoms(top)
+            for bottom in bottoms:
+                to_maps[bottom] = compose(to_maps[top], coord_map(top.fn))
+                frontier.add(bottom)
+        except UndefinedMapException:
+            continue
+
+    # if we got here, we did not find a blob in common
+    raise RuntimeError('Could not compute map between tops; are they '
+                       'connected by spatial layers?')
+
+
+def crop(top_from, top_to):
+    """
+    Define a Crop layer to crop a top (from) to another top (to) by
+    determining the coordinate mapping between the two and net spec'ing
+    the axis and shift parameters of the crop.
+    """
+    ax, a, b = coord_map_from_to(top_from, top_to)
+    assert (a == 1).all(), 'scale mismatch on crop (a = {})'.format(a)
+    assert (b <= 0).all(), 'cannot crop negative offset (b = {})'.format(b)
+    assert (np.round(b) == b).all(), 'cannot crop noninteger offset ' \
+        '(b = {})'.format(b)
+    return L.Crop(top_from, top_to,
+                  crop_param=dict(axis=ax + 1,  # +1 for first cropping dim.
+                                  offset=list(-np.round(b).astype(int))))
diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index 75cd3b12..ef1f9173 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -83,7 +83,7 @@ def detect_windows(self, images_windows):
         for ix, window_in in enumerate(window_inputs):
             caffe_in[ix] = self.transformer.preprocess(in_, window_in)
         out = self.forward_all(**{in_: caffe_in})
-        predictions = out[self.outputs[0]].squeeze(axis=(2, 3))
+        predictions = out[self.outputs[0]]
 
         # Package predictions with images and windows.
         detections = []
diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index cfa3fc5b..8411a41d 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -91,11 +91,11 @@ def get_layer_label(layer, rankdir):
                       separator,
                       layer.type,
                       separator,
-                      layer.convolution_param.kernel_size[0] if len(layer.convolution_param.kernel_size._values) else 1,
+                      layer.convolution_param.kernel_size[0] if len(layer.convolution_param.kernel_size) else 1,
                       separator,
-                      layer.convolution_param.stride[0] if len(layer.convolution_param.stride._values) else 1,
+                      layer.convolution_param.stride[0] if len(layer.convolution_param.stride) else 1,
                       separator,
-                      layer.convolution_param.pad[0] if len(layer.convolution_param.pad._values) else 0)
+                      layer.convolution_param.pad[0] if len(layer.convolution_param.pad) else 0)
     elif layer.type == 'Pooling':
         pooling_types_dict = get_pooling_types_dict()
         node_label = '"%s%s(%s %s)%skernel size: %d%sstride: %d%spad: %d"' %\
@@ -127,7 +127,7 @@ def choose_color_by_layertype(layertype):
     return color
 
 
-def get_pydot_graph(caffe_net, rankdir, label_edges=True):
+def get_pydot_graph(caffe_net, rankdir, label_edges=True, phase=None):
     """Create a data structure which represents the `caffe_net`.
 
     Parameters
@@ -137,17 +137,33 @@ def get_pydot_graph(caffe_net, rankdir, label_edges=True):
         Direction of graph layout.
     label_edges : boolean, optional
         Label the edges (default is True).
+    phase : {caffe_pb2.Phase.TRAIN, caffe_pb2.Phase.TEST, None} optional
+        Include layers from this network phase.  If None, include all layers.
+        (the default is None)
 
     Returns
     -------
     pydot graph object
     """
-    pydot_graph = pydot.Dot(caffe_net.name,
+    pydot_graph = pydot.Dot(caffe_net.name if caffe_net.name else 'Net',
                             graph_type='digraph',
                             rankdir=rankdir)
     pydot_nodes = {}
     pydot_edges = []
     for layer in caffe_net.layer:
+        if phase is not None:
+          included = False
+          if len(layer.include) == 0:
+            included = True
+          if len(layer.include) > 0 and len(layer.exclude) > 0:
+            raise ValueError('layer ' + layer.name + ' has both include '
+                             'and exclude specified.')
+          for layer_phase in layer.include:
+            included = included or layer_phase.phase == phase
+          for layer_phase in layer.exclude:
+            included = included and not layer_phase.phase == phase
+          if not included:
+            continue
         node_label = get_layer_label(layer, rankdir)
         node_name = "%s_%s" % (layer.name, layer.type)
         if (len(layer.bottom) == 1 and len(layer.top) == 1 and
@@ -186,7 +202,7 @@ def get_pydot_graph(caffe_net, rankdir, label_edges=True):
     return pydot_graph
 
 
-def draw_net(caffe_net, rankdir, ext='png'):
+def draw_net(caffe_net, rankdir, ext='png', phase=None):
     """Draws a caffe net and returns the image string encoded using the given
     extension.
 
@@ -195,16 +211,19 @@ def draw_net(caffe_net, rankdir, ext='png'):
     caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer.
     ext : string, optional
         The image extension (the default is 'png').
+    phase : {caffe_pb2.Phase.TRAIN, caffe_pb2.Phase.TEST, None} optional
+        Include layers from this network phase.  If None, include all layers.
+        (the default is None)
 
     Returns
     -------
     string :
         Postscript representation of the graph.
     """
-    return get_pydot_graph(caffe_net, rankdir).create(format=ext)
+    return get_pydot_graph(caffe_net, rankdir, phase=phase).create(format=ext)
 
 
-def draw_net_to_file(caffe_net, filename, rankdir='LR'):
+def draw_net_to_file(caffe_net, filename, rankdir='LR', phase=None):
     """Draws a caffe net, and saves it to file using the format given as the
     file extension. Use '.raw' to output raw text that you can manually feed
     to graphviz to draw graphs.
@@ -216,7 +235,10 @@ def draw_net_to_file(caffe_net, filename, rankdir='LR'):
         The path to a file where the networks visualization will be stored.
     rankdir : {'LR', 'TB', 'BT'}
         Direction of graph layout.
+    phase : {caffe_pb2.Phase.TRAIN, caffe_pb2.Phase.TEST, None} optional
+        Include layers from this network phase.  If None, include all layers.
+        (the default is None)
     """
     ext = filename[filename.rfind('.')+1:]
     with open(filename, 'wb') as fid:
-        fid.write(draw_net(caffe_net, rankdir, ext))
+        fid.write(draw_net(caffe_net, rankdir, ext, phase))
diff --git a/python/caffe/io.py b/python/caffe/io.py
index 75310589..d61f765b 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -46,7 +46,7 @@ def array_to_blobproto(arr, diff=None):
     return blob
 
 
-def arraylist_to_blobprotovecor_str(arraylist):
+def arraylist_to_blobprotovector_str(arraylist):
     """Converts a list of arrays to a serialized blobprotovec, which could be
     then passed to a network for processing.
     """
@@ -63,7 +63,7 @@ def blobprotovector_str_to_arraylist(str):
     return [blobproto_to_array(blob) for blob in vec.blobs]
 
 
-def array_to_datum(arr, label=0):
+def array_to_datum(arr, label=None):
     """Converts a 3-dimensional array to datum. If the array has dtype uint8,
     the output data will be encoded as a string. Otherwise, the output data
     will be stored in float format.
@@ -75,8 +75,9 @@ def array_to_datum(arr, label=0):
     if arr.dtype == np.uint8:
         datum.data = arr.tostring()
     else:
-        datum.float_data.extend(arr.flat)
-    datum.label = label
+        datum.float_data.extend(arr.astype(float).flat)
+    if label is not None:
+        datum.label = label
     return datum
 
 
@@ -322,7 +323,7 @@ def resize_image(im, new_dims, interp_order=1):
             # skimage is fast but only understands {1,3} channel images
             # in [0, 1].
             im_std = (im - im_min) / (im_max - im_min)
-            resized_std = resize(im_std, new_dims, order=interp_order)
+            resized_std = resize(im_std, new_dims, order=interp_order, mode='constant')
             resized_im = resized_std * (im_max - im_min) + im_min
         else:
             # the image is a constant -- avoid divide by 0
diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py
index 93fc0192..20918f9b 100644
--- a/python/caffe/net_spec.py
+++ b/python/caffe/net_spec.py
@@ -32,7 +32,7 @@ def param_name_dict():
     # get all parameter names (typically underscore case) and corresponding
     # type names (typically camel case), which contain the layer names
     # (note that not all parameters correspond to layers, but we'll ignore that)
-    param_names = [s for s in dir(layer) if s.endswith('_param')]
+    param_names = [f.name for f in layer.DESCRIPTOR.fields if f.name.endswith('_param')]
     param_type_names = [type(getattr(layer, s)).__name__ for s in param_names]
     # strip the final '_param' or 'Parameter'
     param_names = [s[:-len('_param')] for s in param_names]
@@ -103,6 +103,10 @@ class Function(object):
 
     def __init__(self, type_name, inputs, params):
         self.type_name = type_name
+        for index, input in enumerate(inputs):
+            if not isinstance(input, Top):
+                raise TypeError('%s input %d is not a Top (type is %s)' %
+                                (type_name, index, type(input)))
         self.inputs = inputs
         self.params = params
         self.ntop = self.params.get('ntop', 1)
@@ -175,6 +179,12 @@ def __setattr__(self, name, value):
     def __getattr__(self, name):
         return self.tops[name]
 
+    def __setitem__(self, key, value):
+        self.__setattr__(key, value)
+
+    def __getitem__(self, item):
+        return self.__getattr__(item)
+
     def to_proto(self):
         names = {v: k for k, v in six.iteritems(self.tops)}
         autonames = Counter()
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 30541107..4a7b5a24 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -11,9 +11,11 @@
 import numpy as np
 
 from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, \
-        RMSPropSolver, AdaDeltaSolver, AdamSolver
+        RMSPropSolver, AdaDeltaSolver, AdamSolver, NCCL, Timer
 import caffe.io
 
+import six
+
 # We directly update methods from Net here (rather than using composition or
 # inheritance) so that nets created by caffe (e.g., by SGDSolver) will
 # automatically have the improved interface.
@@ -25,7 +27,9 @@ def _Net_blobs(self):
     An OrderedDict (bottom to top, i.e., input to output) of network
     blobs indexed by name
     """
-    return OrderedDict(zip(self._blob_names, self._blobs))
+    if not hasattr(self, '_blobs_dict'):
+        self._blobs_dict = OrderedDict(zip(self._blob_names, self._blobs))
+    return self._blobs_dict
 
 
 @property
@@ -34,7 +38,20 @@ def _Net_blob_loss_weights(self):
     An OrderedDict (bottom to top, i.e., input to output) of network
     blob loss weights indexed by name
     """
-    return OrderedDict(zip(self._blob_names, self._blob_loss_weights))
+    if not hasattr(self, '_blobs_loss_weights_dict'):
+        self._blob_loss_weights_dict = OrderedDict(zip(self._blob_names,
+                                                       self._blob_loss_weights))
+    return self._blob_loss_weights_dict
+
+@property
+def _Net_layer_dict(self):
+    """
+    An OrderedDict (bottom to top, i.e., input to output) of network
+    layers indexed by name
+    """
+    if not hasattr(self, '_layer_dict'):
+        self._layer_dict = OrderedDict(zip(self._layer_names, self.layers))
+    return self._layer_dict
 
 
 @property
@@ -44,19 +61,28 @@ def _Net_params(self):
     parameters indexed by name; each is a list of multiple blobs (e.g.,
     weights and biases)
     """
-    return OrderedDict([(name, lr.blobs)
-                        for name, lr in zip(self._layer_names, self.layers)
-                        if len(lr.blobs) > 0])
+    if not hasattr(self, '_params_dict'):
+        self._params_dict = OrderedDict([(name, lr.blobs)
+                                        for name, lr in zip(
+                                            self._layer_names, self.layers)
+                                        if len(lr.blobs) > 0])
+    return self._params_dict
 
 
 @property
 def _Net_inputs(self):
-    return [list(self.blobs.keys())[i] for i in self._inputs]
+    if not hasattr(self, '_input_list'):
+        keys = list(self.blobs.keys())
+        self._input_list = [keys[i] for i in self._inputs]
+    return self._input_list
 
 
 @property
 def _Net_outputs(self):
-    return [list(self.blobs.keys())[i] for i in self._outputs]
+    if not hasattr(self, '_output_list'):
+        keys = list(self.blobs.keys())
+        self._output_list = [keys[i] for i in self._outputs]
+    return self._output_list
 
 
 def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
@@ -87,7 +113,7 @@ def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
 
     if end is not None:
         end_ind = list(self._layer_names).index(end)
-        outputs = set([end] + blobs)
+        outputs = set(self.top_names[end] + blobs)
     else:
         end_ind = len(self.layers) - 1
         outputs = set(self.outputs + blobs)
@@ -97,8 +123,8 @@ def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
             raise Exception('Input blob arguments do not match net inputs.')
         # Set input according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
-        for in_, blob in kwargs.iteritems():
-            if blob.shape[0] != self.blobs[in_].num:
+        for in_, blob in six.iteritems(kwargs):
+            if blob.shape[0] != self.blobs[in_].shape[0]:
                 raise Exception('Input is not batch sized')
             self.blobs[in_].data[...] = blob
 
@@ -135,7 +161,7 @@ def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
 
     if end is not None:
         end_ind = list(self._layer_names).index(end)
-        outputs = set([end] + diffs)
+        outputs = set(self.bottom_names[end] + diffs)
     else:
         end_ind = 0
         outputs = set(self.inputs + diffs)
@@ -145,8 +171,8 @@ def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
             raise Exception('Top diff arguments do not match net outputs.')
         # Set top diffs according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
-        for top, diff in kwargs.iteritems():
-            if diff.shape[0] != self.blobs[top].num:
+        for top, diff in six.iteritems(kwargs):
+            if diff.shape[0] != self.blobs[top].shape[0]:
                 raise Exception('Diff is not batch sized')
             self.blobs[top].diff[...] = diff
 
@@ -174,13 +200,13 @@ def _Net_forward_all(self, blobs=None, **kwargs):
     all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
     for batch in self._batch(kwargs):
         outs = self.forward(blobs=blobs, **batch)
-        for out, out_blob in outs.iteritems():
+        for out, out_blob in six.iteritems(outs):
             all_outs[out].extend(out_blob.copy())
     # Package in ndarray.
     for out in all_outs:
         all_outs[out] = np.asarray(all_outs[out])
     # Discard padding.
-    pad = len(all_outs.itervalues().next()) - len(kwargs.itervalues().next())
+    pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs)))
     if pad:
         for out in all_outs:
             all_outs[out] = all_outs[out][:-pad]
@@ -215,16 +241,16 @@ def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs):
     for fb, bb in izip_longest(forward_batches, backward_batches, fillvalue={}):
         batch_blobs = self.forward(blobs=blobs, **fb)
         batch_diffs = self.backward(diffs=diffs, **bb)
-        for out, out_blobs in batch_blobs.iteritems():
+        for out, out_blobs in six.iteritems(batch_blobs):
             all_outs[out].extend(out_blobs.copy())
-        for diff, out_diffs in batch_diffs.iteritems():
+        for diff, out_diffs in six.iteritems(batch_diffs):
             all_diffs[diff].extend(out_diffs.copy())
     # Package in ndarray.
     for out, diff in zip(all_outs, all_diffs):
         all_outs[out] = np.asarray(all_outs[out])
         all_diffs[diff] = np.asarray(all_diffs[diff])
     # Discard padding at the end and package in ndarray.
-    pad = len(all_outs.itervalues().next()) - len(kwargs.itervalues().next())
+    pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs)))
     if pad:
         for out, diff in zip(all_outs, all_diffs):
             all_outs[out] = all_outs[out][:-pad]
@@ -256,10 +282,10 @@ def _Net_batch(self, blobs):
     ------
     batch: {blob name: list of blobs} dict for a single batch.
     """
-    num = len(blobs.itervalues().next())
-    batch_size = self.blobs.itervalues().next().num
+    num = len(six.next(six.itervalues(blobs)))
+    batch_size = six.next(six.itervalues(self.blobs)).shape[0]
     remainder = num % batch_size
-    num_batches = num / batch_size
+    num_batches = num // batch_size
 
     # Yield full batches.
     for b in range(num_batches):
@@ -276,25 +302,36 @@ def _Net_batch(self, blobs):
                                                  padding])
         yield padded_batch
 
-
-class _Net_IdNameWrapper:
-    """
-    A simple wrapper that allows the ids propery to be accessed as a dict
-    indexed by names. Used for top and bottom names
+def _Net_get_id_name(func, field):
     """
-    def __init__(self, net, func):
-        self.net, self.func = net, func
+    Generic property that maps func to the layer names into an OrderedDict.
 
-    def __getitem__(self, name):
-        # Map the layer name to id
-        ids = self.func(self.net, list(self.net._layer_names).index(name))
-        # Map the blob id to name
-        id_to_name = list(self.net.blobs)
-        return [id_to_name[i] for i in ids]
+    Used for top_names and bottom_names.
+
+    Parameters
+    ----------
+    func: function id -> [id]
+    field: implementation field name (cache)
+
+    Returns
+    ------
+    A one-parameter function that can be set as a property.
+    """
+    @property
+    def get_id_name(self):
+        if not hasattr(self, field):
+            id_to_name = list(self.blobs)
+            res = OrderedDict([(self._layer_names[i],
+                                [id_to_name[j] for j in func(self, i)])
+                                for i in range(len(self.layers))])
+            setattr(self, field, res)
+        return getattr(self, field)
+    return get_id_name
 
 # Attach methods to Net.
 Net.blobs = _Net_blobs
 Net.blob_loss_weights = _Net_blob_loss_weights
+Net.layer_dict = _Net_layer_dict
 Net.params = _Net_params
 Net.forward = _Net_forward
 Net.backward = _Net_backward
@@ -304,5 +341,5 @@ def __getitem__(self, name):
 Net._batch = _Net_batch
 Net.inputs = _Net_inputs
 Net.outputs = _Net_outputs
-Net.top_names = property(lambda n: _Net_IdNameWrapper(n, Net._top_ids))
-Net.bottom_names = property(lambda n: _Net_IdNameWrapper(n, Net._bottom_ids))
+Net.top_names = _Net_get_id_name(Net._top_ids, "_top_names")
+Net.bottom_names = _Net_get_id_name(Net._bottom_ids, "_bottom_names")
diff --git a/python/caffe/test/test_coord_map.py b/python/caffe/test/test_coord_map.py
new file mode 100644
index 00000000..613260e2
--- /dev/null
+++ b/python/caffe/test/test_coord_map.py
@@ -0,0 +1,192 @@
+import unittest
+
+import numpy as np
+import random
+
+import caffe
+from caffe import layers as L
+from caffe import params as P
+from caffe.coord_map import coord_map_from_to, crop
+
+
+def coord_net_spec(ks=3, stride=1, pad=0, pool=2, dstride=2, dpad=0):
+    """
+    Define net spec for simple conv-pool-deconv pattern common to all
+    coordinate mapping tests.
+    """
+    n = caffe.NetSpec()
+    n.data = L.Input(shape=dict(dim=[2, 1, 100, 100]))
+    n.aux = L.Input(shape=dict(dim=[2, 1, 20, 20]))
+    n.conv = L.Convolution(
+        n.data, num_output=10, kernel_size=ks, stride=stride, pad=pad)
+    n.pool = L.Pooling(
+        n.conv, pool=P.Pooling.MAX, kernel_size=pool, stride=pool, pad=0)
+    # for upsampling kernel size is 2x stride
+    try:
+        deconv_ks = [s*2 for s in dstride]
+    except:
+        deconv_ks = dstride*2
+    n.deconv = L.Deconvolution(
+        n.pool, num_output=10, kernel_size=deconv_ks, stride=dstride, pad=dpad)
+    return n
+
+
+class TestCoordMap(unittest.TestCase):
+    def setUp(self):
+        pass
+
+    def test_conv_pool_deconv(self):
+        """
+        Map through conv, pool, and deconv.
+        """
+        n = coord_net_spec()
+        # identity for 2x pool, 2x deconv
+        ax, a, b = coord_map_from_to(n.deconv, n.data)
+        self.assertEquals(ax, 1)
+        self.assertEquals(a, 1)
+        self.assertEquals(b, 0)
+        # shift-by-one for 4x pool, 4x deconv
+        n = coord_net_spec(pool=4, dstride=4)
+        ax, a, b = coord_map_from_to(n.deconv, n.data)
+        self.assertEquals(ax, 1)
+        self.assertEquals(a, 1)
+        self.assertEquals(b, -1)
+
+    def test_pass(self):
+        """
+        A pass-through layer (ReLU) and conv (1x1, stride 1, pad 0)
+        both do identity mapping.
+        """
+        n = coord_net_spec()
+        ax, a, b = coord_map_from_to(n.deconv, n.data)
+        n.relu = L.ReLU(n.deconv)
+        n.conv1x1 = L.Convolution(
+            n.relu, num_output=10, kernel_size=1, stride=1, pad=0)
+        for top in [n.relu, n.conv1x1]:
+            ax_pass, a_pass, b_pass = coord_map_from_to(top, n.data)
+            self.assertEquals(ax, ax_pass)
+            self.assertEquals(a, a_pass)
+            self.assertEquals(b, b_pass)
+
+    def test_padding(self):
+        """
+        Padding conv adds offset while padding deconv subtracts offset.
+        """
+        n = coord_net_spec()
+        ax, a, b = coord_map_from_to(n.deconv, n.data)
+        pad = random.randint(0, 10)
+        # conv padding
+        n = coord_net_spec(pad=pad)
+        _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
+        self.assertEquals(a, a_pad)
+        self.assertEquals(b - pad, b_pad)
+        # deconv padding
+        n = coord_net_spec(dpad=pad)
+        _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
+        self.assertEquals(a, a_pad)
+        self.assertEquals(b + pad, b_pad)
+        # pad both to cancel out
+        n = coord_net_spec(pad=pad, dpad=pad)
+        _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
+        self.assertEquals(a, a_pad)
+        self.assertEquals(b, b_pad)
+
+    def test_multi_conv(self):
+        """
+        Multiple bottoms/tops of a layer are identically mapped.
+        """
+        n = coord_net_spec()
+        # multi bottom/top
+        n.conv_data, n.conv_aux = L.Convolution(
+            n.data, n.aux, ntop=2, num_output=10, kernel_size=5, stride=2,
+            pad=0)
+        ax1, a1, b1 = coord_map_from_to(n.conv_data, n.data)
+        ax2, a2, b2 = coord_map_from_to(n.conv_aux, n.aux)
+        self.assertEquals(ax1, ax2)
+        self.assertEquals(a1, a2)
+        self.assertEquals(b1, b2)
+
+    def test_rect(self):
+        """
+        Anisotropic mapping is equivalent to its isotropic parts.
+        """
+        n3x3 = coord_net_spec(ks=3, stride=1, pad=0)
+        n5x5 = coord_net_spec(ks=5, stride=2, pad=10)
+        n3x5 = coord_net_spec(ks=[3, 5], stride=[1, 2], pad=[0, 10])
+        ax_3x3, a_3x3, b_3x3 = coord_map_from_to(n3x3.deconv, n3x3.data)
+        ax_5x5, a_5x5, b_5x5 = coord_map_from_to(n5x5.deconv, n5x5.data)
+        ax_3x5, a_3x5, b_3x5 = coord_map_from_to(n3x5.deconv, n3x5.data)
+        self.assertTrue(ax_3x3 == ax_5x5 == ax_3x5)
+        self.assertEquals(a_3x3, a_3x5[0])
+        self.assertEquals(b_3x3, b_3x5[0])
+        self.assertEquals(a_5x5, a_3x5[1])
+        self.assertEquals(b_5x5, b_3x5[1])
+
+    def test_nd_conv(self):
+        """
+        ND conv maps the same way in more dimensions.
+        """
+        n = caffe.NetSpec()
+        # define data with 3 spatial dimensions, otherwise the same net
+        n.data = L.Input(shape=dict(dim=[2, 3, 100, 100, 100]))
+        n.conv = L.Convolution(
+            n.data, num_output=10, kernel_size=[3, 3, 3], stride=[1, 1, 1],
+            pad=[0, 1, 2])
+        n.pool = L.Pooling(
+            n.conv, pool=P.Pooling.MAX, kernel_size=2, stride=2, pad=0)
+        n.deconv = L.Deconvolution(
+            n.pool, num_output=10, kernel_size=4, stride=2, pad=0)
+        ax, a, b = coord_map_from_to(n.deconv, n.data)
+        self.assertEquals(ax, 1)
+        self.assertTrue(len(a) == len(b))
+        self.assertTrue(np.all(a == 1))
+        self.assertEquals(b[0] - 1, b[1])
+        self.assertEquals(b[1] - 1, b[2])
+
+    def test_crop_of_crop(self):
+        """
+        Map coordinates through Crop layer:
+        crop an already-cropped output to the input and check change in offset.
+        """
+        n = coord_net_spec()
+        offset = random.randint(0, 10)
+        ax, a, b = coord_map_from_to(n.deconv, n.data)
+        n.crop = L.Crop(n.deconv, n.data, axis=2, offset=offset)
+        ax_crop, a_crop, b_crop = coord_map_from_to(n.crop, n.data)
+        self.assertEquals(ax, ax_crop)
+        self.assertEquals(a, a_crop)
+        self.assertEquals(b + offset, b_crop)
+
+    def test_crop_helper(self):
+        """
+        Define Crop layer by crop().
+        """
+        n = coord_net_spec()
+        crop(n.deconv, n.data)
+
+    def test_catch_unconnected(self):
+        """
+        Catch mapping spatially unconnected tops.
+        """
+        n = coord_net_spec()
+        n.ip = L.InnerProduct(n.deconv, num_output=10)
+        with self.assertRaises(RuntimeError):
+            coord_map_from_to(n.ip, n.data)
+
+    def test_catch_scale_mismatch(self):
+        """
+        Catch incompatible scales, such as when the top to be cropped
+        is mapped to a differently strided reference top.
+        """
+        n = coord_net_spec(pool=3, dstride=2)  # pool 3x but deconv 2x
+        with self.assertRaises(AssertionError):
+            crop(n.deconv, n.data)
+
+    def test_catch_negative_crop(self):
+        """
+        Catch impossible offsets, such as when the top to be cropped
+        is mapped to a larger reference top.
+        """
+        n = coord_net_spec(dpad=10)  # make output smaller than input
+        with self.assertRaises(AssertionError):
+            crop(n.deconv, n.data)
diff --git a/python/caffe/test/test_draw.py b/python/caffe/test/test_draw.py
new file mode 100644
index 00000000..835bb5df
--- /dev/null
+++ b/python/caffe/test/test_draw.py
@@ -0,0 +1,37 @@
+import os
+import unittest
+
+from google.protobuf import text_format
+
+import caffe.draw
+from caffe.proto import caffe_pb2
+
+def getFilenames():
+    """Yields files in the source tree which are Net prototxts."""
+    result = []
+
+    root_dir = os.path.abspath(os.path.join(
+        os.path.dirname(__file__), '..', '..', '..'))
+    assert os.path.exists(root_dir)
+
+    for dirname in ('models', 'examples'):
+        dirname = os.path.join(root_dir, dirname)
+        assert os.path.exists(dirname)
+        for cwd, _, filenames in os.walk(dirname):
+            for filename in filenames:
+                filename = os.path.join(cwd, filename)
+                if filename.endswith('.prototxt') and 'solver' not in filename:
+                    yield os.path.join(dirname, filename)
+
+
+class TestDraw(unittest.TestCase):
+    def test_draw_net(self):
+        for filename in getFilenames():
+            net = caffe_pb2.NetParameter()
+            with open(filename) as infile:
+                text_format.Merge(infile.read(), net)
+            caffe.draw.draw_net(net, 'LR')
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/python/caffe/test/test_io.py b/python/caffe/test/test_io.py
index 8c86ef75..4a16b5b9 100644
--- a/python/caffe/test/test_io.py
+++ b/python/caffe/test/test_io.py
@@ -39,3 +39,18 @@ def test_scalar(self):
 
         arr = caffe.io.blobproto_to_array(blob)
         self.assertEqual(arr, 123)
+
+
+class TestArrayToDatum(unittest.TestCase):
+
+    def test_label_none_size(self):
+        # Set label
+        d1 = caffe.io.array_to_datum(
+            np.ones((10,10,3)), label=1)
+        # Don't set label
+        d2 = caffe.io.array_to_datum(
+            np.ones((10,10,3)))
+        # Not setting the label should result in a smaller object
+        self.assertGreater(
+            len(d1.SerializeToString()),
+            len(d2.SerializeToString()))
diff --git a/python/caffe/test/test_nccl.py b/python/caffe/test/test_nccl.py
new file mode 100644
index 00000000..127a9337
--- /dev/null
+++ b/python/caffe/test/test_nccl.py
@@ -0,0 +1,19 @@
+import sys
+import unittest
+
+import caffe
+
+
+class TestNCCL(unittest.TestCase):
+
+    def test_newuid(self):
+        """
+        Test that NCCL uids are of the proper type
+        according to python version
+        """
+        if caffe.has_nccl():
+            uid = caffe.NCCL.new_uid()
+            if sys.version_info.major >= 3:
+                self.assertTrue(isinstance(uid, bytes))
+            else:
+                self.assertTrue(isinstance(uid, str))
diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py
index aad828aa..ee1d38c3 100644
--- a/python/caffe/test/test_net.py
+++ b/python/caffe/test/test_net.py
@@ -3,6 +3,7 @@
 import os
 import numpy as np
 import six
+from collections import OrderedDict
 
 import caffe
 
@@ -24,11 +25,11 @@ def simple_net_file(num_output):
         bias_filler { type: 'constant' value: 2 } }
         param { decay_mult: 1 } param { decay_mult: 0 }
         }
-    layer { type: 'InnerProduct' name: 'ip' bottom: 'conv' top: 'ip'
+    layer { type: 'InnerProduct' name: 'ip' bottom: 'conv' top: 'ip_blob'
       inner_product_param { num_output: """ + str(num_output) + """
         weight_filler { type: 'gaussian' std: 2.5 }
         bias_filler { type: 'constant' value: -3 } } }
-    layer { type: 'SoftmaxWithLoss' name: 'loss' bottom: 'ip' bottom: 'label'
+    layer { type: 'SoftmaxWithLoss' name: 'loss' bottom: 'ip_blob' bottom: 'label'
       top: 'loss' }""")
     f.close()
     return f.name
@@ -59,23 +60,330 @@ def test_memory(self):
         for bl in blobs:
             total += bl.data.sum() + bl.diff.sum()
 
+    def test_layer_dict(self):
+        layer_dict = self.net.layer_dict
+        self.assertEqual(list(layer_dict.keys()), list(self.net._layer_names))
+        for i, name in enumerate(self.net._layer_names):
+            self.assertEqual(layer_dict[name].type,
+                             self.net.layers[i].type)
+
     def test_forward_backward(self):
         self.net.forward()
         self.net.backward()
 
+    def test_forward_start_end(self):
+        conv_blob=self.net.blobs['conv']
+        ip_blob=self.net.blobs['ip_blob']
+        sample_data=np.random.uniform(size=conv_blob.data.shape)
+        sample_data=sample_data.astype(np.float32)
+        conv_blob.data[:]=sample_data
+        forward_blob=self.net.forward(start='ip',end='ip')
+        self.assertIn('ip_blob',forward_blob)
+
+        manual_forward=[]
+        for i in range(0,conv_blob.data.shape[0]):
+          dot=np.dot(self.net.params['ip'][0].data,
+                     conv_blob.data[i].reshape(-1))
+          manual_forward.append(dot+self.net.params['ip'][1].data)
+        manual_forward=np.array(manual_forward)
+
+        np.testing.assert_allclose(ip_blob.data,manual_forward,rtol=1e-3,atol=1e-5)
+
+    def test_backward_start_end(self):
+        conv_blob=self.net.blobs['conv']
+        ip_blob=self.net.blobs['ip_blob']
+        sample_data=np.random.uniform(size=ip_blob.data.shape)
+        sample_data=sample_data.astype(np.float32)
+        ip_blob.diff[:]=sample_data
+        backward_blob=self.net.backward(start='ip',end='ip')
+        self.assertIn('conv',backward_blob)
+
+        manual_backward=[]
+        for i in range(0,conv_blob.data.shape[0]):
+          dot=np.dot(self.net.params['ip'][0].data.transpose(),
+                     sample_data[i].reshape(-1))
+          manual_backward.append(dot)
+        manual_backward=np.array(manual_backward)
+        manual_backward=manual_backward.reshape(conv_blob.data.shape)
+
+        np.testing.assert_allclose(conv_blob.diff,manual_backward,rtol=1e-3,atol=1e-5)
+
+    def test_clear_param_diffs(self):
+        # Run a forward/backward step to have non-zero diffs
+        self.net.forward()
+        self.net.backward()
+        diff = self.net.params["conv"][0].diff
+        # Check that we have non-zero diffs
+        self.assertTrue(diff.max() > 0)
+        self.net.clear_param_diffs()
+        # Check that the diffs are now 0
+        self.assertTrue((diff == 0).all())
+
     def test_inputs_outputs(self):
         self.assertEqual(self.net.inputs, [])
         self.assertEqual(self.net.outputs, ['loss'])
 
+    def test_top_bottom_names(self):
+        self.assertEqual(self.net.top_names,
+                         OrderedDict([('data', ['data', 'label']),
+                                      ('conv', ['conv']),
+                                      ('ip', ['ip_blob']),
+                                      ('loss', ['loss'])]))
+        self.assertEqual(self.net.bottom_names,
+                         OrderedDict([('data', []),
+                                      ('conv', ['data']),
+                                      ('ip', ['conv']),
+                                      ('loss', ['ip_blob', 'label'])]))
+
     def test_save_and_read(self):
         f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
         f.close()
         self.net.save(f.name)
         net_file = simple_net_file(self.num_output)
-        net2 = caffe.Net(net_file, f.name, caffe.TRAIN)
+        # Test legacy constructor
+        #   should print deprecation warning
+        caffe.Net(net_file, f.name, caffe.TRAIN)
+        # Test named constructor
+        net2 = caffe.Net(net_file, caffe.TRAIN, weights=f.name)
         os.remove(net_file)
         os.remove(f.name)
         for name in self.net.params:
             for i in range(len(self.net.params[name])):
                 self.assertEqual(abs(self.net.params[name][i].data
                     - net2.params[name][i].data).sum(), 0)
+
+    def test_save_hdf5(self):
+        f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
+        f.close()
+        self.net.save_hdf5(f.name)
+        net_file = simple_net_file(self.num_output)
+        net2 = caffe.Net(net_file, caffe.TRAIN)
+        net2.load_hdf5(f.name)
+        os.remove(net_file)
+        os.remove(f.name)
+        for name in self.net.params:
+            for i in range(len(self.net.params[name])):
+                self.assertEqual(abs(self.net.params[name][i].data
+                    - net2.params[name][i].data).sum(), 0)
+
+class TestLevels(unittest.TestCase):
+
+    TEST_NET = """
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  dummy_data_param { shape { dim: 1 dim: 1 dim: 10 dim: 10 } }
+}
+layer {
+  name: "NoLevel"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "NoLevel"
+  inner_product_param { num_output: 1 }
+}
+layer {
+  name: "Level0Only"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "Level0Only"
+  include { min_level: 0 max_level: 0 }
+  inner_product_param { num_output: 1 }
+}
+layer {
+  name: "Level1Only"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "Level1Only"
+  include { min_level: 1 max_level: 1 }
+  inner_product_param { num_output: 1 }
+}
+layer {
+  name: "Level>=0"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "Level>=0"
+  include { min_level: 0 }
+  inner_product_param { num_output: 1 }
+}
+layer {
+  name: "Level>=1"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "Level>=1"
+  include { min_level: 1 }
+  inner_product_param { num_output: 1 }
+}
+"""
+
+    def setUp(self):
+        self.f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
+        self.f.write(self.TEST_NET)
+        self.f.close()
+
+    def tearDown(self):
+        os.remove(self.f.name)
+
+    def check_net(self, net, blobs):
+        net_blobs = [b for b in net.blobs.keys() if 'data' not in b]
+        self.assertEqual(net_blobs, blobs)
+
+    def test_0(self):
+        net = caffe.Net(self.f.name, caffe.TEST)
+        self.check_net(net, ['NoLevel', 'Level0Only', 'Level>=0'])
+
+    def test_1(self):
+        net = caffe.Net(self.f.name, caffe.TEST, level=1)
+        self.check_net(net, ['NoLevel', 'Level1Only', 'Level>=0', 'Level>=1'])
+
+
+class TestStages(unittest.TestCase):
+
+    TEST_NET = """
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  dummy_data_param { shape { dim: 1 dim: 1 dim: 10 dim: 10 } }
+}
+layer {
+  name: "A"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "A"
+  include { stage: "A" }
+  inner_product_param { num_output: 1 }
+}
+layer {
+  name: "B"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "B"
+  include { stage: "B" }
+  inner_product_param { num_output: 1 }
+}
+layer {
+  name: "AorB"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "AorB"
+  include { stage: "A" }
+  include { stage: "B" }
+  inner_product_param { num_output: 1 }
+}
+layer {
+  name: "AandB"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "AandB"
+  include { stage: "A" stage: "B" }
+  inner_product_param { num_output: 1 }
+}
+"""
+
+    def setUp(self):
+        self.f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
+        self.f.write(self.TEST_NET)
+        self.f.close()
+
+    def tearDown(self):
+        os.remove(self.f.name)
+
+    def check_net(self, net, blobs):
+        net_blobs = [b for b in net.blobs.keys() if 'data' not in b]
+        self.assertEqual(net_blobs, blobs)
+
+    def test_A(self):
+        net = caffe.Net(self.f.name, caffe.TEST, stages=['A'])
+        self.check_net(net, ['A', 'AorB'])
+
+    def test_B(self):
+        net = caffe.Net(self.f.name, caffe.TEST, stages=['B'])
+        self.check_net(net, ['B', 'AorB'])
+
+    def test_AandB(self):
+        net = caffe.Net(self.f.name, caffe.TEST, stages=['A', 'B'])
+        self.check_net(net, ['A', 'B', 'AorB', 'AandB'])
+
+
+class TestAllInOne(unittest.TestCase):
+
+    TEST_NET = """
+layer {
+  name: "train_data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  dummy_data_param {
+    shape { dim: 1 dim: 1 dim: 10 dim: 10 }
+    shape { dim: 1 dim: 1 dim: 1 dim: 1 }
+  }
+  include { phase: TRAIN stage: "train" }
+}
+layer {
+  name: "val_data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  dummy_data_param {
+    shape { dim: 1 dim: 1 dim: 10 dim: 10 }
+    shape { dim: 1 dim: 1 dim: 1 dim: 1 }
+  }
+  include { phase: TEST stage: "val" }
+}
+layer {
+  name: "deploy_data"
+  type: "Input"
+  top: "data"
+  input_param { shape { dim: 1 dim: 1 dim: 10 dim: 10 } }
+  include { phase: TEST stage: "deploy" }
+}
+layer {
+  name: "ip"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "ip"
+  inner_product_param { num_output: 2 }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip"
+  bottom: "label"
+  top: "loss"
+  include: { phase: TRAIN stage: "train" }
+  include: { phase: TEST stage: "val" }
+}
+layer {
+  name: "pred"
+  type: "Softmax"
+  bottom: "ip"
+  top: "pred"
+  include: { phase: TEST stage: "deploy" }
+}
+"""
+
+    def setUp(self):
+        self.f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
+        self.f.write(self.TEST_NET)
+        self.f.close()
+
+    def tearDown(self):
+        os.remove(self.f.name)
+
+    def check_net(self, net, outputs):
+        self.assertEqual(list(net.blobs['data'].shape), [1,1,10,10])
+        self.assertEqual(net.outputs, outputs)
+
+    def test_train(self):
+        net = caffe.Net(self.f.name, caffe.TRAIN, stages=['train'])
+        self.check_net(net, ['loss'])
+
+    def test_val(self):
+        net = caffe.Net(self.f.name, caffe.TEST, stages=['val'])
+        self.check_net(net, ['loss'])
+
+    def test_deploy(self):
+        net = caffe.Net(self.f.name, caffe.TEST, stages=['deploy'])
+        self.check_net(net, ['pred'])
+
diff --git a/python/caffe/test/test_net_spec.py b/python/caffe/test/test_net_spec.py
index fee3c0aa..ffe71bac 100644
--- a/python/caffe/test/test_net_spec.py
+++ b/python/caffe/test/test_net_spec.py
@@ -79,3 +79,11 @@ def test_zero_tops(self):
         net_proto = silent_net()
         net = self.load_net(net_proto)
         self.assertEqual(len(net.forward()), 0)
+
+    def test_type_error(self):
+        """Test that a TypeError is raised when a Function input isn't a Top."""
+        data = L.DummyData(ntop=2)  # data is a 2-tuple of Tops
+        r = r"^Silence input 0 is not a Top \(type is <(type|class) 'tuple'>\)$"
+        with self.assertRaisesRegexp(TypeError, r):
+            L.Silence(data, ntop=0)  # should raise: data is a tuple, not a Top
+        L.Silence(*data, ntop=0)  # shouldn't raise: each elt of data is a Top
diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py
index e46b7118..899514e9 100644
--- a/python/caffe/test/test_python_layer.py
+++ b/python/caffe/test/test_python_layer.py
@@ -44,6 +44,18 @@ def forward(self, bottom, top):
     def backward(self, top, propagate_down, bottom):
         self.blobs[0].diff[0] = 1
 
+class PhaseLayer(caffe.Layer):
+    """A layer for checking attribute `phase`"""
+
+    def setup(self, bottom, top):
+        pass
+
+    def reshape(self, bootom, top):
+        top[0].reshape()
+
+    def forward(self, bottom, top):
+        top[0].data[()] = self.phase
+
 def python_net_file():
     with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
         f.write("""name: 'pythonnet' force_backward: true
@@ -76,6 +88,14 @@ def parameter_net_file():
           """)
         return f.name
 
+def phase_net_file():
+    with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
+        f.write("""name: 'pythonnet' force_backward: true
+        layer { type: 'Python' name: 'layer' top: 'phase'
+          python_param { module: 'test_python_layer' layer: 'PhaseLayer' } }
+          """)
+        return f.name
+
 
 @unittest.skipIf('Python' not in caffe.layer_type_list(),
     'Caffe built without Python layer support')
@@ -140,3 +160,9 @@ def test_parameter(self):
         self.assertEqual(layer.blobs[0].data[0], 1)
 
         os.remove(net_file)
+
+    def test_phase(self):
+        net_file = phase_net_file()
+        for phase in caffe.TRAIN, caffe.TEST:
+            net = caffe.Net(net_file, phase)
+            self.assertEqual(net.forward()['phase'], phase)
diff --git a/python/draw_net.py b/python/draw_net.py
index ec76a744..dfe70d26 100755
--- a/python/draw_net.py
+++ b/python/draw_net.py
@@ -28,6 +28,11 @@ def parse_args():
                               'http://www.graphviz.org/doc/info/'
                               'attrs.html#k:rankdir'),
                         default='LR')
+    parser.add_argument('--phase',
+                        help=('Which network phase to draw: can be TRAIN, '
+                              'TEST, or ALL.  If ALL, then all layers are drawn '
+                              'regardless of phase.'),
+                        default="ALL")
 
     args = parser.parse_args()
     return args
@@ -38,7 +43,15 @@ def main():
     net = caffe_pb2.NetParameter()
     text_format.Merge(open(args.input_net_proto_file).read(), net)
     print('Drawing net to %s' % args.output_image_file)
-    caffe.draw.draw_net_to_file(net, args.output_image_file, args.rankdir)
+    phase=None;
+    if args.phase == "TRAIN":
+        phase = caffe.TRAIN
+    elif args.phase == "TEST":
+        phase = caffe.TEST
+    elif args.phase != "ALL":
+        raise ValueError("Unknown phase: " + args.phase)
+    caffe.draw.draw_net_to_file(net, args.output_image_file, args.rankdir,
+                                phase)
 
 
 if __name__ == '__main__':
diff --git a/python/train.py b/python/train.py
new file mode 100644
index 00000000..5897f5dc
--- /dev/null
+++ b/python/train.py
@@ -0,0 +1,100 @@
+#!/usr/bin/env python
+"""
+Trains a model using one or more GPUs.
+"""
+from multiprocessing import Process
+
+import caffe
+
+
+def train(
+        solver,  # solver proto definition
+        snapshot,  # solver snapshot to restore
+        gpus,  # list of device ids
+        timing=False,  # show timing info for compute and communications
+):
+    # NCCL uses a uid to identify a session
+    uid = caffe.NCCL.new_uid()
+
+    caffe.init_log()
+    caffe.log('Using devices %s' % str(gpus))
+
+    procs = []
+    for rank in range(len(gpus)):
+        p = Process(target=solve,
+                    args=(solver, snapshot, gpus, timing, uid, rank))
+        p.daemon = True
+        p.start()
+        procs.append(p)
+    for p in procs:
+        p.join()
+
+
+def time(solver, nccl):
+    fprop = []
+    bprop = []
+    total = caffe.Timer()
+    allrd = caffe.Timer()
+    for _ in range(len(solver.net.layers)):
+        fprop.append(caffe.Timer())
+        bprop.append(caffe.Timer())
+    display = solver.param.display
+
+    def show_time():
+        if solver.iter % display == 0:
+            s = '\n'
+            for i in range(len(solver.net.layers)):
+                s += 'forw %3d %8s ' % (i, solver.net._layer_names[i])
+                s += ': %.2f\n' % fprop[i].ms
+            for i in range(len(solver.net.layers) - 1, -1, -1):
+                s += 'back %3d %8s ' % (i, solver.net._layer_names[i])
+                s += ': %.2f\n' % bprop[i].ms
+            s += 'solver total: %.2f\n' % total.ms
+            s += 'allreduce: %.2f\n' % allrd.ms
+            caffe.log(s)
+
+    solver.net.before_forward(lambda layer: fprop[layer].start())
+    solver.net.after_forward(lambda layer: fprop[layer].stop())
+    solver.net.before_backward(lambda layer: bprop[layer].start())
+    solver.net.after_backward(lambda layer: bprop[layer].stop())
+    solver.add_callback(lambda: total.start(), lambda: (total.stop(), allrd.start()))
+    solver.add_callback(nccl)
+    solver.add_callback(lambda: '', lambda: (allrd.stop(), show_time()))
+
+
+def solve(proto, snapshot, gpus, timing, uid, rank):
+    caffe.set_mode_gpu()
+    caffe.set_device(gpus[rank])
+    caffe.set_solver_count(len(gpus))
+    caffe.set_solver_rank(rank)
+    caffe.set_multiprocess(True)
+
+    solver = caffe.SGDSolver(proto)
+    if snapshot and len(snapshot) != 0:
+        solver.restore(snapshot)
+
+    nccl = caffe.NCCL(solver, uid)
+    nccl.bcast()
+
+    if timing and rank == 0:
+        time(solver, nccl)
+    else:
+        solver.add_callback(nccl)
+
+    if solver.param.layer_wise_reduce:
+        solver.net.after_backward(nccl)
+    solver.step(solver.param.max_iter)
+
+
+if __name__ == '__main__':
+    import argparse
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--solver", required=True, help="Solver proto definition.")
+    parser.add_argument("--snapshot", help="Solver snapshot to restore.")
+    parser.add_argument("--gpus", type=int, nargs='+', default=[0],
+                        help="List of device ids.")
+    parser.add_argument("--timing", action='store_true', help="Show timing info.")
+    args = parser.parse_args()
+
+    train(args.solver, args.snapshot, args.gpus, args.timing)
diff --git a/scripts/build_docs.sh b/scripts/build_docs.sh
index 0e28bd71..4837587a 100755
--- a/scripts/build_docs.sh
+++ b/scripts/build_docs.sh
@@ -12,6 +12,9 @@ cd $ROOT_DIR
 # Gather docs.
 scripts/gather_examples.sh
 
+# Split caffe.proto for inclusion by layer catalogue.
+scripts/split_caffe_proto.py
+
 # Generate developer docs.
 make docs
 
diff --git a/scripts/caffe b/scripts/caffe
new file mode 100644
index 00000000..8a0b22af
--- /dev/null
+++ b/scripts/caffe
@@ -0,0 +1,73 @@
+# bash completion for Caffe's command line utility       -*- shell-script -*-
+# COPYRIGHT (C) 2015,2016 Zhou Mo <cdluminate@gmail.com>
+# License: BSD-2-Clause
+# Originally appeard at https://github.com/BVLC/caffe/issues/3149
+
+# Updated for caffe (1.0.0~rc3+20160715-g42cd785)
+_caffe()
+{
+  local cur prev words cword
+  _init_completion -s || return
+
+  local prototxts='@(prototxt)'
+  local caffemodels='@(caffemodel,binaryproto)'
+  local solverstates='@(solverstate)'
+  local caffefiles='@(prototxt|caffemodel|solverstate)'
+
+  local flags='-gpu -iterations -model -snapshot -solver -weights -sighup_effect -sigint_effect -level -stage -phase'
+  
+  if [[ $cword -eq 1 ]]; then
+    COMPREPLY=( $( compgen -W 'train test time device_query' -- "$cur" ) )
+    return 0
+  fi
+  
+  if [[ $cword -eq 2 ]]; then
+    case ${words[1]} in
+    train|test|device_query|time)
+      COMPREPLY=( $( compgen -W "$flags" -- "$cur") )
+      return 0
+      ;;
+    *)
+      return 0
+      ;;
+    esac
+  fi
+
+  case $prev in
+  -gpu|-iterations|-version|-level|-stage)
+    return 0
+    ;;
+  -solver|-model)
+    _filedir $prototxts
+    return 0
+    ;;
+  -weights)
+    _filedir $caffemodels
+    return 0
+    ;;
+  -snapshot)
+    _filedir $solverstates
+    return 0
+    ;;
+  -sighup_effect|-sigint_effect)
+    COMPREPLY=( $( compgen -W 'snapshot stop none' -- "$cur") )
+    return 0
+    ;;
+  -phase)
+    COMPREPLY=( $( compgen -W 'TRAIN TEST' -- "$cur") )
+    return 0
+    ;;
+  *)
+    COMPREPLY=( $( compgen -W "$flags" -- "$cur") )
+    return 0
+    ;;
+  esac
+
+  # file completion on relevant files
+  _filedir "$caffefiles"
+
+  return 0
+}
+complete -F _caffe caffe
+
+# vim
diff --git a/scripts/cpp_lint.py b/scripts/cpp_lint.py
index f750489f..b2016d4b 100755
--- a/scripts/cpp_lint.py
+++ b/scripts/cpp_lint.py
@@ -1,4 +1,4 @@
-#!/usr/bin/python2
+#!/usr/bin/env python
 #
 # Copyright (c) 2009 Google Inc. All rights reserved.
 #
@@ -52,6 +52,10 @@
 import sys
 import unicodedata
 
+import six
+
+from six import iteritems, itervalues
+from six.moves import xrange
 
 _USAGE = """
 Syntax: cpp_lint.py [--verbose=#] [--output=vs7] [--filter=-x,+y,...]
@@ -756,7 +760,7 @@ def IncrementErrorCount(self, category):
 
   def PrintErrorCounts(self):
     """Print a summary of errors by category, and the total."""
-    for category, count in self.errors_by_category.iteritems():
+    for category, count in iteritems(self.errors_by_category):
       sys.stderr.write('Category \'%s\' errors found: %d\n' %
                        (category, count))
     sys.stderr.write('Total errors found: %d\n' % self.error_count)
@@ -1564,7 +1568,7 @@ def CheckForMultilineCommentsAndStrings(filename, clean_lines, linenum, error):
 caffe_alt_function_list = (
     ('memset', ['caffe_set', 'caffe_memset']),
     ('cudaMemset', ['caffe_gpu_set', 'caffe_gpu_memset']),
-    ('memcpy', ['caffe_copy', 'caffe_memcpy']),
+    ('memcpy', ['caffe_copy']),
     ('cudaMemcpy', ['caffe_copy', 'caffe_gpu_memcpy']),
     )
 
@@ -3444,16 +3448,16 @@ def GetLineWidth(line):
     The width of the line in column positions, accounting for Unicode
     combining characters and wide characters.
   """
-  if isinstance(line, unicode):
-    width = 0
-    for uc in unicodedata.normalize('NFC', line):
-      if unicodedata.east_asian_width(uc) in ('W', 'F'):
-        width += 2
-      elif not unicodedata.combining(uc):
-        width += 1
-    return width
-  else:
-    return len(line)
+  if six.PY2:
+    if isinstance(line, unicode):
+      width = 0
+      for uc in unicodedata.normalize('NFC', line):
+        if unicodedata.east_asian_width(uc) in ('W', 'F'):
+          width += 2
+        elif not unicodedata.combining(uc):
+          width += 1
+      return width
+  return len(line)
 
 
 def CheckStyle(filename, clean_lines, linenum, file_extension, nesting_state,
@@ -3774,7 +3778,7 @@ def _GetTextInside(text, start_pattern):
 
   # Give opening punctuations to get the matching close-punctuations.
   matching_punctuation = {'(': ')', '{': '}', '[': ']'}
-  closing_punctuation = set(matching_punctuation.itervalues())
+  closing_punctuation = set(itervalues(matching_punctuation))
 
   # Find the position to start extracting text.
   match = re.search(start_pattern, text, re.M)
@@ -4460,7 +4464,7 @@ def UpdateIncludeState(filename, include_state, io=codecs):
     io: The io factory to use to read the file. Provided for testability.
 
   Returns:
-    True if a header was succesfully added. False otherwise.
+    True if a header was successfully added. False otherwise.
   """
   headerfile = None
   try:
@@ -4532,7 +4536,7 @@ def CheckForIncludeWhatYouUse(filename, clean_lines, include_state, error,
   # Let's copy the include_state so it is only messed up within this function.
   include_state = include_state.copy()
 
-  # Did we find the header for this file (if any) and succesfully load it?
+  # Did we find the header for this file (if any) and successfully load it?
   header_found = False
 
   # Use the absolute path so that matching works properly.
@@ -4833,7 +4837,7 @@ def ParseArguments(args):
       try:
           _valid_extensions = set(val.split(','))
       except ValueError:
-          PrintUsage('Extensions must be comma seperated list.')
+          PrintUsage('Extensions must be comma separated list.')
 
   if not filenames:
     PrintUsage('No files were specified.')
@@ -4851,10 +4855,11 @@ def main():
 
   # Change stderr to write with replacement characters so we don't die
   # if we try to print something containing non-ASCII characters.
-  sys.stderr = codecs.StreamReaderWriter(sys.stderr,
-                                         codecs.getreader('utf8'),
-                                         codecs.getwriter('utf8'),
-                                         'replace')
+  if six.PY2:
+    sys.stderr = codecs.StreamReaderWriter(sys.stderr,
+                                          codecs.getreader('utf8'),
+                                          codecs.getwriter('utf8'),
+                                          'replace')
 
   _cpplint_state.ResetErrorCounts()
   for filename in filenames:
diff --git a/scripts/download_model_binary.py b/scripts/download_model_binary.py
index 66f72f24..a72fd5d7 100755
--- a/scripts/download_model_binary.py
+++ b/scripts/download_model_binary.py
@@ -3,10 +3,11 @@
 import sys
 import time
 import yaml
-import urllib
 import hashlib
 import argparse
 
+from six.moves import urllib
+
 required_keys = ['caffemodel', 'caffemodel_url', 'sha1']
 
 
@@ -60,7 +61,7 @@ def valid_dirname(dirname):
 
     # Closure-d function for checking SHA1.
     def model_checks_out(filename=model_filename, sha1=frontmatter['sha1']):
-        with open(filename, 'r') as f:
+        with open(filename, 'rb') as f:
             return hashlib.sha1(f.read()).hexdigest() == sha1
 
     # Check if model exists.
@@ -69,7 +70,7 @@ def model_checks_out(filename=model_filename, sha1=frontmatter['sha1']):
         sys.exit(0)
 
     # Download and verify model.
-    urllib.urlretrieve(
+    urllib.request.urlretrieve(
         frontmatter['caffemodel_url'], model_filename, reporthook)
     if not model_checks_out():
         print('ERROR: model did not download correctly! Run this again.')
diff --git a/scripts/split_caffe_proto.py b/scripts/split_caffe_proto.py
new file mode 100755
index 00000000..7e9dc3e7
--- /dev/null
+++ b/scripts/split_caffe_proto.py
@@ -0,0 +1,35 @@
+#!/usr/bin/env python
+import mmap
+import re
+import os
+import errno
+
+script_path = os.path.dirname(os.path.realpath(__file__))
+
+# a regex to match the parameter definitions in caffe.proto
+r = re.compile(r'(?://.*\n)*message ([^ ]*) \{\n(?: .*\n|\n)*\}')
+
+# create directory to put caffe.proto fragments
+try:
+    os.mkdir(
+        os.path.join(script_path,
+                     '../docs/_includes/'))
+    os.mkdir(
+        os.path.join(script_path,
+                     '../docs/_includes/proto/'))
+except OSError as exception:
+    if exception.errno != errno.EEXIST:
+        raise
+
+caffe_proto_fn = os.path.join(
+    script_path,
+    '../src/caffe/proto/caffe.proto')
+
+with open(caffe_proto_fn, 'r') as fin:
+
+    for m in r.finditer(fin.read(), re.MULTILINE):
+        fn = os.path.join(
+            script_path,
+            '../docs/_includes/proto/%s.txt' % m.group(1))
+        with open(fn, 'w') as fout:
+            fout.write(m.group(0))
diff --git a/scripts/travis/build.sh b/scripts/travis/build.sh
new file mode 100755
index 00000000..bb9406f0
--- /dev/null
+++ b/scripts/travis/build.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+# build the project
+
+BASEDIR=$(dirname $0)
+source $BASEDIR/defaults.sh
+
+if ! $WITH_CMAKE ; then
+  make --jobs $NUM_THREADS all test pycaffe warn
+else
+  cd build
+  make --jobs $NUM_THREADS all test.testbin
+fi
+make lint
diff --git a/scripts/travis/configure-cmake.sh b/scripts/travis/configure-cmake.sh
new file mode 100644
index 00000000..772f1e2c
--- /dev/null
+++ b/scripts/travis/configure-cmake.sh
@@ -0,0 +1,32 @@
+# CMake configuration
+
+mkdir -p build
+cd build
+
+ARGS="-DCMAKE_BUILD_TYPE=Release -DBLAS=Open"
+
+if $WITH_PYTHON3 ; then
+  ARGS="$ARGS -Dpython_version=3"
+fi
+
+if $WITH_IO ; then
+  ARGS="$ARGS -DUSE_OPENCV=On -DUSE_LMDB=On -DUSE_LEVELDB=On"
+else
+  ARGS="$ARGS -DUSE_OPENCV=Off -DUSE_LMDB=Off -DUSE_LEVELDB=Off"
+fi
+
+if $WITH_CUDA ; then
+  # Only build SM50
+  ARGS="$ARGS -DCPU_ONLY=Off -DCUDA_ARCH_NAME=Manual -DCUDA_ARCH_BIN=\"50\" -DCUDA_ARCH_PTX=\"\""
+else
+  ARGS="$ARGS -DCPU_ONLY=On"
+fi
+
+if $WITH_CUDNN ; then
+  ARGS="$ARGS -DUSE_CUDNN=On"
+else
+  ARGS="$ARGS -DUSE_CUDNN=Off"
+fi
+
+cmake .. $ARGS
+
diff --git a/scripts/travis/configure-make.sh b/scripts/travis/configure-make.sh
new file mode 100644
index 00000000..ddc40fff
--- /dev/null
+++ b/scripts/travis/configure-make.sh
@@ -0,0 +1,36 @@
+# raw Makefile configuration
+
+LINE () {
+  echo "$@" >> Makefile.config
+}
+
+cp Makefile.config.example Makefile.config
+
+LINE "BLAS := open"
+LINE "WITH_PYTHON_LAYER := 1"
+
+if $WITH_PYTHON3 ; then
+  # TODO(lukeyeager) this path is currently disabled because of test errors like:
+  #   ImportError: dynamic module does not define init function (PyInit__caffe)
+  LINE "PYTHON_LIBRARIES := python3.4m boost_python-py34"
+  LINE "PYTHON_INCLUDE := /usr/include/python3.4 /usr/lib/python3/dist-packages/numpy/core/include"
+  LINE "INCLUDE_DIRS := \$(INCLUDE_DIRS) \$(PYTHON_INCLUDE)"
+fi
+
+if ! $WITH_IO ; then
+  LINE "USE_OPENCV := 0"
+  LINE "USE_LEVELDB := 0"
+  LINE "USE_LMDB := 0"
+fi
+
+if $WITH_CUDA ; then
+  # Only build SM50
+  LINE "CUDA_ARCH := -gencode arch=compute_50,code=sm_50"
+else
+  LINE "CPU_ONLY := 1"
+fi
+
+if $WITH_CUDNN ; then
+  LINE "USE_CUDNN := 1"
+fi
+
diff --git a/scripts/travis/configure.sh b/scripts/travis/configure.sh
new file mode 100755
index 00000000..ef740c89
--- /dev/null
+++ b/scripts/travis/configure.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+# configure the project
+
+BASEDIR=$(dirname $0)
+source $BASEDIR/defaults.sh
+
+if ! $WITH_CMAKE ; then
+  source $BASEDIR/configure-make.sh
+else
+  source $BASEDIR/configure-cmake.sh
+fi
diff --git a/scripts/travis/defaults.sh b/scripts/travis/defaults.sh
new file mode 100755
index 00000000..d69c0a7d
--- /dev/null
+++ b/scripts/travis/defaults.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+# set default environment variables
+
+set -e
+
+WITH_CMAKE=${WITH_CMAKE:-false}
+WITH_PYTHON3=${WITH_PYTHON3:-false}
+WITH_IO=${WITH_IO:-true}
+WITH_CUDA=${WITH_CUDA:-false}
+WITH_CUDNN=${WITH_CUDNN:-false}
diff --git a/scripts/travis/install-deps.sh b/scripts/travis/install-deps.sh
new file mode 100755
index 00000000..2fa2a74a
--- /dev/null
+++ b/scripts/travis/install-deps.sh
@@ -0,0 +1,112 @@
+#!/bin/bash
+# install dependencies
+# (this script must be run as root)
+
+BASEDIR=$(dirname $0)
+source $BASEDIR/defaults.sh
+
+apt-get -y update
+apt-get install -y --no-install-recommends \
+  build-essential \
+  graphviz \
+  libboost-filesystem-dev \
+  libboost-python-dev \
+  libboost-system-dev \
+  libboost-thread-dev \
+  libgflags-dev \
+  libgoogle-glog-dev \
+  libhdf5-serial-dev \
+  libopenblas-dev \
+  python-virtualenv \
+  wget
+
+if $WITH_CMAKE ; then
+  apt-get install -y --no-install-recommends cmake
+fi
+
+if ! $WITH_PYTHON3 ; then
+  # Python2
+  apt-get install -y --no-install-recommends \
+    libprotobuf-dev \
+    protobuf-compiler \
+    python-dev \
+    python-numpy \
+    python-protobuf \
+    python-pydot \
+    python-skimage
+else
+  # Python3
+  apt-get install -y --no-install-recommends \
+    python3-dev \
+    python3-numpy \
+    python3-skimage
+
+  # build Protobuf3 since it's needed for Python3
+  PROTOBUF3_DIR=~/protobuf3
+  pushd .
+  if [ -d "$PROTOBUF3_DIR" ] && [ -e "$PROTOBUF3_DIR/src/protoc" ]; then
+    echo "Using cached protobuf3 build ..."
+    cd $PROTOBUF3_DIR
+  else
+    echo "Building protobuf3 from source ..."
+    rm -rf $PROTOBUF3_DIR
+    mkdir $PROTOBUF3_DIR
+
+    # install some more dependencies required to build protobuf3
+    apt-get install -y --no-install-recommends \
+      curl \
+      dh-autoreconf \
+      unzip
+
+    wget https://github.com/google/protobuf/archive/3.0.x.tar.gz -O protobuf3.tar.gz
+    tar -xzf protobuf3.tar.gz -C $PROTOBUF3_DIR --strip 1
+    rm protobuf3.tar.gz
+    cd $PROTOBUF3_DIR
+    ./autogen.sh
+    ./configure --prefix=/usr
+    make --jobs=$NUM_THREADS
+  fi
+  make install
+  popd
+fi
+
+if $WITH_IO ; then
+  apt-get install -y --no-install-recommends \
+    libleveldb-dev \
+    liblmdb-dev \
+    libopencv-dev \
+    libsnappy-dev
+fi
+
+if $WITH_CUDA ; then
+  # install repo packages
+  CUDA_REPO_PKG=cuda-repo-ubuntu1404_7.5-18_amd64.deb
+  wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/$CUDA_REPO_PKG
+  dpkg -i $CUDA_REPO_PKG
+  rm $CUDA_REPO_PKG
+
+  if $WITH_CUDNN ; then
+    ML_REPO_PKG=nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb
+    wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/$ML_REPO_PKG
+    dpkg -i $ML_REPO_PKG
+  fi
+
+  # update package lists
+  apt-get -y update
+
+  # install packages
+  CUDA_PKG_VERSION="7-5"
+  CUDA_VERSION="7.5"
+  apt-get install -y --no-install-recommends \
+    cuda-core-$CUDA_PKG_VERSION \
+    cuda-cudart-dev-$CUDA_PKG_VERSION \
+    cuda-cublas-dev-$CUDA_PKG_VERSION \
+    cuda-curand-dev-$CUDA_PKG_VERSION
+  # manually create CUDA symlink
+  ln -s /usr/local/cuda-$CUDA_VERSION /usr/local/cuda
+
+  if $WITH_CUDNN ; then
+    apt-get install -y --no-install-recommends libcudnn6-dev
+  fi
+fi
+
diff --git a/scripts/travis/install-python-deps.sh b/scripts/travis/install-python-deps.sh
new file mode 100755
index 00000000..910d35a9
--- /dev/null
+++ b/scripts/travis/install-python-deps.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+# install extra Python dependencies
+# (must come after setup-venv)
+
+BASEDIR=$(dirname $0)
+source $BASEDIR/defaults.sh
+
+if ! $WITH_PYTHON3 ; then
+  # Python2
+  :
+else
+  # Python3
+  pip install --pre protobuf==3.0.0b3
+  pip install pydot
+fi
diff --git a/scripts/travis/setup-venv.sh b/scripts/travis/setup-venv.sh
new file mode 100755
index 00000000..81245f14
--- /dev/null
+++ b/scripts/travis/setup-venv.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+# setup a Python virtualenv
+# (must come after install-deps)
+
+BASEDIR=$(dirname $0)
+source $BASEDIR/defaults.sh
+
+VENV_DIR=${1:-~/venv}
+
+# setup our own virtualenv
+if $WITH_PYTHON3; then
+    PYTHON_EXE='/usr/bin/python3'
+else
+    PYTHON_EXE='/usr/bin/python2'
+fi
+
+# use --system-site-packages so that Python will use deb packages
+virtualenv $VENV_DIR -p $PYTHON_EXE --system-site-packages
diff --git a/scripts/travis/test.sh b/scripts/travis/test.sh
new file mode 100755
index 00000000..fedd7e6b
--- /dev/null
+++ b/scripts/travis/test.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+# test the project
+
+BASEDIR=$(dirname $0)
+source $BASEDIR/defaults.sh
+
+if $WITH_CUDA ; then
+  echo "Skipping tests for CUDA build"
+  exit 0
+fi
+
+if ! $WITH_CMAKE ; then
+  make runtest
+  make pytest
+else
+  cd build
+  make runtest
+  make pytest
+fi
diff --git a/scripts/travis/travis_build_and_test.sh b/scripts/travis/travis_build_and_test.sh
deleted file mode 100755
index 174f1ee5..00000000
--- a/scripts/travis/travis_build_and_test.sh
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/bin/bash
-# Script called by Travis to build and test Caffe.
-# Travis CI tests are CPU-only for lack of compatible hardware.
-
-set -e
-MAKE="make --jobs=$NUM_THREADS --keep-going"
-
-if $WITH_CMAKE; then
-  mkdir build
-  cd build
-  CPU_ONLY=" -DCPU_ONLY=ON"
-  if ! $WITH_CUDA; then
-    CPU_ONLY=" -DCPU_ONLY=OFF"
-  fi
-  PYTHON_ARGS=""
-  if [ "$PYTHON_VERSION" = "3" ]; then
-    PYTHON_ARGS="$PYTHON_ARGS -Dpython_version=3 -DBOOST_LIBRARYDIR=$CONDA_DIR/lib/"
-  fi
-  if $WITH_IO; then
-    IO_ARGS="-DUSE_OPENCV=ON -DUSE_LMDB=ON -DUSE_LEVELDB=ON"
-  else
-    IO_ARGS="-DUSE_OPENCV=OFF -DUSE_LMDB=OFF -DUSE_LEVELDB=OFF"
-  fi
-  cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release $CPU_ONLY $PYTHON_ARGS -DCMAKE_INCLUDE_PATH="$CONDA_DIR/include/" -DCMAKE_LIBRARY_PATH="$CONDA_DIR/lib/" $IO_ARGS ..
-  $MAKE
-  $MAKE pytest
-  if ! $WITH_CUDA; then
-    $MAKE runtest
-    $MAKE lint
-  fi
-  $MAKE clean
-  cd -
-else
-  if ! $WITH_CUDA; then
-    export CPU_ONLY=1
-  fi
-  if $WITH_IO; then
-    export USE_LMDB=1
-    export USE_LEVELDB=1
-    export USE_OPENCV=1
-  fi
-  $MAKE all test pycaffe warn lint || true
-  if ! $WITH_CUDA; then
-    $MAKE runtest
-  fi
-  $MAKE all
-  $MAKE test
-  $MAKE pycaffe
-  $MAKE pytest
-  $MAKE warn
-  if ! $WITH_CUDA; then
-    $MAKE lint
-  fi
-fi
diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
deleted file mode 100755
index d18dc223..00000000
--- a/scripts/travis/travis_install.sh
+++ /dev/null
@@ -1,99 +0,0 @@
-#!/bin/bash
-# This script must be run with sudo.
-
-set -e
-
-MAKE="make --jobs=$NUM_THREADS"
-# Install apt packages where the Ubuntu 12.04 default and ppa works for Caffe
-
-# This ppa is for gflags and glog
-add-apt-repository -y ppa:tuleu/precise-backports
-apt-get -y update
-apt-get install \
-    wget git curl \
-    python-dev python-numpy python3-dev\
-    libleveldb-dev libsnappy-dev libopencv-dev \
-    libprotobuf-dev protobuf-compiler \
-    libatlas-dev libatlas-base-dev \
-    libhdf5-serial-dev libgflags-dev libgoogle-glog-dev \
-    bc
-
-# Add a special apt-repository to install CMake 2.8.9 for CMake Caffe build,
-# if needed.  By default, Aptitude in Ubuntu 12.04 installs CMake 2.8.7, but
-# Caffe requires a minimum CMake version of 2.8.8.
-if $WITH_CMAKE; then
-  # cmake 3 will make sure that the python interpreter and libraries match
-  wget --no-check-certificate http://www.cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh -O cmake3.sh
-  chmod +x cmake3.sh
-  ./cmake3.sh --prefix=/usr/ --skip-license --exclude-subdir
-fi
-
-# Install CUDA, if needed
-if $WITH_CUDA; then
-  CUDA_URL=http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.5-14_amd64.deb
-  CUDA_FILE=/tmp/cuda_install.deb
-  curl $CUDA_URL -o $CUDA_FILE
-  dpkg -i $CUDA_FILE
-  rm -f $CUDA_FILE
-  apt-get -y update
-  # Install the minimal CUDA subpackages required to test Caffe build.
-  # For a full CUDA installation, add 'cuda' to the list of packages.
-  apt-get -y install cuda-core-6-5 cuda-cublas-6-5 cuda-cublas-dev-6-5 cuda-cudart-6-5 cuda-cudart-dev-6-5 cuda-curand-6-5 cuda-curand-dev-6-5
-  # Create CUDA symlink at /usr/local/cuda
-  # (This would normally be created by the CUDA installer, but we create it
-  # manually since we did a partial installation.)
-  ln -s /usr/local/cuda-6.5 /usr/local/cuda
-fi
-
-# Install LMDB
-LMDB_URL=https://github.com/LMDB/lmdb/archive/LMDB_0.9.14.tar.gz
-LMDB_FILE=/tmp/lmdb.tar.gz
-pushd .
-wget $LMDB_URL -O $LMDB_FILE
-tar -C /tmp -xzvf $LMDB_FILE
-cd /tmp/lmdb*/libraries/liblmdb/
-$MAKE
-$MAKE install
-popd
-rm -f $LMDB_FILE
-
-# Install the Python runtime dependencies via miniconda (this is much faster
-# than using pip for everything).
-export PATH=$CONDA_DIR/bin:$PATH
-if [ ! -d $CONDA_DIR ]; then
-  if [ "$PYTHON_VERSION" -eq "3" ]; then
-    wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
-  else
-    wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
-  fi
-  chmod +x miniconda.sh
-  ./miniconda.sh -b -p $CONDA_DIR
-
-  conda update --yes conda
-  # The version of boost we're using for Python 3 depends on 3.4 for now.
-  if [ "$PYTHON_VERSION" -eq "3" ]; then
-    conda install --yes python=3.4
-  fi
-  conda install --yes numpy scipy matplotlib scikit-image pip
-  # Let conda install boost (so that boost_python matches)
-  conda install --yes -c https://conda.binstar.org/menpo boost=1.56.0
-fi
-
-# install protobuf 3 (just use the miniconda3 directory to avoid having to setup the path again)
-if [ "$PYTHON_VERSION" -eq "3" ] && [ ! -e "$CONDA_DIR/bin/protoc" ]; then
-  pushd .
-  wget https://github.com/google/protobuf/archive/v3.0.0-alpha-3.1.tar.gz -O protobuf-3.tar.gz
-  tar -C /tmp -xzvf protobuf-3.tar.gz
-  cd /tmp/protobuf-3*/
-  ./autogen.sh
-  ./configure --prefix=$CONDA_DIR
-  $MAKE
-  $MAKE install
-  popd
-fi
-
-if [ "$PYTHON_VERSION" -eq "3" ]; then
-  pip install --pre protobuf
-else
-  pip install protobuf
-fi
diff --git a/scripts/travis/travis_setup_makefile_config.sh b/scripts/travis/travis_setup_makefile_config.sh
deleted file mode 100755
index 83aacf11..00000000
--- a/scripts/travis/travis_setup_makefile_config.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-
-set -e
-
-mv Makefile.config.example Makefile.config
-
-if $WITH_CUDA; then
-  # Only generate compute_50.
-  GENCODE="-gencode arch=compute_50,code=sm_50"
-  GENCODE="$GENCODE -gencode arch=compute_50,code=compute_50"
-  echo "CUDA_ARCH := $GENCODE" >> Makefile.config
-fi
-
-# Remove IO library settings from Makefile.config
-# to avoid conflicts with CI configuration
-sed -i -e '/USE_LMDB/d' Makefile.config
-sed -i -e '/USE_LEVELDB/d' Makefile.config
-sed -i -e '/USE_OPENCV/d' Makefile.config
-
-cat << 'EOF' >> Makefile.config
-# Travis' nvcc doesn't like newer boost versions
-NVCCFLAGS := -Xcudafe --diag_suppress=cc_clobber_ignored -Xcudafe --diag_suppress=useless_using_declaration -Xcudafe --diag_suppress=set_but_not_used
-ANACONDA_HOME := $(CONDA_DIR)
-PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
-		$(ANACONDA_HOME)/include/python2.7 \
-		$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include
-PYTHON_LIB := $(ANACONDA_HOME)/lib
-INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
-LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
-WITH_PYTHON_LAYER := 1
-EOF
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index 8a80c940..4a805568 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -3,9 +3,12 @@ file(GLOB proto_files proto/*.proto)
 caffe_protobuf_generate_cpp_py(${proto_gen_folder} proto_srcs proto_hdrs proto_python ${proto_files})
 
 # include python files either to force generation
-add_library(proto STATIC ${proto_hdrs} ${proto_srcs} ${proto_python})
-set(Caffe_LINKER_LIBS proto ${Caffe_LINKER_LIBS}) # note, crucial to prepend!
-caffe_default_properties(proto)
+add_library(caffeproto STATIC ${proto_hdrs} ${proto_srcs} ${proto_python})
+caffe_default_properties(caffeproto)
+target_link_libraries(caffeproto PUBLIC ${PROTOBUF_LIBRARIES})
+target_include_directories(caffeproto PUBLIC ${PROTOBUF_INCLUDE_DIR})
+
+list(INSERT Caffe_LINKER_LIBS 0 PUBLIC caffeproto) # note, crucial to prepend!
 
 # --[ Caffe library
 
@@ -18,8 +21,16 @@ if(HAVE_CUDA)
 endif()
 
 add_library(caffe ${srcs})
-target_link_libraries(caffe proto ${Caffe_LINKER_LIBS})
 caffe_default_properties(caffe)
+target_link_libraries(caffe ${Caffe_LINKER_LIBS})
+target_include_directories(caffe ${Caffe_INCLUDE_DIRS}
+                                 PUBLIC
+                                 $<BUILD_INTERFACE:${Caffe_INCLUDE_DIR}>
+                                 $<INSTALL_INTERFACE:include>)
+target_compile_definitions(caffe ${Caffe_DEFINITIONS})
+if(Caffe_COMPILE_OPTIONS)
+  target_compile_options(caffe ${Caffe_COMPILE_OPTIONS})
+endif()
 set_target_properties(caffe PROPERTIES
     VERSION   ${CAFFE_TARGET_VERSION}
     SOVERSION ${CAFFE_TARGET_SOVERSION}
@@ -29,12 +40,11 @@ set_target_properties(caffe PROPERTIES
  add_subdirectory(test)
 
 # ---[ Install
-install(DIRECTORY ${Caffe_INCLUDE_DIR}/caffe DESTINATION include)
-install(FILES ${proto_hdrs} DESTINATION include/caffe/proto)
-install(TARGETS caffe proto EXPORT CaffeTargets DESTINATION lib)
+install(DIRECTORY ${Caffe_INCLUDE_DIR}/caffe DESTINATION ${CMAKE_INSTALL_INCLUDEDIR})
+install(FILES ${proto_hdrs} DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/caffe/proto)
+install(TARGETS caffe caffeproto EXPORT CaffeTargets DESTINATION ${CMAKE_INSTALL_LIBDIR})
 
 file(WRITE ${PROJECT_BINARY_DIR}/__init__.py)
 list(APPEND proto_python ${PROJECT_BINARY_DIR}/__init__.py)
 install(PROGRAMS ${proto_python} DESTINATION python/caffe/proto)
 
-
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index c86fd5d1..603e52f7 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -30,7 +30,9 @@ void Blob<Dtype>::Reshape(const vector<int>& shape) {
   int* shape_data = static_cast<int*>(shape_data_->mutable_cpu_data());
   for (int i = 0; i < shape.size(); ++i) {
     CHECK_GE(shape[i], 0);
-    CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";
+    if (count_ != 0) {
+      CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";
+    }
     count_ *= shape[i];
     shape_[i] = shape[i];
     shape_data[i] = shape[i];
@@ -87,6 +89,12 @@ const Dtype* Blob<Dtype>::cpu_data() const {
 template <typename Dtype>
 void Blob<Dtype>::set_cpu_data(Dtype* data) {
   CHECK(data);
+  // Make sure CPU and GPU sizes remain equal
+  size_t size = count_ * sizeof(Dtype);
+  if (data_->size() != size) {
+    data_.reset(new SyncedMemory(size));
+    diff_.reset(new SyncedMemory(size));
+  }
   data_->set_cpu_data(data);
 }
 
@@ -96,6 +104,18 @@ const Dtype* Blob<Dtype>::gpu_data() const {
   return (const Dtype*)data_->gpu_data();
 }
 
+template <typename Dtype>
+void Blob<Dtype>::set_gpu_data(Dtype* data) {
+  CHECK(data);
+  // Make sure CPU and GPU sizes remain equal
+  size_t size = count_ * sizeof(Dtype);
+  if (data_->size() != size) {
+    data_.reset(new SyncedMemory(size));
+    diff_.reset(new SyncedMemory(size));
+  }
+  data_->set_gpu_data(data);
+}
+
 template <typename Dtype>
 const Dtype* Blob<Dtype>::cpu_diff() const {
   CHECK(diff_);
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 299d67d4..4f6f9bcc 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -53,7 +53,7 @@ void GlobalInit(int* pargc, char*** pargv) {
 
 Caffe::Caffe()
     : random_generator_(), mode_(Caffe::CPU),
-      solver_count_(1), root_solver_(true) { }
+      solver_count_(1), solver_rank_(0), multiprocess_(false) { }
 
 Caffe::~Caffe() { }
 
@@ -70,6 +70,15 @@ void Caffe::DeviceQuery() {
   NO_GPU;
 }
 
+bool Caffe::CheckDevice(const int device_id) {
+  NO_GPU;
+  return false;
+}
+
+int Caffe::FindDevice(const int start_id) {
+  NO_GPU;
+  return -1;
+}
 
 class Caffe::RNG::Generator {
  public:
@@ -97,7 +106,8 @@ void* Caffe::RNG::generator() {
 
 Caffe::Caffe()
     : cublas_handle_(NULL), curand_generator_(NULL), random_generator_(),
-    mode_(Caffe::CPU), solver_count_(1), root_solver_(true) {
+    mode_(Caffe::CPU),
+    solver_count_(1), solver_rank_(0), multiprocess_(false) {
   // Try to create a cublas handler, and report an error if failed (but we will
   // keep the program running as one might just want to run CPU code).
   if (cublasCreate(&cublas_handle_) != CUBLAS_STATUS_SUCCESS) {
@@ -192,6 +202,39 @@ void Caffe::DeviceQuery() {
   return;
 }
 
+bool Caffe::CheckDevice(const int device_id) {
+  // This function checks the availability of GPU #device_id.
+  // It attempts to create a context on the device by calling cudaFree(0).
+  // cudaSetDevice() alone is not sufficient to check the availability.
+  // It lazily records device_id, however, does not initialize a
+  // context. So it does not know if the host thread has the permission to use
+  // the device or not.
+  //
+  // In a shared environment where the devices are set to EXCLUSIVE_PROCESS
+  // or EXCLUSIVE_THREAD mode, cudaSetDevice() returns cudaSuccess
+  // even if the device is exclusively occupied by another process or thread.
+  // Cuda operations that initialize the context are needed to check
+  // the permission. cudaFree(0) is one of those with no side effect,
+  // except the context initialization.
+  bool r = ((cudaSuccess == cudaSetDevice(device_id)) &&
+            (cudaSuccess == cudaFree(0)));
+  // reset any error that may have occurred.
+  cudaGetLastError();
+  return r;
+}
+
+int Caffe::FindDevice(const int start_id) {
+  // This function finds the first available device by checking devices with
+  // ordinal from start_id to the highest available value. In the
+  // EXCLUSIVE_PROCESS or EXCLUSIVE_THREAD mode, if it succeeds, it also
+  // claims the device due to the initialization of the context.
+  int count = 0;
+  CUDA_CHECK(cudaGetDeviceCount(&count));
+  for (int i = start_id; i < count; i++) {
+    if (CheckDevice(i)) return i;
+  }
+  return -1;
+}
 
 class Caffe::RNG::Generator {
  public:
diff --git a/src/caffe/data_reader.cpp b/src/caffe/data_reader.cpp
deleted file mode 100644
index 9f019bbf..00000000
--- a/src/caffe/data_reader.cpp
+++ /dev/null
@@ -1,119 +0,0 @@
-#include <boost/thread.hpp>
-#include <map>
-#include <string>
-#include <vector>
-
-#include "caffe/common.hpp"
-#include "caffe/data_reader.hpp"
-#include "caffe/layers/data_layer.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-namespace caffe {
-
-using boost::weak_ptr;
-
-map<const string, weak_ptr<DataReader::Body> > DataReader::bodies_;
-static boost::mutex bodies_mutex_;
-
-DataReader::DataReader(const LayerParameter& param)
-    : queue_pair_(new QueuePair(  //
-        param.data_param().prefetch() * param.data_param().batch_size())) {
-  // Get or create a body
-  boost::mutex::scoped_lock lock(bodies_mutex_);
-  string key = source_key(param);
-  weak_ptr<Body>& weak = bodies_[key];
-  body_ = weak.lock();
-  if (!body_) {
-    body_.reset(new Body(param));
-    bodies_[key] = weak_ptr<Body>(body_);
-  }
-  body_->new_queue_pairs_.push(queue_pair_);
-}
-
-DataReader::~DataReader() {
-  string key = source_key(body_->param_);
-  body_.reset();
-  boost::mutex::scoped_lock lock(bodies_mutex_);
-  if (bodies_[key].expired()) {
-    bodies_.erase(key);
-  }
-}
-
-//
-
-DataReader::QueuePair::QueuePair(int size) {
-  // Initialize the free queue with requested number of datums
-  for (int i = 0; i < size; ++i) {
-    free_.push(new Datum());
-  }
-}
-
-DataReader::QueuePair::~QueuePair() {
-  Datum* datum;
-  while (free_.try_pop(&datum)) {
-    delete datum;
-  }
-  while (full_.try_pop(&datum)) {
-    delete datum;
-  }
-}
-
-//
-
-DataReader::Body::Body(const LayerParameter& param)
-    : param_(param),
-      new_queue_pairs_() {
-  StartInternalThread();
-}
-
-DataReader::Body::~Body() {
-  StopInternalThread();
-}
-
-void DataReader::Body::InternalThreadEntry() {
-  shared_ptr<db::DB> db(db::GetDB(param_.data_param().backend()));
-  db->Open(param_.data_param().source(), db::READ);
-  shared_ptr<db::Cursor> cursor(db->NewCursor());
-  vector<shared_ptr<QueuePair> > qps;
-  try {
-    int solver_count = param_.phase() == TRAIN ? Caffe::solver_count() : 1;
-
-    // To ensure deterministic runs, only start running once all solvers
-    // are ready. But solvers need to peek on one item during initialization,
-    // so read one item, then wait for the next solver.
-    for (int i = 0; i < solver_count; ++i) {
-      shared_ptr<QueuePair> qp(new_queue_pairs_.pop());
-      read_one(cursor.get(), qp.get());
-      qps.push_back(qp);
-    }
-    // Main loop
-    while (!must_stop()) {
-      for (int i = 0; i < solver_count; ++i) {
-        read_one(cursor.get(), qps[i].get());
-      }
-      // Check no additional readers have been created. This can happen if
-      // more than one net is trained at a time per process, whether single
-      // or multi solver. It might also happen if two data layers have same
-      // name and same source.
-      CHECK_EQ(new_queue_pairs_.size(), 0);
-    }
-  } catch (boost::thread_interrupted&) {
-    // Interrupted exception is expected on shutdown
-  }
-}
-
-void DataReader::Body::read_one(db::Cursor* cursor, QueuePair* qp) {
-  Datum* datum = qp->free_.pop();
-  // TODO deserialize in-place instead of copy?
-  datum->ParseFromString(cursor->value());
-  qp->full_.push(datum);
-
-  // go to the next iter
-  cursor->Next();
-  if (!cursor->valid()) {
-    DLOG(INFO) << "Restarting data prefetching from start.";
-    cursor->SeekToFirst();
-  }
-}
-
-}  // namespace caffe
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 7189d67e..3012251e 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -130,7 +130,7 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const Datum& datum,
                                        Blob<Dtype>* transformed_blob) {
-  // If datum is encoded, decoded and transform the cv::image.
+  // If datum is encoded, decode and transform the cv::image.
   if (datum.encoded()) {
 #ifdef USE_OPENCV
     CHECK(!(param_.force_color() && param_.force_gray()))
diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp
index 104884e0..11de4979 100644
--- a/src/caffe/internal_thread.cpp
+++ b/src/caffe/internal_thread.cpp
@@ -28,25 +28,27 @@ void InternalThread::StartInternalThread() {
   Caffe::Brew mode = Caffe::mode();
   int rand_seed = caffe_rng_rand();
   int solver_count = Caffe::solver_count();
-  bool root_solver = Caffe::root_solver();
+  int solver_rank = Caffe::solver_rank();
+  bool multiprocess = Caffe::multiprocess();
 
   try {
     thread_.reset(new boost::thread(&InternalThread::entry, this, device, mode,
-          rand_seed, solver_count, root_solver));
+          rand_seed, solver_count, solver_rank, multiprocess));
   } catch (std::exception& e) {
     LOG(FATAL) << "Thread exception: " << e.what();
   }
 }
 
 void InternalThread::entry(int device, Caffe::Brew mode, int rand_seed,
-    int solver_count, bool root_solver) {
+    int solver_count, int solver_rank, bool multiprocess) {
 #ifndef CPU_ONLY
   CUDA_CHECK(cudaSetDevice(device));
 #endif
   Caffe::set_mode(mode);
   Caffe::set_random_seed(rand_seed);
   Caffe::set_solver_count(solver_count);
-  Caffe::set_root_solver(root_solver);
+  Caffe::set_solver_rank(solver_rank);
+  Caffe::set_multiprocess(multiprocess);
 
   InternalThreadEntry();
 }
diff --git a/src/caffe/layer.cpp b/src/caffe/layer.cpp
index 3b912898..684ae88b 100644
--- a/src/caffe/layer.cpp
+++ b/src/caffe/layer.cpp
@@ -1,27 +1,7 @@
-#include <boost/thread.hpp>
 #include "caffe/layer.hpp"
 
 namespace caffe {
 
-template <typename Dtype>
-void Layer<Dtype>::InitMutex() {
-  forward_mutex_.reset(new boost::mutex());
-}
-
-template <typename Dtype>
-void Layer<Dtype>::Lock() {
-  if (IsShared()) {
-    forward_mutex_->lock();
-  }
-}
-
-template <typename Dtype>
-void Layer<Dtype>::Unlock() {
-  if (IsShared()) {
-    forward_mutex_->unlock();
-  }
-}
-
 INSTANTIATE_CLASS(Layer);
 
 }  // namespace caffe
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index e967bd61..f14253a5 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -67,6 +67,7 @@ shared_ptr<Layer<Dtype> > GetConvolutionLayer(
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+    throw;  // Avoids missing return warning
   }
 }
 
@@ -104,6 +105,7 @@ shared_ptr<Layer<Dtype> > GetPoolingLayer(const LayerParameter& param) {
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+    throw;  // Avoids missing return warning
   }
 }
 
@@ -141,6 +143,7 @@ shared_ptr<Layer<Dtype> > GetLRNLayer(const LayerParameter& param) {
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+    throw;  // Avoids missing return warning
   }
 }
 
@@ -164,6 +167,7 @@ shared_ptr<Layer<Dtype> > GetReLULayer(const LayerParameter& param) {
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+    throw;  // Avoids missing return warning
   }
 }
 
@@ -187,6 +191,7 @@ shared_ptr<Layer<Dtype> > GetSigmoidLayer(const LayerParameter& param) {
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+    throw;  // Avoids missing return warning
   }
 }
 
@@ -210,6 +215,7 @@ shared_ptr<Layer<Dtype> > GetSoftmaxLayer(const LayerParameter& param) {
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+    throw;  // Avoids missing return warning
   }
 }
 
@@ -233,6 +239,7 @@ shared_ptr<Layer<Dtype> > GetTanHLayer(const LayerParameter& param) {
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+    throw;  // Avoids missing return warning
   }
 }
 
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 4eddbb5c..392829e6 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -52,8 +52,6 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_label = bottom[1]->cpu_data();
   const int dim = bottom[0]->count() / outer_num_;
   const int num_labels = bottom[0]->shape(label_axis_);
-  vector<Dtype> maxval(top_k_+1);
-  vector<int> max_id(top_k_+1);
   if (top.size() > 1) {
     caffe_set(nums_buffer_.count(), Dtype(0), nums_buffer_.mutable_cpu_data());
     caffe_set(top[1]->count(), Dtype(0), top[1]->mutable_cpu_data());
@@ -66,25 +64,22 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       if (has_ignore_label_ && label_value == ignore_label_) {
         continue;
       }
-      if (top.size() > 1) ++nums_buffer_.mutable_cpu_data()[label_value];
       DCHECK_GE(label_value, 0);
       DCHECK_LT(label_value, num_labels);
+      if (top.size() > 1) ++nums_buffer_.mutable_cpu_data()[label_value];
+      const Dtype prob_of_true_class = bottom_data[i * dim
+                                                   + label_value * inner_num_
+                                                   + j];
+      int num_better_predictions = -1;  // true_class also counts as "better"
       // Top-k accuracy
-      std::vector<std::pair<Dtype, int> > bottom_data_vector;
-      for (int k = 0; k < num_labels; ++k) {
-        bottom_data_vector.push_back(std::make_pair(
-            bottom_data[i * dim + k * inner_num_ + j], k));
+      for (int k = 0; k < num_labels && num_better_predictions < top_k_; ++k) {
+        num_better_predictions +=
+          (bottom_data[i * dim + k * inner_num_ + j] >= prob_of_true_class);
       }
-      std::partial_sort(
-          bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
-          bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());
-      // check if true label is in top k predictions
-      for (int k = 0; k < top_k_; k++) {
-        if (bottom_data_vector[k].second == label_value) {
-          ++accuracy;
-          if (top.size() > 1) ++top[1]->mutable_cpu_data()[label_value];
-          break;
-        }
+      // check if there are less than top_k_ predictions
+      if (num_better_predictions < top_k_) {
+        ++accuracy;
+        if (top.size() > 1) ++top[1]->mutable_cpu_data()[label_value];
       }
       ++count;
     }
@@ -102,6 +97,10 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   // Accuracy layer should not be used as a loss function.
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(AccuracyLayer);
+#endif
+
 INSTANTIATE_CLASS(AccuracyLayer);
 REGISTER_LAYER_CLASS(Accuracy);
 
diff --git a/src/caffe/layers/accuracy_layer.cu b/src/caffe/layers/accuracy_layer.cu
new file mode 100644
index 00000000..a8cff936
--- /dev/null
+++ b/src/caffe/layers/accuracy_layer.cu
@@ -0,0 +1,147 @@
+#include <vector>
+
+#include "caffe/layers/accuracy_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void AccuracyForwardGPU(const int nthreads,
+          const Dtype* bottom_data, const Dtype* label, Dtype* acc,
+          const int num, const int dim, const int spatial_dim,
+          const int num_labels, const int top_k,
+          const bool has_ignore_label_, const int ignore_label_,
+          Dtype* counts) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int n = index / spatial_dim;
+    const int s = index % spatial_dim;
+    const int label_value = static_cast<int>(label[n * spatial_dim + s]);
+    const Dtype prob_of_true_class = bottom_data[n * dim
+                                                 + label_value * spatial_dim
+                                                 + s];
+    int num_better_predictions = -1;  // true_class also counts as "better"
+    if (has_ignore_label_ && label_value == ignore_label_) {
+      acc[index] = 0;
+      counts[index] = 0;
+    } else {
+      for (int k = 0; k < num_labels & num_better_predictions < top_k; k++) {
+        num_better_predictions +=
+          (bottom_data[n * dim + k * spatial_dim + s] >= prob_of_true_class);
+      }
+      acc[index] = (num_better_predictions < top_k);
+      counts[index] = 1;
+    }
+  }
+}
+
+template <typename Dtype>
+__global__ void AccuracyForwardWithPerClassGPU(const int nthreads,
+          const Dtype* bottom_data, const Dtype* label,
+          Dtype* acc, Dtype* counts,
+          const int num, const int dim, const int spatial_dim,
+          const int num_labels, const int top_k,
+          const bool has_ignore_label_, const int ignore_label_) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int n = index / spatial_dim;
+    const int s = index % spatial_dim;
+    const int label_value = static_cast<int>(label[n * spatial_dim + s]);
+    const Dtype prob_of_true_class = bottom_data[n * dim
+                                                 + label_value * spatial_dim
+                                                 + s];
+    if (has_ignore_label_ && label_value == ignore_label_) {
+      // nothing to be done.
+    } else {
+      int num_better_predictions = -1;  // true_class also counts as "better"
+      for (int k = 0; k < num_labels & num_better_predictions < top_k; k++) {
+        num_better_predictions +=
+          (bottom_data[n * dim + k * spatial_dim + s] >= prob_of_true_class);
+      }
+      acc[label_value*nthreads + index] += (num_better_predictions < top_k);
+      counts[label_value*nthreads + index] = 1;
+    }
+  }
+}
+
+template <typename Dtype>
+void AccuracyLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  const Dtype* bottom_label = bottom[1]->gpu_data();
+  const int dim = bottom[0]->count() / outer_num_;
+  const int num_labels = bottom[0]->shape(label_axis_);
+  const int nthreads = outer_num_ * inner_num_;
+  // Since this memory is not used for anything,
+  // we use it here to avoid having to allocate new GPU
+  // memory to accumulate intermediate results in the kernel.
+  Dtype* acc_data = bottom[0]->mutable_gpu_diff();
+  if (top.size() == 1) {
+    // simple case - report only global accuracy.
+
+    // Similarly, this memory is never used elsewhere, and thus we can use it
+    // to avoid having to allocate additional GPU memory.
+    Dtype* counts = bottom[1]->mutable_gpu_diff();
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    AccuracyForwardGPU<Dtype><<<CAFFE_GET_BLOCKS(nthreads),
+        CAFFE_CUDA_NUM_THREADS>>>(nthreads, bottom_data, bottom_label,
+        acc_data, outer_num_, dim, inner_num_, num_labels, top_k_,
+        has_ignore_label_, ignore_label_, counts);
+    Dtype acc;
+    caffe_gpu_asum(nthreads, acc_data, &acc);
+    Dtype valid_count;
+    caffe_gpu_asum(nthreads, counts, &valid_count);
+    if (valid_count > 0) {
+      top[0]->mutable_cpu_data()[0] = acc / valid_count;
+    } else {
+      top[0]->mutable_cpu_data()[0] = 0;
+    }
+  } else {
+    // need to report per-class accuracy as well
+
+    // allocate space for more detailed "counts"
+    nums_buffer_.ReshapeLike(*bottom[0]);
+    Dtype* counts = nums_buffer_.mutable_gpu_data();
+
+    caffe_gpu_set(bottom[0]->count(), Dtype(0), acc_data);
+    caffe_gpu_set(nums_buffer_.count(), Dtype(0), counts);
+
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    AccuracyForwardWithPerClassGPU<Dtype><<<CAFFE_GET_BLOCKS(nthreads),
+        CAFFE_CUDA_NUM_THREADS>>>(nthreads, bottom_data, bottom_label,
+        acc_data, counts, outer_num_, dim, inner_num_, num_labels, top_k_,
+        has_ignore_label_, ignore_label_);
+
+    // get the overall accuracy
+    Dtype acc;
+    caffe_gpu_asum(bottom[0]->count(), acc_data, &acc);
+    Dtype valid_count;
+    caffe_gpu_asum(nums_buffer_.count(), counts, &valid_count);
+    if (valid_count > 0) {
+      top[0]->mutable_cpu_data()[0] = acc / valid_count;
+    } else {
+      top[0]->mutable_cpu_data()[0] = 0;
+    }
+
+    // get per-class accuracy
+    Dtype* per_class_acc = top[1]->mutable_cpu_data();
+    for (int l = 0; l < num_labels; l++) {
+      caffe_gpu_asum(nthreads, acc_data + l*nthreads, per_class_acc+l);
+      caffe_gpu_asum(nthreads, counts + l*nthreads, &valid_count);
+      if (valid_count > 0) {
+        per_class_acc[l] /= valid_count;
+      } else {
+        per_class_acc[l] = 0;
+      }
+    }
+  }
+}
+
+
+template <typename Dtype>
+void AccuracyLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[1]) {  NOT_IMPLEMENTED;  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(AccuracyLayer);
+}  // namespace caffe
diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp
index 4a4c68e0..35c90145 100644
--- a/src/caffe/layers/base_conv_layer.cpp
+++ b/src/caffe/layers/base_conv_layer.cpp
@@ -19,7 +19,6 @@ void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   const int num_axes = bottom[0]->num_axes();
   num_spatial_axes_ = num_axes - first_spatial_axis;
   CHECK_GE(num_spatial_axes_, 0);
-  vector<int> bottom_dim_blob_shape(1, num_spatial_axes_ + 1);
   vector<int> spatial_dim_blob_shape(1, std::max(num_spatial_axes_, 1));
   // Setup filter kernel dimensions (kernel_shape_).
   kernel_shape_.Reshape(spatial_dim_blob_shape);
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 989319f1..93a798f3 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -36,9 +36,11 @@ template <typename Dtype>
 BasePrefetchingDataLayer<Dtype>::BasePrefetchingDataLayer(
     const LayerParameter& param)
     : BaseDataLayer<Dtype>(param),
-      prefetch_free_(), prefetch_full_() {
-  for (int i = 0; i < PREFETCH_COUNT; ++i) {
-    prefetch_free_.push(&prefetch_[i]);
+      prefetch_(param.data_param().prefetch()),
+      prefetch_free_(), prefetch_full_(), prefetch_current_() {
+  for (int i = 0; i < prefetch_.size(); ++i) {
+    prefetch_[i].reset(new Batch<Dtype>());
+    prefetch_free_.push(prefetch_[i].get());
   }
 }
 
@@ -46,22 +48,23 @@ template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::LayerSetUp(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   BaseDataLayer<Dtype>::LayerSetUp(bottom, top);
+
   // Before starting the prefetch thread, we make cpu_data and gpu_data
   // calls so that the prefetch thread does not accidentally make simultaneous
   // cudaMalloc calls when the main thread is running. In some GPUs this
   // seems to cause failures if we do not so.
-  for (int i = 0; i < PREFETCH_COUNT; ++i) {
-    prefetch_[i].data_.mutable_cpu_data();
+  for (int i = 0; i < prefetch_.size(); ++i) {
+    prefetch_[i]->data_.mutable_cpu_data();
     if (this->output_labels_) {
-      prefetch_[i].label_.mutable_cpu_data();
+      prefetch_[i]->label_.mutable_cpu_data();
     }
   }
 #ifndef CPU_ONLY
   if (Caffe::mode() == Caffe::GPU) {
-    for (int i = 0; i < PREFETCH_COUNT; ++i) {
-      prefetch_[i].data_.mutable_gpu_data();
+    for (int i = 0; i < prefetch_.size(); ++i) {
+      prefetch_[i]->data_.mutable_gpu_data();
       if (this->output_labels_) {
-        prefetch_[i].label_.mutable_gpu_data();
+        prefetch_[i]->label_.mutable_gpu_data();
       }
     }
   }
@@ -88,6 +91,9 @@ void BasePrefetchingDataLayer<Dtype>::InternalThreadEntry() {
 #ifndef CPU_ONLY
       if (Caffe::mode() == Caffe::GPU) {
         batch->data_.data().get()->async_gpu_push(stream);
+        if (this->output_labels_) {
+          batch->label_.data().get()->async_gpu_push(stream);
+        }
         CUDA_CHECK(cudaStreamSynchronize(stream));
       }
 #endif
@@ -106,22 +112,18 @@ void BasePrefetchingDataLayer<Dtype>::InternalThreadEntry() {
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
-  Batch<Dtype>* batch = prefetch_full_.pop("Data layer prefetch queue empty");
+  if (prefetch_current_) {
+    prefetch_free_.push(prefetch_current_);
+  }
+  prefetch_current_ = prefetch_full_.pop("Waiting for data");
   // Reshape to loaded data.
-  top[0]->ReshapeLike(batch->data_);
-  // Copy the data
-  caffe_copy(batch->data_.count(), batch->data_.cpu_data(),
-             top[0]->mutable_cpu_data());
-  DLOG(INFO) << "Prefetch copied";
+  top[0]->ReshapeLike(prefetch_current_->data_);
+  top[0]->set_cpu_data(prefetch_current_->data_.mutable_cpu_data());
   if (this->output_labels_) {
     // Reshape to loaded labels.
-    top[1]->ReshapeLike(batch->label_);
-    // Copy the labels.
-    caffe_copy(batch->label_.count(), batch->label_.cpu_data(),
-        top[1]->mutable_cpu_data());
+    top[1]->ReshapeLike(prefetch_current_->label_);
+    top[1]->set_cpu_data(prefetch_current_->label_.mutable_cpu_data());
   }
-
-  prefetch_free_.push(batch);
 }
 
 #ifdef CPU_ONLY
diff --git a/src/caffe/layers/base_data_layer.cu b/src/caffe/layers/base_data_layer.cu
index 4056d36a..64c621a7 100644
--- a/src/caffe/layers/base_data_layer.cu
+++ b/src/caffe/layers/base_data_layer.cu
@@ -7,23 +7,18 @@ namespace caffe {
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
-  Batch<Dtype>* batch = prefetch_full_.pop("Data layer prefetch queue empty");
+  if (prefetch_current_) {
+    prefetch_free_.push(prefetch_current_);
+  }
+  prefetch_current_ = prefetch_full_.pop("Waiting for data");
   // Reshape to loaded data.
-  top[0]->ReshapeLike(batch->data_);
-  // Copy the data
-  caffe_copy(batch->data_.count(), batch->data_.gpu_data(),
-      top[0]->mutable_gpu_data());
+  top[0]->ReshapeLike(prefetch_current_->data_);
+  top[0]->set_gpu_data(prefetch_current_->data_.mutable_gpu_data());
   if (this->output_labels_) {
     // Reshape to loaded labels.
-    top[1]->ReshapeLike(batch->label_);
-    // Copy the labels.
-    caffe_copy(batch->label_.count(), batch->label_.gpu_data(),
-        top[1]->mutable_gpu_data());
+    top[1]->ReshapeLike(prefetch_current_->label_);
+    top[1]->set_gpu_data(prefetch_current_->label_.mutable_gpu_data());
   }
-  // Ensure the copy is synchronous wrt the host, so that the next batch isn't
-  // copied in meanwhile.
-  CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
-  prefetch_free_.push(batch);
 }
 
 INSTANTIATE_LAYER_GPU_FORWARD(BasePrefetchingDataLayer);
diff --git a/src/caffe/layers/batch_norm_layer.cpp b/src/caffe/layers/batch_norm_layer.cpp
index a69d8f99..c6a1d5b1 100644
--- a/src/caffe/layers/batch_norm_layer.cpp
+++ b/src/caffe/layers/batch_norm_layer.cpp
@@ -27,13 +27,25 @@ void BatchNormLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     sz.push_back(channels_);
     this->blobs_[0].reset(new Blob<Dtype>(sz));
     this->blobs_[1].reset(new Blob<Dtype>(sz));
-    sz[0]=1;
+    sz[0] = 1;
     this->blobs_[2].reset(new Blob<Dtype>(sz));
     for (int i = 0; i < 3; ++i) {
       caffe_set(this->blobs_[i]->count(), Dtype(0),
                 this->blobs_[i]->mutable_cpu_data());
     }
   }
+  // Mask statistics from optimization by setting local learning rates
+  // for mean, variance, and the bias correction to zero.
+  for (int i = 0; i < this->blobs_.size(); ++i) {
+    if (this->layer_param_.param_size() == i) {
+      ParamSpec* fixed_param_spec = this->layer_param_.add_param();
+      fixed_param_spec->set_lr_mult(0.f);
+    } else {
+      CHECK_EQ(this->layer_param_.param(i).lr_mult(), 0.f)
+          << "Cannot configure batch normalization statistics as layer "
+          << "parameters.";
+    }
+  }
 }
 
 template <typename Dtype>
@@ -49,7 +61,7 @@ void BatchNormLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
   variance_.Reshape(sz);
   temp_.ReshapeLike(*bottom[0]);
   x_norm_.ReshapeLike(*bottom[0]);
-  sz[0]=bottom[0]->shape(0);
+  sz[0] = bottom[0]->shape(0);
   batch_sum_multiplier_.Reshape(sz);
 
   int spatial_dim = bottom[0]->count()/(channels_*bottom[0]->shape(0));
@@ -112,8 +124,8 @@ void BatchNormLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
   if (!use_global_stats_) {
     // compute variance using var(X) = E((X-EX)^2)
-    caffe_powx(top[0]->count(), top_data, Dtype(2),
-        temp_.mutable_cpu_data());  // (X-EX)^2
+    caffe_sqr<Dtype>(top[0]->count(), top_data,
+                     temp_.mutable_cpu_data());  // (X-EX)^2
     caffe_cpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
         1. / (num * spatial_dim), temp_.cpu_data(),
         spatial_sum_multiplier_.cpu_data(), 0.,
@@ -136,7 +148,7 @@ void BatchNormLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
   // normalize variance
   caffe_add_scalar(variance_.count(), eps_, variance_.mutable_cpu_data());
-  caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
+  caffe_sqrt(variance_.count(), variance_.cpu_data(),
              variance_.mutable_cpu_data());
 
   // replicate variance to input size
diff --git a/src/caffe/layers/batch_norm_layer.cu b/src/caffe/layers/batch_norm_layer.cu
index c21713c8..a35e778e 100644
--- a/src/caffe/layers/batch_norm_layer.cu
+++ b/src/caffe/layers/batch_norm_layer.cu
@@ -48,14 +48,14 @@ void BatchNormLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
   if (!use_global_stats_) {
     // compute variance using var(X) = E((X-EX)^2)
-    caffe_gpu_powx(top[0]->count(), top_data, Dtype(2),
+    caffe_gpu_mul(top[0]->count(), top[0]->gpu_data(), top[0]->gpu_data(),
         temp_.mutable_gpu_data());  // (X-EX)^2
     caffe_gpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
         1. / (num * spatial_dim), temp_.gpu_data(),
         spatial_sum_multiplier_.gpu_data(), 0.,
         num_by_chans_.mutable_gpu_data());
-    caffe_gpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
-        num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0.,
+    caffe_gpu_gemv<Dtype>(CblasTrans, num, channels_, Dtype(1.),
+        num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), Dtype(0.),
         variance_.mutable_gpu_data());  // E((X_EX)^2)
 
     // compute and save moving average
@@ -72,7 +72,7 @@ void BatchNormLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
   // normalize variance
   caffe_gpu_add_scalar(variance_.count(), eps_, variance_.mutable_gpu_data());
-  caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
+  caffe_gpu_sqrt(variance_.count(), variance_.gpu_data(),
       variance_.mutable_gpu_data());
 
   // replicate variance to input size
diff --git a/src/caffe/layers/crop_layer.cpp b/src/caffe/layers/crop_layer.cpp
index 76409bd7..65ea8f8b 100644
--- a/src/caffe/layers/crop_layer.cpp
+++ b/src/caffe/layers/crop_layer.cpp
@@ -1,8 +1,10 @@
 #include <algorithm>
+#include <functional>
 #include <map>
 #include <set>
 #include <vector>
 
+
 #include "caffe/layer.hpp"
 #include "caffe/layers/crop_layer.hpp"
 #include "caffe/net.hpp"
@@ -13,40 +15,117 @@ namespace caffe {
 template <typename Dtype>
 void CropLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
+  // LayerSetup() handles the number of dimensions; Reshape() handles the sizes.
+  // bottom[0] supplies the data
+  // bottom[1] supplies the size
   const CropParameter& param = this->layer_param_.crop_param();
   CHECK_EQ(bottom.size(), 2) << "Wrong number of bottom blobs.";
-  CHECK_EQ(bottom[0]->num_axes(), 4) << "Only works with 4D blobs.";
-  CHECK_EQ(bottom[1]->num_axes(), 4) << "Only works with 4D blobs.";
-  crop_h_ = param.offset_height();
-  crop_w_ = param.offset_width();
+  int input_dim = bottom[0]->num_axes();
+  const int start_axis = bottom[0]->CanonicalAxisIndex(param.axis());
+  CHECK_LT(start_axis, input_dim) << "crop axis bigger than input dim";
+  if (param.offset_size() > 1) {
+    // the number of crop values specified must be equal to the number
+    // of dimensions following axis
+    CHECK_EQ(start_axis + param.offset_size(), input_dim)
+      << "number of offset values specified must be equal to the number of "
+      << "dimensions following axis.";
+  }
 }
 
 template <typename Dtype>
 void CropLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
-  // Check that the image we are cropping minus the margin is bigger than the
-  // destination image.
-  CHECK_GT(bottom[0]->height()-crop_h_, bottom[1]->height())
-      << "invalid offset";
-  CHECK_GT(bottom[0]->width()-crop_w_, bottom[1]->width()) << "invalid offset";
-  top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(), bottom[1]->height(),
-      bottom[1]->width());
+  const CropParameter& param = this->layer_param_.crop_param();
+  int input_dim = bottom[0]->num_axes();
+  const int start_axis = bottom[0]->CanonicalAxisIndex(param.axis());
+
+  // Initialize offsets to 0 and the new shape to the current shape of the data.
+  vector<int> new_shape(bottom[0]->shape());
+  vector<int> offsets_shape(1, input_dim);
+  offsets.Reshape(offsets_shape);
+  int* offset_data = offsets.mutable_cpu_data();
+
+  // Determine crop offsets and the new shape post-crop.
+  for (int i = 0; i < input_dim; ++i) {
+    int crop_offset = 0;
+    int new_size = bottom[0]->shape(i);
+    if (i >= start_axis) {
+      new_size = bottom[1]->shape(i);
+      if (param.offset_size() == 1) {
+        // If only one offset is given, all crops have the same offset.
+        crop_offset = param.offset(0);
+      } else if (param.offset_size() > 1) {
+        // For several offsets, the number of offsets must be equal to the
+        // number of dimensions to crop, that is dimensions after the axis.
+        crop_offset = param.offset(i - start_axis);
+      }
+      // Check that the crop and offset are within the dimension's bounds.
+      CHECK_GE(bottom[0]->shape(i) - crop_offset, bottom[1]->shape(i))
+          << "the crop for dimension " << i << " is out-of-bounds with "
+          << "size " << bottom[1]->shape(i) << " and offset " << crop_offset;
+    }
+    new_shape[i] = new_size;
+    offset_data[i] = crop_offset;
+  }
+  top[0]->Reshape(new_shape);
+  // Compute strides
+  src_strides_.Reshape(offsets_shape);
+  dest_strides_.Reshape(offsets_shape);
+  for (int i = 0; i < input_dim; ++i) {
+    src_strides_.mutable_cpu_data()[i] = bottom[0]->count(i + 1, input_dim);
+    dest_strides_.mutable_cpu_data()[i] = top[0]->count(i + 1, input_dim);
+  }
+}
+
+template <typename Dtype>
+void CropLayer<Dtype>::crop_copy(const vector<Blob<Dtype>*>& bottom,
+             const vector<Blob<Dtype>*>& top,
+             const int* offsets,
+             vector<int> indices,
+             int cur_dim,
+             const Dtype* src_data,
+             Dtype* dest_data,
+             bool is_forward) {
+  if (cur_dim + 1 < top[0]->num_axes()) {
+    // We are not yet at the final dimension, call copy recursively
+    for (int i = 0; i < top[0]->shape(cur_dim); ++i) {
+      indices[cur_dim] = i;
+      crop_copy(bottom, top, offsets, indices, cur_dim+1,
+                src_data, dest_data, is_forward);
+    }
+  } else {
+    // We are at the last dimensions, which is stored continuously in memory
+    // prepare index vector reduced(red) and with offsets(off)
+    std::vector<int> ind_red(cur_dim, 0);
+    std::vector<int> ind_off(cur_dim+1, 0);
+    for (int j = 0; j < cur_dim; ++j) {
+      ind_red[j] = indices[j];
+      ind_off[j] = indices[j] + offsets[j];
+    }
+    ind_off[cur_dim] = offsets[cur_dim];
+    // do the copy
+    if (is_forward) {
+      caffe_copy(top[0]->shape(cur_dim),
+          src_data + bottom[0]->offset(ind_off),
+          dest_data + top[0]->offset(ind_red));
+    } else {
+      // in the backwards pass the src_data is top_diff
+      // and the dest_data is bottom_diff
+      caffe_copy(top[0]->shape(cur_dim),
+          src_data + top[0]->offset(ind_red),
+          dest_data + bottom[0]->offset(ind_off));
+    }
+  }
 }
 
 template <typename Dtype>
 void CropLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
+  std::vector<int> indices(top[0]->num_axes(), 0);
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = top[0]->mutable_cpu_data();
-  for (int n = 0; n < top[0]->num(); ++n) {
-    for (int c = 0; c < top[0]->channels(); ++c) {
-      for (int h = 0; h < top[0]->height(); ++h) {
-        caffe_copy(top[0]->width(),
-            bottom_data + bottom[0]->offset(n, c, crop_h_ + h, crop_w_),
-            top_data + top[0]->offset(n, c, h));
-      }
-    }
-  }
+  crop_copy(bottom, top, offsets.cpu_data(), indices, 0, bottom_data, top_data,
+      true);
 }
 
 template <typename Dtype>
@@ -54,17 +133,12 @@ void CropLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+
   if (propagate_down[0]) {
     caffe_set(bottom[0]->count(), static_cast<Dtype>(0), bottom_diff);
-    for (int n = 0; n < top[0]->num(); ++n) {
-      for (int c = 0; c < top[0]->channels(); ++c) {
-        for (int h = 0; h < top[0]->height(); ++h) {
-          caffe_copy(top[0]->width(),
-              top_diff + top[0]->offset(n, c, h),
-              bottom_diff + bottom[0]->offset(n, c, crop_h_ + h, crop_w_));
-        }
-      }
-    }
+    std::vector<int> indices(top[0]->num_axes(), 0);
+    crop_copy(bottom, top, offsets.cpu_data(), indices, 0, top_diff,
+        bottom_diff, false);
   }
 }
 
diff --git a/src/caffe/layers/crop_layer.cu b/src/caffe/layers/crop_layer.cu
index 262f5fa8..4ece9cd1 100644
--- a/src/caffe/layers/crop_layer.cu
+++ b/src/caffe/layers/crop_layer.cu
@@ -4,21 +4,47 @@
 
 namespace caffe {
 
-// Copy (one line per thread) from one array to another, with arbitrary
-// strides in the last two dimensions.
+__device__ int compute_uncropped_index(
+    int index,
+    const int ndims,
+    const int* src_strides,
+    const int* dest_strides,
+    const int* offsets) {
+  int dest_index = index;
+  int src_index = 0;
+  for (int i = 0; i < ndims; ++i) {
+      int coord = dest_index / dest_strides[i];
+      dest_index -= coord * dest_strides[i];
+      src_index += src_strides[i] * (coord + offsets[i]);
+  }
+  return src_index;
+}
+
 template <typename Dtype>
-__global__ void copy_kernel(const int n, const int height, const int width,
-    const int src_outer_stride, const int src_inner_stride,
-    const int dest_outer_stride, const int dest_inner_stride,
+__global__ void crop_kernel_forward(const int nthreads,
+    const int ndims,
+    const int* src_strides,
+    const int* dest_strides,
+    const int* offsets,
     const Dtype* src, Dtype* dest) {
-  CUDA_KERNEL_LOOP(index, n) {
-    int src_start = index / height * src_outer_stride
-                  + index % height * src_inner_stride;
-    int dest_start = index / height * dest_outer_stride
-                   + index % height * dest_inner_stride;
-    for (int i = 0; i < width; ++i) {
-      dest[dest_start + i] = src[src_start + i];
-    }
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    int src_index = compute_uncropped_index(
+        index, ndims, src_strides, dest_strides, offsets);
+    dest[index] = src[src_index];
+  }
+}
+
+template <typename Dtype>
+__global__ void crop_kernel_backward(const int nthreads,
+    const int ndims,
+    const int* src_strides,
+    const int* dest_strides,
+    const int* offsets,
+    Dtype* src, const Dtype* dest) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    int src_index = compute_uncropped_index(
+        index, ndims, src_strides, dest_strides, offsets);
+    src[src_index] = dest[index];
   }
 }
 
@@ -27,14 +53,14 @@ void CropLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
-  const int lines = top[0]->count() / top[0]->width();
-
+  int n = top[0]->count();
   // NOLINT_NEXT_LINE(whitespace/operators)
-  copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
-      lines, top[0]->height(), top[0]->width(),
-      bottom[0]->height() * bottom[0]->width(), bottom[0]->width(),
-      top[0]->height() * top[0]->width(), top[0]->width(),
-      bottom_data + bottom[0]->offset(0, 0, crop_h_, crop_w_), top_data);
+  crop_kernel_forward<<<CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS>>>(n,
+      bottom[0]->num_axes(),
+      src_strides_.gpu_data(),
+      dest_strides_.gpu_data(),
+      offsets.gpu_data(),
+      bottom_data, top_data);
 }
 
 template <typename Dtype>
@@ -42,16 +68,17 @@ void CropLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
-  const int lines = top[0]->count() / top[0]->width();
+  int n = top[0]->count();
 
   if (propagate_down[0]) {
     caffe_gpu_set(bottom[0]->count(), static_cast<Dtype>(0), bottom_diff);
     // NOLINT_NEXT_LINE(whitespace/operators)
-    copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
-        lines, top[0]->height(), top[0]->width(),
-        top[0]->height() * top[0]->width(), top[0]->width(),
-        bottom[0]->height() * bottom[0]->width(), bottom[0]->width(),
-        top_diff, bottom_diff + bottom[0]->offset(0, 0, crop_h_, crop_w_));
+    crop_kernel_backward<<<CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS>>>(n,
+        bottom[0]->num_axes(),
+        src_strides_.gpu_data(),
+        dest_strides_.gpu_data(),
+        offsets.gpu_data(),
+        bottom_diff, top_diff);
   }
 }
 
diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp
index 1987fb09..efc9e04e 100644
--- a/src/caffe/layers/cudnn_conv_layer.cpp
+++ b/src/caffe/layers/cudnn_conv_layer.cpp
@@ -252,6 +252,7 @@ CuDNNConvolutionLayer<Dtype>::~CuDNNConvolutionLayer() {
   }
 
   cudaFree(workspaceData);
+  delete [] workspace;
   delete [] stream_;
   delete [] handle_;
   delete [] fwd_algo_;
diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index 42c4fd02..8bc53462 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -30,19 +30,11 @@ void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
       // Bias.
       if (this->bias_term_) {
         const Dtype* bias_data = this->blobs_[1]->gpu_data();
-#if CUDNN_VERSION_MIN(4, 0, 0)
         CUDNN_CHECK(cudnnAddTensor(handle_[g],
               cudnn::dataType<Dtype>::one,
               bias_desc_, bias_data + bias_offset_ * g,
               cudnn::dataType<Dtype>::one,
               top_descs_[i], top_data + top_offset_ * g));
-#else
-        CUDNN_CHECK(cudnnAddTensor(handle_[g], CUDNN_ADD_SAME_C,
-              cudnn::dataType<Dtype>::one,
-              bias_desc_, bias_data + bias_offset_ * g,
-              cudnn::dataType<Dtype>::one,
-              top_descs_[i], top_data + top_offset_ * g));
-#endif
       }
     }
 
@@ -82,7 +74,7 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       // Gradient w.r.t. weights.
       if (this->param_propagate_down_[0]) {
         const Dtype* bottom_data = bottom[i]->gpu_data();
-        CUDNN_CHECK(cudnnConvolutionBackwardFilter_v3(
+        CUDNN_CHECK(cudnnConvolutionBackwardFilter(
               handle_[1*this->group_ + g],
               cudnn::dataType<Dtype>::one,
               bottom_descs_[i], bottom_data + bottom_offset_ * g,
@@ -100,7 +92,7 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
           weight = this->blobs_[0]->gpu_data();
         }
         Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
-        CUDNN_CHECK(cudnnConvolutionBackwardData_v3(
+        CUDNN_CHECK(cudnnConvolutionBackwardData(
               handle_[2*this->group_ + g],
               cudnn::dataType<Dtype>::one,
               filter_desc_, weight + this->weight_offset_ * g,
diff --git a/src/caffe/layers/cudnn_relu_layer.cpp b/src/caffe/layers/cudnn_relu_layer.cpp
index c86c6907..687c9057 100644
--- a/src/caffe/layers/cudnn_relu_layer.cpp
+++ b/src/caffe/layers/cudnn_relu_layer.cpp
@@ -13,6 +13,7 @@ void CuDNNReLULayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+  cudnn::createActivationDescriptor<Dtype>(&activ_desc_, CUDNN_ACTIVATION_RELU);
   handles_setup_ = true;
 }
 
@@ -35,6 +36,7 @@ CuDNNReLULayer<Dtype>::~CuDNNReLULayer() {
 
   cudnnDestroyTensorDescriptor(this->bottom_desc_);
   cudnnDestroyTensorDescriptor(this->top_desc_);
+  cudnnDestroyActivationDescriptor(this->activ_desc_);
   cudnnDestroy(this->handle_);
 }
 
diff --git a/src/caffe/layers/cudnn_relu_layer.cu b/src/caffe/layers/cudnn_relu_layer.cu
index 9f617183..e7928bbd 100644
--- a/src/caffe/layers/cudnn_relu_layer.cu
+++ b/src/caffe/layers/cudnn_relu_layer.cu
@@ -15,12 +15,21 @@ void CuDNNReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
+#if CUDNN_VERSION_MIN(5, 0, 0)
   CUDNN_CHECK(cudnnActivationForward(this->handle_,
-        CUDNN_ACTIVATION_RELU,
+        activ_desc_,
         cudnn::dataType<Dtype>::one,
         this->bottom_desc_, bottom_data,
         cudnn::dataType<Dtype>::zero,
         this->top_desc_, top_data));
+#else
+  CUDNN_CHECK(cudnnActivationForward_v4(this->handle_,
+        activ_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->top_desc_, top_data));
+#endif
 }
 
 template <typename Dtype>
@@ -40,13 +49,23 @@ void CuDNNReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+#if CUDNN_VERSION_MIN(5, 0, 0)
   CUDNN_CHECK(cudnnActivationBackward(this->handle_,
-        CUDNN_ACTIVATION_RELU,
+        activ_desc_,
         cudnn::dataType<Dtype>::one,
         this->top_desc_, top_data, this->top_desc_, top_diff,
         this->bottom_desc_, bottom_data,
         cudnn::dataType<Dtype>::zero,
         this->bottom_desc_, bottom_diff));
+#else
+  CUDNN_CHECK(cudnnActivationBackward_v4(this->handle_,
+        activ_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->top_desc_, top_data, this->top_desc_, top_diff,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->bottom_desc_, bottom_diff));
+#endif
 }
 
 INSTANTIATE_LAYER_GPU_FUNCS(CuDNNReLULayer);
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cpp b/src/caffe/layers/cudnn_sigmoid_layer.cpp
index ccb955cd..3ce6aef1 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cpp
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cpp
@@ -13,6 +13,8 @@ void CuDNNSigmoidLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+  cudnn::createActivationDescriptor<Dtype>(&activ_desc_,
+      CUDNN_ACTIVATION_SIGMOID);
   handles_setup_ = true;
 }
 
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cu b/src/caffe/layers/cudnn_sigmoid_layer.cu
index e2a4b460..48d6cbab 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cu
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cu
@@ -10,12 +10,21 @@ void CuDNNSigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
+#if CUDNN_VERSION_MIN(5, 0, 0)
   CUDNN_CHECK(cudnnActivationForward(this->handle_,
-        CUDNN_ACTIVATION_SIGMOID,
+        activ_desc_,
         cudnn::dataType<Dtype>::one,
         this->bottom_desc_, bottom_data,
         cudnn::dataType<Dtype>::zero,
         this->top_desc_, top_data));
+#else
+  CUDNN_CHECK(cudnnActivationForward_v4(this->handle_,
+        activ_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->top_desc_, top_data));
+#endif
 }
 
 template <typename Dtype>
@@ -30,13 +39,23 @@ void CuDNNSigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+#if CUDNN_VERSION_MIN(5, 0, 0)
   CUDNN_CHECK(cudnnActivationBackward(this->handle_,
-        CUDNN_ACTIVATION_SIGMOID,
+        activ_desc_,
         cudnn::dataType<Dtype>::one,
         this->top_desc_, top_data, this->top_desc_, top_diff,
         this->bottom_desc_, bottom_data,
         cudnn::dataType<Dtype>::zero,
         this->bottom_desc_, bottom_diff));
+#else
+  CUDNN_CHECK(cudnnActivationBackward_v4(this->handle_,
+        activ_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->top_desc_, top_data, this->top_desc_, top_diff,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->bottom_desc_, bottom_diff));
+#endif
 }
 
 INSTANTIATE_LAYER_GPU_FUNCS(CuDNNSigmoidLayer);
diff --git a/src/caffe/layers/cudnn_tanh_layer.cpp b/src/caffe/layers/cudnn_tanh_layer.cpp
index 1a564182..e87dd9de 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cpp
+++ b/src/caffe/layers/cudnn_tanh_layer.cpp
@@ -13,6 +13,7 @@ void CuDNNTanHLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+  cudnn::createActivationDescriptor<Dtype>(&activ_desc_, CUDNN_ACTIVATION_TANH);
   handles_setup_ = true;
 }
 
diff --git a/src/caffe/layers/cudnn_tanh_layer.cu b/src/caffe/layers/cudnn_tanh_layer.cu
index 89df28a3..6b5d7ae7 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cu
+++ b/src/caffe/layers/cudnn_tanh_layer.cu
@@ -10,12 +10,21 @@ void CuDNNTanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
+#if CUDNN_VERSION_MIN(5, 0, 0)
   CUDNN_CHECK(cudnnActivationForward(this->handle_,
-        CUDNN_ACTIVATION_TANH,
+        activ_desc_,
         cudnn::dataType<Dtype>::one,
         this->bottom_desc_, bottom_data,
         cudnn::dataType<Dtype>::zero,
         this->top_desc_, top_data));
+#else
+  CUDNN_CHECK(cudnnActivationForward_v4(this->handle_,
+        activ_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->top_desc_, top_data));
+#endif
 }
 
 template <typename Dtype>
@@ -31,13 +40,23 @@ void CuDNNTanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
 
+#if CUDNN_VERSION_MIN(5, 0, 0)
   CUDNN_CHECK(cudnnActivationBackward(this->handle_,
-        CUDNN_ACTIVATION_TANH,
+        activ_desc_,
         cudnn::dataType<Dtype>::one,
         this->top_desc_, top_data, this->top_desc_, top_diff,
         this->bottom_desc_, bottom_data,
         cudnn::dataType<Dtype>::zero,
         this->bottom_desc_, bottom_diff));
+#else
+  CUDNN_CHECK(cudnnActivationBackward_v4(this->handle_,
+        activ_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->top_desc_, top_data, this->top_desc_, top_diff,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->bottom_desc_, bottom_diff));
+#endif
 }
 
 INSTANTIATE_LAYER_GPU_FUNCS(CuDNNTanHLayer);
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 66e6301f..0f1296bb 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -14,7 +14,10 @@ namespace caffe {
 template <typename Dtype>
 DataLayer<Dtype>::DataLayer(const LayerParameter& param)
   : BasePrefetchingDataLayer<Dtype>(param),
-    reader_(param) {
+    offset_() {
+  db_.reset(db::GetDB(param.data_param().backend()));
+  db_->Open(param.data_param().source(), db::READ);
+  cursor_.reset(db_->NewCursor());
 }
 
 template <typename Dtype>
@@ -27,7 +30,8 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const int batch_size = this->layer_param_.data_param().batch_size();
   // Read a data point, and use it to initialize the top blob.
-  Datum& datum = *(reader_.full().peek());
+  Datum datum;
+  datum.ParseFromString(cursor_->value());
 
   // Use data_transformer to infer the expected blob shape from datum.
   vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
@@ -35,22 +39,44 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // Reshape top[0] and prefetch_data according to the batch_size.
   top_shape[0] = batch_size;
   top[0]->Reshape(top_shape);
-  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
-    this->prefetch_[i].data_.Reshape(top_shape);
+  for (int i = 0; i < this->prefetch_.size(); ++i) {
+    this->prefetch_[i]->data_.Reshape(top_shape);
   }
-  LOG(INFO) << "output data size: " << top[0]->num() << ","
+  LOG_IF(INFO, Caffe::root_solver())
+      << "output data size: " << top[0]->num() << ","
       << top[0]->channels() << "," << top[0]->height() << ","
       << top[0]->width();
   // label
   if (this->output_labels_) {
     vector<int> label_shape(1, batch_size);
     top[1]->Reshape(label_shape);
-    for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
-      this->prefetch_[i].label_.Reshape(label_shape);
+    for (int i = 0; i < this->prefetch_.size(); ++i) {
+      this->prefetch_[i]->label_.Reshape(label_shape);
     }
   }
 }
 
+template <typename Dtype>
+bool DataLayer<Dtype>::Skip() {
+  int size = Caffe::solver_count();
+  int rank = Caffe::solver_rank();
+  bool keep = (offset_ % size) == rank ||
+              // In test mode, only rank 0 runs, so avoid skipping
+              this->layer_param_.phase() == TEST;
+  return !keep;
+}
+
+template<typename Dtype>
+void DataLayer<Dtype>::Next() {
+  cursor_->Next();
+  if (!cursor_->valid()) {
+    LOG_IF(INFO, Caffe::root_solver())
+        << "Restarting data prefetching from start.";
+    cursor_->SeekToFirst();
+  }
+  offset_++;
+}
+
 // This function is called on prefetch thread
 template<typename Dtype>
 void DataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
@@ -61,41 +87,41 @@ void DataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
   CPUTimer timer;
   CHECK(batch->data_.count());
   CHECK(this->transformed_data_.count());
-
-  // Reshape according to the first datum of each batch
-  // on single input batches allows for inputs of varying dimension.
   const int batch_size = this->layer_param_.data_param().batch_size();
-  Datum& datum = *(reader_.full().peek());
-  // Use data_transformer to infer the expected blob shape from datum.
-  vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
-  this->transformed_data_.Reshape(top_shape);
-  // Reshape batch according to the batch_size.
-  top_shape[0] = batch_size;
-  batch->data_.Reshape(top_shape);
-
-  Dtype* top_data = batch->data_.mutable_cpu_data();
-  Dtype* top_label = NULL;  // suppress warnings about uninitialized variables
 
-  if (this->output_labels_) {
-    top_label = batch->label_.mutable_cpu_data();
-  }
+  Datum datum;
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     timer.Start();
-    // get a datum
-    Datum& datum = *(reader_.full().pop("Waiting for data"));
+    while (Skip()) {
+      Next();
+    }
+    datum.ParseFromString(cursor_->value());
     read_time += timer.MicroSeconds();
-    timer.Start();
+
+    if (item_id == 0) {
+      // Reshape according to the first datum of each batch
+      // on single input batches allows for inputs of varying dimension.
+      // Use data_transformer to infer the expected blob shape from datum.
+      vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
+      this->transformed_data_.Reshape(top_shape);
+      // Reshape batch according to the batch_size.
+      top_shape[0] = batch_size;
+      batch->data_.Reshape(top_shape);
+    }
+
     // Apply data transformations (mirror, scale, crop...)
+    timer.Start();
     int offset = batch->data_.offset(item_id);
+    Dtype* top_data = batch->data_.mutable_cpu_data();
     this->transformed_data_.set_cpu_data(top_data + offset);
     this->data_transformer_->Transform(datum, &(this->transformed_data_));
     // Copy label.
     if (this->output_labels_) {
+      Dtype* top_label = batch->label_.mutable_cpu_data();
       top_label[item_id] = datum.label();
     }
     trans_time += timer.MicroSeconds();
-
-    reader_.free().push(const_cast<Datum*>(&datum));
+    Next();
   }
   timer.Stop();
   batch_timer.Stop();
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index 9cb64d97..533ab26c 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -23,8 +23,8 @@ void DropoutLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   NeuronLayer<Dtype>::Reshape(bottom, top);
   // Set up the cache for random number generation
-  rand_vec_.Reshape(bottom[0]->num(), bottom[0]->channels(),
-      bottom[0]->height(), bottom[0]->width());
+  // ReshapeLike does not work because rand_vec_ is of Dtype uint
+  rand_vec_.Reshape(bottom[0]->shape());
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index 21256166..3d82b0e1 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -31,7 +31,9 @@ template <typename Dtype>
 void EltwiseLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   for (int i = 1; i < bottom.size(); ++i) {
-    CHECK(bottom[i]->shape() == bottom[0]->shape());
+    CHECK(bottom[0]->shape() == bottom[i]->shape())
+        << "bottom[0]: " << bottom[0]->shape_string()
+        << ", bottom[" << i << "]: " << bottom[i]->shape_string();
   }
   top[0]->ReshapeLike(*bottom[0]);
   // If max operation, we will initialize the vector index part.
diff --git a/src/caffe/layers/exp_layer.cpp b/src/caffe/layers/exp_layer.cpp
index 1f4a309f..0c1b463a 100644
--- a/src/caffe/layers/exp_layer.cpp
+++ b/src/caffe/layers/exp_layer.cpp
@@ -23,7 +23,8 @@ void ExpLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   const Dtype input_scale = this->layer_param_.exp_param().scale();
   const Dtype input_shift = this->layer_param_.exp_param().shift();
   inner_scale_ = log_base * input_scale;
-  outer_scale_ = (input_shift == Dtype(0)) ? Dtype(1) : pow(base, input_shift);
+  outer_scale_ = (input_shift == Dtype(0)) ? Dtype(1) :
+     ( (base != Dtype(-1)) ? pow(base, input_shift) : exp(input_shift) );
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 2f13dc64..00716a92 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -39,8 +39,9 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
 
   for (int i = 0; i < top_size; ++i) {
     hdf_blobs_[i] = shared_ptr<Blob<Dtype> >(new Blob<Dtype>());
+    // Allow reshape here, as we are loading data not params
     hdf5_load_nd_dataset(file_id, this->layer_param_.top(i).c_str(),
-        MIN_DATA_DIM, MAX_DATA_DIM, hdf_blobs_[i].get());
+        MIN_DATA_DIM, MAX_DATA_DIM, hdf_blobs_[i].get(), true);
   }
 
   herr_t status = H5Fclose(file_id);
@@ -61,10 +62,10 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
   // Shuffle if needed.
   if (this->layer_param_.hdf5_data_param().shuffle()) {
     std::random_shuffle(data_permutation_.begin(), data_permutation_.end());
-    DLOG(INFO) << "Successully loaded " << hdf_blobs_[0]->shape(0)
+    DLOG(INFO) << "Successfully loaded " << hdf_blobs_[0]->shape(0)
                << " rows (shuffled)";
   } else {
-    DLOG(INFO) << "Successully loaded " << hdf_blobs_[0]->shape(0) << " rows";
+    DLOG(INFO) << "Successfully loaded " << hdf_blobs_[0]->shape(0) << " rows";
   }
 }
 
@@ -124,28 +125,46 @@ void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   }
 }
 
+template <typename Dtype>
+bool HDF5DataLayer<Dtype>::Skip() {
+  int size = Caffe::solver_count();
+  int rank = Caffe::solver_rank();
+  bool keep = (offset_ % size) == rank ||
+              // In test mode, only rank 0 runs, so avoid skipping
+              this->layer_param_.phase() == TEST;
+  return !keep;
+}
+
+template<typename Dtype>
+void HDF5DataLayer<Dtype>::Next() {
+  if (++current_row_ == hdf_blobs_[0]->shape(0)) {
+    if (num_files_ > 1) {
+      ++current_file_;
+      if (current_file_ == num_files_) {
+        current_file_ = 0;
+        if (this->layer_param_.hdf5_data_param().shuffle()) {
+          std::random_shuffle(file_permutation_.begin(),
+                              file_permutation_.end());
+        }
+        DLOG(INFO) << "Looping around to first file.";
+      }
+      LoadHDF5FileData(
+        hdf_filenames_[file_permutation_[current_file_]].c_str());
+    }
+    current_row_ = 0;
+    if (this->layer_param_.hdf5_data_param().shuffle())
+      std::random_shuffle(data_permutation_.begin(), data_permutation_.end());
+  }
+  offset_++;
+}
+
 template <typename Dtype>
 void HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
-  for (int i = 0; i < batch_size; ++i, ++current_row_) {
-    if (current_row_ == hdf_blobs_[0]->shape(0)) {
-      if (num_files_ > 1) {
-        ++current_file_;
-        if (current_file_ == num_files_) {
-          current_file_ = 0;
-          if (this->layer_param_.hdf5_data_param().shuffle()) {
-            std::random_shuffle(file_permutation_.begin(),
-                                file_permutation_.end());
-          }
-          DLOG(INFO) << "Looping around to first file.";
-        }
-        LoadHDF5FileData(
-            hdf_filenames_[file_permutation_[current_file_]].c_str());
-      }
-      current_row_ = 0;
-      if (this->layer_param_.hdf5_data_param().shuffle())
-        std::random_shuffle(data_permutation_.begin(), data_permutation_.end());
+  for (int i = 0; i < batch_size; ++i) {
+    while (Skip()) {
+      Next();
     }
     for (int j = 0; j < this->layer_param_.top_size(); ++j) {
       int data_dim = top[j]->count() / top[j]->shape(0);
@@ -153,6 +172,7 @@ void HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
           &hdf_blobs_[j]->cpu_data()[data_permutation_[current_row_]
             * data_dim], &top[j]->mutable_cpu_data()[i * data_dim]);
     }
+    Next();
   }
 }
 
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index 595d2230..33eebd41 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -17,24 +17,9 @@ template <typename Dtype>
 void HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
-  for (int i = 0; i < batch_size; ++i, ++current_row_) {
-    if (current_row_ == hdf_blobs_[0]->shape(0)) {
-      if (num_files_ > 1) {
-        current_file_ += 1;
-        if (current_file_ == num_files_) {
-          current_file_ = 0;
-          if (this->layer_param_.hdf5_data_param().shuffle()) {
-            std::random_shuffle(file_permutation_.begin(),
-                                file_permutation_.end());
-          }
-          DLOG(INFO) << "Looping around to first file.";
-        }
-        LoadHDF5FileData(
-            hdf_filenames_[file_permutation_[current_file_]].c_str());
-      }
-      current_row_ = 0;
-      if (this->layer_param_.hdf5_data_param().shuffle())
-        std::random_shuffle(data_permutation_.begin(), data_permutation_.end());
+  for (int i = 0; i < batch_size; ++i) {
+    while (Skip()) {
+      Next();
     }
     for (int j = 0; j < this->layer_param_.top_size(); ++j) {
       int data_dim = top[j]->count() / top[j]->shape(0);
@@ -42,6 +27,7 @@ void HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
           &hdf_blobs_[j]->cpu_data()[data_permutation_[current_row_]
             * data_dim], &top[j]->mutable_gpu_data()[i * data_dim]);
     }
+    Next();
   }
 }
 
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 62fda4ac..ec0fc5b0 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -37,18 +37,28 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   const string& source = this->layer_param_.image_data_param().source();
   LOG(INFO) << "Opening file " << source;
   std::ifstream infile(source.c_str());
-  string filename;
+  string line;
+  size_t pos;
   int label;
-  while (infile >> filename >> label) {
-    lines_.push_back(std::make_pair(filename, label));
+  while (std::getline(infile, line)) {
+    pos = line.find_last_of(' ');
+    label = atoi(line.substr(pos + 1).c_str());
+    lines_.push_back(std::make_pair(line.substr(0, pos), label));
   }
 
+  CHECK(!lines_.empty()) << "File is empty";
+
   if (this->layer_param_.image_data_param().shuffle()) {
     // randomly shuffle data
     LOG(INFO) << "Shuffling data";
     const unsigned int prefetch_rng_seed = caffe_rng_rand();
     prefetch_rng_.reset(new Caffe::RNG(prefetch_rng_seed));
     ShuffleImages();
+  } else {
+    if (this->phase_ == TRAIN && Caffe::solver_rank() > 0 &&
+        this->layer_param_.image_data_param().rand_skip() == 0) {
+      LOG(WARNING) << "Shuffling or skipping recommended for multi-GPU";
+    }
   }
   LOG(INFO) << "A total of " << lines_.size() << " images.";
 
@@ -72,8 +82,8 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   const int batch_size = this->layer_param_.image_data_param().batch_size();
   CHECK_GT(batch_size, 0) << "Positive batch size required";
   top_shape[0] = batch_size;
-  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
-    this->prefetch_[i].data_.Reshape(top_shape);
+  for (int i = 0; i < this->prefetch_.size(); ++i) {
+    this->prefetch_[i]->data_.Reshape(top_shape);
   }
   top[0]->Reshape(top_shape);
 
@@ -83,8 +93,8 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // label
   vector<int> label_shape(1, batch_size);
   top[1]->Reshape(label_shape);
-  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
-    this->prefetch_[i].label_.Reshape(label_shape);
+  for (int i = 0; i < this->prefetch_.size(); ++i) {
+    this->prefetch_[i]->label_.Reshape(label_shape);
   }
 }
 
diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp
index 624d3118..3c3f460e 100644
--- a/src/caffe/layers/infogain_loss_layer.cpp
+++ b/src/caffe/layers/infogain_loss_layer.cpp
@@ -3,7 +3,8 @@
 #include <vector>
 
 #include "caffe/layers/infogain_loss_layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/util/io.hpp"  // for bolb reading of matrix H
+#include "caffe/util/math_functions.hpp"
 
 namespace caffe {
 
@@ -11,6 +12,31 @@ template <typename Dtype>
 void InfogainLossLayer<Dtype>::LayerSetUp(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::LayerSetUp(bottom, top);
+  // internal softmax layer
+  LayerParameter softmax_layer_param(this->layer_param_);
+  SoftmaxParameter* softmax_param = softmax_layer_param.mutable_softmax_param();
+  softmax_param->set_axis(this->layer_param_.infogain_loss_param().axis());
+  softmax_layer_param.set_type("Softmax");
+  softmax_layer_param.clear_loss_weight();
+  softmax_layer_param.add_loss_weight(1);
+  softmax_layer_ = LayerRegistry<Dtype>::CreateLayer(softmax_layer_param);
+  softmax_bottom_vec_.clear();
+  softmax_bottom_vec_.push_back(bottom[0]);
+  softmax_top_vec_.clear();
+  softmax_top_vec_.push_back(&prob_);
+  softmax_layer_->SetUp(softmax_bottom_vec_, softmax_top_vec_);
+
+  // ignore label
+  has_ignore_label_ =
+    this->layer_param_.loss_param().has_ignore_label();
+  if (has_ignore_label_) {
+    ignore_label_ = this->layer_param_.loss_param().ignore_label();
+  }
+  // normalization
+  CHECK(!this->layer_param_.loss_param().has_normalize())
+    << "normalize is deprecated. use \"normalization\"";
+  normalization_ = this->layer_param_.loss_param().normalization();
+  // matrix H
   if (bottom.size() < 3) {
     CHECK(this->layer_param_.infogain_loss_param().has_source())
         << "Infogain matrix source must be specified.";
@@ -25,28 +51,86 @@ template <typename Dtype>
 void InfogainLossLayer<Dtype>::Reshape(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::Reshape(bottom, top);
+  softmax_layer_->Reshape(softmax_bottom_vec_, softmax_top_vec_);
+  infogain_axis_ =
+    bottom[0]->CanonicalAxisIndex(
+      this->layer_param_.infogain_loss_param().axis());
+  outer_num_ = bottom[0]->count(0, infogain_axis_);
+  inner_num_ = bottom[0]->count(infogain_axis_ + 1);
+  CHECK_EQ(outer_num_ * inner_num_, bottom[1]->count())
+      << "Number of labels must match number of predictions; "
+      << "e.g., if infogain axis == 1 and prediction shape is (N, C, H, W), "
+      << "label count (number of labels) must be N*H*W, "
+      << "with integer values in {0, 1, ..., C-1}.";
+  num_labels_ = bottom[0]->shape(infogain_axis_);
   Blob<Dtype>* infogain = NULL;
   if (bottom.size() < 3) {
     infogain = &infogain_;
   } else {
     infogain = bottom[2];
   }
-  CHECK_EQ(bottom[1]->channels(), 1);
-  CHECK_EQ(bottom[1]->height(), 1);
-  CHECK_EQ(bottom[1]->width(), 1);
-  const int num = bottom[0]->num();
-  const int dim = bottom[0]->count() / num;
-  CHECK_EQ(infogain->num(), 1);
-  CHECK_EQ(infogain->channels(), 1);
-  CHECK_EQ(infogain->height(), dim);
-  CHECK_EQ(infogain->width(), dim);
+  CHECK_EQ(infogain->count(), num_labels_*num_labels_);
+  sum_rows_H_.Reshape(vector<int>(1, num_labels_));
+  if (bottom.size() == 2) {
+    // H is provided as a parameter and will not change. sum rows once
+    sum_rows_of_H(infogain);
+  }
+  if (top.size() >= 2) {
+    // softmax output
+    top[1]->ReshapeLike(*bottom[0]);
+  }
+}
+
+template <typename Dtype>
+Dtype InfogainLossLayer<Dtype>::get_normalizer(
+    LossParameter_NormalizationMode normalization_mode, int valid_count) {
+  Dtype normalizer;
+  switch (normalization_mode) {
+    case LossParameter_NormalizationMode_FULL:
+      normalizer = Dtype(outer_num_ * inner_num_);
+      break;
+    case LossParameter_NormalizationMode_VALID:
+      if (valid_count == -1) {
+        normalizer = Dtype(outer_num_ * inner_num_);
+      } else {
+        normalizer = Dtype(valid_count);
+      }
+      break;
+    case LossParameter_NormalizationMode_BATCH_SIZE:
+      normalizer = Dtype(outer_num_);
+      break;
+    case LossParameter_NormalizationMode_NONE:
+      normalizer = Dtype(1);
+      break;
+    default:
+      LOG(FATAL) << "Unknown normalization mode: "
+          << LossParameter_NormalizationMode_Name(normalization_mode);
+  }
+  // Some users will have no labels for some examples in order to 'turn off' a
+  // particular loss in a multi-task setup. The max prevents NaNs in that case.
+  return std::max(Dtype(1.0), normalizer);
 }
 
+template <typename Dtype>
+void InfogainLossLayer<Dtype>::sum_rows_of_H(const Blob<Dtype>* H) {
+  CHECK_EQ(H->count(), num_labels_*num_labels_)
+    << "H must be " << num_labels_ << "x" << num_labels_;
+  const Dtype* infogain_mat = H->cpu_data();
+  Dtype* sum = sum_rows_H_.mutable_cpu_data();
+  for ( int row = 0; row < num_labels_ ; row++ ) {
+    sum[row] = 0;
+    for ( int col = 0; col < num_labels_ ; col++ ) {
+      sum[row] += infogain_mat[row*num_labels_+col];
+    }
+  }
+}
 
 template <typename Dtype>
 void InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
-  const Dtype* bottom_data = bottom[0]->cpu_data();
+  // The forward pass computes the softmax prob values.
+  softmax_layer_->Forward(softmax_bottom_vec_, softmax_top_vec_);
+  const Dtype* prob_data = prob_.cpu_data();
   const Dtype* bottom_label = bottom[1]->cpu_data();
   const Dtype* infogain_mat = NULL;
   if (bottom.size() < 3) {
@@ -54,17 +138,30 @@ void InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   } else {
     infogain_mat = bottom[2]->cpu_data();
   }
-  int num = bottom[0]->num();
-  int dim = bottom[0]->count() / bottom[0]->num();
+  int count = 0;
   Dtype loss = 0;
-  for (int i = 0; i < num; ++i) {
-    int label = static_cast<int>(bottom_label[i]);
-    for (int j = 0; j < dim; ++j) {
-      Dtype prob = std::max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
-      loss -= infogain_mat[label * dim + j] * log(prob);
+  for (int i = 0; i < outer_num_; ++i) {
+    for (int j = 0; j < inner_num_; j++) {
+      const int label_value =
+        static_cast<int>(bottom_label[i * inner_num_ + j]);
+      if (has_ignore_label_ && label_value == ignore_label_) {
+        continue;
+      }
+      DCHECK_GE(label_value, 0);
+      DCHECK_LT(label_value, num_labels_);
+      for (int l = 0; l < num_labels_; l++) {
+        loss -= infogain_mat[label_value * num_labels_ + l] *
+          log(std::max(
+                prob_data[i * inner_num_*num_labels_ + l * inner_num_ + j],
+                Dtype(kLOG_THRESHOLD)));
+      }
+      ++count;
     }
   }
-  top[0]->mutable_cpu_data()[0] = loss / num;
+  top[0]->mutable_cpu_data()[0] = loss / get_normalizer(normalization_, count);
+  if (top.size() == 2) {
+    top[1]->ShareData(prob_);
+  }
 }
 
 template <typename Dtype>
@@ -80,25 +177,44 @@ void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
                << " Layer cannot backpropagate to infogain inputs.";
   }
   if (propagate_down[0]) {
-    const Dtype* bottom_data = bottom[0]->cpu_data();
+    const Dtype* prob_data = prob_.cpu_data();
     const Dtype* bottom_label = bottom[1]->cpu_data();
     const Dtype* infogain_mat = NULL;
     if (bottom.size() < 3) {
       infogain_mat = infogain_.cpu_data();
     } else {
       infogain_mat = bottom[2]->cpu_data();
+      // H is provided as a "bottom" and might change. sum rows every time.
+      sum_rows_of_H(bottom[2]);
     }
+    const Dtype* sum_rows_H = sum_rows_H_.cpu_data();
     Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
-    int num = bottom[0]->num();
-    int dim = bottom[0]->count() / bottom[0]->num();
-    const Dtype scale = - top[0]->cpu_diff()[0] / num;
-    for (int i = 0; i < num; ++i) {
-      const int label = static_cast<int>(bottom_label[i]);
-      for (int j = 0; j < dim; ++j) {
-        Dtype prob = std::max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
-        bottom_diff[i * dim + j] = scale * infogain_mat[label * dim + j] / prob;
+    const int dim = bottom[0]->count() / outer_num_;
+    int count = 0;
+    for (int i = 0; i < outer_num_; ++i) {
+      for (int j = 0; j < inner_num_; ++j) {
+        const int label_value =
+          static_cast<int>(bottom_label[i * inner_num_ + j]);
+        DCHECK_GE(label_value, 0);
+        DCHECK_LT(label_value, num_labels_);
+        if (has_ignore_label_ && label_value == ignore_label_) {
+          for (int l = 0; l < num_labels_; ++l) {
+            bottom_diff[i * dim + l * inner_num_ + j] = 0;
+          }
+        } else {
+          for (int l = 0; l < num_labels_; ++l) {
+            bottom_diff[i * dim + l * inner_num_ + j] =
+               prob_data[i*dim + l*inner_num_ + j]*sum_rows_H[label_value]
+               - infogain_mat[label_value * num_labels_ + l];
+          }
+          ++count;
+        }
       }
     }
+    // Scale gradient
+    Dtype loss_weight = top[0]->cpu_diff()[0] /
+                        get_normalizer(normalization_, count);
+    caffe_scal(bottom[0]->count(), loss_weight, bottom_diff);
   }
 }
 
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index d9088805..e65349f0 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -11,6 +11,7 @@ void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const int num_output = this->layer_param_.inner_product_param().num_output();
   bias_term_ = this->layer_param_.inner_product_param().bias_term();
+  transpose_ = this->layer_param_.inner_product_param().transpose();
   N_ = num_output;
   const int axis = bottom[0]->CanonicalAxisIndex(
       this->layer_param_.inner_product_param().axis());
@@ -27,10 +28,15 @@ void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     } else {
       this->blobs_.resize(1);
     }
-    // Intialize the weight
+    // Initialize the weights
     vector<int> weight_shape(2);
-    weight_shape[0] = N_;
-    weight_shape[1] = K_;
+    if (transpose_) {
+      weight_shape[0] = K_;
+      weight_shape[1] = N_;
+    } else {
+      weight_shape[0] = N_;
+      weight_shape[1] = K_;
+    }
     this->blobs_[0].reset(new Blob<Dtype>(weight_shape));
     // fill the weights
     shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
@@ -80,7 +86,8 @@ void InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = top[0]->mutable_cpu_data();
   const Dtype* weight = this->blobs_[0]->cpu_data();
-  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1.,
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, transpose_ ? CblasNoTrans : CblasTrans,
+      M_, N_, K_, (Dtype)1.,
       bottom_data, weight, (Dtype)0., top_data);
   if (bias_term_) {
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
@@ -97,8 +104,17 @@ void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = top[0]->cpu_diff();
     const Dtype* bottom_data = bottom[0]->cpu_data();
     // Gradient with respect to weight
-    caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
-        top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_cpu_diff());
+    if (transpose_) {
+      caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
+          K_, N_, M_,
+          (Dtype)1., bottom_data, top_diff,
+          (Dtype)1., this->blobs_[0]->mutable_cpu_diff());
+    } else {
+      caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
+          N_, K_, M_,
+          (Dtype)1., top_diff, bottom_data,
+          (Dtype)1., this->blobs_[0]->mutable_cpu_diff());
+    }
   }
   if (bias_term_ && this->param_propagate_down_[1]) {
     const Dtype* top_diff = top[0]->cpu_diff();
@@ -110,9 +126,17 @@ void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->cpu_diff();
     // Gradient with respect to bottom data
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
-        top_diff, this->blobs_[0]->cpu_data(), (Dtype)0.,
-        bottom[0]->mutable_cpu_diff());
+    if (transpose_) {
+      caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans,
+          M_, K_, N_,
+          (Dtype)1., top_diff, this->blobs_[0]->cpu_data(),
+          (Dtype)0., bottom[0]->mutable_cpu_diff());
+    } else {
+      caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
+          M_, K_, N_,
+          (Dtype)1., top_diff, this->blobs_[0]->cpu_data(),
+          (Dtype)0., bottom[0]->mutable_cpu_diff());
+    }
   }
 }
 
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index dc25aa33..a58b56e3 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -19,7 +19,9 @@ void InnerProductLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       caffe_gpu_axpy<Dtype>(N_, bias_multiplier_.cpu_data()[0],
                             this->blobs_[1]->gpu_data(), top_data);
   } else {
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1.,
+    caffe_gpu_gemm<Dtype>(CblasNoTrans,
+                          transpose_ ? CblasNoTrans : CblasTrans,
+                          M_, N_, K_, (Dtype)1.,
                           bottom_data, weight, (Dtype)0., top_data);
     if (bias_term_)
       caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
@@ -36,8 +38,17 @@ void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = top[0]->gpu_diff();
     const Dtype* bottom_data = bottom[0]->gpu_data();
     // Gradient with respect to weight
-    caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
-        top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_gpu_diff());
+    if (transpose_) {
+      caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
+          K_, N_, M_,
+          (Dtype)1., bottom_data, top_diff,
+          (Dtype)1., this->blobs_[0]->mutable_gpu_diff());
+    } else {
+      caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
+          N_, K_, M_,
+          (Dtype)1., top_diff, bottom_data,
+          (Dtype)1., this->blobs_[0]->mutable_gpu_diff());
+    }
   }
   if (bias_term_ && this->param_propagate_down_[1]) {
     const Dtype* top_diff = top[0]->gpu_diff();
@@ -49,9 +60,17 @@ void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->gpu_diff();
     // Gradient with respect to bottom data
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
-        top_diff, this->blobs_[0]->gpu_data(), (Dtype)0.,
-        bottom[0]->mutable_gpu_diff());
+    if (transpose_) {
+      caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans,
+          M_, K_, N_,
+          (Dtype)1., top_diff, this->blobs_[0]->gpu_data(),
+          (Dtype)0., bottom[0]->mutable_gpu_diff());
+    } else {
+      caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
+          M_, K_, N_,
+         (Dtype)1., top_diff, this->blobs_[0]->gpu_data(),
+         (Dtype)0., bottom[0]->mutable_gpu_diff());
+    }
   }
 }
 
diff --git a/src/caffe/layers/input_layer.cpp b/src/caffe/layers/input_layer.cpp
new file mode 100644
index 00000000..667d8ad6
--- /dev/null
+++ b/src/caffe/layers/input_layer.cpp
@@ -0,0 +1,27 @@
+#include <vector>
+
+#include "caffe/layers/input_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void InputLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const int num_top = top.size();
+  const InputParameter& param = this->layer_param_.input_param();
+  const int num_shape = param.shape_size();
+  CHECK(num_shape == 0 || num_shape == 1 || num_shape == num_top)
+      << "Must specify 'shape' once, once per top blob, or not at all: "
+      << num_top << " tops vs. " << num_shape << " shapes.";
+  if (num_shape > 0) {
+    for (int i = 0; i < num_top; ++i) {
+      const int shape_index = (param.shape_size() == 1) ? 0 : i;
+      top[i]->Reshape(param.shape(shape_index));
+    }
+  }
+}
+
+INSTANTIATE_CLASS(InputLayer);
+REGISTER_LAYER_CLASS(Input);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index c0b7a862..afb1ce94 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -16,8 +16,8 @@ void LossLayer<Dtype>::LayerSetUp(
 template <typename Dtype>
 void LossLayer<Dtype>::Reshape(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
-  CHECK_EQ(bottom[0]->num(), bottom[1]->num())
-      << "The data and label should have the same number.";
+  CHECK_EQ(bottom[0]->shape(0), bottom[1]->shape(0))
+      << "The data and label should have the same first dimension.";
   vector<int> loss_shape(0);  // Loss layers output a scalar; 0 axes.
   top[0]->Reshape(loss_shape);
 }
diff --git a/src/caffe/layers/lstm_layer.cpp b/src/caffe/layers/lstm_layer.cpp
new file mode 100644
index 00000000..da48dba4
--- /dev/null
+++ b/src/caffe/layers/lstm_layer.cpp
@@ -0,0 +1,244 @@
+#include <string>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/lstm_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void LSTMLayer<Dtype>::RecurrentInputBlobNames(vector<string>* names) const {
+  names->resize(2);
+  (*names)[0] = "h_0";
+  (*names)[1] = "c_0";
+}
+
+template <typename Dtype>
+void LSTMLayer<Dtype>::RecurrentOutputBlobNames(vector<string>* names) const {
+  names->resize(2);
+  (*names)[0] = "h_" + format_int(this->T_);
+  (*names)[1] = "c_T";
+}
+
+template <typename Dtype>
+void LSTMLayer<Dtype>::RecurrentInputShapes(vector<BlobShape>* shapes) const {
+  const int num_output = this->layer_param_.recurrent_param().num_output();
+  const int num_blobs = 2;
+  shapes->resize(num_blobs);
+  for (int i = 0; i < num_blobs; ++i) {
+    (*shapes)[i].Clear();
+    (*shapes)[i].add_dim(1);  // a single timestep
+    (*shapes)[i].add_dim(this->N_);
+    (*shapes)[i].add_dim(num_output);
+  }
+}
+
+template <typename Dtype>
+void LSTMLayer<Dtype>::OutputBlobNames(vector<string>* names) const {
+  names->resize(1);
+  (*names)[0] = "h";
+}
+
+template <typename Dtype>
+void LSTMLayer<Dtype>::FillUnrolledNet(NetParameter* net_param) const {
+  const int num_output = this->layer_param_.recurrent_param().num_output();
+  CHECK_GT(num_output, 0) << "num_output must be positive";
+  const FillerParameter& weight_filler =
+      this->layer_param_.recurrent_param().weight_filler();
+  const FillerParameter& bias_filler =
+      this->layer_param_.recurrent_param().bias_filler();
+
+  // Add generic LayerParameter's (without bottoms/tops) of layer types we'll
+  // use to save redundant code.
+  LayerParameter hidden_param;
+  hidden_param.set_type("InnerProduct");
+  hidden_param.mutable_inner_product_param()->set_num_output(num_output * 4);
+  hidden_param.mutable_inner_product_param()->set_bias_term(false);
+  hidden_param.mutable_inner_product_param()->set_axis(2);
+  hidden_param.mutable_inner_product_param()->
+      mutable_weight_filler()->CopyFrom(weight_filler);
+
+  LayerParameter biased_hidden_param(hidden_param);
+  biased_hidden_param.mutable_inner_product_param()->set_bias_term(true);
+  biased_hidden_param.mutable_inner_product_param()->
+      mutable_bias_filler()->CopyFrom(bias_filler);
+
+  LayerParameter sum_param;
+  sum_param.set_type("Eltwise");
+  sum_param.mutable_eltwise_param()->set_operation(
+      EltwiseParameter_EltwiseOp_SUM);
+
+  LayerParameter scale_param;
+  scale_param.set_type("Scale");
+  scale_param.mutable_scale_param()->set_axis(0);
+
+  LayerParameter slice_param;
+  slice_param.set_type("Slice");
+  slice_param.mutable_slice_param()->set_axis(0);
+
+  LayerParameter split_param;
+  split_param.set_type("Split");
+
+  vector<BlobShape> input_shapes;
+  RecurrentInputShapes(&input_shapes);
+  CHECK_EQ(2, input_shapes.size());
+
+  LayerParameter* input_layer_param = net_param->add_layer();
+  input_layer_param->set_type("Input");
+  InputParameter* input_param = input_layer_param->mutable_input_param();
+
+  input_layer_param->add_top("c_0");
+  input_param->add_shape()->CopyFrom(input_shapes[0]);
+
+  input_layer_param->add_top("h_0");
+  input_param->add_shape()->CopyFrom(input_shapes[1]);
+
+  LayerParameter* cont_slice_param = net_param->add_layer();
+  cont_slice_param->CopyFrom(slice_param);
+  cont_slice_param->set_name("cont_slice");
+  cont_slice_param->add_bottom("cont");
+  cont_slice_param->mutable_slice_param()->set_axis(0);
+
+  // Add layer to transform all timesteps of x to the hidden state dimension.
+  //     W_xc_x = W_xc * x + b_c
+  {
+    LayerParameter* x_transform_param = net_param->add_layer();
+    x_transform_param->CopyFrom(biased_hidden_param);
+    x_transform_param->set_name("x_transform");
+    x_transform_param->add_param()->set_name("W_xc");
+    x_transform_param->add_param()->set_name("b_c");
+    x_transform_param->add_bottom("x");
+    x_transform_param->add_top("W_xc_x");
+    x_transform_param->add_propagate_down(true);
+  }
+
+  if (this->static_input_) {
+    // Add layer to transform x_static to the gate dimension.
+    //     W_xc_x_static = W_xc_static * x_static
+    LayerParameter* x_static_transform_param = net_param->add_layer();
+    x_static_transform_param->CopyFrom(hidden_param);
+    x_static_transform_param->mutable_inner_product_param()->set_axis(1);
+    x_static_transform_param->set_name("W_xc_x_static");
+    x_static_transform_param->add_param()->set_name("W_xc_static");
+    x_static_transform_param->add_bottom("x_static");
+    x_static_transform_param->add_top("W_xc_x_static_preshape");
+    x_static_transform_param->add_propagate_down(true);
+
+    LayerParameter* reshape_param = net_param->add_layer();
+    reshape_param->set_type("Reshape");
+    BlobShape* new_shape =
+         reshape_param->mutable_reshape_param()->mutable_shape();
+    new_shape->add_dim(1);  // One timestep.
+    // Should infer this->N as the dimension so we can reshape on batch size.
+    new_shape->add_dim(-1);
+    new_shape->add_dim(
+        x_static_transform_param->inner_product_param().num_output());
+    reshape_param->set_name("W_xc_x_static_reshape");
+    reshape_param->add_bottom("W_xc_x_static_preshape");
+    reshape_param->add_top("W_xc_x_static");
+  }
+
+  LayerParameter* x_slice_param = net_param->add_layer();
+  x_slice_param->CopyFrom(slice_param);
+  x_slice_param->add_bottom("W_xc_x");
+  x_slice_param->set_name("W_xc_x_slice");
+
+  LayerParameter output_concat_layer;
+  output_concat_layer.set_name("h_concat");
+  output_concat_layer.set_type("Concat");
+  output_concat_layer.add_top("h");
+  output_concat_layer.mutable_concat_param()->set_axis(0);
+
+  for (int t = 1; t <= this->T_; ++t) {
+    string tm1s = format_int(t - 1);
+    string ts = format_int(t);
+
+    cont_slice_param->add_top("cont_" + ts);
+    x_slice_param->add_top("W_xc_x_" + ts);
+
+    // Add layers to flush the hidden state when beginning a new
+    // sequence, as indicated by cont_t.
+    //     h_conted_{t-1} := cont_t * h_{t-1}
+    //
+    // Normally, cont_t is binary (i.e., 0 or 1), so:
+    //     h_conted_{t-1} := h_{t-1} if cont_t == 1
+    //                       0   otherwise
+    {
+      LayerParameter* cont_h_param = net_param->add_layer();
+      cont_h_param->CopyFrom(scale_param);
+      cont_h_param->set_name("h_conted_" + tm1s);
+      cont_h_param->add_bottom("h_" + tm1s);
+      cont_h_param->add_bottom("cont_" + ts);
+      cont_h_param->add_top("h_conted_" + tm1s);
+    }
+
+    // Add layer to compute
+    //     W_hc_h_{t-1} := W_hc * h_conted_{t-1}
+    {
+      LayerParameter* w_param = net_param->add_layer();
+      w_param->CopyFrom(hidden_param);
+      w_param->set_name("transform_" + ts);
+      w_param->add_param()->set_name("W_hc");
+      w_param->add_bottom("h_conted_" + tm1s);
+      w_param->add_top("W_hc_h_" + tm1s);
+      w_param->mutable_inner_product_param()->set_axis(2);
+    }
+
+    // Add the outputs of the linear transformations to compute the gate input.
+    //     gate_input_t := W_hc * h_conted_{t-1} + W_xc * x_t + b_c
+    //                   = W_hc_h_{t-1} + W_xc_x_t + b_c
+    {
+      LayerParameter* input_sum_layer = net_param->add_layer();
+      input_sum_layer->CopyFrom(sum_param);
+      input_sum_layer->set_name("gate_input_" + ts);
+      input_sum_layer->add_bottom("W_hc_h_" + tm1s);
+      input_sum_layer->add_bottom("W_xc_x_" + ts);
+      if (this->static_input_) {
+        input_sum_layer->add_bottom("W_xc_x_static");
+      }
+      input_sum_layer->add_top("gate_input_" + ts);
+    }
+
+    // Add LSTMUnit layer to compute the cell & hidden vectors c_t and h_t.
+    // Inputs: c_{t-1}, gate_input_t = (i_t, f_t, o_t, g_t), cont_t
+    // Outputs: c_t, h_t
+    //     [ i_t' ]
+    //     [ f_t' ] := gate_input_t
+    //     [ o_t' ]
+    //     [ g_t' ]
+    //         i_t := \sigmoid[i_t']
+    //         f_t := \sigmoid[f_t']
+    //         o_t := \sigmoid[o_t']
+    //         g_t := \tanh[g_t']
+    //         c_t := cont_t * (f_t .* c_{t-1}) + (i_t .* g_t)
+    //         h_t := o_t .* \tanh[c_t]
+    {
+      LayerParameter* lstm_unit_param = net_param->add_layer();
+      lstm_unit_param->set_type("LSTMUnit");
+      lstm_unit_param->add_bottom("c_" + tm1s);
+      lstm_unit_param->add_bottom("gate_input_" + ts);
+      lstm_unit_param->add_bottom("cont_" + ts);
+      lstm_unit_param->add_top("c_" + ts);
+      lstm_unit_param->add_top("h_" + ts);
+      lstm_unit_param->set_name("unit_" + ts);
+    }
+    output_concat_layer.add_bottom("h_" + ts);
+  }  // for (int t = 1; t <= this->T_; ++t)
+
+  {
+    LayerParameter* c_T_copy_param = net_param->add_layer();
+    c_T_copy_param->CopyFrom(split_param);
+    c_T_copy_param->add_bottom("c_" + format_int(this->T_));
+    c_T_copy_param->add_top("c_T");
+  }
+  net_param->add_layer()->CopyFrom(output_concat_layer);
+}
+
+INSTANTIATE_CLASS(LSTMLayer);
+REGISTER_LAYER_CLASS(LSTM);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/lstm_unit_layer.cpp b/src/caffe/layers/lstm_unit_layer.cpp
new file mode 100644
index 00000000..d1ab59c4
--- /dev/null
+++ b/src/caffe/layers/lstm_unit_layer.cpp
@@ -0,0 +1,130 @@
+#include <algorithm>
+#include <cmath>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/layers/lstm_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+inline Dtype sigmoid(Dtype x) {
+  return 1. / (1. + exp(-x));
+}
+
+template <typename Dtype>
+inline Dtype tanh(Dtype x) {
+  return 2. * sigmoid(2. * x) - 1.;
+}
+
+template <typename Dtype>
+void LSTMUnitLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int num_instances = bottom[0]->shape(1);
+  for (int i = 0; i < bottom.size(); ++i) {
+    if (i == 2) {
+      CHECK_EQ(2, bottom[i]->num_axes());
+    } else {
+      CHECK_EQ(3, bottom[i]->num_axes());
+    }
+    CHECK_EQ(1, bottom[i]->shape(0));
+    CHECK_EQ(num_instances, bottom[i]->shape(1));
+  }
+  hidden_dim_ = bottom[0]->shape(2);
+  CHECK_EQ(4 * hidden_dim_, bottom[1]->shape(2));
+  top[0]->ReshapeLike(*bottom[0]);
+  top[1]->ReshapeLike(*bottom[0]);
+  X_acts_.ReshapeLike(*bottom[1]);
+}
+
+template <typename Dtype>
+void LSTMUnitLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int num = bottom[0]->shape(1);
+  const int x_dim = hidden_dim_ * 4;
+  const Dtype* C_prev = bottom[0]->cpu_data();
+  const Dtype* X = bottom[1]->cpu_data();
+  const Dtype* cont = bottom[2]->cpu_data();
+  Dtype* C = top[0]->mutable_cpu_data();
+  Dtype* H = top[1]->mutable_cpu_data();
+  for (int n = 0; n < num; ++n) {
+    for (int d = 0; d < hidden_dim_; ++d) {
+      const Dtype i = sigmoid(X[d]);
+      const Dtype f = (*cont == 0) ? 0 :
+          (*cont * sigmoid(X[1 * hidden_dim_ + d]));
+      const Dtype o = sigmoid(X[2 * hidden_dim_ + d]);
+      const Dtype g = tanh(X[3 * hidden_dim_ + d]);
+      const Dtype c_prev = C_prev[d];
+      const Dtype c = f * c_prev + i * g;
+      C[d] = c;
+      const Dtype tanh_c = tanh(c);
+      H[d] = o * tanh_c;
+    }
+    C_prev += hidden_dim_;
+    X += x_dim;
+    C += hidden_dim_;
+    H += hidden_dim_;
+    ++cont;
+  }
+}
+
+template <typename Dtype>
+void LSTMUnitLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  CHECK(!propagate_down[2]) << "Cannot backpropagate to sequence indicators.";
+  if (!propagate_down[0] && !propagate_down[1]) { return; }
+
+  const int num = bottom[0]->shape(1);
+  const int x_dim = hidden_dim_ * 4;
+  const Dtype* C_prev = bottom[0]->cpu_data();
+  const Dtype* X = bottom[1]->cpu_data();
+  const Dtype* cont = bottom[2]->cpu_data();
+  const Dtype* C = top[0]->cpu_data();
+  const Dtype* H = top[1]->cpu_data();
+  const Dtype* C_diff = top[0]->cpu_diff();
+  const Dtype* H_diff = top[1]->cpu_diff();
+  Dtype* C_prev_diff = bottom[0]->mutable_cpu_diff();
+  Dtype* X_diff = bottom[1]->mutable_cpu_diff();
+  for (int n = 0; n < num; ++n) {
+    for (int d = 0; d < hidden_dim_; ++d) {
+      const Dtype i = sigmoid(X[d]);
+      const Dtype f = (*cont == 0) ? 0 :
+          (*cont * sigmoid(X[1 * hidden_dim_ + d]));
+      const Dtype o = sigmoid(X[2 * hidden_dim_ + d]);
+      const Dtype g = tanh(X[3 * hidden_dim_ + d]);
+      const Dtype c_prev = C_prev[d];
+      const Dtype c = C[d];
+      const Dtype tanh_c = tanh(c);
+      Dtype* c_prev_diff = C_prev_diff + d;
+      Dtype* i_diff = X_diff + d;
+      Dtype* f_diff = X_diff + 1 * hidden_dim_ + d;
+      Dtype* o_diff = X_diff + 2 * hidden_dim_ + d;
+      Dtype* g_diff = X_diff + 3 * hidden_dim_ + d;
+      const Dtype c_term_diff =
+          C_diff[d] + H_diff[d] * o * (1 - tanh_c * tanh_c);
+      *c_prev_diff = c_term_diff * f;
+      *i_diff = c_term_diff * g * i * (1 - i);
+      *f_diff = c_term_diff * c_prev * f * (1 - f);
+      *o_diff = H_diff[d] * tanh_c * o * (1 - o);
+      *g_diff = c_term_diff * i * (1 - g * g);
+    }
+    C_prev += hidden_dim_;
+    X += x_dim;
+    C += hidden_dim_;
+    H += hidden_dim_;
+    C_diff += hidden_dim_;
+    H_diff += hidden_dim_;
+    X_diff += x_dim;
+    C_prev_diff += hidden_dim_;
+    ++cont;
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(LSTMUnitLayer);
+#endif
+
+INSTANTIATE_CLASS(LSTMUnitLayer);
+REGISTER_LAYER_CLASS(LSTMUnit);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/lstm_unit_layer.cu b/src/caffe/layers/lstm_unit_layer.cu
new file mode 100644
index 00000000..15bb451d
--- /dev/null
+++ b/src/caffe/layers/lstm_unit_layer.cu
@@ -0,0 +1,154 @@
+#include <algorithm>
+#include <cmath>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/layers/lstm_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__device__ Dtype sigmoid(const Dtype x) {
+  return Dtype(1) / (Dtype(1) + exp(-x));
+}
+
+template <typename Dtype>
+__device__ Dtype tanh(const Dtype x) {
+  return Dtype(2) * sigmoid(Dtype(2) * x) - Dtype(1);
+}
+
+template <typename Dtype>
+__global__ void LSTMActsForward(const int nthreads, const int dim,
+                                const Dtype* X, Dtype* X_acts) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int x_dim = 4 * dim;
+    const int d = index % x_dim;
+    if (d < 3 * dim) {
+      X_acts[index] = sigmoid(X[index]);
+    } else {
+      X_acts[index] = tanh(X[index]);
+    }
+  }
+}
+
+template <typename Dtype>
+__global__ void LSTMUnitForward(const int nthreads, const int dim,
+    const Dtype* C_prev, const Dtype* X, const Dtype* cont,
+    Dtype* C, Dtype* H) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int n = index / dim;
+    const int d = index % dim;
+    const Dtype* X_offset = X + 4 * dim * n;
+    const Dtype i = X_offset[d];
+    const Dtype f = X_offset[1 * dim + d];
+    const Dtype o = X_offset[2 * dim + d];
+    const Dtype g = X_offset[3 * dim + d];
+    const Dtype c_prev = C_prev[index];
+    const Dtype c = cont[n] * f * c_prev + i * g;
+    C[index] = c;
+    const Dtype tanh_c = tanh(c);
+    H[index] = o * tanh_c;
+  }
+}
+
+template <typename Dtype>
+void LSTMUnitLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int count = top[1]->count();
+  const Dtype* C_prev = bottom[0]->gpu_data();
+  const Dtype* X = bottom[1]->gpu_data();
+  const Dtype* cont = bottom[2]->gpu_data();
+  Dtype* X_acts = X_acts_.mutable_gpu_data();
+  Dtype* C = top[0]->mutable_gpu_data();
+  Dtype* H = top[1]->mutable_gpu_data();
+  const int X_count = bottom[1]->count();
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  LSTMActsForward<Dtype><<<CAFFE_GET_BLOCKS(X_count), CAFFE_CUDA_NUM_THREADS>>>(
+      X_count, hidden_dim_, X, X_acts);
+  CUDA_POST_KERNEL_CHECK;
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  LSTMUnitForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, hidden_dim_, C_prev, X_acts, cont, C, H);
+  CUDA_POST_KERNEL_CHECK;
+}
+
+template <typename Dtype>
+__global__ void LSTMUnitBackward(const int nthreads, const int dim,
+    const Dtype* C_prev, const Dtype* X, const Dtype* C, const Dtype* H,
+    const Dtype* cont, const Dtype* C_diff, const Dtype* H_diff,
+    Dtype* C_prev_diff, Dtype* X_diff) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int n = index / dim;
+    const int d = index % dim;
+    const Dtype* X_offset = X + 4 * dim * n;
+    const Dtype i = X_offset[d];
+    const Dtype f = X_offset[1 * dim + d];
+    const Dtype o = X_offset[2 * dim + d];
+    const Dtype g = X_offset[3 * dim + d];
+    const Dtype c_prev = C_prev[index];
+    const Dtype c = C[index];
+    const Dtype tanh_c = tanh(c);
+    Dtype* c_prev_diff = C_prev_diff + index;
+    Dtype* X_diff_offset = X_diff + 4 * dim * n;
+    Dtype* i_diff = X_diff_offset + d;
+    Dtype* f_diff = X_diff_offset + 1 * dim + d;
+    Dtype* o_diff = X_diff_offset + 2 * dim + d;
+    Dtype* g_diff = X_diff_offset + 3 * dim + d;
+    const Dtype c_term_diff =
+        C_diff[index] + H_diff[index] * o * (1 - tanh_c * tanh_c);
+    const Dtype cont_n = cont[n];
+    *c_prev_diff = cont_n * c_term_diff * f;
+    *i_diff = c_term_diff * g;
+    *f_diff = cont_n * c_term_diff * c_prev;
+    *o_diff = H_diff[index] * tanh_c;
+    *g_diff = c_term_diff * i;
+  }
+}
+
+template <typename Dtype>
+__global__ void LSTMActsBackward(const int nthreads, const int dim,
+    const Dtype* X_acts, const Dtype* X_acts_diff, Dtype* X_diff) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int x_dim = 4 * dim;
+    const int d = index % x_dim;
+    const Dtype X_act = X_acts[index];
+    if (d < 3 * dim) {
+      X_diff[index] = X_acts_diff[index] * X_act * (Dtype(1) - X_act);
+    } else {
+      X_diff[index] = X_acts_diff[index] * (Dtype(1) - X_act * X_act);
+    }
+  }
+}
+
+template <typename Dtype>
+void LSTMUnitLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  CHECK(!propagate_down[2]) << "Cannot backpropagate to sequence indicators.";
+  if (!propagate_down[0] && !propagate_down[1]) { return; }
+
+  const int count = top[1]->count();
+  const Dtype* C_prev = bottom[0]->gpu_data();
+  const Dtype* X_acts = X_acts_.gpu_data();
+  const Dtype* cont = bottom[2]->gpu_data();
+  const Dtype* C = top[0]->gpu_data();
+  const Dtype* H = top[1]->gpu_data();
+  const Dtype* C_diff = top[0]->gpu_diff();
+  const Dtype* H_diff = top[1]->gpu_diff();
+  Dtype* C_prev_diff = bottom[0]->mutable_gpu_diff();
+  Dtype* X_acts_diff = X_acts_.mutable_gpu_diff();
+  LSTMUnitBackward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(count, hidden_dim_,
+      C_prev, X_acts, C, H, cont, C_diff, H_diff, C_prev_diff, X_acts_diff);
+  CUDA_POST_KERNEL_CHECK;
+  const int X_count = bottom[1]->count();
+  Dtype* X_diff = bottom[1]->mutable_gpu_diff();
+  LSTMActsBackward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(X_count), CAFFE_CUDA_NUM_THREADS>>>(
+      X_count, hidden_dim_, X_acts, X_acts_diff, X_diff);
+  CUDA_POST_KERNEL_CHECK;
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(LSTMUnitLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 82909874..975f4841 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -107,7 +107,7 @@ void MemoryDataLayer<Dtype>::set_batch_size(int new_size) {
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  CHECK(data_) << "MemoryDataLayer needs to be initalized by calling Reset";
+  CHECK(data_) << "MemoryDataLayer needs to be initialized by calling Reset";
   top[0]->Reshape(batch_size_, channels_, height_, width_);
   top[1]->Reshape(batch_size_, 1, 1, 1);
   top[0]->set_cpu_data(data_ + pos_ * size_);
diff --git a/src/caffe/layers/parameter_layer.cpp b/src/caffe/layers/parameter_layer.cpp
new file mode 100644
index 00000000..fbd326f8
--- /dev/null
+++ b/src/caffe/layers/parameter_layer.cpp
@@ -0,0 +1,8 @@
+#include "caffe/layers/parameter_layer.hpp"
+
+namespace caffe {
+
+INSTANTIATE_CLASS(ParameterLayer);
+REGISTER_LAYER_CLASS(Parameter);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 1ea46cc8..46eddb94 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -138,7 +138,7 @@ __global__ void StoPoolForwardTest(const int nthreads,
     const int wstart = pw * stride_w;
     const int wend = min(wstart + kernel_w, width);
     // We set cumsum to be 0 to avoid divide-by-zero problems
-    Dtype cumsum = FLT_MIN;
+    Dtype cumsum = 0.;
     Dtype cumvalues = 0.;
     const Dtype* const bottom_slice =
         bottom_data + (n * channels + c) * height * width;
@@ -149,7 +149,7 @@ __global__ void StoPoolForwardTest(const int nthreads,
         cumvalues += bottom_slice[h * width + w] * bottom_slice[h * width + w];
       }
     }
-    top_data[index] = cumvalues / cumsum;
+    top_data[index] = (cumsum > 0.) ? cumvalues / cumsum : 0.;
   }
 }
 
diff --git a/src/caffe/layers/recurrent_layer.cpp b/src/caffe/layers/recurrent_layer.cpp
new file mode 100644
index 00000000..e0c82773
--- /dev/null
+++ b/src/caffe/layers/recurrent_layer.cpp
@@ -0,0 +1,295 @@
+#include <string>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/recurrent_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void RecurrentLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  CHECK_GE(bottom[0]->num_axes(), 2)
+      << "bottom[0] must have at least 2 axes -- (#timesteps, #streams, ...)";
+  T_ = bottom[0]->shape(0);
+  N_ = bottom[0]->shape(1);
+  LOG(INFO) << "Initializing recurrent layer: assuming input batch contains "
+            << T_ << " timesteps of " << N_ << " independent streams.";
+
+  CHECK_EQ(bottom[1]->num_axes(), 2)
+      << "bottom[1] must have exactly 2 axes -- (#timesteps, #streams)";
+  CHECK_EQ(T_, bottom[1]->shape(0));
+  CHECK_EQ(N_, bottom[1]->shape(1));
+
+  // If expose_hidden is set, we take as input and produce as output
+  // the hidden state blobs at the first and last timesteps.
+  expose_hidden_ = this->layer_param_.recurrent_param().expose_hidden();
+
+  // Get (recurrent) input/output names.
+  vector<string> output_names;
+  OutputBlobNames(&output_names);
+  vector<string> recur_input_names;
+  RecurrentInputBlobNames(&recur_input_names);
+  vector<string> recur_output_names;
+  RecurrentOutputBlobNames(&recur_output_names);
+  const int num_recur_blobs = recur_input_names.size();
+  CHECK_EQ(num_recur_blobs, recur_output_names.size());
+
+  // If provided, bottom[2] is a static input to the recurrent net.
+  const int num_hidden_exposed = expose_hidden_ * num_recur_blobs;
+  static_input_ = (bottom.size() > 2 + num_hidden_exposed);
+  if (static_input_) {
+    CHECK_GE(bottom[2]->num_axes(), 1);
+    CHECK_EQ(N_, bottom[2]->shape(0));
+  }
+
+  // Create a NetParameter; setup the inputs that aren't unique to particular
+  // recurrent architectures.
+  NetParameter net_param;
+
+  LayerParameter* input_layer_param = net_param.add_layer();
+  input_layer_param->set_type("Input");
+  InputParameter* input_param = input_layer_param->mutable_input_param();
+  input_layer_param->add_top("x");
+  BlobShape input_shape;
+  for (int i = 0; i < bottom[0]->num_axes(); ++i) {
+    input_shape.add_dim(bottom[0]->shape(i));
+  }
+  input_param->add_shape()->CopyFrom(input_shape);
+
+  input_shape.Clear();
+  for (int i = 0; i < bottom[1]->num_axes(); ++i) {
+    input_shape.add_dim(bottom[1]->shape(i));
+  }
+  input_layer_param->add_top("cont");
+  input_param->add_shape()->CopyFrom(input_shape);
+
+  if (static_input_) {
+    input_shape.Clear();
+    for (int i = 0; i < bottom[2]->num_axes(); ++i) {
+      input_shape.add_dim(bottom[2]->shape(i));
+    }
+    input_layer_param->add_top("x_static");
+    input_param->add_shape()->CopyFrom(input_shape);
+  }
+
+  // Call the child's FillUnrolledNet implementation to specify the unrolled
+  // recurrent architecture.
+  this->FillUnrolledNet(&net_param);
+
+  // Prepend this layer's name to the names of each layer in the unrolled net.
+  const string& layer_name = this->layer_param_.name();
+  if (layer_name.size()) {
+    for (int i = 0; i < net_param.layer_size(); ++i) {
+      LayerParameter* layer = net_param.mutable_layer(i);
+      layer->set_name(layer_name + "_" + layer->name());
+    }
+  }
+
+  // Add "pseudo-losses" to all outputs to force backpropagation.
+  // (Setting force_backward is too aggressive as we may not need to backprop to
+  // all inputs, e.g., the sequence continuation indicators.)
+  vector<string> pseudo_losses(output_names.size());
+  for (int i = 0; i < output_names.size(); ++i) {
+    LayerParameter* layer = net_param.add_layer();
+    pseudo_losses[i] = output_names[i] + "_pseudoloss";
+    layer->set_name(pseudo_losses[i]);
+    layer->set_type("Reduction");
+    layer->add_bottom(output_names[i]);
+    layer->add_top(pseudo_losses[i]);
+    layer->add_loss_weight(1);
+  }
+
+  // Create the unrolled net.
+  unrolled_net_.reset(new Net<Dtype>(net_param));
+  unrolled_net_->set_debug_info(
+      this->layer_param_.recurrent_param().debug_info());
+
+  // Setup pointers to the inputs.
+  x_input_blob_ = CHECK_NOTNULL(unrolled_net_->blob_by_name("x").get());
+  cont_input_blob_ = CHECK_NOTNULL(unrolled_net_->blob_by_name("cont").get());
+  if (static_input_) {
+    x_static_input_blob_ =
+        CHECK_NOTNULL(unrolled_net_->blob_by_name("x_static").get());
+  }
+
+  // Setup pointers to paired recurrent inputs/outputs.
+  recur_input_blobs_.resize(num_recur_blobs);
+  recur_output_blobs_.resize(num_recur_blobs);
+  for (int i = 0; i < recur_input_names.size(); ++i) {
+    recur_input_blobs_[i] =
+        CHECK_NOTNULL(unrolled_net_->blob_by_name(recur_input_names[i]).get());
+    recur_output_blobs_[i] =
+        CHECK_NOTNULL(unrolled_net_->blob_by_name(recur_output_names[i]).get());
+  }
+
+  // Setup pointers to outputs.
+  CHECK_EQ(top.size() - num_hidden_exposed, output_names.size())
+      << "OutputBlobNames must provide an output blob name for each top.";
+  output_blobs_.resize(output_names.size());
+  for (int i = 0; i < output_names.size(); ++i) {
+    output_blobs_[i] =
+        CHECK_NOTNULL(unrolled_net_->blob_by_name(output_names[i]).get());
+  }
+
+  // We should have 2 inputs (x and cont), plus a number of recurrent inputs,
+  // plus maybe a static input.
+  CHECK_EQ(2 + num_recur_blobs + static_input_,
+           unrolled_net_->input_blobs().size());
+
+  // This layer's parameters are any parameters in the layers of the unrolled
+  // net. We only want one copy of each parameter, so check that the parameter
+  // is "owned" by the layer, rather than shared with another.
+  this->blobs_.clear();
+  for (int i = 0; i < unrolled_net_->params().size(); ++i) {
+    if (unrolled_net_->param_owners()[i] == -1) {
+      LOG(INFO) << "Adding parameter " << i << ": "
+                << unrolled_net_->param_display_names()[i];
+      this->blobs_.push_back(unrolled_net_->params()[i]);
+    }
+  }
+  // Check that param_propagate_down is set for all of the parameters in the
+  // unrolled net; set param_propagate_down to true in this layer.
+  for (int i = 0; i < unrolled_net_->layers().size(); ++i) {
+    for (int j = 0; j < unrolled_net_->layers()[i]->blobs().size(); ++j) {
+      CHECK(unrolled_net_->layers()[i]->param_propagate_down(j))
+          << "param_propagate_down not set for layer " << i << ", param " << j;
+    }
+  }
+  this->param_propagate_down_.clear();
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+
+  // Set the diffs of recurrent outputs to 0 -- we can't backpropagate across
+  // batches.
+  for (int i = 0; i < recur_output_blobs_.size(); ++i) {
+    caffe_set(recur_output_blobs_[i]->count(), Dtype(0),
+              recur_output_blobs_[i]->mutable_cpu_diff());
+  }
+
+  // Check that the last output_names.size() layers are the pseudo-losses;
+  // set last_layer_index so that we don't actually run these layers.
+  const vector<string>& layer_names = unrolled_net_->layer_names();
+  last_layer_index_ = layer_names.size() - 1 - pseudo_losses.size();
+  for (int i = last_layer_index_ + 1, j = 0; i < layer_names.size(); ++i, ++j) {
+    CHECK_EQ(layer_names[i], pseudo_losses[j]);
+  }
+}
+
+template <typename Dtype>
+void RecurrentLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  CHECK_GE(bottom[0]->num_axes(), 2)
+      << "bottom[0] must have at least 2 axes -- (#timesteps, #streams, ...)";
+  CHECK_EQ(T_, bottom[0]->shape(0)) << "input number of timesteps changed";
+  N_ = bottom[0]->shape(1);
+  CHECK_EQ(bottom[1]->num_axes(), 2)
+      << "bottom[1] must have exactly 2 axes -- (#timesteps, #streams)";
+  CHECK_EQ(T_, bottom[1]->shape(0));
+  CHECK_EQ(N_, bottom[1]->shape(1));
+  x_input_blob_->ReshapeLike(*bottom[0]);
+  vector<int> cont_shape = bottom[1]->shape();
+  cont_input_blob_->Reshape(cont_shape);
+  if (static_input_) {
+    x_static_input_blob_->ReshapeLike(*bottom[2]);
+  }
+  vector<BlobShape> recur_input_shapes;
+  RecurrentInputShapes(&recur_input_shapes);
+  CHECK_EQ(recur_input_shapes.size(), recur_input_blobs_.size());
+  for (int i = 0; i < recur_input_shapes.size(); ++i) {
+    recur_input_blobs_[i]->Reshape(recur_input_shapes[i]);
+  }
+  unrolled_net_->Reshape();
+  x_input_blob_->ShareData(*bottom[0]);
+  x_input_blob_->ShareDiff(*bottom[0]);
+  cont_input_blob_->ShareData(*bottom[1]);
+  if (static_input_) {
+    x_static_input_blob_->ShareData(*bottom[2]);
+    x_static_input_blob_->ShareDiff(*bottom[2]);
+  }
+  if (expose_hidden_) {
+    const int bottom_offset = 2 + static_input_;
+    for (int i = bottom_offset, j = 0; i < bottom.size(); ++i, ++j) {
+      CHECK(recur_input_blobs_[j]->shape() == bottom[i]->shape())
+          << "bottom[" << i << "] shape must match hidden state input shape: "
+          << recur_input_blobs_[j]->shape_string();
+      recur_input_blobs_[j]->ShareData(*bottom[i]);
+    }
+  }
+  for (int i = 0; i < output_blobs_.size(); ++i) {
+    top[i]->ReshapeLike(*output_blobs_[i]);
+    top[i]->ShareData(*output_blobs_[i]);
+    top[i]->ShareDiff(*output_blobs_[i]);
+  }
+  if (expose_hidden_) {
+    const int top_offset = output_blobs_.size();
+    for (int i = top_offset, j = 0; i < top.size(); ++i, ++j) {
+      top[i]->ReshapeLike(*recur_output_blobs_[j]);
+    }
+  }
+}
+
+template <typename Dtype>
+void RecurrentLayer<Dtype>::Reset() {
+  // "Reset" the hidden state of the net by zeroing out all recurrent outputs.
+  for (int i = 0; i < recur_output_blobs_.size(); ++i) {
+    caffe_set(recur_output_blobs_[i]->count(), Dtype(0),
+              recur_output_blobs_[i]->mutable_cpu_data());
+  }
+}
+
+template <typename Dtype>
+void RecurrentLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  // Hacky fix for test time: reshare all the internal shared blobs, which may
+  // currently point to a stale owner blob that was dropped when Solver::Test
+  // called test_net->ShareTrainedLayersWith(net_.get()).
+  // TODO: somehow make this work non-hackily.
+  if (this->phase_ == TEST) {
+    unrolled_net_->ShareWeights();
+  }
+
+  DCHECK_EQ(recur_input_blobs_.size(), recur_output_blobs_.size());
+  if (!expose_hidden_) {
+    for (int i = 0; i < recur_input_blobs_.size(); ++i) {
+      const int count = recur_input_blobs_[i]->count();
+      DCHECK_EQ(count, recur_output_blobs_[i]->count());
+      const Dtype* timestep_T_data = recur_output_blobs_[i]->cpu_data();
+      Dtype* timestep_0_data = recur_input_blobs_[i]->mutable_cpu_data();
+      caffe_copy(count, timestep_T_data, timestep_0_data);
+    }
+  }
+
+  unrolled_net_->ForwardTo(last_layer_index_);
+
+  if (expose_hidden_) {
+    const int top_offset = output_blobs_.size();
+    for (int i = top_offset, j = 0; i < top.size(); ++i, ++j) {
+      top[i]->ShareData(*recur_output_blobs_[j]);
+    }
+  }
+}
+
+template <typename Dtype>
+void RecurrentLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  CHECK(!propagate_down[1]) << "Cannot backpropagate to sequence indicators.";
+
+  // TODO: skip backpropagation to inputs and parameters inside the unrolled
+  // net according to propagate_down[0] and propagate_down[2]. For now just
+  // backprop to inputs and parameters unconditionally, as either the inputs or
+  // the parameters do need backward (or Net would have set
+  // layer_needs_backward_[i] == false for this layer).
+  unrolled_net_->BackwardFrom(last_layer_index_);
+}
+
+#ifdef CPU_ONLY
+STUB_GPU_FORWARD(RecurrentLayer, Forward);
+#endif
+
+INSTANTIATE_CLASS(RecurrentLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/recurrent_layer.cu b/src/caffe/layers/recurrent_layer.cu
new file mode 100644
index 00000000..4dd2b0e2
--- /dev/null
+++ b/src/caffe/layers/recurrent_layer.cu
@@ -0,0 +1,44 @@
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/recurrent_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void RecurrentLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  // Hacky fix for test time... reshare all the shared blobs.
+  // TODO: somehow make this work non-hackily.
+  if (this->phase_ == TEST) {
+    unrolled_net_->ShareWeights();
+  }
+
+  DCHECK_EQ(recur_input_blobs_.size(), recur_output_blobs_.size());
+  if (!expose_hidden_) {
+    for (int i = 0; i < recur_input_blobs_.size(); ++i) {
+      const int count = recur_input_blobs_[i]->count();
+      DCHECK_EQ(count, recur_output_blobs_[i]->count());
+      const Dtype* timestep_T_data = recur_output_blobs_[i]->gpu_data();
+      Dtype* timestep_0_data = recur_input_blobs_[i]->mutable_gpu_data();
+      caffe_copy(count, timestep_T_data, timestep_0_data);
+    }
+  }
+
+  unrolled_net_->ForwardTo(last_layer_index_);
+
+  if (expose_hidden_) {
+    const int top_offset = output_blobs_.size();
+    for (int i = top_offset, j = 0; i < top.size(); ++i, ++j) {
+      top[i]->ShareData(*recur_output_blobs_[j]);
+    }
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FORWARD(RecurrentLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/rnn_layer.cpp b/src/caffe/layers/rnn_layer.cpp
new file mode 100644
index 00000000..8c2fa22e
--- /dev/null
+++ b/src/caffe/layers/rnn_layer.cpp
@@ -0,0 +1,236 @@
+#include <string>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/rnn_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void RNNLayer<Dtype>::RecurrentInputBlobNames(vector<string>* names) const {
+  names->resize(1);
+  (*names)[0] = "h_0";
+}
+
+template <typename Dtype>
+void RNNLayer<Dtype>::RecurrentOutputBlobNames(vector<string>* names) const {
+  names->resize(1);
+  (*names)[0] = "h_" + format_int(this->T_);
+}
+
+template <typename Dtype>
+void RNNLayer<Dtype>::RecurrentInputShapes(vector<BlobShape>* shapes) const {
+  const int num_output = this->layer_param_.recurrent_param().num_output();
+  shapes->resize(1);
+  (*shapes)[0].Clear();
+  (*shapes)[0].add_dim(1);  // a single timestep
+  (*shapes)[0].add_dim(this->N_);
+  (*shapes)[0].add_dim(num_output);
+}
+
+template <typename Dtype>
+void RNNLayer<Dtype>::OutputBlobNames(vector<string>* names) const {
+  names->resize(1);
+  (*names)[0] = "o";
+}
+
+template <typename Dtype>
+void RNNLayer<Dtype>::FillUnrolledNet(NetParameter* net_param) const {
+  const int num_output = this->layer_param_.recurrent_param().num_output();
+  CHECK_GT(num_output, 0) << "num_output must be positive";
+  const FillerParameter& weight_filler =
+      this->layer_param_.recurrent_param().weight_filler();
+  const FillerParameter& bias_filler =
+      this->layer_param_.recurrent_param().bias_filler();
+
+  // Add generic LayerParameter's (without bottoms/tops) of layer types we'll
+  // use to save redundant code.
+  LayerParameter hidden_param;
+  hidden_param.set_type("InnerProduct");
+  hidden_param.mutable_inner_product_param()->set_num_output(num_output);
+  hidden_param.mutable_inner_product_param()->set_bias_term(false);
+  hidden_param.mutable_inner_product_param()->set_axis(2);
+  hidden_param.mutable_inner_product_param()->
+      mutable_weight_filler()->CopyFrom(weight_filler);
+
+  LayerParameter biased_hidden_param(hidden_param);
+  biased_hidden_param.mutable_inner_product_param()->set_bias_term(true);
+  biased_hidden_param.mutable_inner_product_param()->
+      mutable_bias_filler()->CopyFrom(bias_filler);
+
+  LayerParameter sum_param;
+  sum_param.set_type("Eltwise");
+  sum_param.mutable_eltwise_param()->set_operation(
+      EltwiseParameter_EltwiseOp_SUM);
+
+  LayerParameter tanh_param;
+  tanh_param.set_type("TanH");
+
+  LayerParameter scale_param;
+  scale_param.set_type("Scale");
+  scale_param.mutable_scale_param()->set_axis(0);
+
+  LayerParameter slice_param;
+  slice_param.set_type("Slice");
+  slice_param.mutable_slice_param()->set_axis(0);
+
+  vector<BlobShape> input_shapes;
+  RecurrentInputShapes(&input_shapes);
+  CHECK_EQ(1, input_shapes.size());
+
+  LayerParameter* input_layer_param = net_param->add_layer();
+  input_layer_param->set_type("Input");
+  InputParameter* input_param = input_layer_param->mutable_input_param();
+  input_layer_param->add_top("h_0");
+  input_param->add_shape()->CopyFrom(input_shapes[0]);
+
+  LayerParameter* cont_slice_param = net_param->add_layer();
+  cont_slice_param->CopyFrom(slice_param);
+  cont_slice_param->set_name("cont_slice");
+  cont_slice_param->add_bottom("cont");
+  cont_slice_param->mutable_slice_param()->set_axis(0);
+
+  // Add layer to transform all timesteps of x to the hidden state dimension.
+  //     W_xh_x = W_xh * x + b_h
+  {
+    LayerParameter* x_transform_param = net_param->add_layer();
+    x_transform_param->CopyFrom(biased_hidden_param);
+    x_transform_param->set_name("x_transform");
+    x_transform_param->add_param()->set_name("W_xh");
+    x_transform_param->add_param()->set_name("b_h");
+    x_transform_param->add_bottom("x");
+    x_transform_param->add_top("W_xh_x");
+    x_transform_param->add_propagate_down(true);
+  }
+
+  if (this->static_input_) {
+    // Add layer to transform x_static to the hidden state dimension.
+    //     W_xh_x_static = W_xh_static * x_static
+    LayerParameter* x_static_transform_param = net_param->add_layer();
+    x_static_transform_param->CopyFrom(hidden_param);
+    x_static_transform_param->mutable_inner_product_param()->set_axis(1);
+    x_static_transform_param->set_name("W_xh_x_static");
+    x_static_transform_param->add_param()->set_name("W_xh_static");
+    x_static_transform_param->add_bottom("x_static");
+    x_static_transform_param->add_top("W_xh_x_static_preshape");
+    x_static_transform_param->add_propagate_down(true);
+
+    LayerParameter* reshape_param = net_param->add_layer();
+    reshape_param->set_type("Reshape");
+    BlobShape* new_shape =
+         reshape_param->mutable_reshape_param()->mutable_shape();
+    new_shape->add_dim(1);  // One timestep.
+    // Should infer this->N as the dimension so we can reshape on batch size.
+    new_shape->add_dim(-1);
+    new_shape->add_dim(
+        x_static_transform_param->inner_product_param().num_output());
+    reshape_param->set_name("W_xh_x_static_reshape");
+    reshape_param->add_bottom("W_xh_x_static_preshape");
+    reshape_param->add_top("W_xh_x_static");
+  }
+
+  LayerParameter* x_slice_param = net_param->add_layer();
+  x_slice_param->CopyFrom(slice_param);
+  x_slice_param->set_name("W_xh_x_slice");
+  x_slice_param->add_bottom("W_xh_x");
+
+  LayerParameter output_concat_layer;
+  output_concat_layer.set_name("o_concat");
+  output_concat_layer.set_type("Concat");
+  output_concat_layer.add_top("o");
+  output_concat_layer.mutable_concat_param()->set_axis(0);
+
+  for (int t = 1; t <= this->T_; ++t) {
+    string tm1s = format_int(t - 1);
+    string ts = format_int(t);
+
+    cont_slice_param->add_top("cont_" + ts);
+    x_slice_param->add_top("W_xh_x_" + ts);
+
+    // Add layer to flush the hidden state when beginning a new sequence,
+    // as indicated by cont_t.
+    //     h_conted_{t-1} := cont_t * h_{t-1}
+    //
+    // Normally, cont_t is binary (i.e., 0 or 1), so:
+    //     h_conted_{t-1} := h_{t-1} if cont_t == 1
+    //                       0   otherwise
+    {
+      LayerParameter* cont_h_param = net_param->add_layer();
+      cont_h_param->CopyFrom(scale_param);
+      cont_h_param->set_name("h_conted_" + tm1s);
+      cont_h_param->add_bottom("h_" + tm1s);
+      cont_h_param->add_bottom("cont_" + ts);
+      cont_h_param->add_top("h_conted_" + tm1s);
+    }
+
+    // Add layer to compute
+    //     W_hh_h_{t-1} := W_hh * h_conted_{t-1}
+    {
+      LayerParameter* w_param = net_param->add_layer();
+      w_param->CopyFrom(hidden_param);
+      w_param->set_name("W_hh_h_" + tm1s);
+      w_param->add_param()->set_name("W_hh");
+      w_param->add_bottom("h_conted_" + tm1s);
+      w_param->add_top("W_hh_h_" + tm1s);
+      w_param->mutable_inner_product_param()->set_axis(2);
+    }
+
+    // Add layers to compute
+    //     h_t := \tanh( W_hh * h_conted_{t-1} + W_xh * x_t + b_h )
+    //          = \tanh( W_hh_h_{t-1} + W_xh_t )
+    {
+      LayerParameter* h_input_sum_param = net_param->add_layer();
+      h_input_sum_param->CopyFrom(sum_param);
+      h_input_sum_param->set_name("h_input_sum_" + ts);
+      h_input_sum_param->add_bottom("W_hh_h_" + tm1s);
+      h_input_sum_param->add_bottom("W_xh_x_" + ts);
+      if (this->static_input_) {
+        h_input_sum_param->add_bottom("W_xh_x_static");
+      }
+      h_input_sum_param->add_top("h_neuron_input_" + ts);
+    }
+    {
+      LayerParameter* h_neuron_param = net_param->add_layer();
+      h_neuron_param->CopyFrom(tanh_param);
+      h_neuron_param->set_name("h_neuron_" + ts);
+      h_neuron_param->add_bottom("h_neuron_input_" + ts);
+      h_neuron_param->add_top("h_" + ts);
+    }
+
+    // Add layer to compute
+    //     W_ho_h_t := W_ho * h_t + b_o
+    {
+      LayerParameter* w_param = net_param->add_layer();
+      w_param->CopyFrom(biased_hidden_param);
+      w_param->set_name("W_ho_h_" + ts);
+      w_param->add_param()->set_name("W_ho");
+      w_param->add_param()->set_name("b_o");
+      w_param->add_bottom("h_" + ts);
+      w_param->add_top("W_ho_h_" + ts);
+      w_param->mutable_inner_product_param()->set_axis(2);
+    }
+
+    // Add layers to compute
+    //     o_t := \tanh( W_ho * h_t + b_o)
+    //          = \tanh( W_ho_h_t )
+    {
+      LayerParameter* o_neuron_param = net_param->add_layer();
+      o_neuron_param->CopyFrom(tanh_param);
+      o_neuron_param->set_name("o_neuron_" + ts);
+      o_neuron_param->add_bottom("W_ho_h_" + ts);
+      o_neuron_param->add_top("o_" + ts);
+    }
+    output_concat_layer.add_bottom("o_" + ts);
+  }  // for (int t = 1; t <= this->T_; ++t)
+
+  net_param->add_layer()->CopyFrom(output_concat_layer);
+}
+
+INSTANTIATE_CLASS(RNNLayer);
+REGISTER_LAYER_CLASS(RNN);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/scale_layer.cpp b/src/caffe/layers/scale_layer.cpp
index ecdbb123..e652dad6 100644
--- a/src/caffe/layers/scale_layer.cpp
+++ b/src/caffe/layers/scale_layer.cpp
@@ -56,9 +56,17 @@ void ScaleLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     bias_bottom_vec_.resize(1);
     bias_bottom_vec_[0] = bottom[0];
     bias_layer_->SetUp(bias_bottom_vec_, top);
-    bias_param_id_ = this->blobs_.size();
-    this->blobs_.resize(bias_param_id_ + 1);
-    this->blobs_[bias_param_id_] = bias_layer_->blobs()[0];
+    if (this->blobs_.size() + bottom.size() < 3) {
+      // case: blobs.size == 1 && bottom.size == 1
+      // or blobs.size == 0 && bottom.size == 2
+      bias_param_id_ = this->blobs_.size();
+      this->blobs_.resize(bias_param_id_ + 1);
+      this->blobs_[bias_param_id_] = bias_layer_->blobs()[0];
+    } else {
+      // bias param already initialized
+      bias_param_id_ = this->blobs_.size() - 1;
+      bias_layer_->blobs()[0] = this->blobs_[bias_param_id_];
+    }
     bias_propagate_down_.resize(1, false);
   }
   this->param_propagate_down_.resize(this->blobs_.size(), true);
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index 10ac9470..99fa3eb6 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -1,3 +1,4 @@
+#include <algorithm>
 #include <vector>
 
 #include "caffe/layers/sigmoid_cross_entropy_loss_layer.hpp"
@@ -14,17 +15,66 @@ void SigmoidCrossEntropyLossLayer<Dtype>::LayerSetUp(
   sigmoid_top_vec_.clear();
   sigmoid_top_vec_.push_back(sigmoid_output_.get());
   sigmoid_layer_->SetUp(sigmoid_bottom_vec_, sigmoid_top_vec_);
+
+  has_ignore_label_ =
+    this->layer_param_.loss_param().has_ignore_label();
+  if (has_ignore_label_) {
+    ignore_label_ = this->layer_param_.loss_param().ignore_label();
+  }
+  if (this->layer_param_.loss_param().has_normalization()) {
+    normalization_ = this->layer_param_.loss_param().normalization();
+  } else if (this->layer_param_.loss_param().has_normalize()) {
+    normalization_ = this->layer_param_.loss_param().normalize() ?
+                     LossParameter_NormalizationMode_VALID :
+                     LossParameter_NormalizationMode_BATCH_SIZE;
+  } else {
+    normalization_ = LossParameter_NormalizationMode_BATCH_SIZE;
+  }
 }
 
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Reshape(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::Reshape(bottom, top);
+  outer_num_ = bottom[0]->shape(0);  // batch size
+  inner_num_ = bottom[0]->count(1);  // instance size: |output| == |target|
   CHECK_EQ(bottom[0]->count(), bottom[1]->count()) <<
       "SIGMOID_CROSS_ENTROPY_LOSS layer inputs must have the same count.";
   sigmoid_layer_->Reshape(sigmoid_bottom_vec_, sigmoid_top_vec_);
 }
 
+// TODO(shelhamer) loss normalization should be pulled up into LossLayer,
+// instead of duplicated here and in SoftMaxWithLossLayer
+template <typename Dtype>
+Dtype SigmoidCrossEntropyLossLayer<Dtype>::get_normalizer(
+    LossParameter_NormalizationMode normalization_mode, int valid_count) {
+  Dtype normalizer;
+  switch (normalization_mode) {
+    case LossParameter_NormalizationMode_FULL:
+      normalizer = Dtype(outer_num_ * inner_num_);
+      break;
+    case LossParameter_NormalizationMode_VALID:
+      if (valid_count == -1) {
+        normalizer = Dtype(outer_num_ * inner_num_);
+      } else {
+        normalizer = Dtype(valid_count);
+      }
+      break;
+    case LossParameter_NormalizationMode_BATCH_SIZE:
+      normalizer = Dtype(outer_num_);
+      break;
+    case LossParameter_NormalizationMode_NONE:
+      normalizer = Dtype(1);
+      break;
+    default:
+      LOG(FATAL) << "Unknown normalization mode: "
+          << LossParameter_NormalizationMode_Name(normalization_mode);
+  }
+  // Some users will have no labels for some examples in order to 'turn off' a
+  // particular loss in a multi-task setup. The max prevents NaNs in that case.
+  return std::max(Dtype(1.0), normalizer);
+}
+
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
@@ -32,17 +82,22 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
   sigmoid_bottom_vec_[0] = bottom[0];
   sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_);
   // Compute the loss (negative log likelihood)
-  const int count = bottom[0]->count();
-  const int num = bottom[0]->num();
   // Stable version of loss computation from input data
   const Dtype* input_data = bottom[0]->cpu_data();
   const Dtype* target = bottom[1]->cpu_data();
+  int valid_count = 0;
   Dtype loss = 0;
-  for (int i = 0; i < count; ++i) {
+  for (int i = 0; i < bottom[0]->count(); ++i) {
+    const int target_value = static_cast<int>(target[i]);
+    if (has_ignore_label_ && target_value == ignore_label_) {
+      continue;
+    }
     loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
         log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
+    ++valid_count;
   }
-  top[0]->mutable_cpu_data()[0] = loss / num;
+  normalizer_ = get_normalizer(normalization_, valid_count);
+  top[0]->mutable_cpu_data()[0] = loss / normalizer_;
 }
 
 template <typename Dtype>
@@ -56,19 +111,27 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_cpu(
   if (propagate_down[0]) {
     // First, compute the diff
     const int count = bottom[0]->count();
-    const int num = bottom[0]->num();
     const Dtype* sigmoid_output_data = sigmoid_output_->cpu_data();
     const Dtype* target = bottom[1]->cpu_data();
     Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
     caffe_sub(count, sigmoid_output_data, target, bottom_diff);
+    // Zero out gradient of ignored targets.
+    if (has_ignore_label_) {
+      for (int i = 0; i < count; ++i) {
+        const int target_value = static_cast<int>(target[i]);
+        if (target_value == ignore_label_) {
+          bottom_diff[i] = 0;
+        }
+      }
+    }
     // Scale down gradient
-    const Dtype loss_weight = top[0]->cpu_diff()[0];
-    caffe_scal(count, loss_weight / num, bottom_diff);
+    Dtype loss_weight = top[0]->cpu_diff()[0] / normalizer_;
+    caffe_scal(count, loss_weight, bottom_diff);
   }
 }
 
 #ifdef CPU_ONLY
-STUB_GPU_BACKWARD(SigmoidCrossEntropyLossLayer, Backward);
+STUB_GPU(SigmoidCrossEntropyLossLayer);
 #endif
 
 INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer);
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 046cb9d3..b9877e6a 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -5,6 +5,72 @@
 
 namespace caffe {
 
+
+template <typename Dtype>
+__global__ void SigmoidCrossEntropyLossForwardGPU(const int nthreads,
+          const Dtype* input_data, const Dtype* target, Dtype* loss,
+          const bool has_ignore_label_, const int ignore_label_,
+          Dtype* counts) {
+  CUDA_KERNEL_LOOP(i, nthreads) {
+    const int target_value = static_cast<int>(target[i]);
+    if (has_ignore_label_ && target_value == ignore_label_) {
+      loss[i] = 0;
+      counts[i] = 0;
+    } else {
+      loss[i] = input_data[i] * (target[i] - (input_data[i] >= 0)) -
+          log(1 + exp(input_data[i] - 2 * input_data[i] *
+          (input_data[i] >= 0)));
+      counts[i] = 1;
+    }
+  }
+}
+
+template <typename Dtype>
+__global__ void SigmoidCrossEntropyLossIgnoreDiffGPU(const int count,
+    const int ignore_label, const Dtype* target, Dtype* diff) {
+  CUDA_KERNEL_LOOP(i, count) {
+    const int target_value = static_cast<int>(target[i]);
+    if (target_value == ignore_label) {
+      diff[i] = 0;
+    }
+  }
+}
+
+
+template <typename Dtype>
+void SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  // The forward pass computes the sigmoid outputs.
+  sigmoid_bottom_vec_[0] = bottom[0];
+  sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_);
+  // Compute the loss (negative log likelihood)
+  const int count = bottom[0]->count();
+  // Stable version of loss computation from input data
+  const Dtype* input_data = bottom[0]->gpu_data();
+  const Dtype* target = bottom[1]->gpu_data();
+  // Since this memory is not used for anything until it is overwritten
+  // on the backward pass, we use it here to avoid having to allocate new GPU
+  // memory to accumulate intermediate results in the kernel.
+  Dtype* loss_data = bottom[0]->mutable_gpu_diff();
+  Dtype* count_data = bottom[1]->mutable_gpu_diff();
+  Dtype valid_count;
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  SigmoidCrossEntropyLossForwardGPU<Dtype><<<CAFFE_GET_BLOCKS(count),
+      CAFFE_CUDA_NUM_THREADS>>>(count, input_data, target, loss_data,
+      has_ignore_label_, ignore_label_, count_data);
+  // Only launch another CUDA kernel if we actually need the valid count.
+  if (normalization_ == LossParameter_NormalizationMode_VALID &&
+      has_ignore_label_) {
+    caffe_gpu_asum(count, count_data, &valid_count);
+  } else {
+    valid_count = count;
+  }
+  Dtype loss;
+  caffe_gpu_asum(count, loss_data, &loss);
+  normalizer_ = get_normalizer(normalization_, valid_count);
+  top[0]->mutable_cpu_data()[0] = loss / normalizer_;
+}
+
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
@@ -16,19 +82,23 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
   if (propagate_down[0]) {
     // First, compute the diff
     const int count = bottom[0]->count();
-    const int num = bottom[0]->num();
     const Dtype* sigmoid_output_data = sigmoid_output_->gpu_data();
     const Dtype* target = bottom[1]->gpu_data();
     Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
     caffe_copy(count, sigmoid_output_data, bottom_diff);
     caffe_gpu_axpy(count, Dtype(-1), target, bottom_diff);
+    // Zero out gradient of ignored targets.
+    if (has_ignore_label_) {
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      SigmoidCrossEntropyLossIgnoreDiffGPU<Dtype><<<CAFFE_GET_BLOCKS(count),
+        CAFFE_CUDA_NUM_THREADS>>>(count, ignore_label_, target, bottom_diff);
+    }
     // Scale down gradient
-    const Dtype loss_weight = top[0]->cpu_diff()[0];
-    caffe_gpu_scal(count, loss_weight / num, bottom_diff);
+    Dtype loss_weight = top[0]->cpu_diff()[0] / normalizer_;
+    caffe_gpu_scal(count, loss_weight, bottom_diff);
   }
 }
 
-INSTANTIATE_LAYER_GPU_BACKWARD(SigmoidCrossEntropyLossLayer);
-
+INSTANTIATE_LAYER_GPU_FUNCS(SigmoidCrossEntropyLossLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/sigmoid_layer.cpp b/src/caffe/layers/sigmoid_layer.cpp
index 85fd9676..f8aa769a 100644
--- a/src/caffe/layers/sigmoid_layer.cpp
+++ b/src/caffe/layers/sigmoid_layer.cpp
@@ -7,7 +7,7 @@ namespace caffe {
 
 template <typename Dtype>
 inline Dtype sigmoid(Dtype x) {
-  return 1. / (1. + exp(-x));
+  return 0.5 * tanh(0.5 * x) + 0.5;
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/sigmoid_layer.cu b/src/caffe/layers/sigmoid_layer.cu
index 184c61ed..8a4ea661 100644
--- a/src/caffe/layers/sigmoid_layer.cu
+++ b/src/caffe/layers/sigmoid_layer.cu
@@ -8,7 +8,7 @@ namespace caffe {
 template <typename Dtype>
 __global__ void SigmoidForward(const int n, const Dtype* in, Dtype* out) {
   CUDA_KERNEL_LOOP(index, n) {
-    out[index] = 1. / (1. + exp(-in[index]));
+    out[index] = 0.5 * tanh(0.5 * in[index]) + 0.5;
   }
 }
 
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 4ca8315d..1bf3760e 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -173,8 +173,8 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_GT(crop_size, 0);
   const int batch_size = this->layer_param_.window_data_param().batch_size();
   top[0]->Reshape(batch_size, channels, crop_size, crop_size);
-  for (int i = 0; i < this->PREFETCH_COUNT; ++i)
-    this->prefetch_[i].data_.Reshape(
+  for (int i = 0; i < this->prefetch_.size(); ++i)
+    this->prefetch_[i]->data_.Reshape(
         batch_size, channels, crop_size, crop_size);
 
   LOG(INFO) << "output data size: " << top[0]->num() << ","
@@ -183,8 +183,8 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // label
   vector<int> label_shape(1, batch_size);
   top[1]->Reshape(label_shape);
-  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
-    this->prefetch_[i].label_.Reshape(label_shape);
+  for (int i = 0; i < this->prefetch_.size(); ++i) {
+    this->prefetch_[i]->label_.Reshape(label_shape);
   }
 
   // data mean
@@ -265,6 +265,9 @@ void WindowDataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
   const int num_samples[2] = { batch_size - num_fg, num_fg };
 
   int item_id = 0;
+  CHECK_GT(fg_windows_.size(), 0);
+  CHECK_GT(bg_windows_.size(), 0);
+
   // sample from bg set then fg set
   for (int is_fg = 0; is_fg < 2; ++is_fg) {
     for (int dummy = 0; dummy < num_samples[is_fg]; ++dummy) {
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 05bee798..353c2f95 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -17,29 +17,31 @@
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/upgrade_proto.hpp"
 
-#include "caffe/test/test_caffe_main.hpp"
-
 namespace caffe {
 
 template <typename Dtype>
-Net<Dtype>::Net(const NetParameter& param, const Net* root_net)
-    : root_net_(root_net) {
+Net<Dtype>::Net(const NetParameter& param) {
   Init(param);
 }
 
 template <typename Dtype>
-Net<Dtype>::Net(const string& param_file, Phase phase, const Net* root_net)
-    : root_net_(root_net) {
+Net<Dtype>::Net(const string& param_file, Phase phase,
+    const int level, const vector<string>* stages) {
   NetParameter param;
   ReadNetParamsFromTextFileOrDie(param_file, &param);
+  // Set phase, stages and level
   param.mutable_state()->set_phase(phase);
+  if (stages != NULL) {
+    for (int i = 0; i < stages->size(); i++) {
+      param.mutable_state()->add_stage((*stages)[i]);
+    }
+  }
+  param.mutable_state()->set_level(level);
   Init(param);
 }
 
 template <typename Dtype>
 void Net<Dtype>::Init(const NetParameter& in_param) {
-  CHECK(Caffe::root_solver() || root_net_)
-      << "root_net_ needs to be set for all non-root solvers";
   // Set phase from the state.
   phase_ = in_param.state().phase();
   // Filter layers based on their include/exclude rules and
@@ -56,22 +58,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   name_ = param.name();
   map<string, int> blob_name_to_idx;
   set<string> available_blobs;
-  CHECK(param.input_dim_size() == 0 || param.input_shape_size() == 0)
-      << "Must specify either input_shape OR deprecated input_dim, not both.";
-  if (param.input_dim_size() > 0) {
-    // Deprecated 4D dimensions.
-    CHECK_EQ(param.input_size() * 4, param.input_dim_size())
-        << "Incorrect input blob dimension specifications.";
-  } else {
-    CHECK_EQ(param.input_size(), param.input_shape_size())
-        << "Exactly one input_shape must be specified per input.";
-  }
   memory_used_ = 0;
-  // set the input blobs
-  for (int input_id = 0; input_id < param.input_size(); ++input_id) {
-    const int layer_id = -1;  // inputs have fake layer ID -1
-    AppendTop(param, layer_id, input_id, &available_blobs, &blob_name_to_idx);
-  }
   // For each layer, set up its input and output
   bottom_vecs_.resize(param.layer_size());
   top_vecs_.resize(param.layer_size());
@@ -80,9 +67,6 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   top_id_vecs_.resize(param.layer_size());
   bottom_need_backward_.resize(param.layer_size());
   for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) {
-    // For non-root solvers, whether this layer is shared from root_net_.
-    bool share_from_root = !Caffe::root_solver()
-        && root_net_->layers_[layer_id]->ShareInParallel();
     // Inherit phase from net if unset.
     if (!param.layer(layer_id).has_phase()) {
       param.mutable_layer(layer_id)->set_phase(phase_);
@@ -95,13 +79,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
           << "propagate_down param must be specified "
           << "either 0 or bottom_size times ";
     }
-    if (share_from_root) {
-      LOG(INFO) << "Sharing layer " << layer_param.name() << " from root net";
-      layers_.push_back(root_net_->layers_[layer_id]);
-      layers_[layer_id]->SetShared(true);
-    } else {
-      layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
-    }
+    layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
     layer_names_.push_back(layer_param.name());
     LOG_IF(INFO, Caffe::root_solver())
         << "Creating Layer " << layer_param.name();
@@ -118,6 +96,12 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     int num_top = layer_param.top_size();
     for (int top_id = 0; top_id < num_top; ++top_id) {
       AppendTop(param, layer_id, top_id, &available_blobs, &blob_name_to_idx);
+      // Collect Input layer tops as Net inputs.
+      if (layer_param.type() == "Input") {
+        const int blob_id = blobs_.size() - 1;
+        net_input_blob_indices_.push_back(blob_id);
+        net_input_blobs_.push_back(blobs_[blob_id].get());
+      }
     }
     // If the layer specifies that AutoTopBlobs() -> true and the LayerParameter
     // specified fewer than the required number (as specified by
@@ -134,19 +118,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
       }
     }
     // After this layer is connected, set it up.
-    if (share_from_root) {
-      // Set up size of top blobs using root_net_
-      const vector<Blob<Dtype>*>& base_top = root_net_->top_vecs_[layer_id];
-      const vector<Blob<Dtype>*>& this_top = this->top_vecs_[layer_id];
-      for (int top_id = 0; top_id < base_top.size(); ++top_id) {
-        this_top[top_id]->ReshapeLike(*base_top[top_id]);
-        LOG(INFO) << "Created top blob " << top_id << " (shape: "
-            << this_top[top_id]->shape_string() <<  ") for shared layer "
-            << layer_param.name();
-      }
-    } else {
-      layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);
-    }
+    layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);
     LOG_IF(INFO, Caffe::root_solver())
         << "Setting up " << layer_names_[layer_id];
     for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
@@ -379,19 +351,17 @@ bool Net<Dtype>::StateMeetsRule(const NetState& state,
   return true;
 }
 
-// Helper for Net::Init: add a new input or top blob to the net.  (Inputs have
-// layer_id == -1, tops have layer_id >= 0.)
+// Helper for Net::Init: add a new top blob to the net.
 template <typename Dtype>
 void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
                            const int top_id, set<string>* available_blobs,
                            map<string, int>* blob_name_to_idx) {
-  shared_ptr<LayerParameter> layer_param((layer_id >= 0) ?
-    (new LayerParameter(param.layer(layer_id))) : NULL);
-  const string& blob_name = layer_param ?
-      (layer_param->top_size() > top_id ?
-          layer_param->top(top_id) : "(automatic)") : param.input(top_id);
+  shared_ptr<LayerParameter> layer_param(
+      new LayerParameter(param.layer(layer_id)));
+  const string& blob_name = (layer_param->top_size() > top_id) ?
+      layer_param->top(top_id) : "(automatic)";
   // Check if we are doing in-place computation
-  if (blob_name_to_idx && layer_param && layer_param->bottom_size() > top_id &&
+  if (blob_name_to_idx && layer_param->bottom_size() > top_id &&
       blob_name == layer_param->bottom(top_id)) {
     // In-place computation
     LOG_IF(INFO, Caffe::root_solver())
@@ -407,11 +377,7 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
   } else {
     // Normal output.
     if (Caffe::root_solver()) {
-      if (layer_param) {
-        LOG(INFO) << layer_param->name() << " -> " << blob_name;
-      } else {
-        LOG(INFO) << "Input " << top_id << " -> " << blob_name;
-      }
+      LOG(INFO) << layer_param->name() << " -> " << blob_name;
     }
     shared_ptr<Blob<Dtype> > blob_pointer(new Blob<Dtype>());
     const int blob_id = blobs_.size();
@@ -419,22 +385,8 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
     blob_names_.push_back(blob_name);
     blob_need_backward_.push_back(false);
     if (blob_name_to_idx) { (*blob_name_to_idx)[blob_name] = blob_id; }
-    if (layer_id == -1) {
-      // Set the (explicitly specified) dimensions of the input blob.
-      if (param.input_dim_size() > 0) {
-        blob_pointer->Reshape(param.input_dim(top_id * 4),
-                              param.input_dim(top_id * 4 + 1),
-                              param.input_dim(top_id * 4 + 2),
-                              param.input_dim(top_id * 4 + 3));
-      } else {
-        blob_pointer->Reshape(param.input_shape(top_id));
-      }
-      net_input_blob_indices_.push_back(blob_id);
-      net_input_blobs_.push_back(blob_pointer.get());
-    } else {
-      top_id_vecs_[layer_id].push_back(blob_id);
-      top_vecs_[layer_id].push_back(blob_pointer.get());
-    }
+    top_id_vecs_[layer_id].push_back(blob_id);
+    top_vecs_[layer_id].push_back(blob_pointer.get());
   }
   if (available_blobs) { available_blobs->insert(blob_name); }
 }
@@ -456,12 +408,11 @@ int Net<Dtype>::AppendBottom(const NetParameter& param, const int layer_id,
   bottom_vecs_[layer_id].push_back(blobs_[blob_id].get());
   bottom_id_vecs_[layer_id].push_back(blob_id);
   available_blobs->erase(blob_name);
-  bool propagate_down = true;
+  bool need_backward = blob_need_backward_[blob_id];
   // Check if the backpropagation on bottom_id should be skipped
-  if (layer_param.propagate_down_size() > 0)
-    propagate_down = layer_param.propagate_down(bottom_id);
-  const bool need_backward = blob_need_backward_[blob_id] &&
-                          propagate_down;
+  if (layer_param.propagate_down_size() > 0) {
+    need_backward = layer_param.propagate_down(bottom_id);
+  }
   bottom_need_backward_[layer_id].push_back(need_backward);
   return blob_id;
 }
@@ -566,16 +517,16 @@ Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
   CHECK_GE(start, 0);
   CHECK_LT(end, layers_.size());
   Dtype loss = 0;
-  if (debug_info_) {
-    for (int i = 0; i < net_input_blobs_.size(); ++i) {
-      InputDebugInfo(i);
-    }
-  }
   for (int i = start; i <= end; ++i) {
-    // LOG(ERROR) << "Forwarding " << layer_names_[i];
+    for (int c = 0; c < before_forward_.size(); ++c) {
+      before_forward_[c]->run(i);
+    }
     Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], top_vecs_[i]);
     loss += layer_loss;
     if (debug_info_) { ForwardDebugInfo(i); }
+    for (int c = 0; c < after_forward_.size(); ++c) {
+      after_forward_[c]->run(i);
+    }
   }
   return loss;
 }
@@ -591,7 +542,7 @@ Dtype Net<Dtype>::ForwardTo(int end) {
 }
 
 template <typename Dtype>
-const vector<Blob<Dtype>*>& Net<Dtype>::ForwardPrefilled(Dtype* loss) {
+const vector<Blob<Dtype>*>& Net<Dtype>::Forward(Dtype* loss) {
   if (loss != NULL) {
     *loss = ForwardFromTo(0, layers_.size() - 1);
   } else {
@@ -603,32 +554,13 @@ const vector<Blob<Dtype>*>& Net<Dtype>::ForwardPrefilled(Dtype* loss) {
 template <typename Dtype>
 const vector<Blob<Dtype>*>& Net<Dtype>::Forward(
     const vector<Blob<Dtype>*> & bottom, Dtype* loss) {
-  // Copy bottom to internal bottom
+  LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: Forward(bottom, loss) "
+      << "will be removed in a future version. Use Forward(loss).";
+  // Copy bottom to net bottoms
   for (int i = 0; i < bottom.size(); ++i) {
     net_input_blobs_[i]->CopyFrom(*bottom[i]);
   }
-  return ForwardPrefilled(loss);
-}
-
-template <typename Dtype>
-string Net<Dtype>::Forward(const string& input_blob_protos, Dtype* loss) {
-  BlobProtoVector blob_proto_vec;
-  if (net_input_blobs_.size()) {
-    blob_proto_vec.ParseFromString(input_blob_protos);
-    CHECK_EQ(blob_proto_vec.blobs_size(), net_input_blobs_.size())
-        << "Incorrect input size.";
-    for (int i = 0; i < blob_proto_vec.blobs_size(); ++i) {
-      net_input_blobs_[i]->FromProto(blob_proto_vec.blobs(i));
-    }
-  }
-  ForwardPrefilled(loss);
-  blob_proto_vec.Clear();
-  for (int i = 0; i < net_output_blobs_.size(); ++i) {
-    net_output_blobs_[i]->ToProto(blob_proto_vec.add_blobs());
-  }
-  string output;
-  blob_proto_vec.SerializeToString(&output);
-  return output;
+  return Forward(loss);
 }
 
 template <typename Dtype>
@@ -636,24 +568,20 @@ void Net<Dtype>::BackwardFromTo(int start, int end) {
   CHECK_GE(end, 0);
   CHECK_LT(start, layers_.size());
   for (int i = start; i >= end; --i) {
+    for (int c = 0; c < before_backward_.size(); ++c) {
+      before_backward_[c]->run(i);
+    }
     if (layer_need_backward_[i]) {
       layers_[i]->Backward(
           top_vecs_[i], bottom_need_backward_[i], bottom_vecs_[i]);
       if (debug_info_) { BackwardDebugInfo(i); }
     }
+    for (int c = 0; c < after_backward_.size(); ++c) {
+      after_backward_[c]->run(i);
+    }
   }
 }
 
-template <typename Dtype>
-void Net<Dtype>::InputDebugInfo(const int input_id) {
-  const Blob<Dtype>& blob = *net_input_blobs_[input_id];
-  const string& blob_name = blob_names_[net_input_blob_indices_[input_id]];
-  const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
-  LOG_IF(INFO, Caffe::root_solver())
-      << "    [Forward] "
-      << "Input " << blob_name << " data: " << data_abs_val_mean;
-}
-
 template <typename Dtype>
 void Net<Dtype>::ForwardDebugInfo(const int layer_id) {
   for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
@@ -841,8 +769,7 @@ void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
 
 template <typename Dtype>
 void Net<Dtype>::CopyTrainedLayersFrom(const string trained_filename) {
-  if (trained_filename.size() >= 3 &&
-      trained_filename.compare(trained_filename.size() - 3, 3, ".h5") == 0) {
+  if (H5Fis_hdf5(trained_filename.c_str())) {
     CopyTrainedLayersFromHDF5(trained_filename);
   } else {
     CopyTrainedLayersFromBinaryProto(trained_filename);
@@ -912,9 +839,6 @@ void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) const {
   param->Clear();
   param->set_name(name_);
   // Add bottom and top
-  for (int i = 0; i < net_input_blob_indices_.size(); ++i) {
-    param->add_input(blob_names_[net_input_blob_indices_[i]]);
-  }
   DLOG(INFO) << "Serializing " << layers_.size() << " layers";
   for (int i = 0; i < layers_.size(); ++i) {
     LayerParameter* layer_param = param->add_layer();
diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp
index 62f5d738..d9433917 100644
--- a/src/caffe/parallel.cpp
+++ b/src/caffe/parallel.cpp
@@ -1,16 +1,15 @@
-#ifndef CPU_ONLY
+#ifdef USE_NCCL
+
 #include <cuda_runtime.h>
-#endif
 #include <glog/logging.h>
 #include <stdio.h>
-
 #include <sstream>
 #include <string>
 #include <vector>
 
-#include "boost/thread.hpp"
 #include "caffe/caffe.hpp"
 #include "caffe/parallel.hpp"
+#include "caffe/sgd_solvers.hpp"
 
 namespace caffe {
 
@@ -68,15 +67,14 @@ static size_t total_size(const vector<Blob<Dtype>*>& params) {
 
 template<typename Dtype>
 Params<Dtype>::Params(shared_ptr<Solver<Dtype> > root_solver)
-    : size_(total_size<Dtype>(root_solver->net()->learnable_params())),
-      data_(),
-      diff_() {
+  : size_(total_size<Dtype>(root_solver->net()->learnable_params())),
+    data_(),
+    diff_() {
 }
 
 template<typename Dtype>
 GPUParams<Dtype>::GPUParams(shared_ptr<Solver<Dtype> > root_solver, int device)
-    : Params<Dtype>(root_solver) {
-#ifndef CPU_ONLY
+  : Params<Dtype>(root_solver) {
   int initial_device;
   CUDA_CHECK(cudaGetDevice(&initial_device));
 
@@ -86,352 +84,288 @@ GPUParams<Dtype>::GPUParams(shared_ptr<Solver<Dtype> > root_solver, int device)
 
   // Copy blob values
   const vector<Blob<Dtype>*>& net =
-      root_solver->net()->learnable_params();
+    root_solver->net()->learnable_params();
   apply_buffers(net, data_, size_, copy);
 
   CUDA_CHECK(cudaMalloc(&diff_, size_ * sizeof(Dtype)));
   caffe_gpu_set(size_, Dtype(0), diff_);
 
   CUDA_CHECK(cudaSetDevice(initial_device));
-#else
-  NO_GPU;
-#endif
 }
 
 template<typename Dtype>
 GPUParams<Dtype>::~GPUParams() {
-#ifndef CPU_ONLY
   CUDA_CHECK(cudaFree(data_));
   CUDA_CHECK(cudaFree(diff_));
-#endif
 }
 
 template<typename Dtype>
-void GPUParams<Dtype>::configure(Solver<Dtype>* solver) const {
+void GPUParams<Dtype>::Configure(Solver<Dtype>* solver) const {
   const vector<Blob<Dtype>*>& net =
-      solver->net()->learnable_params();
+    solver->net()->learnable_params();
   apply_buffers(net, data_, size_, replace_gpu);
   apply_buffers(net, diff_, size_, replace_gpu_diff);
 }
 
-void DevicePair::compute(const vector<int> devices, vector<DevicePair>* pairs) {
-#ifndef CPU_ONLY
-  vector<int> remaining(devices);
-
-  // Depth for reduction tree
-  int remaining_depth = static_cast<int>(ceil(log2(remaining.size())));
-
-  // Group GPUs by board
-  for (int d = 0; d < remaining_depth; ++d) {
-    for (int i = 0; i < remaining.size(); ++i) {
-      for (int j = i + 1; j < remaining.size(); ++j) {
-        cudaDeviceProp a, b;
-        CUDA_CHECK(cudaGetDeviceProperties(&a, remaining[i]));
-        CUDA_CHECK(cudaGetDeviceProperties(&b, remaining[j]));
-        if (a.isMultiGpuBoard && b.isMultiGpuBoard) {
-          if (a.multiGpuBoardGroupID == b.multiGpuBoardGroupID) {
-            pairs->push_back(DevicePair(remaining[i], remaining[j]));
-            DLOG(INFO) << "GPU board: " << remaining[i] << ":" << remaining[j];
-            remaining.erase(remaining.begin() + j);
-            break;
-          }
-        }
-      }
-    }
-  }
-  ostringstream s;
-  for (int i = 0; i < remaining.size(); ++i) {
-    s << (i ? ", " : "") << remaining[i];
-  }
-  DLOG(INFO) << "GPUs paired by boards, remaining: " << s.str();
-
-  // Group by P2P accessibility
-  remaining_depth = ceil(log2(remaining.size()));
-  for (int d = 0; d < remaining_depth; ++d) {
-    for (int i = 0; i < remaining.size(); ++i) {
-      for (int j = i + 1; j < remaining.size(); ++j) {
-        int access;
-        CUDA_CHECK(
-            cudaDeviceCanAccessPeer(&access, remaining[i], remaining[j]));
-        if (access) {
-          pairs->push_back(DevicePair(remaining[i], remaining[j]));
-          DLOG(INFO) << "P2P pair: " << remaining[i] << ":" << remaining[j];
-          remaining.erase(remaining.begin() + j);
-          break;
-        }
-      }
-    }
-  }
-  s.str("");
-  for (int i = 0; i < remaining.size(); ++i) {
-    s << (i ? ", " : "") << remaining[i];
-  }
-  DLOG(INFO) << "GPUs paired by P2P access, remaining: " << s.str();
-
-  // Group remaining
-  remaining_depth = ceil(log2(remaining.size()));
-  for (int d = 0; d < remaining_depth; ++d) {
-    for (int i = 0; i < remaining.size(); ++i) {
-      pairs->push_back(DevicePair(remaining[i], remaining[i + 1]));
-      DLOG(INFO) << "Remaining pair: " << remaining[i] << ":"
-                 << remaining[i + 1];
-      remaining.erase(remaining.begin() + i + 1);
-    }
-  }
+static int getDevice() {
+  int device = 0;
+  CUDA_CHECK(cudaGetDevice(&device));
+  return device;
+}
 
-  // Should only be the parent node remaining
-  CHECK_EQ(remaining.size(), 1);
+template<typename Dtype>
+NCCL<Dtype>::NCCL(shared_ptr<Solver<Dtype> > solver)
+  : GPUParams<Dtype>(solver, getDevice()),
+    comm_(), solver_(solver), barrier_() {
+  this->Configure(solver.get());
+  Init();
+}
 
-  pairs->insert(pairs->begin(), DevicePair(-1, remaining[0]));
+template<typename Dtype>
+NCCL<Dtype>::NCCL(shared_ptr<Solver<Dtype> > solver, const string& uid)
+  : GPUParams<Dtype>(solver, getDevice()),
+    solver_(solver), barrier_() {
+  this->Configure(solver.get());
+  Caffe::set_multiprocess(true);
+  ncclUniqueId nccl_uid;
+  memcpy(&nccl_uid, &uid[0], NCCL_UNIQUE_ID_BYTES);  // NOLINT(caffe/alt_fn)
+  NCCL_CHECK(ncclCommInitRank(&comm_,
+                              Caffe::solver_count(),
+                              nccl_uid,
+                              Caffe::solver_rank()));
+  Init();
+}
 
-  CHECK(pairs->size() == devices.size());
-  for (int i = 0; i < pairs->size(); ++i) {
-    CHECK((*pairs)[i].parent() != (*pairs)[i].device());
-    for (int j = i + 1; j < pairs->size(); ++j) {
-      CHECK((*pairs)[i].device() != (*pairs)[j].device());
-    }
+template<typename Dtype>
+void NCCL<Dtype>::Init() {
+  if (solver_->param().layer_wise_reduce()) {
+    CUDA_CHECK(cudaStreamCreateWithFlags(&stream_, cudaStreamNonBlocking));
   }
-#else
-  NO_GPU;
-#endif
 }
 
-//
-
 template<typename Dtype>
-P2PSync<Dtype>::P2PSync(shared_ptr<Solver<Dtype> > root_solver,
-                        P2PSync<Dtype>* parent, const SolverParameter& param)
-    : GPUParams<Dtype>(root_solver, param.device_id()),
-      parent_(parent),
-      children_(),
-      queue_(),
-      initial_iter_(root_solver->iter()),
-      solver_() {
-#ifndef CPU_ONLY
-  int initial_device;
-  CUDA_CHECK(cudaGetDevice(&initial_device));
-  const int self = param.device_id();
-  CUDA_CHECK(cudaSetDevice(self));
-
-  if (parent == NULL) {
-    solver_ = root_solver;
-  } else {
-    Caffe::set_root_solver(false);
-    solver_.reset(new WorkerSolver<Dtype>(param, root_solver.get()));
-    Caffe::set_root_solver(true);
+NCCL<Dtype>::~NCCL() {
+  if (solver_->param().layer_wise_reduce()) {
+    CUDA_CHECK(cudaStreamDestroy(stream_));
   }
-  this->configure(solver_.get());
-  solver_->add_callback(this);
-
-  if (parent) {
-    // Enable p2p access between devices
-    const int peer = parent->solver_->param().device_id();
-    int access;
-    CUDA_CHECK(cudaDeviceCanAccessPeer(&access, self, peer));
-    if (access) {
-      CUDA_CHECK(cudaDeviceEnablePeerAccess(peer, 0));
-    } else {
-      LOG(INFO)<< "GPU " << self << " does not have p2p access to GPU " << peer;
-    }
-    // Allocate receiving buffer on parent
-    CUDA_CHECK(cudaSetDevice(peer));
-    CUDA_CHECK(cudaMalloc(&parent_grads_, size_ * sizeof(Dtype)));
-    CUDA_CHECK(cudaSetDevice(self));
+  if (comm_) {
+    ncclCommDestroy(comm_);
   }
-
-  CUDA_CHECK(cudaSetDevice(initial_device));
-#else
-  NO_GPU;
-#endif
 }
 
 template<typename Dtype>
-P2PSync<Dtype>::~P2PSync() {
-#ifndef CPU_ONLY
-  int initial_device;
-  CUDA_CHECK(cudaGetDevice(&initial_device));
-  const int self = solver_->param().device_id();
-  CUDA_CHECK(cudaSetDevice(self));
-
-  if (parent_) {
-    CUDA_CHECK(cudaFree(parent_grads_));
-    const int peer = parent_->solver_->param().device_id();
-    int access;
-    CUDA_CHECK(cudaDeviceCanAccessPeer(&access, self, peer));
-    if (access) {
-      CUDA_CHECK(cudaDeviceDisablePeerAccess(peer));
-    }
-  }
-
-  CUDA_CHECK(cudaSetDevice(initial_device));
-#endif
+boost::barrier* NCCL<Dtype>::barrier() {
+  return barrier_;
+}
+template<typename Dtype>
+void NCCL<Dtype>::set_barrier(boost::barrier* value) {
+  barrier_ = value;
 }
 
 template<typename Dtype>
-void P2PSync<Dtype>::InternalThreadEntry() {
-  Caffe::SetDevice(solver_->param().device_id());
-  CHECK(Caffe::root_solver());
-  Caffe::set_root_solver(false);
-  // See if there is a defined seed and reset random state if so
-  if (solver_->param().random_seed() >= 0) {
-    // Fetch random seed and modulate by device ID to make sure
-    // everyone doesn't have the same seed.  We seem to have some
-    // solver instability if we have everyone with the same seed
-    Caffe::set_random_seed(
-        solver_->param().random_seed() + solver_->param().device_id());
+void NCCL<Dtype>::InitSingleProcess(vector<NCCL<Dtype>*>* nccls) {
+  ncclComm_t* comms = new ncclComm_t[nccls->size()];
+  int* gpu_list = new int[nccls->size()];
+  for (int i = 0; i < nccls->size(); ++i) {
+    gpu_list[i] = (*nccls)[i]->solver_->param().device_id();
+  }
+  NCCL_CHECK(ncclCommInitAll(comms, static_cast<int>(nccls->size()), gpu_list));
+  for (int i = 0; i < nccls->size(); ++i) {
+    (*nccls)[i]->comm_ = comms[i];
   }
-  solver_->Step(solver_->param().max_iter() - initial_iter_);
 }
 
 template<typename Dtype>
-void P2PSync<Dtype>::on_start() {
-#ifndef CPU_ONLY
-#ifdef DEBUG
-  int device;
-  CUDA_CHECK(cudaGetDevice(&device));
-  CHECK(device == solver_->param().device_id());
-#else
-//  CHECK(false);
-#endif
+string NCCL<Dtype>::new_uid() {
+  string uid;
+  uid.resize(NCCL_UNIQUE_ID_BYTES);
+  ncclUniqueId nccl_uid;
+  NCCL_CHECK(ncclGetUniqueId(&nccl_uid));
+  memcpy(&uid[0], &nccl_uid, NCCL_UNIQUE_ID_BYTES);  // NOLINT(caffe/alt_fn)
+  return uid;
+}
 
-  // Wait for update from parent
-  if (parent_) {
-    P2PSync<Dtype> *parent = queue_.pop();
-    CHECK(parent == parent_);
+template<typename Dtype>
+void NCCL<Dtype>::Broadcast() {
+  if (barrier_) {  // NULL in multi process case
+    barrier_->wait();
   }
-
-  // Update children
-  for (int i = children_.size() - 1; i >= 0; i--) {
-    Dtype* src = data_;
-    Dtype* dst = children_[i]->data_;
-
-#ifdef DEBUG
-    cudaPointerAttributes attributes;
-    CUDA_CHECK(cudaPointerGetAttributes(&attributes, src));
-    CHECK(attributes.device == device);
-    CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst));
-    CHECK(attributes.device == children_[i]->solver_->param().device_id());
-#endif
-
-    CUDA_CHECK(cudaMemcpyAsync(dst, src, size_ * sizeof(Dtype),
-        cudaMemcpyDeviceToDevice, cudaStreamDefault));
-    CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
-    children_[i]->queue_.push(this);
+  NCCL_CHECK(ncclBcast(data_, static_cast<int>(size_),
+                       nccl::dataType<Dtype>::type, 0,
+                       comm_, cudaStreamDefault));
+  if (barrier_) {
+    barrier_->wait();
   }
-#endif
 }
 
 template<typename Dtype>
-void P2PSync<Dtype>::on_gradients_ready() {
-#ifndef CPU_ONLY
+void NCCL<Dtype>::run(int layer) {
+  CHECK(solver_->param().layer_wise_reduce());
+  vector<shared_ptr<Blob<Dtype> > >& blobs =
+    solver_->net()->layers()[layer]->blobs();
 #ifdef DEBUG
-  int device;
-  CUDA_CHECK(cudaGetDevice(&device));
-  CHECK(device == solver_->param().device_id());
+  // Assert blobs are contiguous to reduce in one step (e.g. bias often small)
+  for (int i = 1; i < blobs.size(); ++i) {
+    CHECK_EQ(blobs[i - 1]->gpu_diff() + blobs[i - 1]->count(),
+             blobs[i + 0]->gpu_diff());
+  }
 #endif
+  if (blobs.size() > 0) {
+    // Make sure default stream is done computing gradients. Could be
+    // replaced by cudaEventRecord+cudaStreamWaitEvent to avoid
+    // blocking the default stream, but it's actually slower.
+    CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
 
-  // Sum children gradients as they appear in the queue
-  for (int i = 0; i < children_.size(); ++i) {
-    P2PSync<Dtype> *child = queue_.pop();
-    Dtype* src = child->parent_grads_;
-    Dtype* dst = diff_;
-
-#ifdef DEBUG
-    bool ok = false;
-    for (int j = 0; j < children_.size(); ++j) {
-      if (child == children_[j]) {
-        ok = true;
-      }
+    // Reduce asynchronously
+    int size = 0;
+    for (int i = 0; i < blobs.size(); ++i) {
+      size += blobs[i]->count();
     }
-    CHECK(ok);
-    cudaPointerAttributes attributes;
-    CUDA_CHECK(cudaPointerGetAttributes(&attributes, src));
-    CHECK(attributes.device == device);
-    CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst));
-    CHECK(attributes.device == device);
-#endif
-
-    caffe_gpu_add(size_, src, dst, dst);
+    if (barrier_) {  // NULL in multi process case
+      barrier_->wait();
+    }
+    NCCL_CHECK(ncclAllReduce(blobs[0]->mutable_gpu_diff(),
+                             blobs[0]->mutable_gpu_diff(),
+                             size,
+                             nccl::dataType<Dtype>::type,
+                             ncclSum, comm_, stream_));
+    caffe_gpu_scal(size, (Dtype) 1.0 / Caffe::solver_count(),
+                   blobs[0]->mutable_gpu_diff(), stream_);
   }
+}
 
-  // Send gradients to parent
-  if (parent_) {
-    Dtype* src = diff_;
-    Dtype* dst = parent_grads_;
-
-#ifdef DEBUG
-    cudaPointerAttributes attributes;
-    CUDA_CHECK(cudaPointerGetAttributes(&attributes, src));
-    CHECK(attributes.device == device);
-    CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst));
-    CHECK(attributes.device == parent_->solver_->param().device_id());
-#endif
-
-    CUDA_CHECK(cudaMemcpyAsync(dst, src, size_ * sizeof(Dtype),  //
-        cudaMemcpyDeviceToDevice, cudaStreamDefault));
-    CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
-    parent_->queue_.push(this);
+template<typename Dtype>
+void NCCL<Dtype>::on_gradients_ready() {
+  if (solver_->param().layer_wise_reduce()) {
+    CHECK_EQ(solver_->net()->params().size(),
+             solver_->net()->learnable_params().size())
+      << "Layer-wise reduce is not supported for nets with shared weights.";
+
+    // Make sure reduction is done before applying gradients
+    CUDA_CHECK(cudaStreamSynchronize(stream_));
   } else {
-    // Loss functions divide gradients by the batch size, so to compensate
-    // for split batch, the root solver divides by number of solvers.
-    caffe_gpu_scal(size_, Dtype(1.0 / Caffe::solver_count()), diff_);
+    if (barrier_) {  // NULL in multi process case
+      barrier_->wait();
+    }
+    NCCL_CHECK(ncclAllReduce(diff_, diff_, static_cast<int>(size_),
+                             nccl::dataType<Dtype>::type, ncclSum, comm_,
+                             cudaStreamDefault));
+    caffe_gpu_scal(static_cast<int>(size_),
+                   (Dtype) 1.0 / Caffe::solver_count(), diff_);
   }
-#endif
 }
 
 template<typename Dtype>
-void P2PSync<Dtype>::run(const vector<int>& gpus) {
-  // Pair devices for map-reduce synchronization
-  vector<DevicePair> pairs;
-  DevicePair::compute(gpus, &pairs);
-  ostringstream s;
-  for (int i = 1; i < pairs.size(); ++i) {
-    s << (i == 1 ? "" : ", ") << pairs[i].parent() << ":" << pairs[i].device();
+class Worker : public InternalThread {
+ public:
+  explicit Worker(shared_ptr<Solver<Dtype> > rank0, int device,
+                  boost::barrier* barrier, vector<NCCL<Dtype>*>* nccls,
+                  const char* restore)
+    : rank0_(rank0), device_(device), barrier_(barrier),
+      nccls_(nccls), restore_(restore) {
   }
-  LOG(INFO)<< "GPUs pairs " << s.str();
-
-  SolverParameter param(solver_->param());
-  vector<shared_ptr<P2PSync<Dtype> > > syncs(gpus.size());
-
-  // Build the GPU tree by finding the parent for each solver
-  for (int attempts = 0; attempts < pairs.size(); ++attempts) {
-    for (int i = 1; i < pairs.size(); ++i) {
-      if (!syncs[i].get()) {
-        P2PSync<Dtype>* parent = NULL;
-        for (int j = 0; j < syncs.size(); ++j) {
-          P2PSync<Dtype>* sync = j == 0 ? this : syncs[j].get();
-          if (sync) {
-            const SolverParameter& p = sync->solver()->param();
-            if (p.device_id() == pairs[i].parent()) {
-              parent = sync;
-            }
-          }
-        }
-        if (parent) {
-          param.set_device_id(pairs[i].device());
-          syncs[i].reset(new P2PSync<Dtype>(solver_, parent, param));
-          parent->children_.push_back((P2PSync<Dtype>*) syncs[i].get());
-        }
+  virtual ~Worker() {}
+
+ protected:
+  void InternalThreadEntry() {
+    // Create solver and install callbacks
+    SolverParameter param(rank0_->param());
+    param.set_device_id(device_);
+#ifdef DEBUG
+    int device;
+    CUDA_CHECK(cudaGetDevice(&device));
+    CHECK_EQ(device, device_);
+#endif
+    param.set_type(rank0_->type());
+    shared_ptr<Solver<Dtype> > s(SolverRegistry<Dtype>::CreateSolver(param));
+    CHECK_EQ(s->type(), rank0_->type());
+    if (restore_) {
+      // Could not make NCCL broadcast solver state, it seems to crash
+      // if called in a tight loop, regardless of barriers etc. so
+      // restore all solvers from file.
+      s->Restore(restore_);
+    }
+    NCCL<Dtype> nccl(s);
+    nccl.set_barrier(barrier_);
+    s->add_callback(&nccl);
+    if (s->param().layer_wise_reduce()) {
+      s->net()->add_after_backward(&nccl);
+    }
+    (*nccls_)[Caffe::solver_rank()] = &nccl;
+    // Wait for other threads
+    barrier_->wait();
+    // Wait for NCCL init
+    barrier_->wait();
+    // Broadcast rank 0 state
+    nccl.Broadcast();
+    // Solve
+    s->Step(param.max_iter() - s->iter());
+    barrier_->wait();
+#ifdef DEBUG
+    // Check all solvers have same state
+    SGDSolver<Dtype>* sa = static_cast<SGDSolver<Dtype>*>(rank0_.get());
+    SGDSolver<Dtype>* sb = static_cast<SGDSolver<Dtype>*>(s.get());
+    for (int h = 0; h < sa->history().size(); ++h) {
+      CUDA_CHECK(cudaSetDevice(sa->param().device_id()));
+      const Dtype* a = sa->history()[h]->cpu_data();
+      CUDA_CHECK(cudaSetDevice(sb->param().device_id()));
+      const Dtype* b = sb->history()[h]->cpu_data();
+      for (int v = 0; v < sa->history()[h]->count(); ++v) {
+        CHECK_DOUBLE_EQ(a[v], b[v]);
       }
     }
+#endif
   }
 
-  LOG(INFO)<< "Starting Optimization";
+  shared_ptr<Solver<Dtype> > rank0_;
+  int device_;
+  boost::barrier* barrier_;
+  vector<NCCL<Dtype>*>* nccls_;
+  const char* restore_;
+};
 
-  for (int i = 1; i < syncs.size(); ++i) {
-    syncs[i]->StartInternalThread();
+template<typename Dtype>
+void NCCL<Dtype>::Run(const vector<int>& gpus, const char* restore) {
+  boost::barrier barrier(static_cast<int>(gpus.size()));
+  vector<NCCL<Dtype>*> nccls(gpus.size());
+  // Create workers
+  vector<shared_ptr<Worker<Dtype> > > workers(gpus.size());
+  for (int i = 1; i < gpus.size(); ++i) {
+    CUDA_CHECK(cudaSetDevice(gpus[i]));
+    Caffe::set_solver_rank(i);
+    Worker<Dtype>* w = new Worker<Dtype>(solver_, gpus[i], &barrier,
+                                         &nccls, restore);
+    w->StartInternalThread();
+    workers[i].reset(w);
   }
-
-  // Run root solver on current thread
+  CUDA_CHECK(cudaSetDevice(gpus[0]));
+  Caffe::set_solver_rank(0);
+  barrier_ = &barrier;
+  solver_->add_callback(this);
+  if (solver_->param().layer_wise_reduce()) {
+    solver_->net()->add_after_backward(this);
+  }
+  nccls[0] = this;
+  // Wait for workers
+  barrier.wait();
+  // Init NCCL
+  InitSingleProcess(&nccls);
+  barrier.wait();
+  // Run first solver on current thread
+  Broadcast();
   solver_->Solve();
-
-  for (int i = 1; i < syncs.size(); ++i) {
-    syncs[i]->StopInternalThread();
+  barrier.wait();  // Hangs without it when running tests
+  // Wait for shutdown
+  for (int i = 1; i < gpus.size(); ++i) {
+    workers[i]->StopInternalThread();
   }
 }
 
 INSTANTIATE_CLASS(Params);
 INSTANTIATE_CLASS(GPUParams);
-INSTANTIATE_CLASS(P2PSync);
+INSTANTIATE_CLASS(Worker);
+INSTANTIATE_CLASS(NCCL);
 
 }  // namespace caffe
+
+#endif  // USE_NCCL
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index f23eba3a..6e3f4abd 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -63,12 +63,12 @@ message FillerParameter {
 
 message NetParameter {
   optional string name = 1; // consider giving the network a name
-  // The input blobs to the network.
+  // DEPRECATED. See InputParameter. The input blobs to the network.
   repeated string input = 3;
-  // The shape of the input blobs.
+  // DEPRECATED. See InputParameter. The shape of the input blobs.
   repeated BlobShape input_shape = 8;
 
-  // 4D input dimensions -- deprecated.  Use "shape" instead.
+  // 4D input dimensions -- deprecated.  Use "input_shape" instead.
   // If specified, for each input blob there should be four
   // values specifying the num, channels, height and width of the input blob.
   // Thus, there should be a total of (4 * #input) numbers.
@@ -98,7 +98,7 @@ message NetParameter {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 41 (last added: type)
+// SolverParameter next available ID: 43 (last added: layer_wise_reduce)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -128,8 +128,7 @@ message SolverParameter {
   // The states for the train/test nets. Must be unspecified or
   // specified once per net.
   //
-  // By default, all states will have solver = true;
-  // train_state will have phase = TRAIN,
+  // By default, train_state will have phase = TRAIN,
   // and all test_state's will have phase = TEST.
   // Other defaults are set according to the NetState defaults.
   optional NetState train_state = 26;
@@ -137,7 +136,7 @@ message SolverParameter {
 
   // The number of iterations for each test net.
   repeated int32 test_iter = 3;
-  repeated float multistep_lr = 41;
+  repeated float multistep_lr = 42;
 
   // The number of iterations between two testing phases.
   optional int32 test_interval = 4 [default = 0];
@@ -220,7 +219,7 @@ message SolverParameter {
 
   // RMSProp decay value
   // MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)
-  optional float rms_decay = 38;
+  optional float rms_decay = 38 [default = 0.99];
 
   // If true, print information about the state of the net that may help with
   // debugging learning problems.
@@ -240,6 +239,9 @@ message SolverParameter {
   }
   // DEPRECATED: use type instead of solver_type
   optional SolverType solver_type = 30 [default = SGD];
+
+  // Overlap compute and communication for data parallel training
+  optional bool layer_wise_reduce = 41 [default = true];
 }
 
 // A message that stores the solver snapshots
@@ -307,7 +309,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 143 (last added: scale_param)
+// LayerParameter next available layer-specific ID: 147 (last added: recurrent_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -329,7 +331,12 @@ message LayerParameter {
   // The blobs containing the numeric parameters of the layer.
   repeated BlobProto blobs = 7;
 
-  // Specifies on which bottoms the backpropagation should be skipped.
+  // Specifies whether to backpropagate to each bottom. If unspecified,
+  // Caffe will automatically infer whether each input needs backpropagation
+  // to compute parameter gradients. If set to true for some inputs,
+  // backpropagation to those inputs is forced; if set false for some inputs,
+  // backpropagation to those inputs is skipped.
+  //
   // The size must be either 0 or equal to the number of bottoms.
   repeated bool propagate_down = 11;
 
@@ -361,7 +368,7 @@ message LayerParameter {
   optional ConcatParameter concat_param = 104;
   optional ContrastiveLossParameter contrastive_loss_param = 105;
   optional ConvolutionParameter convolution_param = 106;
-  optional CropParameter crop_param = 143;
+  optional CropParameter crop_param = 144;
   optional DataParameter data_param = 107;
   optional DropoutParameter dropout_param = 108;
   optional DummyDataParameter dummy_data_param = 109;
@@ -376,14 +383,17 @@ message LayerParameter {
   optional ImageDataParameter image_data_param = 115;
   optional InfogainLossParameter infogain_loss_param = 116;
   optional InnerProductParameter inner_product_param = 117;
+  optional InputParameter input_param = 143;
   optional LogParameter log_param = 134;
   optional LRNParameter lrn_param = 118;
   optional MemoryDataParameter memory_data_param = 119;
   optional MVNParameter mvn_param = 120;
+  optional ParameterParameter parameter_param = 145;
   optional PoolingParameter pooling_param = 121;
   optional PowerParameter power_param = 122;
   optional PReLUParameter prelu_param = 131;
   optional PythonParameter python_param = 130;
+  optional RecurrentParameter recurrent_param = 146;
   optional ReductionParameter reduction_param = 136;
   optional ReLUParameter relu_param = 123;
   optional ReshapeParameter reshape_param = 133;
@@ -413,7 +423,7 @@ message TransformationParameter {
   optional uint32 crop_size = 3 [default = 0];
   // mean_file and mean_value cannot be specified at the same time
   optional string mean_file = 4;
-  // if specified can be repeated once (would substract it from all the channels)
+  // if specified can be repeated once (would subtract it from all the channels)
   // or can be repeated the same number of times as channels
   // (would subtract them from the corresponding channel)
   repeated float mean_value = 5;
@@ -429,13 +439,13 @@ message LossParameter {
   optional int32 ignore_label = 1;
   // How to normalize the loss for loss layers that aggregate across batches,
   // spatial dimensions, or other dimensions.  Currently only implemented in
-  // SoftmaxWithLoss layer.
+  // SoftmaxWithLoss and SigmoidCrossEntropyLoss layers.
   enum NormalizationMode {
     // Divide by the number of examples in the batch times spatial dimensions.
     // Outputs that receive the ignore label will NOT be ignored in computing
     // the normalization factor.
     FULL = 0;
-    // Divide by the total number of output locations that do not take the 
+    // Divide by the total number of output locations that do not take the
     // ignore_label.  If ignore_label is not set, this behaves like FULL.
     VALID = 1;
     // Divide by the batch size.
@@ -443,6 +453,8 @@ message LossParameter {
     // Do not normalize the loss.
     NONE = 3;
   }
+  // For historical reasons, the default normalization for
+  // SigmoidCrossEntropyLoss is BATCH_SIZE and *not* VALID.
   optional NormalizationMode normalization = 3 [default = VALID];
   // Deprecated.  Ignored if normalization is specified.  If normalization
   // is not specified, then setting this to false will be equivalent to
@@ -499,11 +511,21 @@ message ConcatParameter {
 }
 
 message BatchNormParameter {
-  // If false, accumulate global mean/variance values via a moving average. If
-  // true, use those accumulated values instead of computing mean/variance
-  // across the batch.
+  // If false, normalization is performed over the current mini-batch
+  // and global statistics are accumulated (but not yet used) by a moving
+  // average.
+  // If true, those accumulated mean and variance values are used for the
+  // normalization.
+  // By default, it is set to false when the network is in the training
+  // phase and true when the network is in the testing phase.
   optional bool use_global_stats = 1;
-  // How much does the moving average decay each iteration?
+  // What fraction of the moving average remains each iteration?
+  // Smaller values make the moving average decay faster, giving more
+  // weight to the recent values.
+  // Each iteration updates the moving average @f$S_{t-1}@f$ with the
+  // current mean @f$ Y_t @f$ by
+  // @f$ S_t = (1-\beta)Y_t + \beta \cdot S_{t-1} @f$, where @f$ \beta @f$
+  // is the moving_average_fraction parameter.
   optional float moving_average_fraction = 2 [default = .999];
   // Small value to add to the variance estimate so that we don't divide by
   // zero.
@@ -608,10 +630,21 @@ message ConvolutionParameter {
 }
 
 message CropParameter {
-  // Assumes standard dimensions: ( N,C,H,W )
-  // This could possibly be extended to  use "optional BlobShape offsets"
-  optional uint32 offset_height = 1[default = 0];
-  optional uint32 offset_width = 2[default = 0];
+  // To crop, elements of the first bottom are selected to fit the dimensions
+  // of the second, reference bottom. The crop is configured by
+  // - the crop `axis` to pick the dimensions for cropping
+  // - the crop `offset` to set the shift for all/each dimension
+  // to align the cropped bottom with the reference bottom.
+  // All dimensions up to but excluding `axis` are preserved, while
+  // the dimensions including and trailing `axis` are cropped.
+  // If only one `offset` is set, then all dimensions are offset by this amount.
+  // Otherwise, the number of offsets must equal the number of cropped axes to
+  // shift the crop in each dimension accordingly.
+  // Note: standard dimensions are N,C,H,W so the default is a spatial crop,
+  // and `axis` may be negative to index from the end (e.g., -1 for the last
+  // axis).
+  optional int32 axis = 1 [default = 2];
+  repeated uint32 offset = 2;
 }
 
 message DataParameter {
@@ -643,8 +676,8 @@ message DataParameter {
   optional bool mirror = 6 [default = false];
   // Force the encoded image to have 3 color channels
   optional bool force_encoded_color = 9 [default = false];
-  // Prefetch queue (Number of batches to prefetch to host memory, increase if
-  // data access bandwidth varies).
+  // Prefetch queue (Increase if data feeding bandwidth varies, within the
+  // limit of device memory for GPU training)
   optional uint32 prefetch = 10 [default = 4];
 }
 
@@ -688,7 +721,7 @@ message EltwiseParameter {
 // Message that stores parameters used by ELULayer
 message ELUParameter {
   // Described in:
-  // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate 
+  // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate
   // Deep Network Learning by Exponential Linear Units (ELUs). arXiv
   optional float alpha = 1 [default = 1];
 }
@@ -791,6 +824,7 @@ message ImageDataParameter {
 message InfogainLossParameter {
   // Specify the infogain matrix source.
   optional string source = 1;
+  optional int32 axis = 2 [default = 1]; // axis of prob
 }
 
 message InnerProductParameter {
@@ -803,6 +837,19 @@ message InnerProductParameter {
   // all preceding axes are retained in the output.
   // May be negative to index from the end (e.g., -1 for the last axis).
   optional int32 axis = 5 [default = 1];
+  // Specify whether to transpose the weight matrix or not.
+  // If transpose == true, any operations will be performed on the transpose
+  // of the weight matrix. The weight matrix itself is not going to be transposed
+  // but rather the transfer flag of operations will be toggled accordingly.
+  optional bool transpose = 6 [default = false];
+}
+
+message InputParameter {
+  // This layer produces N >= 1 top blob(s) to be assigned manually.
+  // Define N shapes to set a shape for each top.
+  // Define 1 shape to set the same shape for every top.
+  // Define no shape to defer to reshaping manually.
+  repeated BlobShape shape = 1;
 }
 
 // Message that stores parameters used by LogLayer
@@ -852,6 +899,10 @@ message MVNParameter {
   optional float eps = 3 [default = 1e-9];
 }
 
+message ParameterParameter {
+  optional BlobShape shape = 1;
+}
+
 message PoolingParameter {
   enum PoolMethod {
     MAX = 0;
@@ -896,12 +947,29 @@ message PythonParameter {
   // string, dictionary in Python dict format, JSON, etc. You may parse this
   // string in `setup` method and use it in `forward` and `backward`.
   optional string param_str = 3 [default = ''];
-  // Whether this PythonLayer is shared among worker solvers during data parallelism.
-  // If true, each worker solver sequentially run forward from this layer.
-  // This value should be set true if you are using it as a data layer.
+  // DEPRECATED
   optional bool share_in_parallel = 4 [default = false];
 }
 
+// Message that stores parameters used by RecurrentLayer
+message RecurrentParameter {
+  // The dimension of the output (and usually hidden state) representation --
+  // must be explicitly set to non-zero.
+  optional uint32 num_output = 1 [default = 0];
+
+  optional FillerParameter weight_filler = 2; // The filler for the weight
+  optional FillerParameter bias_filler = 3; // The filler for the bias
+
+  // Whether to enable displaying debug_info in the unrolled recurrent net.
+  optional bool debug_info = 4 [default = false];
+
+  // Whether to add as additional inputs (bottoms) the initial hidden state
+  // blobs, and add as additional outputs (tops) the final timestep hidden state
+  // blobs.  The number of additional bottom/top blobs required depends on the
+  // recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.
+  optional bool expose_hidden = 5 [default = false];
+}
+
 // Message that stores parameters used by ReductionLayer
 message ReductionParameter {
   enum ReductionOp {
@@ -966,7 +1034,7 @@ message ReshapeParameter {
   //   reshape_param { shape { dim:  2  dim: 2  dim:  4 } }
   //   reshape_param { shape { dim:  0  dim: 2  dim:  4 } }
   //   reshape_param { shape { dim:  0  dim: 2  dim: -1 } }
-  //   reshape_param { shape { dim: -1  dim: 0  dim:  2 } }
+  //   reshape_param { shape { dim:  0  dim:-1  dim:  4 } }
   //
   optional BlobShape shape = 1;
 
@@ -1394,6 +1462,6 @@ message PReLUParameter {
 
   // Initial value of a_i. Default is a_i=0.25 for all i.
   optional FillerParameter filler = 1;
-  // Whether or not slope paramters are shared across channels.
+  // Whether or not slope parameters are shared across channels.
   optional bool channel_shared = 2 [default = false];
 }
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index a5ccf9c7..04426937 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -26,16 +26,14 @@ SolverAction::Enum Solver<Dtype>::GetRequestedAction() {
 }
 
 template <typename Dtype>
-Solver<Dtype>::Solver(const SolverParameter& param, const Solver* root_solver)
-    : net_(), callbacks_(), root_solver_(root_solver),
-      requested_early_exit_(false) {
+Solver<Dtype>::Solver(const SolverParameter& param)
+    : net_(), callbacks_(), requested_early_exit_(false) {
   Init(param);
 }
 
 template <typename Dtype>
-Solver<Dtype>::Solver(const string& param_file, const Solver* root_solver)
-    : net_(), callbacks_(), root_solver_(root_solver),
-      requested_early_exit_(false) {
+Solver<Dtype>::Solver(const string& param_file)
+    : net_(), callbacks_(), requested_early_exit_(false) {
   SolverParameter param;
   ReadSolverParamsFromTextFileOrDie(param_file, &param);
   Init(param);
@@ -43,20 +41,18 @@ Solver<Dtype>::Solver(const string& param_file, const Solver* root_solver)
 
 template <typename Dtype>
 void Solver<Dtype>::Init(const SolverParameter& param) {
-  CHECK(Caffe::root_solver() || root_solver_)
-      << "root_solver_ needs to be set for all non-root solvers";
   LOG_IF(INFO, Caffe::root_solver()) << "Initializing solver from parameters: "
     << std::endl << param.DebugString();
   param_ = param;
   CHECK_GE(param_.average_loss(), 1) << "average_loss should be non-negative.";
   CheckSnapshotWritePermissions();
-  if (Caffe::root_solver() && param_.random_seed() >= 0) {
-    Caffe::set_random_seed(param_.random_seed());
+  if (param_.random_seed() >= 0) {
+    Caffe::set_random_seed(param_.random_seed() + Caffe::solver_rank());
   }
   // Scaffolding code
   InitTrainNet();
+  InitTestNets();
   if (Caffe::root_solver()) {
-    InitTestNets();
     LOG(INFO) << "Solver scaffolding done.";
   }
   iter_ = 0;
@@ -101,16 +97,11 @@ void Solver<Dtype>::InitTrainNet() {
   net_state.MergeFrom(net_param.state());
   net_state.MergeFrom(param_.train_state());
   net_param.mutable_state()->CopyFrom(net_state);
-  if (Caffe::root_solver()) {
-    net_.reset(new Net<Dtype>(net_param));
-  } else {
-    net_.reset(new Net<Dtype>(net_param, root_solver_->net_.get()));
-  }
+  net_.reset(new Net<Dtype>(net_param));
 }
 
 template <typename Dtype>
 void Solver<Dtype>::InitTestNets() {
-  CHECK(Caffe::root_solver());
   const bool has_net_param = param_.has_net_param();
   const bool has_net_file = param_.has_net();
   const int num_generic_nets = has_net_param + has_net_file;
@@ -180,32 +171,28 @@ void Solver<Dtype>::InitTestNets() {
     net_params[i].mutable_state()->CopyFrom(net_state);
     LOG(INFO)
         << "Creating test net (#" << i << ") specified by " << sources[i];
-    if (Caffe::root_solver()) {
-      test_nets_[i].reset(new Net<Dtype>(net_params[i]));
-    } else {
-      test_nets_[i].reset(new Net<Dtype>(net_params[i],
-          root_solver_->test_nets_[i].get()));
-    }
+    test_nets_[i].reset(new Net<Dtype>(net_params[i]));
     test_nets_[i]->set_debug_info(param_.debug_info());
   }
 }
 
 template <typename Dtype>
 void Solver<Dtype>::Step(int iters) {
-  vector<Blob<Dtype>*> bottom_vec;
   const int start_iter = iter_;
   const int stop_iter = iter_ + iters;
   int average_loss = this->param_.average_loss();
   losses_.clear();
   smoothed_loss_ = 0;
+  iteration_timer_.Start();
 
   while (iter_ < stop_iter) {
     // zero-init the params
     net_->ClearParamDiffs();
     if (param_.test_interval() && iter_ % param_.test_interval() == 0
-        && (iter_ > 0 || param_.test_initialization())
-        && Caffe::root_solver()) {
-      TestAll();
+        && (iter_ > 0 || param_.test_initialization())) {
+      if (Caffe::root_solver()) {
+        TestAll();
+      }
       if (requested_early_exit_) {
         // Break out of the while loop because stop was requested while testing.
         break;
@@ -220,14 +207,19 @@ void Solver<Dtype>::Step(int iters) {
     // accumulate the loss and gradient
     Dtype loss = 0;
     for (int i = 0; i < param_.iter_size(); ++i) {
-      loss += net_->ForwardBackward(bottom_vec);
+      loss += net_->ForwardBackward();
     }
     loss /= param_.iter_size();
     // average the loss across iterations for smoothed reporting
     UpdateSmoothedLoss(loss, start_iter, average_loss);
     if (display) {
+      float lapse = iteration_timer_.Seconds();
+      float per_s = (iter_ - iterations_last_) / (lapse ? lapse : 1);
       LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << iter_
-          << ", loss = " << smoothed_loss_;
+          << " (" << per_s << " iter/s, " << lapse << "s/"
+          << param_.display() << " iters), loss = " << smoothed_loss_;
+      iteration_timer_.Start();
+      iterations_last_ = iter_;
       const vector<Blob<Dtype>*>& result = net_->output_blobs();
       int score_index = 0;
       for (int j = 0; j < result.size(); ++j) {
@@ -311,7 +303,7 @@ void Solver<Dtype>::Solve(const char* resume_file) {
   if (param_.display() && iter_ % param_.display() == 0) {
     int average_loss = this->param_.average_loss();
     Dtype loss;
-    net_->ForwardPrefilled(&loss);
+    net_->Forward(&loss);
 
     UpdateSmoothedLoss(loss, start_iter, average_loss);
 
@@ -341,7 +333,6 @@ void Solver<Dtype>::Test(const int test_net_id) {
       ShareTrainedLayersWith(net_.get());
   vector<Dtype> test_score;
   vector<int> test_score_output_id;
-  vector<Blob<Dtype>*> bottom_vec;
   const shared_ptr<Net<Dtype> >& test_net = test_nets_[test_net_id];
   Dtype loss = 0;
   for (int i = 0; i < param_.test_iter(test_net_id); ++i) {
@@ -362,7 +353,7 @@ void Solver<Dtype>::Test(const int test_net_id) {
 
     Dtype iter_loss;
     const vector<Blob<Dtype>*>& result =
-        test_net->Forward(bottom_vec, &iter_loss);
+        test_net->Forward(&iter_loss);
     if (param_.test_compute_loss()) {
       loss += iter_loss;
     }
@@ -470,7 +461,6 @@ string Solver<Dtype>::SnapshotToHDF5() {
 
 template <typename Dtype>
 void Solver<Dtype>::Restore(const char* state_file) {
-  CHECK(Caffe::root_solver());
   string state_filename(state_file);
   if (state_filename.size() >= 3 &&
       state_filename.compare(state_filename.size() - 3, 3, ".h5") == 0) {
diff --git a/src/caffe/solvers/adagrad_solver.cpp b/src/caffe/solvers/adagrad_solver.cpp
index e78eadca..d8107e1e 100644
--- a/src/caffe/solvers/adagrad_solver.cpp
+++ b/src/caffe/solvers/adagrad_solver.cpp
@@ -12,7 +12,6 @@ void adagrad_update_gpu(int N, Dtype* g, Dtype* h, Dtype delta,
 
 template <typename Dtype>
 void AdaGradSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
-  CHECK(Caffe::root_solver());
   const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
   const vector<float>& net_params_lr = this->net_->params_lr();
   Dtype delta = this->param_.delta();
diff --git a/src/caffe/solvers/nesterov_solver.cpp b/src/caffe/solvers/nesterov_solver.cpp
index 23ab2d43..7c1fac1f 100644
--- a/src/caffe/solvers/nesterov_solver.cpp
+++ b/src/caffe/solvers/nesterov_solver.cpp
@@ -12,7 +12,6 @@ void nesterov_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum,
 
 template <typename Dtype>
 void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
-  CHECK(Caffe::root_solver());
   const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
   const vector<float>& net_params_lr = this->net_->params_lr();
   Dtype momentum = this->param_.momentum();
diff --git a/src/caffe/solvers/sgd_solver.cpp b/src/caffe/solvers/sgd_solver.cpp
index f4631185..dc4f1419 100644
--- a/src/caffe/solvers/sgd_solver.cpp
+++ b/src/caffe/solvers/sgd_solver.cpp
@@ -103,10 +103,10 @@ void SGDSolver<Dtype>::ClipGradients() {
 
 template <typename Dtype>
 void SGDSolver<Dtype>::ApplyUpdate() {
-  CHECK(Caffe::root_solver());
   Dtype rate = GetLearningRate();
   if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
-    LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
+    LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << this->iter_
+        << ", lr = " << rate;
   }
   ClipGradients();
   for (int param_id = 0; param_id < this->net_->learnable_params().size();
diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index 4d356417..88d9b785 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -3,26 +3,41 @@
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
+SyncedMemory::SyncedMemory()
+  : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(0), head_(UNINITIALIZED),
+    own_cpu_data_(false), cpu_malloc_use_cuda_(false), own_gpu_data_(false) {
+#ifndef CPU_ONLY
+#ifdef DEBUG
+  CUDA_CHECK(cudaGetDevice(&device_));
+#endif
+#endif
+}
+
+SyncedMemory::SyncedMemory(size_t size)
+  : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(size), head_(UNINITIALIZED),
+    own_cpu_data_(false), cpu_malloc_use_cuda_(false), own_gpu_data_(false) {
+#ifndef CPU_ONLY
+#ifdef DEBUG
+  CUDA_CHECK(cudaGetDevice(&device_));
+#endif
+#endif
+}
 
 SyncedMemory::~SyncedMemory() {
+  check_device();
   if (cpu_ptr_ && own_cpu_data_) {
     CaffeFreeHost(cpu_ptr_, cpu_malloc_use_cuda_);
   }
 
 #ifndef CPU_ONLY
   if (gpu_ptr_ && own_gpu_data_) {
-    int initial_device;
-    cudaGetDevice(&initial_device);
-    if (gpu_device_ != -1) {
-      CUDA_CHECK(cudaSetDevice(gpu_device_));
-    }
     CUDA_CHECK(cudaFree(gpu_ptr_));
-    cudaSetDevice(initial_device);
   }
 #endif  // CPU_ONLY
 }
 
 inline void SyncedMemory::to_cpu() {
+  check_device();
   switch (head_) {
   case UNINITIALIZED:
     CaffeMallocHost(&cpu_ptr_, size_, &cpu_malloc_use_cuda_);
@@ -49,10 +64,10 @@ inline void SyncedMemory::to_cpu() {
 }
 
 inline void SyncedMemory::to_gpu() {
+  check_device();
 #ifndef CPU_ONLY
   switch (head_) {
   case UNINITIALIZED:
-    CUDA_CHECK(cudaGetDevice(&gpu_device_));
     CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
     caffe_gpu_memset(size_, 0, gpu_ptr_);
     head_ = HEAD_AT_GPU;
@@ -60,7 +75,6 @@ inline void SyncedMemory::to_gpu() {
     break;
   case HEAD_AT_CPU:
     if (gpu_ptr_ == NULL) {
-      CUDA_CHECK(cudaGetDevice(&gpu_device_));
       CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
       own_gpu_data_ = true;
     }
@@ -77,11 +91,13 @@ inline void SyncedMemory::to_gpu() {
 }
 
 const void* SyncedMemory::cpu_data() {
+  check_device();
   to_cpu();
   return (const void*)cpu_ptr_;
 }
 
 void SyncedMemory::set_cpu_data(void* data) {
+  check_device();
   CHECK(data);
   if (own_cpu_data_) {
     CaffeFreeHost(cpu_ptr_, cpu_malloc_use_cuda_);
@@ -92,6 +108,7 @@ void SyncedMemory::set_cpu_data(void* data) {
 }
 
 const void* SyncedMemory::gpu_data() {
+  check_device();
 #ifndef CPU_ONLY
   to_gpu();
   return (const void*)gpu_ptr_;
@@ -102,16 +119,11 @@ const void* SyncedMemory::gpu_data() {
 }
 
 void SyncedMemory::set_gpu_data(void* data) {
+  check_device();
 #ifndef CPU_ONLY
   CHECK(data);
   if (own_gpu_data_) {
-    int initial_device;
-    cudaGetDevice(&initial_device);
-    if (gpu_device_ != -1) {
-      CUDA_CHECK(cudaSetDevice(gpu_device_));
-    }
     CUDA_CHECK(cudaFree(gpu_ptr_));
-    cudaSetDevice(initial_device);
   }
   gpu_ptr_ = data;
   head_ = HEAD_AT_GPU;
@@ -122,12 +134,14 @@ void SyncedMemory::set_gpu_data(void* data) {
 }
 
 void* SyncedMemory::mutable_cpu_data() {
+  check_device();
   to_cpu();
   head_ = HEAD_AT_CPU;
   return cpu_ptr_;
 }
 
 void* SyncedMemory::mutable_gpu_data() {
+  check_device();
 #ifndef CPU_ONLY
   to_gpu();
   head_ = HEAD_AT_GPU;
@@ -140,9 +154,9 @@ void* SyncedMemory::mutable_gpu_data() {
 
 #ifndef CPU_ONLY
 void SyncedMemory::async_gpu_push(const cudaStream_t& stream) {
+  check_device();
   CHECK(head_ == HEAD_AT_CPU);
   if (gpu_ptr_ == NULL) {
-    CUDA_CHECK(cudaGetDevice(&gpu_device_));
     CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
     own_gpu_data_ = true;
   }
@@ -153,5 +167,20 @@ void SyncedMemory::async_gpu_push(const cudaStream_t& stream) {
 }
 #endif
 
+void SyncedMemory::check_device() {
+#ifndef CPU_ONLY
+#ifdef DEBUG
+  int device;
+  cudaGetDevice(&device);
+  CHECK(device == device_);
+  if (gpu_ptr_ && own_gpu_data_) {
+    cudaPointerAttributes attributes;
+    CUDA_CHECK(cudaPointerGetAttributes(&attributes, gpu_ptr_));
+    CHECK(attributes.device == device_);
+  }
+#endif
+#endif
+}
+
 }  // namespace caffe
 
diff --git a/src/caffe/test/CMakeLists.txt b/src/caffe/test/CMakeLists.txt
index 35a803f2..d8afc30b 100644
--- a/src/caffe/test/CMakeLists.txt
+++ b/src/caffe/test/CMakeLists.txt
@@ -1,7 +1,7 @@
 # The option allows to include in build only selected test files and exclude all others
 # Usage example:
 #  cmake -DBUILD_only_tests="common,net,blob,im2col_kernel"
-set(BUILD_only_tests "" CACHE STRING "Blank or comma-separated list of test files to build without 'test_' prefix and extention")
+set(BUILD_only_tests "" CACHE STRING "Blank or comma-separated list of test files to build without 'test_' prefix and extension")
 caffe_leave_only_selected_tests(test_srcs ${BUILD_only_tests})
 caffe_leave_only_selected_tests(test_cuda ${BUILD_only_tests})
 
diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
index 6fe808bd..e5cc9d5e 100644
--- a/src/caffe/test/test_accuracy_layer.cpp
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -13,8 +13,10 @@
 
 namespace caffe {
 
-template <typename Dtype>
-class AccuracyLayerTest : public CPUDeviceTest<Dtype> {
+template <typename TypeParam>
+class AccuracyLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   AccuracyLayerTest()
       : blob_bottom_data_(new Blob<Dtype>()),
@@ -69,11 +71,12 @@ class AccuracyLayerTest : public CPUDeviceTest<Dtype> {
   int top_k_;
 };
 
-TYPED_TEST_CASE(AccuracyLayerTest, TestDtypes);
+TYPED_TEST_CASE(AccuracyLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(AccuracyLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  AccuracyLayer<TypeParam> layer(layer_param);
+  AccuracyLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
@@ -82,11 +85,12 @@ TYPED_TEST(AccuracyLayerTest, TestSetup) {
 }
 
 TYPED_TEST(AccuracyLayerTest, TestSetupTopK) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   AccuracyParameter* accuracy_param =
       layer_param.mutable_accuracy_param();
   accuracy_param->set_top_k(5);
-  AccuracyLayer<TypeParam> layer(layer_param);
+  AccuracyLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
@@ -95,8 +99,9 @@ TYPED_TEST(AccuracyLayerTest, TestSetupTopK) {
 }
 
 TYPED_TEST(AccuracyLayerTest, TestSetupOutputPerClass) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  AccuracyLayer<TypeParam> layer(layer_param);
+  AccuracyLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
@@ -108,33 +113,39 @@ TYPED_TEST(AccuracyLayerTest, TestSetupOutputPerClass) {
   EXPECT_EQ(this->blob_top_per_class_->width(), 1);
 }
 
-TYPED_TEST(AccuracyLayerTest, TestForwardCPU) {
+TYPED_TEST(AccuracyLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  AccuracyLayer<TypeParam> layer(layer_param);
+  AccuracyLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
-  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
-
-  TypeParam max_value;
-  int max_id;
-  int num_correct_labels = 0;
-  for (int i = 0; i < 100; ++i) {
-    max_value = -FLT_MAX;
-    max_id = 0;
-    for (int j = 0; j < 10; ++j) {
-      if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
-        max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
-        max_id = j;
+
+  // repeat the forward
+  for (int iter = 0; iter < 3; iter++) {
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+    Dtype max_value;
+    int max_id;
+    int num_correct_labels = 0;
+    for (int i = 0; i < 100; ++i) {
+      max_value = -FLT_MAX;
+      max_id = 0;
+      for (int j = 0; j < 10; ++j) {
+        if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
+          max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+          max_id = j;
+        }
+      }
+      if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+        ++num_correct_labels;
       }
     }
-    if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
-      ++num_correct_labels;
-    }
+    EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+                num_correct_labels / Dtype(100.0), 1e-4);
   }
-  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
-              num_correct_labels / 100.0, 1e-4);
 }
 
 TYPED_TEST(AccuracyLayerTest, TestForwardWithSpatialAxes) {
+  typedef typename TypeParam::Dtype Dtype;
   this->blob_bottom_data_->Reshape(2, 10, 4, 5);
   vector<int> label_shape(3);
   label_shape[0] = 2; label_shape[1] = 4; label_shape[2] = 5;
@@ -142,195 +153,218 @@ TYPED_TEST(AccuracyLayerTest, TestForwardWithSpatialAxes) {
   this->FillBottoms();
   LayerParameter layer_param;
   layer_param.mutable_accuracy_param()->set_axis(1);
-  AccuracyLayer<TypeParam> layer(layer_param);
+  AccuracyLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
-  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
-
-  TypeParam max_value;
-  const int num_labels = this->blob_bottom_label_->count();
-  int max_id;
-  int num_correct_labels = 0;
-  vector<int> label_offset(3);
-  for (int n = 0; n < this->blob_bottom_data_->num(); ++n) {
-    for (int h = 0; h < this->blob_bottom_data_->height(); ++h) {
-      for (int w = 0; w < this->blob_bottom_data_->width(); ++w) {
-        max_value = -FLT_MAX;
-        max_id = 0;
-        for (int c = 0; c < this->blob_bottom_data_->channels(); ++c) {
-          const TypeParam pred_value =
-              this->blob_bottom_data_->data_at(n, c, h, w);
-          if (pred_value > max_value) {
-            max_value = pred_value;
-            max_id = c;
+
+  // repeat the forward
+  for (int iter = 0; iter < 3; iter++) {
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+    Dtype max_value;
+    const int num_labels = this->blob_bottom_label_->count();
+    int max_id;
+    int num_correct_labels = 0;
+    vector<int> label_offset(3);
+    for (int n = 0; n < this->blob_bottom_data_->num(); ++n) {
+      for (int h = 0; h < this->blob_bottom_data_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_data_->width(); ++w) {
+          max_value = -FLT_MAX;
+          max_id = 0;
+          for (int c = 0; c < this->blob_bottom_data_->channels(); ++c) {
+            const Dtype pred_value =
+                this->blob_bottom_data_->data_at(n, c, h, w);
+            if (pred_value > max_value) {
+              max_value = pred_value;
+              max_id = c;
+            }
+          }
+          label_offset[0] = n; label_offset[1] = h; label_offset[2] = w;
+          const int correct_label =
+              static_cast<int>(this->blob_bottom_label_->data_at(label_offset));
+          if (max_id == correct_label) {
+            ++num_correct_labels;
           }
-        }
-        label_offset[0] = n; label_offset[1] = h; label_offset[2] = w;
-        const int correct_label =
-            static_cast<int>(this->blob_bottom_label_->data_at(label_offset));
-        if (max_id == correct_label) {
-          ++num_correct_labels;
         }
       }
     }
+    EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+                num_correct_labels / Dtype(num_labels), 1e-4);
   }
-  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
-              num_correct_labels / TypeParam(num_labels), 1e-4);
 }
 
 TYPED_TEST(AccuracyLayerTest, TestForwardIgnoreLabel) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  const TypeParam kIgnoreLabelValue = -1;
+  const Dtype kIgnoreLabelValue = -1;
   layer_param.mutable_accuracy_param()->set_ignore_label(kIgnoreLabelValue);
-  AccuracyLayer<TypeParam> layer(layer_param);
+  AccuracyLayer<Dtype> layer(layer_param);
   // Manually set some labels to the ignore label value (-1).
   this->blob_bottom_label_->mutable_cpu_data()[2] = kIgnoreLabelValue;
   this->blob_bottom_label_->mutable_cpu_data()[5] = kIgnoreLabelValue;
   this->blob_bottom_label_->mutable_cpu_data()[32] = kIgnoreLabelValue;
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
-  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
-
-  TypeParam max_value;
-  int max_id;
-  int num_correct_labels = 0;
-  int count = 0;
-  for (int i = 0; i < 100; ++i) {
-    if (kIgnoreLabelValue == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
-      continue;
-    }
-    ++count;
-    max_value = -FLT_MAX;
-    max_id = 0;
-    for (int j = 0; j < 10; ++j) {
-      if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
-        max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
-        max_id = j;
+
+  // repeat the forward
+  for (int iter = 0; iter < 3; iter++) {
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+    Dtype max_value;
+    int max_id;
+    int num_correct_labels = 0;
+    int count = 0;
+    for (int i = 0; i < 100; ++i) {
+      if (kIgnoreLabelValue == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+        continue;
+      }
+      ++count;
+      max_value = -FLT_MAX;
+      max_id = 0;
+      for (int j = 0; j < 10; ++j) {
+        if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
+          max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+          max_id = j;
+        }
+      }
+      if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+        ++num_correct_labels;
       }
     }
-    if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
-      ++num_correct_labels;
-    }
+    EXPECT_EQ(count, 97);  // We set 3 out of 100 labels to kIgnoreLabelValue.
+    EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+                num_correct_labels / Dtype(count), 1e-4);
   }
-  EXPECT_EQ(count, 97);  // We set 3 out of 100 labels to kIgnoreLabelValue.
-  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
-              num_correct_labels / TypeParam(count), 1e-4);
 }
 
-TYPED_TEST(AccuracyLayerTest, TestForwardCPUTopK) {
+TYPED_TEST(AccuracyLayerTest, TestForwardTopK) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   AccuracyParameter* accuracy_param = layer_param.mutable_accuracy_param();
   accuracy_param->set_top_k(this->top_k_);
-  AccuracyLayer<TypeParam> layer(layer_param);
+  AccuracyLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
-  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
-
-  TypeParam current_value;
-  int current_rank;
-  int num_correct_labels = 0;
-  for (int i = 0; i < 100; ++i) {
-    for (int j = 0; j < 10; ++j) {
-      current_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
-      current_rank = 0;
-      for (int k = 0; k < 10; ++k) {
-        if (this->blob_bottom_data_->data_at(i, k, 0, 0) > current_value) {
-          ++current_rank;
+
+  // repeat the forward
+  for (int iter = 0; iter < 3; iter++) {
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+    Dtype current_value;
+    int current_rank;
+    int num_correct_labels = 0;
+    for (int i = 0; i < 100; ++i) {
+      for (int j = 0; j < 10; ++j) {
+        current_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+        current_rank = 0;
+        for (int k = 0; k < 10; ++k) {
+          if (this->blob_bottom_data_->data_at(i, k, 0, 0) > current_value) {
+            ++current_rank;
+          }
+        }
+        if (current_rank < this->top_k_ &&
+            j == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+          ++num_correct_labels;
         }
-      }
-      if (current_rank < this->top_k_ &&
-          j == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
-        ++num_correct_labels;
       }
     }
-  }
 
-  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
-              num_correct_labels / 100.0, 1e-4);
+    EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+                num_correct_labels / Dtype(100.0), 1e-4);
+  }
 }
 
-TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClass) {
+TYPED_TEST(AccuracyLayerTest, TestForwardPerClass) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  AccuracyLayer<TypeParam> layer(layer_param);
+  AccuracyLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
-  layer.Forward(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
-
-  TypeParam max_value;
-  int max_id;
-  int num_correct_labels = 0;
-  const int num_class = this->blob_top_per_class_->num();
-  vector<int> correct_per_class(num_class, 0);
-  vector<int> num_per_class(num_class, 0);
-  for (int i = 0; i < 100; ++i) {
-    max_value = -FLT_MAX;
-    max_id = 0;
-    for (int j = 0; j < 10; ++j) {
-      if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
-        max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
-        max_id = j;
+  // repeat the forward
+  for (int iter = 0; iter < 3; iter++) {
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+
+    Dtype max_value;
+    int max_id;
+    int num_correct_labels = 0;
+    const int num_class = this->blob_top_per_class_->num();
+    vector<int> correct_per_class(num_class, 0);
+    vector<int> num_per_class(num_class, 0);
+    for (int i = 0; i < 100; ++i) {
+      max_value = -FLT_MAX;
+      max_id = 0;
+      for (int j = 0; j < 10; ++j) {
+        if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
+          max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+          max_id = j;
+        }
+      }
+      ++num_per_class[this->blob_bottom_label_->data_at(i, 0, 0, 0)];
+      if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+        ++num_correct_labels;
+        ++correct_per_class[max_id];
       }
     }
-    ++num_per_class[this->blob_bottom_label_->data_at(i, 0, 0, 0)];
-    if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
-      ++num_correct_labels;
-      ++correct_per_class[max_id];
+    EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+                num_correct_labels / 100.0, 1e-4);
+    for (int i = 0; i < num_class; ++i) {
+      Dtype accuracy_per_class = (num_per_class[i] > 0 ?
+         static_cast<Dtype>(correct_per_class[i]) / num_per_class[i] : 0);
+      EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0),
+                  accuracy_per_class, 1e-4);
     }
   }
-  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
-              num_correct_labels / 100.0, 1e-4);
-  for (int i = 0; i < num_class; ++i) {
-    TypeParam accuracy_per_class = (num_per_class[i] > 0 ?
-       static_cast<TypeParam>(correct_per_class[i]) / num_per_class[i] : 0);
-    EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0),
-                accuracy_per_class, 1e-4);
-  }
 }
 
 
-TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClassWithIgnoreLabel) {
+TYPED_TEST(AccuracyLayerTest, TestForwardPerClassWithIgnoreLabel) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  const TypeParam kIgnoreLabelValue = -1;
+  const Dtype kIgnoreLabelValue = -1;
   layer_param.mutable_accuracy_param()->set_ignore_label(kIgnoreLabelValue);
-  AccuracyLayer<TypeParam> layer(layer_param);
+  AccuracyLayer<Dtype> layer(layer_param);
   // Manually set some labels to the ignore label value (-1).
   this->blob_bottom_label_->mutable_cpu_data()[2] = kIgnoreLabelValue;
   this->blob_bottom_label_->mutable_cpu_data()[5] = kIgnoreLabelValue;
   this->blob_bottom_label_->mutable_cpu_data()[32] = kIgnoreLabelValue;
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
-  layer.Forward(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
-
-  TypeParam max_value;
-  int max_id;
-  int num_correct_labels = 0;
-  const int num_class = this->blob_top_per_class_->num();
-  vector<int> correct_per_class(num_class, 0);
-  vector<int> num_per_class(num_class, 0);
-  int count = 0;
-  for (int i = 0; i < 100; ++i) {
-    if (kIgnoreLabelValue == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
-      continue;
-    }
-    ++count;
-    max_value = -FLT_MAX;
-    max_id = 0;
-    for (int j = 0; j < 10; ++j) {
-      if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
-        max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
-        max_id = j;
+
+  // repeat the forward
+  for (int iter = 0; iter < 3; iter++) {
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+
+    Dtype max_value;
+    int max_id;
+    int num_correct_labels = 0;
+    const int num_class = this->blob_top_per_class_->num();
+    vector<int> correct_per_class(num_class, 0);
+    vector<int> num_per_class(num_class, 0);
+    int count = 0;
+    for (int i = 0; i < 100; ++i) {
+      if (kIgnoreLabelValue == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+        continue;
+      }
+      ++count;
+      max_value = -FLT_MAX;
+      max_id = 0;
+      for (int j = 0; j < 10; ++j) {
+        if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
+          max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+          max_id = j;
+        }
+      }
+      ++num_per_class[this->blob_bottom_label_->data_at(i, 0, 0, 0)];
+      if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+        ++num_correct_labels;
+        ++correct_per_class[max_id];
       }
     }
-    ++num_per_class[this->blob_bottom_label_->data_at(i, 0, 0, 0)];
-    if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
-      ++num_correct_labels;
-      ++correct_per_class[max_id];
+    EXPECT_EQ(count, 97);
+    EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+                num_correct_labels / Dtype(count), 1e-4);
+    for (int i = 0; i < 10; ++i) {
+      Dtype accuracy_per_class = (num_per_class[i] > 0 ?
+         static_cast<Dtype>(correct_per_class[i]) / num_per_class[i] : 0);
+      EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0),
+                  accuracy_per_class, 1e-4);
     }
   }
-  EXPECT_EQ(count, 97);
-  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
-              num_correct_labels / TypeParam(count), 1e-4);
-  for (int i = 0; i < 10; ++i) {
-    TypeParam accuracy_per_class = (num_per_class[i] > 0 ?
-       static_cast<TypeParam>(correct_per_class[i]) / num_per_class[i] : 0);
-    EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0),
-                accuracy_per_class, 1e-4);
-  }
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_blob.cpp b/src/caffe/test/test_blob.cpp
index a9d7d519..b8856222 100644
--- a/src/caffe/test/test_blob.cpp
+++ b/src/caffe/test/test_blob.cpp
@@ -51,6 +51,14 @@ TYPED_TEST(BlobSimpleTest, TestReshape) {
   EXPECT_EQ(this->blob_->count(), 120);
 }
 
+TYPED_TEST(BlobSimpleTest, TestReshapeZero) {
+  vector<int> shape(2);
+  shape[0] = 0;
+  shape[1] = 5;
+  this->blob_->Reshape(shape);
+  EXPECT_EQ(this->blob_->count(), 0);
+}
+
 TYPED_TEST(BlobSimpleTest, TestLegacyBlobProtoShapeEquals) {
   BlobProto blob_proto;
 
diff --git a/src/caffe/test/test_caffe_main.cpp b/src/caffe/test/test_caffe_main.cpp
index fccf6f16..8f333bd7 100644
--- a/src/caffe/test/test_caffe_main.cpp
+++ b/src/caffe/test/test_caffe_main.cpp
@@ -1,6 +1,3 @@
-// The main caffe test code. Your test cpp code should include this hpp
-// to allow a main function to be compiled into the binary.
-
 #include "caffe/caffe.hpp"
 #include "caffe/test/test_caffe_main.hpp"
 
@@ -18,7 +15,7 @@ int main(int argc, char** argv) {
   ::testing::InitGoogleTest(&argc, argv);
   caffe::GlobalInit(&argc, &argv);
 #ifndef CPU_ONLY
-  // Before starting testing, let's first print out a few cuda defice info.
+  // Before starting testing, let's first print out a few cuda device info.
   int device;
   cudaGetDeviceCount(&device);
   cout << "Cuda number of devices: " << device << endl;
diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index 9bb19d13..85c10a29 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -695,7 +695,7 @@ TYPED_TEST(ConvolutionLayerTest, TestNDAgainst2D) {
   }
   ASSERT_EQ(backward_result_nd.count(), backward_result_2d.count());
   for (int i = 0; i < backward_result_2d.count(); ++i) {
-    EXPECT_EQ(backward_result_2d.cpu_diff()[i],
+    EXPECT_FLOAT_EQ(backward_result_2d.cpu_diff()[i],
               backward_result_nd.cpu_diff()[i]);
   }
   ASSERT_EQ(backward_weight_result_nd.count(),
diff --git a/src/caffe/test/test_crop_layer.cpp b/src/caffe/test/test_crop_layer.cpp
new file mode 100644
index 00000000..ce2c736f
--- /dev/null
+++ b/src/caffe/test/test_crop_layer.cpp
@@ -0,0 +1,283 @@
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/crop_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class CropLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  CropLayerTest()
+      : blob_bottom_0_(new Blob<Dtype>(2, 4, 5, 4)),
+        blob_bottom_1_(new Blob<Dtype>(2, 3, 4, 2)),
+        blob_top_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_0_);
+    filler.Fill(this->blob_bottom_1_);
+
+    blob_bottom_vec_.push_back(blob_bottom_0_);
+    blob_bottom_vec_.push_back(blob_bottom_1_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+
+  virtual ~CropLayerTest() {
+    delete blob_bottom_0_; delete blob_bottom_1_;
+    delete blob_top_;
+  }
+
+  Blob<Dtype>* const blob_bottom_0_;
+  Blob<Dtype>* const blob_bottom_1_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+
+TYPED_TEST_CASE(CropLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(CropLayerTest, TestSetupShapeAll) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  // Crop all dimensions
+  layer_param.mutable_crop_param()->set_axis(0);
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->num_axes(); ++i) {
+    EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i));
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestSetupShapeDefault) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  // Crop last two dimensions, axis is 2 by default
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->num_axes(); ++i) {
+    if (i < 2) {
+      EXPECT_EQ(this->blob_bottom_0_->shape(i), this->blob_top_->shape(i));
+    } else {
+      EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i));
+    }
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestSetupShapeNegativeIndexing) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  // Crop last dimension by negative indexing
+  layer_param.mutable_crop_param()->set_axis(-1);
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->num_axes(); ++i) {
+    if (i < 3) {
+      EXPECT_EQ(this->blob_bottom_0_->shape(i), this->blob_top_->shape(i));
+    } else {
+      EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i));
+    }
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestDimensionsCheck) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  // Reshape size blob to have incompatible sizes for uncropped dimensions:
+  // the size blob has more channels than the data blob, but this is fine
+  // since the channels dimension is not cropped in this configuration.
+  this->blob_bottom_1_->Reshape(2, 5, 4, 2);
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->num_axes(); ++i) {
+    if (i < 2) {
+      EXPECT_EQ(this->blob_bottom_0_->shape(i), this->blob_top_->shape(i));
+    } else {
+      EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i));
+    }
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestCropAll) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(0);
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_0_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_0_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_0_->width(); ++w) {
+          if ( n < this->blob_top_->shape(0) &&
+              c < this->blob_top_->shape(1) &&
+              h < this->blob_top_->shape(2) &&
+              w < this->blob_top_->shape(3) ) {
+            EXPECT_EQ(this->blob_top_->data_at(n, c, h, w),
+                this->blob_bottom_0_->data_at(n, c, h, w));
+          }
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestCropAllOffset) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(0);
+  layer_param.mutable_crop_param()->add_offset(0);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(2);
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_0_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_0_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_0_->width(); ++w) {
+          if ( n < this->blob_top_->shape(0) &&
+              c < this->blob_top_->shape(1) &&
+              h < this->blob_top_->shape(2) &&
+              w < this->blob_top_->shape(3) ) {
+            EXPECT_EQ(this->blob_top_->data_at(n, c, h, w),
+                this->blob_bottom_0_->data_at(n, c+1, h+1, w+2));
+          }
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestCropHW) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(2);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(2);
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_0_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_0_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_0_->width(); ++w) {
+          if (n < this->blob_top_->shape(0) &&
+              c < this->blob_top_->shape(1) &&
+              h < this->blob_top_->shape(2) &&
+              w < this->blob_top_->shape(3)) {
+            EXPECT_EQ(this->blob_top_->data_at(n, c, h, w),
+                this->blob_bottom_0_->data_at(n, c, h+1, w+2));
+          }
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestCrop5D) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Add dimension to each bottom for >4D check
+  vector<int> bottom_0_shape = this->blob_bottom_0_->shape();
+  vector<int> bottom_1_shape = this->blob_bottom_1_->shape();
+  bottom_0_shape.push_back(2);
+  bottom_1_shape.push_back(1);
+  this->blob_bottom_0_->Reshape(bottom_0_shape);
+  this->blob_bottom_1_->Reshape(bottom_1_shape);
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(this->blob_bottom_0_);
+  filler.Fill(this->blob_bottom_1_);
+  // Make layer
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(2);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(2);
+  layer_param.mutable_crop_param()->add_offset(0);
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  vector<int> bottom_idx = vector<int>(5, 0);
+  vector<int> top_idx = vector<int>(5, 0);
+  for (int n = 0; n < this->blob_bottom_0_->shape(0); ++n) {
+    for (int c = 0; c < this->blob_bottom_0_->shape(1); ++c) {
+      for (int z = 0; z < this->blob_bottom_0_->shape(2); ++z) {
+        for (int h = 0; h < this->blob_bottom_0_->shape(3); ++h) {
+          for (int w = 0; w < this->blob_bottom_0_->shape(4); ++w) {
+            if (n < this->blob_top_->shape(0) &&
+                c < this->blob_top_->shape(1) &&
+                z < this->blob_top_->shape(2) &&
+                h < this->blob_top_->shape(3) &&
+                w < this->blob_top_->shape(4)) {
+              bottom_idx[0] = top_idx[0] = n;
+              bottom_idx[1] = top_idx[1] = c;
+              bottom_idx[2] = z;
+              bottom_idx[3] = h;
+              bottom_idx[4] = top_idx[4] = w;
+              top_idx[2] = z+1;
+              top_idx[3] = h+2;
+              EXPECT_EQ(this->blob_top_->data_at(bottom_idx),
+                  this->blob_bottom_0_->data_at(top_idx));
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestCropAllGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(0);
+  CropLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(CropLayerTest, TestCropHWGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(2);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(2);
+  CropLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(CropLayerTest, TestCrop5DGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(2);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(2);
+  layer_param.mutable_crop_param()->add_offset(0);
+  CropLayer<Dtype> layer(layer_param);
+  // Add dimension to each bottom for >4D check
+  vector<int> bottom_0_shape = this->blob_bottom_0_->shape();
+  vector<int> bottom_1_shape = this->blob_bottom_1_->shape();
+  bottom_0_shape.push_back(2);
+  bottom_1_shape.push_back(1);
+  this->blob_bottom_0_->Reshape(bottom_0_shape);
+  this->blob_bottom_1_->Reshape(bottom_1_shape);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 3e8d113d..3835af1f 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -105,6 +105,32 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     }
   }
 
+  void TestSkip() {
+    LayerParameter param;
+    param.set_phase(TRAIN);
+    DataParameter* data_param = param.mutable_data_param();
+    int batch_size = 5;
+    data_param->set_batch_size(batch_size);
+    data_param->set_source(filename_->c_str());
+    data_param->set_backend(backend_);
+    Caffe::set_solver_count(8);
+    for (int dev = 0; dev < Caffe::solver_count(); ++dev) {
+      Caffe::set_solver_rank(dev);
+      DataLayer<Dtype> layer(param);
+      layer.SetUp(blob_bottom_vec_, blob_top_vec_);
+      int label = dev;
+      for (int iter = 0; iter < 10; ++iter) {
+        layer.Forward(blob_bottom_vec_, blob_top_vec_);
+        for (int i = 0; i < batch_size; ++i) {
+          EXPECT_EQ(label % batch_size, blob_top_label_->cpu_data()[i]);
+          label += Caffe::solver_count();
+        }
+      }
+    }
+    Caffe::set_solver_count(1);
+    Caffe::set_solver_rank(0);
+  }
+
   void TestReshape(DataParameter_DB backend) {
     const int num_inputs = 5;
     // Save data of varying shapes.
@@ -356,6 +382,11 @@ TYPED_TEST(DataLayerTest, TestReadLevelDB) {
   this->TestRead();
 }
 
+TYPED_TEST(DataLayerTest, TestSkipLevelDB) {
+  this->Fill(false, DataParameter_DB_LEVELDB);
+  this->TestSkip();
+}
+
 TYPED_TEST(DataLayerTest, TestReshapeLevelDB) {
   this->TestReshape(DataParameter_DB_LEVELDB);
 }
@@ -396,6 +427,11 @@ TYPED_TEST(DataLayerTest, TestReadLMDB) {
   this->TestRead();
 }
 
+TYPED_TEST(DataLayerTest, TestSkipLMDB) {
+  this->Fill(false, DataParameter_DB_LMDB);
+  this->TestSkip();
+}
+
 TYPED_TEST(DataLayerTest, TestReshapeLMDB) {
   this->TestReshape(DataParameter_DB_LMDB);
 }
diff --git a/src/caffe/test/test_data_transformer.cpp b/src/caffe/test/test_data_transformer.cpp
index 6103918f..31bf1c1f 100644
--- a/src/caffe/test/test_data_transformer.cpp
+++ b/src/caffe/test/test_data_transformer.cpp
@@ -3,7 +3,6 @@
 #include <vector>
 
 #include "gtest/gtest.h"
-#include "leveldb/db.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
diff --git a/src/caffe/test/test_embed_layer.cpp b/src/caffe/test/test_embed_layer.cpp
index dc7f5c4a..13f13a87 100644
--- a/src/caffe/test/test_embed_layer.cpp
+++ b/src/caffe/test/test_embed_layer.cpp
@@ -124,7 +124,7 @@ TYPED_TEST(EmbedLayerTest, TestForwardWithBias) {
     top_offset[4] = 0;
     bias_offset[0] = 0;
     for (int j = 0; j < kNumOutput; ++j) {
-      EXPECT_EQ(layer->blobs()[0]->data_at(weight_offset) +
+      EXPECT_FLOAT_EQ(layer->blobs()[0]->data_at(weight_offset) +
                 layer->blobs()[1]->data_at(bias_offset),
                 this->blob_top_->data_at(top_offset));
       ++top_offset[4];
diff --git a/src/caffe/test/test_euclidean_loss_layer.cpp b/src/caffe/test/test_euclidean_loss_layer.cpp
index f253f9fd..b026f5b2 100644
--- a/src/caffe/test/test_euclidean_loss_layer.cpp
+++ b/src/caffe/test/test_euclidean_loss_layer.cpp
@@ -39,7 +39,7 @@ class EuclideanLossLayerTest : public MultiDeviceTest<TypeParam> {
 
   void TestForward() {
     // Get the loss without a specified objective weight -- should be
-    // equivalent to explicitly specifiying a weight of 1.
+    // equivalent to explicitly specifying a weight of 1.
     LayerParameter layer_param;
     EuclideanLossLayer<Dtype> layer_weight_1(layer_param);
     layer_weight_1.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
diff --git a/src/caffe/test/test_filler.cpp b/src/caffe/test/test_filler.cpp
index 26e9b217..f84d707b 100644
--- a/src/caffe/test/test_filler.cpp
+++ b/src/caffe/test/test_filler.cpp
@@ -29,7 +29,7 @@ TYPED_TEST(ConstantFillerTest, TestFill) {
   const int count = this->blob_->count();
   const TypeParam* data = this->blob_->cpu_data();
   for (int i = 0; i < count; ++i) {
-    EXPECT_GE(data[i], this->filler_param_.value());
+    EXPECT_EQ(data[i], this->filler_param_.value());
   }
 }
 
@@ -238,4 +238,45 @@ TYPED_TEST(MSRAFillerTest, TestFillAverage) {
   this->test_params(FillerParameter_VarianceNorm_AVERAGE, n);
 }
 
+template <typename Dtype>
+class BilinearFillerTest : public ::testing::Test {
+ protected:
+  BilinearFillerTest() : filler_param_() {}
+  virtual void test_params(const int n) {
+    this->blob_ = new Blob<Dtype>(1000, 2, n, n);
+    this->filler_.reset(new BilinearFiller<Dtype>(this->filler_param_));
+    this->filler_->Fill(blob_);
+    EXPECT_TRUE(this->blob_);
+    const int outer_num = this->blob_->count(0, 2);
+    const int inner_num = this->blob_->count(2, 4);
+    const Dtype* data = this->blob_->cpu_data();
+    int f = ceil(this->blob_->width() / 2.);
+    Dtype c = (this->blob_->width() - 1) / (2. * f);
+    for (int i = 0; i < outer_num; ++i) {
+      for (int j = 0; j < inner_num; ++j) {
+        Dtype x = j % this->blob_->width();
+        Dtype y = (j / this->blob_->width()) % this->blob_->height();
+        Dtype expected_value = (1 - fabs(x / f - c)) * (1 - fabs(y / f - c));
+        const Dtype actual_value = data[i * inner_num + j];
+        EXPECT_NEAR(expected_value, actual_value, 0.01);
+      }
+    }
+  }
+  virtual ~BilinearFillerTest() { delete blob_; }
+  Blob<Dtype>* blob_;
+  FillerParameter filler_param_;
+  shared_ptr<BilinearFiller<Dtype> > filler_;
+};
+
+TYPED_TEST_CASE(BilinearFillerTest, TestDtypes);
+
+TYPED_TEST(BilinearFillerTest, TestFillOdd) {
+  const int n = 7;
+  this->test_params(n);
+}
+TYPED_TEST(BilinearFillerTest, TestFillEven) {
+  const int n = 6;
+  this->test_params(n);
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 84c6747f..f4395f53 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -28,7 +28,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
       seed_(1701), num_(4), channels_(3), height_(10), width_(10),
       share_(false) {
         input_file_ = new string(
-        CMAKE_SOURCE_DIR "caffe/test/test_data/solver_data_list.txt" CMAKE_EXT);
+        ABS_TEST_DATA_DIR "/solver_data_list.txt");
       }
   ~GradientBasedSolverTest() {
     delete input_file_;
@@ -36,7 +36,9 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
 
   string snapshot_prefix_;
   shared_ptr<SGDSolver<Dtype> > solver_;
-  shared_ptr<P2PSync<Dtype> > sync_;
+#ifdef USE_NCCL
+  shared_ptr<NCCL<Dtype> > nccl_;
+#endif
   int seed_;
   // Dimensions are determined by generate_sample_data.py
   // TODO this is brittle and the hdf5 file should be checked instead.
@@ -85,6 +87,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "lr_policy: 'fixed' "
        "iter_size: " << iter_size << " "
        "device_id: " << device_id << " "
+       "layer_wise_reduce: " << (!share_) << " "
        "net_param { "
        "  name: 'TestNetwork' "
        "  layer { "
@@ -183,11 +186,10 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     }
     Caffe::set_random_seed(this->seed_);
     this->InitSolverFromProtoString(proto.str());
-    if (from_snapshot != NULL) {
+    if (from_snapshot) {
       this->solver_->Restore(from_snapshot);
-      vector<Blob<Dtype>*> empty_bottom_vec;
       for (int i = 0; i < this->solver_->iter(); ++i) {
-        this->solver_->net()->Forward(empty_bottom_vec);
+        this->solver_->net()->Forward();
       }
     }
     if (devices == 1) {
@@ -203,9 +205,10 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
           gpus.push_back(i);
       }
       Caffe::set_solver_count(gpus.size());
-      this->sync_.reset(new P2PSync<Dtype>(
-          this->solver_, NULL, this->solver_->param()));
-      this->sync_->run(gpus);
+#ifdef USE_NCCL
+      this->nccl_.reset(new NCCL<Dtype>(this->solver_));
+      this->nccl_->Run(gpus, from_snapshot);
+#endif
       Caffe::set_solver_count(1);
     }
     if (snapshot) {
@@ -231,8 +234,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     // Run a forward pass, and manually compute the update values from the
     // result.
     Net<Dtype>& net = *this->solver_->net();
-    vector<Blob<Dtype>*> empty_bottom_vec;
-    net.Forward(empty_bottom_vec);
+    net.Forward();
     ASSERT_TRUE(net.has_blob("data"));
     const Blob<Dtype>& data = *net.blob_by_name("data");
     ASSERT_TRUE(net.has_blob("targets"));
@@ -459,12 +461,28 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     const int kIterSize = 1;
     // Test over all numbers of devices.
     int available_devices = 1;
-#ifndef CPU_ONLY
+#ifdef USE_NCCL
     if (Caffe::mode() == Caffe::GPU) {
       CUDA_CHECK(cudaGetDeviceCount(&available_devices));
     }
 #endif
-    for (int devices = 1; devices <= available_devices; ++devices) {
+    // Takes a while to test all sizes for each test so sparse
+    vector<int> sizes;
+    sizes.push_back(1);
+    if (available_devices >= 2) {
+      sizes.push_back(2);
+    }
+    if (available_devices >= 3) {
+      sizes.push_back(3);
+    }
+    if (available_devices >= 8) {
+      sizes.push_back(8);
+    }
+    if (available_devices >= 16) {
+      sizes.push_back(16);
+    }
+    for (int i = 0; i < sizes.size(); ++i) {
+      int devices = sizes[i];
       // Configure batch size for single / multi device equivalence.
       // Constant data is needed for multi device as for accumulation.
       num_ = kNum * devices;
@@ -540,9 +558,11 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     const vector<Blob<Dtype>*>& params = solver_->net()->learnable_params();
     for (int i = 0; i < params.size(); ++i) {
       for (int j = 0; j < params[i]->count(); ++j) {
-        EXPECT_EQ(param_copies[i]->cpu_data()[j], params[i]->cpu_data()[j])
+        EXPECT_FLOAT_EQ(param_copies[i]->cpu_data()[j],
+            params[i]->cpu_data()[j])
             << "param " << i << " data differed at dim " << j;
-        EXPECT_EQ(param_copies[i]->cpu_diff()[j], params[i]->cpu_diff()[j])
+        EXPECT_FLOAT_EQ(param_copies[i]->cpu_diff()[j],
+            params[i]->cpu_diff()[j])
             << "param " << i << " diff differed at dim " << j;
       }
     }
@@ -551,9 +571,11 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
     for (int i = 0; i < history.size(); ++i) {
       for (int j = 0; j < history[i]->count(); ++j) {
-        EXPECT_EQ(history_copies[i]->cpu_data()[j], history[i]->cpu_data()[j])
+        EXPECT_FLOAT_EQ(history_copies[i]->cpu_data()[j],
+            history[i]->cpu_data()[j])
             << "history blob " << i << " data differed at dim " << j;
-        EXPECT_EQ(history_copies[i]->cpu_diff()[j], history[i]->cpu_diff()[j])
+        EXPECT_FLOAT_EQ(history_copies[i]->cpu_diff()[j],
+            history[i]->cpu_diff()[j])
             << "history blob " << i << " diff differed at dim " << j;
       }
     }
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index 3833ebff..f94dd57e 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -20,8 +20,7 @@ class HDF5OutputLayerTest : public MultiDeviceTest<TypeParam> {
 
  protected:
   HDF5OutputLayerTest()
-      : input_file_name_(
-        CMAKE_SOURCE_DIR "caffe/test/test_data/sample_data.h5"),
+      : input_file_name_(ABS_TEST_DATA_DIR "/sample_data.h5"),
         blob_data_(new Blob<Dtype>()),
         blob_label_(new Blob<Dtype>()),
         num_(5),
@@ -77,10 +76,12 @@ TYPED_TEST(HDF5OutputLayerTest, TestForward) {
                           H5P_DEFAULT);
   ASSERT_GE(file_id, 0)<< "Failed to open HDF5 file" <<
       this->input_file_name_;
+  // Allow reshape here as we are loading data not params
+  bool reshape = true;
   hdf5_load_nd_dataset(file_id, HDF5_DATA_DATASET_NAME, 0, 4,
-                       this->blob_data_);
+                       this->blob_data_, reshape);
   hdf5_load_nd_dataset(file_id, HDF5_DATA_LABEL_NAME, 0, 4,
-                       this->blob_label_);
+                       this->blob_label_, reshape);
   herr_t status = H5Fclose(file_id);
   EXPECT_GE(status, 0)<< "Failed to close HDF5 file " <<
       this->input_file_name_;
@@ -105,12 +106,12 @@ TYPED_TEST(HDF5OutputLayerTest, TestForward) {
 
   Blob<Dtype>* blob_data = new Blob<Dtype>();
   hdf5_load_nd_dataset(file_id, HDF5_DATA_DATASET_NAME, 0, 4,
-                       blob_data);
+                       blob_data, reshape);
   this->CheckBlobEqual(*(this->blob_data_), *blob_data);
 
   Blob<Dtype>* blob_label = new Blob<Dtype>();
   hdf5_load_nd_dataset(file_id, HDF5_DATA_LABEL_NAME, 0, 4,
-                       blob_label);
+                       blob_label, reshape);
   this->CheckBlobEqual(*(this->blob_label_), *blob_label);
 
   status = H5Fclose(file_id);
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index 8884ce95..3977c486 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -30,8 +30,7 @@ class HDF5DataLayerTest : public MultiDeviceTest<TypeParam> {
     blob_top_vec_.push_back(blob_top_label2_);
 
     // Check out generate_sample_data.py in the same directory.
-    filename = new string(
-    CMAKE_SOURCE_DIR "caffe/test/test_data/sample_data_list.txt" CMAKE_EXT);
+    filename = new string(ABS_TEST_DATA_DIR "/sample_data_list.txt");
     LOG(INFO)<< "Using sample HDF5 data file " << filename;
   }
 
@@ -70,7 +69,7 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
   int height = 6;
   int width = 5;
 
-  // Test that the layer setup got the correct parameters.
+  // Test that the layer setup gives correct parameters.
   HDF5DataLayer<Dtype> layer(param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), batch_size);
@@ -133,4 +132,34 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
   }
 }
 
+TYPED_TEST(HDF5DataLayerTest, TestSkip) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter param;
+  param.add_top("data");
+  param.add_top("label");
+
+  HDF5DataParameter* hdf5_data_param = param.mutable_hdf5_data_param();
+  int batch_size = 5;
+  hdf5_data_param->set_batch_size(batch_size);
+  hdf5_data_param->set_source(*(this->filename));
+
+  Caffe::set_solver_count(8);
+  for (int dev = 0; dev < Caffe::solver_count(); ++dev) {
+    Caffe::set_solver_rank(dev);
+
+    HDF5DataLayer<Dtype> layer(param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    int label = dev;
+    for (int iter = 0; iter < 1; ++iter) {
+      layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+      for (int i = 0; i < batch_size; ++i) {
+        EXPECT_EQ(1 + label, this->blob_top_label_->cpu_data()[i]);
+        label = (label + Caffe::solver_count()) % (batch_size * 2);
+      }
+    }
+  }
+  Caffe::set_solver_count(1);
+  Caffe::set_solver_rank(0);
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index a4080ccd..ce5e0bc6 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -34,16 +34,24 @@ class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
     std::ofstream outfile(filename_.c_str(), std::ofstream::out);
     LOG(INFO) << "Using temporary file " << filename_;
     for (int i = 0; i < 5; ++i) {
-      outfile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << i;
+      outfile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << i << std::endl;
     }
     outfile.close();
     // Create test input file for images of distinct sizes.
     MakeTempFilename(&filename_reshape_);
     std::ofstream reshapefile(filename_reshape_.c_str(), std::ofstream::out);
     LOG(INFO) << "Using temporary file " << filename_reshape_;
-    reshapefile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << 0;
-    reshapefile << EXAMPLES_SOURCE_DIR "images/fish-bike.jpg " << 1;
+    reshapefile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << 0 << std::endl;
+    reshapefile << EXAMPLES_SOURCE_DIR "images/fish-bike.jpg " << 1
+                << std::endl;
     reshapefile.close();
+    // Create test input file for images with space in names
+    MakeTempFilename(&filename_space_);
+    std::ofstream spacefile(filename_space_.c_str(), std::ofstream::out);
+    LOG(INFO) << "Using temporary file " << filename_space_;
+    spacefile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << 0 << std::endl;
+    spacefile << EXAMPLES_SOURCE_DIR "images/cat gray.jpg " << 1 << std::endl;
+    spacefile.close();
   }
 
   virtual ~ImageDataLayerTest() {
@@ -54,6 +62,7 @@ class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
   int seed_;
   string filename_;
   string filename_reshape_;
+  string filename_space_;
   Blob<Dtype>* const blob_top_data_;
   Blob<Dtype>* const blob_top_label_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
@@ -177,5 +186,34 @@ TYPED_TEST(ImageDataLayerTest, TestShuffle) {
   }
 }
 
+TYPED_TEST(ImageDataLayerTest, TestSpace) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter param;
+  ImageDataParameter* image_data_param = param.mutable_image_data_param();
+  image_data_param->set_batch_size(1);
+  image_data_param->set_source(this->filename_space_.c_str());
+  image_data_param->set_shuffle(false);
+  ImageDataLayer<Dtype> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_label_->num(), 1);
+  EXPECT_EQ(this->blob_top_label_->channels(), 1);
+  EXPECT_EQ(this->blob_top_label_->height(), 1);
+  EXPECT_EQ(this->blob_top_label_->width(), 1);
+  // cat.jpg
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_data_->num(), 1);
+  EXPECT_EQ(this->blob_top_data_->channels(), 3);
+  EXPECT_EQ(this->blob_top_data_->height(), 360);
+  EXPECT_EQ(this->blob_top_data_->width(), 480);
+  EXPECT_EQ(this->blob_top_label_->cpu_data()[0], 0);
+  // cat gray.jpg
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_data_->num(), 1);
+  EXPECT_EQ(this->blob_top_data_->channels(), 3);
+  EXPECT_EQ(this->blob_top_data_->height(), 360);
+  EXPECT_EQ(this->blob_top_data_->width(), 480);
+  EXPECT_EQ(this->blob_top_label_->cpu_data()[0], 1);
+}
+
 }  // namespace caffe
 #endif  // USE_OPENCV
diff --git a/src/caffe/test/test_infogain_loss_layer.cpp b/src/caffe/test/test_infogain_loss_layer.cpp
index a24ac683..34f21271 100644
--- a/src/caffe/test/test_infogain_loss_layer.cpp
+++ b/src/caffe/test/test_infogain_loss_layer.cpp
@@ -1,3 +1,4 @@
+#include <algorithm>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -18,17 +19,22 @@ class InfogainLossLayerTest : public MultiDeviceTest<TypeParam> {
 
  protected:
   InfogainLossLayerTest()
-      : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
-        blob_bottom_label_(new Blob<Dtype>(10, 1, 1, 1)),
+      : blob_bottom_data_(new Blob<Dtype>(4, 2, 5, 2)),
+        blob_bottom_label_(new Blob<Dtype>(4, 2, 1, 2)),
         blob_bottom_infogain_(new Blob<Dtype>(1, 1, 5, 5)),
-        blob_top_loss_(new Blob<Dtype>()) {
+        blob_top_loss_(new Blob<Dtype>()),
+        blob_top_prob_(new Blob<Dtype>()),
+        inner_(2), outer_(4*2), num_labels_(5) {
     Caffe::set_random_seed(1701);
     FillerParameter filler_param;
-    PositiveUnitballFiller<Dtype> filler(filler_param);
+    filler_param.set_min(-0.5);
+    filler_param.set_max(2.0);
+    UniformFiller<Dtype> filler(filler_param);
     filler.Fill(this->blob_bottom_data_);
     blob_bottom_vec_.push_back(blob_bottom_data_);
     for (int i = 0; i < blob_bottom_label_->count(); ++i) {
-      blob_bottom_label_->mutable_cpu_data()[i] = caffe_rng_rand() % 5;
+      blob_bottom_label_->mutable_cpu_data()[i] =
+        caffe_rng_rand() % num_labels_;
     }
     blob_bottom_vec_.push_back(blob_bottom_label_);
     filler_param.set_min(0.1);
@@ -37,29 +43,94 @@ class InfogainLossLayerTest : public MultiDeviceTest<TypeParam> {
     infogain_filler.Fill(this->blob_bottom_infogain_);
     blob_bottom_vec_.push_back(blob_bottom_infogain_);
     blob_top_vec_.push_back(blob_top_loss_);
+    blob_top_vec_.push_back(blob_top_prob_);
   }
   virtual ~InfogainLossLayerTest() {
     delete blob_bottom_data_;
     delete blob_bottom_label_;
     delete blob_bottom_infogain_;
     delete blob_top_loss_;
+    delete blob_top_prob_;
   }
   Blob<Dtype>* const blob_bottom_data_;
   Blob<Dtype>* const blob_bottom_label_;
   Blob<Dtype>* const blob_bottom_infogain_;
   Blob<Dtype>* const blob_top_loss_;
+  Blob<Dtype>* const blob_top_prob_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
+  int inner_, outer_, num_labels_;
 };
 
 TYPED_TEST_CASE(InfogainLossLayerTest, TestDtypesAndDevices);
 
+TYPED_TEST(InfogainLossLayerTest, TestInfogainLoss) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_infogain_loss_param()->set_axis(2);
+  layer_param.clear_loss_weight();
+  layer_param.add_loss_weight(1);
+  layer_param.add_loss_weight(0);
+  /*vector<float>* lw = layer_param.mutable_loss_weight();
+  lw->clear();
+  lw->push_back(1);
+  lw->push_back(1);*/
+  InfogainLossLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Now, check values
+  const Dtype* data = this->blob_bottom_vec_[0]->cpu_data();
+  const Dtype* prob = this->blob_top_vec_[1]->cpu_data();
+  const Dtype* labels = this->blob_bottom_vec_[1]->cpu_data();
+  const Dtype* H = this->blob_bottom_vec_[2]->cpu_data();
+  // first. test the prob top
+  CHECK_EQ(this->blob_bottom_vec_[0]->num_axes(),
+    this->blob_top_vec_[1]->num_axes())
+      << "prob top shape not match bottom data";
+  for (int ai = 0 ; ai < this->blob_bottom_vec_[0]->num_axes(); ai++) {
+    CHECK_EQ(this->blob_bottom_vec_[0]->shape(ai),
+      this->blob_top_vec_[1]->shape(ai))
+        << "prob top shape not match bottom data";
+  }
+  vector<Dtype> est_prob(this->num_labels_, 0);
+  for ( int i = 0 ; i < this->outer_; i++ ) {
+    for ( int j = 0; j < this->inner_; j++ ) {
+      Dtype den = 0;
+      for ( int  l = 0; l < this->num_labels_; l++ ) {
+        est_prob[l] = std::exp(
+          data[i*this->num_labels_*this->inner_ + l*this->inner_ + j]);
+        den += est_prob[l];
+      }
+      for ( int l = 0; l < this->num_labels_; l++ ) {
+        EXPECT_NEAR(prob[i*this->num_labels_*this->inner_ + l*this->inner_ + j],
+          est_prob[l]/den, 1e-6);
+      }
+    }
+  }
+  Dtype loss = 0;  // loss from prob top
+  for ( int i = 0 ; i < this->outer_; i++ ) {
+    for ( int j = 0; j < this->inner_; j++ ) {
+      int gt = static_cast<int>(labels[i*this->inner_+j]);
+      for ( int l = 0; l < this->num_labels_; l++ ) {
+        loss -= H[gt*this->num_labels_ + l] *
+          log(std::max(
+            prob[i*this->num_labels_*this->inner_ + l*this->inner_ + j],
+            Dtype(kLOG_THRESHOLD)));
+      }
+    }
+  }
+  EXPECT_NEAR(this->blob_top_loss_->cpu_data()[0],
+    loss/(this->outer_*this->inner_), 1e-6);
+}
 
 TYPED_TEST(InfogainLossLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
+  layer_param.mutable_infogain_loss_param()->set_axis(2);
   InfogainLossLayer<Dtype> layer(layer_param);
-  GradientChecker<Dtype> checker(1e-4, 2e-2, 1701, 1, 0.01);
+  this->blob_top_vec_.clear();  // ignore prob top.
+  this->blob_top_vec_.push_back(this->blob_top_loss_);
+  GradientChecker<Dtype> checker(1e-4, 2e-2, 1701);  // no "kink"
   checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
       this->blob_top_vec_, 0);
 }
diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp
index b888b510..6d84d292 100644
--- a/src/caffe/test/test_inner_product_layer.cpp
+++ b/src/caffe/test/test_inner_product_layer.cpp
@@ -60,6 +60,50 @@ TYPED_TEST(InnerProductLayerTest, TestSetUp) {
   EXPECT_EQ(this->blob_top_->channels(), 10);
 }
 
+/** @brief TestSetUp while toggling transpose flag
+ */
+TYPED_TEST(InnerProductLayerTest, TestSetUpTransposeFalse) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
+  LayerParameter layer_param;
+  InnerProductParameter* inner_product_param =
+      layer_param.mutable_inner_product_param();
+  inner_product_param->set_num_output(10);
+  inner_product_param->set_transpose(false);
+  shared_ptr<InnerProductLayer<Dtype> > layer(
+      new InnerProductLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(2, this->blob_top_->num());
+  EXPECT_EQ(1, this->blob_top_->height());
+  EXPECT_EQ(1, this->blob_top_->width());
+  EXPECT_EQ(10, this->blob_top_->channels());
+  EXPECT_EQ(2, layer->blobs()[0]->num_axes());
+  EXPECT_EQ(10, layer->blobs()[0]->shape(0));
+  EXPECT_EQ(60, layer->blobs()[0]->shape(1));
+}
+
+/** @brief TestSetUp while toggling transpose flag
+ */
+TYPED_TEST(InnerProductLayerTest, TestSetUpTransposeTrue) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
+  LayerParameter layer_param;
+  InnerProductParameter* inner_product_param =
+      layer_param.mutable_inner_product_param();
+  inner_product_param->set_num_output(10);
+  inner_product_param->set_transpose(true);
+  shared_ptr<InnerProductLayer<Dtype> > layer(
+      new InnerProductLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(2, this->blob_top_->num());
+  EXPECT_EQ(1, this->blob_top_->height());
+  EXPECT_EQ(1, this->blob_top_->width());
+  EXPECT_EQ(10, this->blob_top_->channels());
+  EXPECT_EQ(2, layer->blobs()[0]->num_axes());
+  EXPECT_EQ(60, layer->blobs()[0]->shape(0));
+  EXPECT_EQ(10, layer->blobs()[0]->shape(1));
+}
+
 TYPED_TEST(InnerProductLayerTest, TestForward) {
   typedef typename TypeParam::Dtype Dtype;
   this->blob_bottom_vec_.push_back(this->blob_bottom_);
@@ -91,6 +135,79 @@ TYPED_TEST(InnerProductLayerTest, TestForward) {
   }
 }
 
+/**
+ * @brief Init. an IP layer without transpose + random weights,
+ * run Forward, save the result.
+ * Init. another IP layer with transpose.
+ * manually copy and transpose the weights from the first IP layer,
+ * then run Forward on the same input and check that the result is the same
+ */
+TYPED_TEST(InnerProductLayerTest, TestForwardTranspose) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
+  bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+  IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
+  if (Caffe::mode() == Caffe::CPU ||
+      sizeof(Dtype) == 4 || IS_VALID_CUDA) {
+    LayerParameter layer_param;
+    InnerProductParameter* inner_product_param =
+        layer_param.mutable_inner_product_param();
+    inner_product_param->set_num_output(10);
+    inner_product_param->mutable_weight_filler()->set_type("uniform");
+    inner_product_param->mutable_bias_filler()->set_type("uniform");
+    inner_product_param->mutable_bias_filler()->set_min(1);
+    inner_product_param->mutable_bias_filler()->set_max(2);
+    inner_product_param->set_transpose(false);
+    shared_ptr<InnerProductLayer<Dtype> > layer(
+        new InnerProductLayer<Dtype>(layer_param));
+    layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    const int count = this->blob_top_->count();
+    Blob<Dtype>* const top = new Blob<Dtype>();
+    top->ReshapeLike(*this->blob_top_);
+    caffe_copy(count, this->blob_top_->cpu_data(), top->mutable_cpu_data());
+    this->blob_top_vec_.clear();
+    this->blob_top_vec_.push_back(new Blob<Dtype>());
+    inner_product_param->set_transpose(true);
+    shared_ptr<InnerProductLayer<Dtype> > ip_t(
+        new InnerProductLayer<Dtype>(layer_param));
+    ip_t->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    const int count_w = layer->blobs()[0]->count();
+    EXPECT_EQ(count_w, ip_t->blobs()[0]->count());
+    // manually copy and transpose the weights from 1st IP layer into 2nd
+    const Dtype* w = layer->blobs()[0]->cpu_data();
+    Dtype* w_t = ip_t->blobs()[0]->mutable_cpu_data();
+    const int width = layer->blobs()[0]->shape(1);
+    const int width_t = ip_t->blobs()[0]->shape(1);
+    for (int i = 0; i < count_w; ++i) {
+      int r = i / width;
+      int c = i % width;
+      w_t[c*width_t+r] = w[r*width+c];  // copy while transposing
+    }
+    // copy bias from 1st IP layer to 2nd IP layer
+    ASSERT_EQ(layer->blobs()[1]->count(), ip_t->blobs()[1]->count());
+    caffe_copy(layer->blobs()[1]->count(), layer->blobs()[1]->cpu_data(),
+        ip_t->blobs()[1]->mutable_cpu_data());
+    ip_t->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    EXPECT_EQ(count, this->blob_top_->count())
+        << "Invalid count for top blob for IP with transpose.";
+    Blob<Dtype>* const top_t = new Blob<Dtype>();\
+    top_t->ReshapeLike(*this->blob_top_vec_[0]);
+    caffe_copy(count,
+      this->blob_top_vec_[0]->cpu_data(),
+      top_t->mutable_cpu_data());
+    const Dtype* data = top->cpu_data();
+    const Dtype* data_t = top_t->cpu_data();
+    for (int i = 0; i < count; ++i) {
+      EXPECT_FLOAT_EQ(data[i], data_t[i]);
+    }
+  } else {
+    LOG(ERROR) << "Skipping test due to old architecture.";
+  }
+}
+
 TYPED_TEST(InnerProductLayerTest, TestForwardNoBatch) {
   typedef typename TypeParam::Dtype Dtype;
   this->blob_bottom_vec_.push_back(this->blob_bottom_nobatch_);
@@ -148,4 +265,127 @@ TYPED_TEST(InnerProductLayerTest, TestGradient) {
   }
 }
 
+TYPED_TEST(InnerProductLayerTest, TestGradientTranspose) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
+  bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+  IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
+  if (Caffe::mode() == Caffe::CPU ||
+      sizeof(Dtype) == 4 || IS_VALID_CUDA) {
+    LayerParameter layer_param;
+    InnerProductParameter* inner_product_param =
+        layer_param.mutable_inner_product_param();
+    inner_product_param->set_num_output(11);
+    inner_product_param->mutable_weight_filler()->set_type("gaussian");
+    inner_product_param->mutable_bias_filler()->set_type("gaussian");
+    inner_product_param->mutable_bias_filler()->set_min(1);
+    inner_product_param->mutable_bias_filler()->set_max(2);
+    inner_product_param->set_transpose(true);
+    InnerProductLayer<Dtype> layer(layer_param);
+    GradientChecker<Dtype> checker(1e-2, 1e-3);
+    checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+        this->blob_top_vec_);
+  } else {
+    LOG(ERROR) << "Skipping test due to old architecture.";
+  }
+}
+
+TYPED_TEST(InnerProductLayerTest, TestBackwardTranspose) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
+  bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+  IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
+  if (Caffe::mode() == Caffe::CPU ||
+      sizeof(Dtype) == 4 || IS_VALID_CUDA) {
+    LayerParameter layer_param;
+    InnerProductParameter* inner_product_param =
+        layer_param.mutable_inner_product_param();
+    inner_product_param->set_num_output(10);
+    inner_product_param->mutable_weight_filler()->set_type("uniform");
+    inner_product_param->mutable_bias_filler()->set_type("uniform");
+    inner_product_param->mutable_bias_filler()->set_min(1);
+    inner_product_param->mutable_bias_filler()->set_max(2);
+    inner_product_param->set_transpose(false);
+    shared_ptr<InnerProductLayer<Dtype> > layer(
+        new InnerProductLayer<Dtype>(layer_param));
+    layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    // copy top blob
+    Blob<Dtype>* const top = new Blob<Dtype>();
+    top->CopyFrom(*this->blob_top_, false, true);
+    // fake top diff
+    Blob<Dtype>* const diff = new Blob<Dtype>();
+    diff->ReshapeLike(*this->blob_top_);
+    {
+      FillerParameter filler_param;
+      UniformFiller<Dtype> filler(filler_param);
+      filler.Fill(diff);
+    }
+    caffe_copy(this->blob_top_vec_[0]->count(),
+      diff->cpu_data(),
+      this->blob_top_vec_[0]->mutable_cpu_diff());
+    vector<bool> propagate_down(1, true);
+    layer->Backward(this->blob_top_vec_,
+        propagate_down,
+        this->blob_bottom_vec_);
+    // copy first ip's weights and their diffs
+    Blob<Dtype>* const w = new Blob<Dtype>();
+    w->CopyFrom(*layer->blobs()[0], false, true);
+    w->CopyFrom(*layer->blobs()[0], true, true);
+    // copy bottom diffs
+    Blob<Dtype>* const bottom_diff = new Blob<Dtype>();
+    bottom_diff->CopyFrom(*this->blob_bottom_vec_[0], true, true);
+    // repeat original top with transposed ip
+    this->blob_top_vec_.clear();
+    this->blob_top_vec_.push_back(new Blob<Dtype>());
+    inner_product_param->set_transpose(true);
+    shared_ptr<InnerProductLayer<Dtype> > ip_t(
+        new InnerProductLayer<Dtype>(layer_param));
+    ip_t->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    // manually copy and transpose the weights from 1st IP layer into 2nd
+    {
+      const Dtype* w_src = w->cpu_data();
+      Dtype* w_t = ip_t->blobs()[0]->mutable_cpu_data();
+      const int width = layer->blobs()[0]->shape(1);
+      const int width_t = ip_t->blobs()[0]->shape(1);
+      for (int i = 0; i < layer->blobs()[0]->count(); ++i) {
+        int r = i / width;
+        int c = i % width;
+        w_t[c*width_t+r] = w_src[r*width+c];  // copy while transposing
+      }
+      // copy bias from 1st IP layer to 2nd IP layer
+      ASSERT_EQ(layer->blobs()[1]->count(), ip_t->blobs()[1]->count());
+      caffe_copy(layer->blobs()[1]->count(), layer->blobs()[1]->cpu_data(),
+          ip_t->blobs()[1]->mutable_cpu_data());
+    }
+    ip_t->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    caffe_copy(this->blob_top_vec_[0]->count(),
+      diff->cpu_data(),
+      this->blob_top_vec_[0]->mutable_cpu_diff());
+    ip_t->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+    const Dtype* data = w->cpu_diff();
+    const Dtype* data_t = ip_t->blobs()[0]->cpu_diff();
+    const int WIDTH = layer->blobs()[0]->shape(1);
+    const int WIDTH_T = ip_t->blobs()[0]->shape(1);
+    for (int i = 0; i < layer->blobs()[0]->count(); ++i) {
+      int r = i / WIDTH;
+      int c = i % WIDTH;
+      EXPECT_NE(Dtype(0.), data[r*WIDTH+c]);
+      EXPECT_FLOAT_EQ(data[r*WIDTH+c], data_t[c*WIDTH_T+r]);
+    }
+    data = bottom_diff->cpu_diff();
+    data_t = this->blob_bottom_vec_[0]->cpu_diff();
+    for (int i = 0; i < this->blob_bottom_vec_[0]->count(); ++i) {
+      EXPECT_NE(Dtype(0.), data[i]);
+      EXPECT_FLOAT_EQ(data[i], data_t[i]);
+    }
+  } else {
+    LOG(ERROR) << "Skipping test due to old architecture.";
+  }
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_lstm_layer.cpp b/src/caffe/test/test_lstm_layer.cpp
new file mode 100644
index 00000000..51905baa
--- /dev/null
+++ b/src/caffe/test/test_lstm_layer.cpp
@@ -0,0 +1,288 @@
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/lstm_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class LSTMLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  LSTMLayerTest() : num_output_(7) {
+    blob_bottom_vec_.push_back(&blob_bottom_);
+    blob_bottom_vec_.push_back(&blob_bottom_cont_);
+    blob_top_vec_.push_back(&blob_top_);
+    unit_blob_bottom_vec_.push_back(&unit_blob_bottom_c_prev_);
+    unit_blob_bottom_vec_.push_back(&unit_blob_bottom_x_);
+    unit_blob_bottom_vec_.push_back(&unit_blob_bottom_cont_);
+    unit_blob_top_vec_.push_back(&unit_blob_top_c_);
+    unit_blob_top_vec_.push_back(&unit_blob_top_h_);
+
+    ReshapeBlobs(1, 3);
+
+    layer_param_.mutable_recurrent_param()->set_num_output(num_output_);
+    FillerParameter* weight_filler =
+        layer_param_.mutable_recurrent_param()->mutable_weight_filler();
+    weight_filler->set_type("gaussian");
+    weight_filler->set_std(0.2);
+    FillerParameter* bias_filler =
+        layer_param_.mutable_recurrent_param()->mutable_bias_filler();
+    bias_filler->set_type("gaussian");
+    bias_filler->set_std(0.1);
+
+    layer_param_.set_phase(TEST);
+  }
+
+  void ReshapeBlobs(int num_timesteps, int num_instances) {
+    blob_bottom_.Reshape(num_timesteps, num_instances, 3, 2);
+    blob_bottom_static_.Reshape(num_instances, 2, 3, 4);
+    vector<int> shape(2);
+    shape[0] = num_timesteps;
+    shape[1] = num_instances;
+    blob_bottom_cont_.Reshape(shape);
+    shape.push_back(num_output_);
+
+    shape[0] = 1; shape[1] = num_instances; shape[2] = 4 * num_output_;
+    unit_blob_bottom_x_.Reshape(shape);
+    shape[0] = 1; shape[1] = num_instances; shape[2] = num_output_;
+    unit_blob_bottom_c_prev_.Reshape(shape);
+    shape.resize(2);
+    shape[0] = 1; shape[1] = num_instances;
+    unit_blob_bottom_cont_.Reshape(shape);
+
+    FillerParameter filler_param;
+    filler_param.set_min(-1);
+    filler_param.set_max(1);
+    UniformFiller<Dtype> filler(filler_param);
+    filler.Fill(&blob_bottom_);
+    filler.Fill(&unit_blob_bottom_c_prev_);
+    filler.Fill(&unit_blob_bottom_x_);
+  }
+
+  int num_output_;
+  LayerParameter layer_param_;
+  Blob<Dtype> blob_bottom_;
+  Blob<Dtype> blob_bottom_cont_;
+  Blob<Dtype> blob_bottom_static_;
+  Blob<Dtype> blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+
+  Blob<Dtype> unit_blob_bottom_cont_;
+  Blob<Dtype> unit_blob_bottom_c_prev_;
+  Blob<Dtype> unit_blob_bottom_x_;
+  Blob<Dtype> unit_blob_top_c_;
+  Blob<Dtype> unit_blob_top_h_;
+  vector<Blob<Dtype>*> unit_blob_bottom_vec_;
+  vector<Blob<Dtype>*> unit_blob_top_vec_;
+};
+
+TYPED_TEST_CASE(LSTMLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(LSTMLayerTest, TestSetUp) {
+  typedef typename TypeParam::Dtype Dtype;
+  LSTMLayer<Dtype> layer(this->layer_param_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  vector<int> expected_top_shape = this->blob_bottom_.shape();
+  expected_top_shape.resize(3);
+  expected_top_shape[2] = this->num_output_;
+  EXPECT_TRUE(this->blob_top_.shape() == expected_top_shape);
+}
+
+TYPED_TEST(LSTMLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  const int kNumTimesteps = 3;
+  const int num = this->blob_bottom_.shape(1);
+  this->ReshapeBlobs(kNumTimesteps, num);
+
+  // Fill the cont blob with <0, 1, 1, ..., 1>,
+  // indicating a sequence that begins at the first timestep
+  // then continues for the rest of the sequence.
+  for (int t = 0; t < kNumTimesteps; ++t) {
+    for (int n = 0; n < num; ++n) {
+      this->blob_bottom_cont_.mutable_cpu_data()[t * num + n] = t > 0;
+    }
+  }
+
+  // Process the full sequence in a single batch.
+  FillerParameter filler_param;
+  filler_param.set_mean(0);
+  filler_param.set_std(1);
+  GaussianFiller<Dtype> sequence_filler(filler_param);
+  Caffe::set_random_seed(1);
+  sequence_filler.Fill(&this->blob_bottom_);
+  shared_ptr<LSTMLayer<Dtype> > layer(new LSTMLayer<Dtype>(this->layer_param_));
+  Caffe::set_random_seed(1701);
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  LOG(INFO) << "Calling forward for full sequence LSTM";
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  // Copy the inputs and outputs to reuse/check them later.
+  Blob<Dtype> bottom_copy(this->blob_bottom_.shape());
+  bottom_copy.CopyFrom(this->blob_bottom_);
+  Blob<Dtype> top_copy(this->blob_top_.shape());
+  top_copy.CopyFrom(this->blob_top_);
+
+  // Process the batch one timestep at a time;
+  // check that we get the same result.
+  this->ReshapeBlobs(1, num);
+  layer.reset(new LSTMLayer<Dtype>(this->layer_param_));
+  Caffe::set_random_seed(1701);
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  const int bottom_count = this->blob_bottom_.count();
+  const int top_count = this->blob_top_.count();
+  const Dtype kEpsilon = 1e-5;
+  for (int t = 0; t < kNumTimesteps; ++t) {
+    caffe_copy(bottom_count, bottom_copy.cpu_data() + t * bottom_count,
+               this->blob_bottom_.mutable_cpu_data());
+    for (int n = 0; n < num; ++n) {
+      this->blob_bottom_cont_.mutable_cpu_data()[n] = t > 0;
+    }
+    LOG(INFO) << "Calling forward for LSTM timestep " << t;
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    for (int i = 0; i < top_count; ++i) {
+      ASSERT_LT(t * top_count + i, top_copy.count());
+      EXPECT_NEAR(this->blob_top_.cpu_data()[i],
+                  top_copy.cpu_data()[t * top_count + i], kEpsilon)
+         << "t = " << t << "; i = " << i;
+    }
+  }
+
+  // Process the batch one timestep at a time with all cont blobs set to 0.
+  // Check that we get a different result, except in the first timestep.
+  Caffe::set_random_seed(1701);
+  layer.reset(new LSTMLayer<Dtype>(this->layer_param_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int t = 0; t < kNumTimesteps; ++t) {
+    caffe_copy(bottom_count, bottom_copy.cpu_data() + t * bottom_count,
+               this->blob_bottom_.mutable_cpu_data());
+    for (int n = 0; n < num; ++n) {
+      this->blob_bottom_cont_.mutable_cpu_data()[n] = 0;
+    }
+    LOG(INFO) << "Calling forward for LSTM timestep " << t;
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    for (int i = 0; i < top_count; ++i) {
+      if (t == 0) {
+        EXPECT_NEAR(this->blob_top_.cpu_data()[i],
+                    top_copy.cpu_data()[t * top_count + i], kEpsilon)
+           << "t = " << t << "; i = " << i;
+      } else {
+        EXPECT_NE(this->blob_top_.cpu_data()[i],
+                  top_copy.cpu_data()[t * top_count + i])
+           << "t = " << t << "; i = " << i;
+      }
+    }
+  }
+}
+
+TYPED_TEST(LSTMLayerTest, TestLSTMUnitSetUp) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  LSTMUnitLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->unit_blob_bottom_vec_, this->unit_blob_top_vec_);
+  const int num_axes = this->unit_blob_bottom_c_prev_.num_axes();
+  ASSERT_EQ(num_axes, this->unit_blob_top_c_.num_axes());
+  ASSERT_EQ(num_axes, this->unit_blob_top_h_.num_axes());
+  for (int i = 0; i < num_axes; ++i) {
+    EXPECT_EQ(this->unit_blob_bottom_c_prev_.shape(i),
+              this->unit_blob_top_c_.shape(i));
+    EXPECT_EQ(this->unit_blob_bottom_c_prev_.shape(i),
+              this->unit_blob_top_h_.shape(i));
+  }
+}
+
+TYPED_TEST(LSTMLayerTest, TestLSTMUnitGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  LSTMUnitLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  Dtype* cont_data = this->blob_bottom_cont_.mutable_cpu_data();
+  cont_data[0] = 0;
+  cont_data[1] = 0;
+  cont_data[2] = 0;
+  checker.CheckGradientExhaustive(&layer, this->unit_blob_bottom_vec_,
+      this->unit_blob_top_vec_, 0);
+  checker.CheckGradientExhaustive(&layer, this->unit_blob_bottom_vec_,
+      this->unit_blob_top_vec_, 1);
+}
+
+TYPED_TEST(LSTMLayerTest, TestLSTMUnitGradientNonZeroCont) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  LSTMUnitLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  Dtype* cont_data = this->blob_bottom_cont_.mutable_cpu_data();
+  cont_data[0] = 1;
+  cont_data[1] = 0;
+  cont_data[2] = 1;
+  checker.CheckGradientExhaustive(&layer, this->unit_blob_bottom_vec_,
+      this->unit_blob_top_vec_, 0);
+  checker.CheckGradientExhaustive(&layer, this->unit_blob_bottom_vec_,
+      this->unit_blob_top_vec_, 1);
+}
+
+TYPED_TEST(LSTMLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LSTMLayer<Dtype> layer(this->layer_param_);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(LSTMLayerTest, TestGradientNonZeroCont) {
+  typedef typename TypeParam::Dtype Dtype;
+  LSTMLayer<Dtype> layer(this->layer_param_);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  for (int i = 0; i < this->blob_bottom_cont_.count(); ++i) {
+    this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;
+  }
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(LSTMLayerTest, TestGradientNonZeroContBufferSize2) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->ReshapeBlobs(2, 2);
+  FillerParameter filler_param;
+  UniformFiller<Dtype> filler(filler_param);
+  filler.Fill(&this->blob_bottom_);
+  LSTMLayer<Dtype> layer(this->layer_param_);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  for (int i = 0; i < this->blob_bottom_cont_.count(); ++i) {
+    this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;
+  }
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(LSTMLayerTest, TestGradientNonZeroContBufferSize2WithStaticInput) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->ReshapeBlobs(2, 2);
+  FillerParameter filler_param;
+  UniformFiller<Dtype> filler(filler_param);
+  filler.Fill(&this->blob_bottom_);
+  filler.Fill(&this->blob_bottom_static_);
+  this->blob_bottom_vec_.push_back(&this->blob_bottom_static_);
+  LSTMLayer<Dtype> layer(this->layer_param_);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  for (int i = 0; i < this->blob_bottom_cont_.count(); ++i) {
+    this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;
+  }
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 2);
+}
+
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index ab4afba1..24b957f2 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -9,6 +9,7 @@
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/net.hpp"
+#include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
@@ -29,6 +30,17 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     net_.reset(new Net<Dtype>(param));
   }
 
+  virtual void InitNetFromProtoFileWithState(const string& proto,
+      Phase phase = caffe::TRAIN, const int level = 0,
+      const vector<string>* stages = NULL) {
+    NetParameter param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
+    string param_file;
+    MakeTempFilename(&param_file);
+    WriteProtoToTextFile(param, param_file);
+    net_.reset(new Net<Dtype>(param_file, phase, level, stages));
+  }
+
   virtual void CopyNetBlobs(const bool copy_diff,
       vector<shared_ptr<Blob<Dtype> > >* blobs_copy) {
     CHECK(net_);
@@ -555,11 +567,14 @@ class NetTest : public MultiDeviceTest<TypeParam> {
   virtual void InitReshapableNet() {
     const string& proto =
         "name: 'ReshapableNetwork' "
-        "input: 'data' "
-        "input_dim: 1 "
-        "input_dim: 3 "
-        "input_dim: 100 "
-        "input_dim: 100 "
+        "layer { "
+        "  name: 'data' "
+        "  type: 'Input' "
+        "  top: 'data' "
+        "  input_param { "
+        "  shape: { dim: 1 dim: 3 dim: 100 dim: 100 } "
+        "  } "
+        "} "
         "layer { "
         "  name: 'conv1' "
         "  type: 'Convolution' "
@@ -713,6 +728,117 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     InitNetFromProtoString(proto);
   }
 
+  virtual void InitForcePropNet(bool test_force_true) {
+    string proto =
+      "name: 'ForcePropTestNetwork' "
+      "layer { "
+      "  name: 'data' "
+      "  type: 'DummyData' "
+      "  dummy_data_param { "
+      "    shape { "
+      "      dim: 5 "
+      "      dim: 2 "
+      "      dim: 3 "
+      "      dim: 4 "
+      "    } "
+      "    data_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    shape { "
+      "      dim: 5 "
+      "    } "
+      "    data_filler { "
+      "      type: 'constant' "
+      "      value: 0 "
+      "    } "
+      "  } "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layer { "
+      "  name: 'innerproduct' "
+      "  type: 'InnerProduct' "
+      "  inner_product_param { "
+      "    num_output: 1 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "  } "
+      "  bottom: 'data' "
+      "  top: 'innerproduct' ";
+    if (test_force_true) {
+      proto += "  propagate_down: true ";
+    }
+    proto +=
+      "} "
+      "layer { "
+      "  name: 'loss' "
+      "  bottom: 'innerproduct' "
+      "  bottom: 'label' "
+      "  top: 'cross_entropy_loss' "
+      "  type: 'SigmoidCrossEntropyLoss' "
+      "} ";
+    InitNetFromProtoString(proto);
+  }
+
+  virtual void InitAllInOneNet(Phase phase = caffe::TRAIN,
+      const int level = 0, const vector<string>* stages = NULL) {
+    string proto =
+      "name: 'All-in-one Network'"
+      "layer { "
+      "  name: 'train-data' "
+      "  type: 'DummyData' "
+      "  top: 'data' "
+      "  top: 'label' "
+      "  dummy_data_param { "
+      "    shape { dim: 1 dim: 10 } "
+      "    shape { dim: 1 dim: 1 } "
+      "  } "
+      "  include { phase: TRAIN stage: 'train' } "
+      "} "
+      "layer { "
+      "  name: 'val-data' "
+      "  type: 'DummyData' "
+      "  top: 'data' "
+      "  top: 'label' "
+      "  dummy_data_param { "
+      "    shape { dim: 1 dim: 10 } "
+      "    shape { dim: 1 dim: 1 } "
+      "  } "
+      "  include { phase: TEST stage: 'val' } "
+      "} "
+      "layer { "
+      "  name: 'deploy-data' "
+      "  type: 'Input' "
+      "  top: 'data' "
+      "  input_param { "
+      "    shape { dim: 1 dim: 10 } "
+      "  } "
+      "  include { phase: TEST stage: 'deploy' } "
+      "} "
+      "layer { "
+      "  name: 'ip' "
+      "  type: 'InnerProduct' "
+      "  bottom: 'data' "
+      "  top: 'ip' "
+      "  inner_product_param { "
+      "    num_output: 2 "
+      "  } "
+      "} "
+      "layer { "
+      "  name: 'loss' "
+      "  type: 'SoftmaxWithLoss' "
+      "  bottom: 'ip' "
+      "  bottom: 'label' "
+      "  top: 'loss' "
+      "  include { phase: TRAIN stage: 'train' } "
+      "  include { phase: TEST stage: 'val' } "
+      "} ";
+    InitNetFromProtoFileWithState(proto, phase, level, stages);
+  }
+
   int seed_;
   shared_ptr<Net<Dtype> > net_;
 };
@@ -821,7 +947,7 @@ TYPED_TEST(NetTest, TestLossWeight) {
   Caffe::set_random_seed(this->seed_);
   const bool kForceBackward = true;
   this->InitUnsharedWeightsNet(NULL, NULL, kForceBackward);
-  const Dtype loss = this->net_->ForwardBackward(bottom);
+  const Dtype loss = this->net_->ForwardBackward();
   const bool kCopyDiff = true;
   vector<shared_ptr<Blob<Dtype> > > blob_grads;
   this->CopyNetBlobs(kCopyDiff, &blob_grads);
@@ -836,7 +962,7 @@ TYPED_TEST(NetTest, TestLossWeight) {
   for (int i = 0; i < kNumLossWeights; ++i) {
     Caffe::set_random_seed(this->seed_);
     this->InitUnsharedWeightsNet(&kLossWeights[i], NULL, kForceBackward);
-    const Dtype weighted_loss = this->net_->ForwardBackward(bottom);
+    const Dtype weighted_loss = this->net_->ForwardBackward();
     const Dtype error_margin = kErrorMargin * fabs(kLossWeights[i]);
     EXPECT_NEAR(loss * kLossWeights[i], weighted_loss, error_margin)
         << "loss weight = " << kLossWeights[i];
@@ -865,14 +991,13 @@ TYPED_TEST(NetTest, TestLossWeight) {
 
 TYPED_TEST(NetTest, TestLossWeightMidNet) {
   typedef typename TypeParam::Dtype Dtype;
-  vector<Blob<Dtype>*> bottom;
   Caffe::set_random_seed(this->seed_);
   const bool kForceBackward = true;
   Dtype loss_weight = 0;
   Dtype midnet_loss_weight = 1;
   this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
                                kForceBackward);
-  const Dtype loss = this->net_->ForwardBackward(bottom);
+  const Dtype loss = this->net_->ForwardBackward();
   const bool kCopyDiff = true;
   const bool kReshape = true;
   Blob<Dtype> data_grad;
@@ -887,7 +1012,7 @@ TYPED_TEST(NetTest, TestLossWeightMidNet) {
     Caffe::set_random_seed(this->seed_);
     this->InitUnsharedWeightsNet(&loss_weight, &kLossWeights[i],
                                  kForceBackward);
-    const Dtype weighted_loss = this->net_->ForwardBackward(bottom);
+    const Dtype weighted_loss = this->net_->ForwardBackward();
     const Dtype error_margin = kErrorMargin * fabs(kLossWeights[i]);
     EXPECT_NEAR(loss * kLossWeights[i], weighted_loss, error_margin)
         << "loss weight = " << kLossWeights[i];
@@ -903,7 +1028,6 @@ TYPED_TEST(NetTest, TestLossWeightMidNet) {
 
 TYPED_TEST(NetTest, TestComboLossWeight) {
   typedef typename TypeParam::Dtype Dtype;
-  vector<Blob<Dtype>*> bottom;
   Dtype loss_weight;
   Dtype midnet_loss_weight;
   const bool kForceBackward = true;
@@ -916,7 +1040,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
   Caffe::set_random_seed(this->seed_);
   this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
                                kForceBackward);
-  const Dtype loss = this->net_->ForwardBackward(bottom);
+  const Dtype loss = this->net_->ForwardBackward();
   const bool kCopyDiff = true;
   vector<shared_ptr<Blob<Dtype> > > blob_grads;
   this->CopyNetBlobs(kCopyDiff, &blob_grads);
@@ -928,7 +1052,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
   Caffe::set_random_seed(this->seed_);
   this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
                                kForceBackward);
-  const Dtype loss_main_2 = this->net_->ForwardBackward(bottom);
+  const Dtype loss_main_2 = this->net_->ForwardBackward();
   vector<shared_ptr<Blob<Dtype> > > blob_grads_loss_2;
   this->CopyNetBlobs(kCopyDiff, &blob_grads_loss_2);
   vector<shared_ptr<Blob<Dtype> > > param_grads_loss_2;
@@ -939,7 +1063,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
   Caffe::set_random_seed(this->seed_);
   this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
                                kForceBackward);
-  const Dtype loss_main_3 = this->net_->ForwardBackward(bottom);
+  const Dtype loss_main_3 = this->net_->ForwardBackward();
   const vector<shared_ptr<Blob<Dtype> > >& blob_grads_loss_3 =
       this->net_->blobs();
   ASSERT_EQ(blob_grads.size(), blob_grads_loss_3.size());
@@ -974,7 +1098,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
   Caffe::set_random_seed(this->seed_);
   this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
                                kForceBackward);
-  const Dtype loss_midnet_2 = this->net_->ForwardBackward(bottom);
+  const Dtype loss_midnet_2 = this->net_->ForwardBackward();
   this->CopyNetBlobs(kCopyDiff, &blob_grads_loss_2);
   this->CopyNetParams(kCopyDiff, &param_grads_loss_2);
 
@@ -983,7 +1107,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
   Caffe::set_random_seed(this->seed_);
   this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
                                kForceBackward);
-  const Dtype loss_midnet_3 = this->net_->ForwardBackward(bottom);
+  const Dtype loss_midnet_3 = this->net_->ForwardBackward();
   const vector<shared_ptr<Blob<Dtype> > >& blob_grads_midnet_loss_3 =
       this->net_->blobs();
   ASSERT_EQ(blob_grads.size(), blob_grads_midnet_loss_3.size());
@@ -1032,40 +1156,35 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
 }
 
 TYPED_TEST(NetTest, TestBackwardWithAccuracyLayer) {
-  typedef typename TypeParam::Dtype Dtype;
   const bool kForceBackward = false;
   const bool kAccuracyLayer = true;
   this->InitTinyNet(kForceBackward, kAccuracyLayer);
   EXPECT_TRUE(this->net_->has_blob("accuracy"));
-  vector<Blob<Dtype>*> bottom;
   // Test that we can do Backward even though we have an 'Accuracy' layer.
-  this->net_->ForwardBackward(bottom);
+  this->net_->ForwardBackward();
 }
 
 TYPED_TEST(NetTest, TestUnsharedWeightsDataNet) {
   typedef typename TypeParam::Dtype Dtype;
   this->InitUnsharedWeightsNet();
-  vector<Blob<Dtype>*> bottom;
   Dtype loss;
-  this->net_->Forward(bottom, &loss);
+  this->net_->Forward(&loss);
   EXPECT_GT(loss, 0);
 }
 
 TYPED_TEST(NetTest, TestSharedWeightsDataNet) {
   typedef typename TypeParam::Dtype Dtype;
   this->InitSharedWeightsNet();
-  vector<Blob<Dtype>*> bottom;
   Dtype loss;
-  this->net_->Forward(bottom, &loss);
+  this->net_->Forward(&loss);
   EXPECT_FLOAT_EQ(loss, 0);
 }
 
 TYPED_TEST(NetTest, TestUnsharedWeightsDiffNet) {
   typedef typename TypeParam::Dtype Dtype;
   this->InitUnsharedWeightsNet();
-  vector<Blob<Dtype>*> bottom;
   Net<Dtype>* net = this->net_.get();
-  net->Forward(bottom);
+  net->Forward();
   net->Backward();
   Layer<Dtype>* ip1_layer = net->layer_by_name("innerproduct1").get();
   Layer<Dtype>* ip2_layer = net->layer_by_name("innerproduct2").get();
@@ -1081,10 +1200,9 @@ TYPED_TEST(NetTest, TestUnsharedWeightsDiffNet) {
 TYPED_TEST(NetTest, TestSharedWeightsDiffNet) {
   typedef typename TypeParam::Dtype Dtype;
   this->InitSharedWeightsNet();
-  vector<Blob<Dtype>*> bottom;
   Net<Dtype>* net = this->net_.get();
   Dtype loss;
-  net->Forward(bottom, &loss);
+  net->Forward(&loss);
   net->Backward();
   EXPECT_FLOAT_EQ(loss, 0);
   Layer<Dtype>* ip1_layer = net->layer_by_name("innerproduct1").get();
@@ -1102,7 +1220,6 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
   typedef typename TypeParam::Dtype Dtype;
   Caffe::set_random_seed(this->seed_);
   this->InitDiffDataSharedWeightsNet();
-  vector<Blob<Dtype>*> bottom;
   EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
   EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
   Blob<Dtype>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
@@ -1111,7 +1228,7 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
   // locations.
   EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
   EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
-  this->net_->Forward(bottom);
+  this->net_->Forward();
   this->net_->Backward();
   // Compute the expected update as the data minus the two diffs.
   Blob<Dtype> shared_params;
@@ -1146,7 +1263,7 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
   // locations in memory.
   EXPECT_NE(ip1_weights->cpu_data(), ip2_weights->cpu_data());
   EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
-  this->net_->Forward(bottom);
+  this->net_->Forward();
   this->net_->Backward();
   // Compute the expected update.
   Blob<Dtype> unshared_params1;
@@ -1186,7 +1303,6 @@ TYPED_TEST(NetTest, TestSharedWeightsResume) {
   // Create a net with weight sharing; Update it once.
   Caffe::set_random_seed(this->seed_);
   this->InitDiffDataSharedWeightsNet();
-  vector<Blob<Dtype>*> bottom;
   EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
   EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
   Blob<Dtype>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
@@ -1195,7 +1311,7 @@ TYPED_TEST(NetTest, TestSharedWeightsResume) {
   // locations.
   EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
   EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
-  this->net_->ForwardBackward(bottom);
+  this->net_->ForwardBackward();
   this->net_->Update();
   Blob<Dtype> shared_params;
   const bool kReshape = true;
@@ -1228,7 +1344,6 @@ TYPED_TEST(NetTest, TestSharedWeightsResume) {
 
 TYPED_TEST(NetTest, TestParamPropagateDown) {
   typedef typename TypeParam::Dtype Dtype;
-  vector<Blob<Dtype>*> bottom;
   const bool kBiasTerm = true, kForceBackward = false;
   const Dtype* kLossWeight1 = NULL;
   const Dtype* kLossWeight2 = NULL;
@@ -1238,7 +1353,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   Dtype blobs_lr_w1 = 1, blobs_lr_w2 = 1, blobs_lr_b1 = 2, blobs_lr_b2 = 2;
   this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
       kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
-  this->net_->Forward(bottom);
+  this->net_->Forward();
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params = this->net_->params();
   const int num_params = params.size();
@@ -1258,7 +1373,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   blobs_lr_w1 *= 2, blobs_lr_w2 *= 2, blobs_lr_b1 *= 2, blobs_lr_b2 *= 2;
   this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
       kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
-  this->net_->Forward(bottom);
+  this->net_->Forward();
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params2 = this->net_->params();
   ASSERT_EQ(num_params, params2.size());
@@ -1274,7 +1389,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   blobs_lr_w1 = 1, blobs_lr_w2 = 0, blobs_lr_b1 = 0, blobs_lr_b2 = 1;
   this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
       kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
-  this->net_->Forward(bottom);
+  this->net_->Forward();
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params3 = this->net_->params();
   ASSERT_EQ(num_params, params3.size());
@@ -1293,7 +1408,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   blobs_lr_w1 = 0, blobs_lr_w2 = 1, blobs_lr_b1 = 1, blobs_lr_b2 = 0;
   this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
       kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
-  this->net_->Forward(bottom);
+  this->net_->Forward();
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params4 = this->net_->params();
   ASSERT_EQ(num_params, params4.size());
@@ -1315,7 +1430,7 @@ TYPED_TEST(NetTest, TestFromTo) {
   // Run Forward and Backward, recording the data diff and loss.
   Blob<Dtype> data;
   data.ReshapeLike(*this->net_->blob_by_name("data"));
-  this->net_->ForwardPrefilled();
+  this->net_->Forward();
   this->net_->Backward();
   data.CopyFrom(*this->net_->blob_by_name("data"), true, true);
   const Dtype *loss_ptr = this->net_->output_blobs()[0]->cpu_data();
@@ -2277,12 +2392,12 @@ TYPED_TEST(NetTest, TestReshape) {
   filler.Fill(&blob2);
 
   this->InitReshapableNet();
-  Blob<Dtype>* input_blob = this->net_->input_blobs()[0];
+  shared_ptr<Blob<Dtype> > input_blob = this->net_->blob_by_name("data");
   Blob<Dtype>* output_blob = this->net_->output_blobs()[0];
   input_blob->Reshape(blob1.num(), blob1.channels(), blob1.height(),
       blob1.width());
   caffe_copy(blob1.count(), blob1.cpu_data(), input_blob->mutable_cpu_data());
-  this->net_->ForwardPrefilled();
+  this->net_->Forward();
   // call backward just to make sure it runs
   this->net_->Backward();
   Blob<Dtype> output1(output_blob->num(), output_blob->channels(),
@@ -2293,7 +2408,7 @@ TYPED_TEST(NetTest, TestReshape) {
   input_blob->Reshape(blob2.num(), blob2.channels(), blob2.height(),
       blob2.width());
   caffe_copy(blob2.count(), blob2.cpu_data(), input_blob->mutable_cpu_data());
-  this->net_->ForwardPrefilled();
+  this->net_->Forward();
   this->net_->Backward();
   Blob<Dtype> output2(output_blob->num(), output_blob->channels(),
       output_blob->height(), output_blob->width());
@@ -2303,7 +2418,7 @@ TYPED_TEST(NetTest, TestReshape) {
   input_blob->Reshape(blob1.num(), blob1.channels(), blob1.height(),
       blob1.width());
   caffe_copy(blob1.count(), blob1.cpu_data(), input_blob->mutable_cpu_data());
-  this->net_->ForwardPrefilled();
+  this->net_->Forward();
   this->net_->Backward();
   for (int i = 0; i < output1.count(); ++i) {
     EXPECT_FLOAT_EQ(*(output1.cpu_data() + i), *(output_blob->cpu_data() + i));
@@ -2312,7 +2427,7 @@ TYPED_TEST(NetTest, TestReshape) {
   input_blob->Reshape(blob2.num(), blob2.channels(), blob2.height(),
       blob2.width());
   caffe_copy(blob2.count(), blob2.cpu_data(), input_blob->mutable_cpu_data());
-  this->net_->ForwardPrefilled();
+  this->net_->Forward();
   this->net_->Backward();
   for (int i = 0; i < output2.count(); ++i) {
     EXPECT_FLOAT_EQ(*(output2.cpu_data() + i), *(output_blob->cpu_data() + i));
@@ -2379,4 +2494,111 @@ TYPED_TEST(NetTest, TestSkipPropagateDown) {
   }
 }
 
+TYPED_TEST(NetTest, TestForcePropagateDown) {
+  this->InitForcePropNet(false);
+  vector<bool> layer_need_backward = this->net_->layer_need_backward();
+  for (int layer_id = 0; layer_id < this->net_->layers().size(); ++layer_id) {
+    const string& layer_name = this->net_->layer_names()[layer_id];
+    const vector<bool> need_backward =
+        this->net_->bottom_need_backward()[layer_id];
+    if (layer_name == "data") {
+      ASSERT_EQ(need_backward.size(), 0);
+      EXPECT_FALSE(layer_need_backward[layer_id]);
+    } else if (layer_name == "innerproduct") {
+      ASSERT_EQ(need_backward.size(), 1);
+      EXPECT_FALSE(need_backward[0]);  // data
+      EXPECT_TRUE(layer_need_backward[layer_id]);
+    } else if (layer_name == "loss") {
+      ASSERT_EQ(need_backward.size(), 2);
+      EXPECT_TRUE(need_backward[0]);   // innerproduct
+      EXPECT_FALSE(need_backward[1]);  // label
+      EXPECT_TRUE(layer_need_backward[layer_id]);
+    } else {
+      LOG(FATAL) << "Unknown layer: " << layer_name;
+    }
+  }
+  this->InitForcePropNet(true);
+  layer_need_backward = this->net_->layer_need_backward();
+  for (int layer_id = 0; layer_id < this->net_->layers().size(); ++layer_id) {
+    const string& layer_name = this->net_->layer_names()[layer_id];
+    const vector<bool> need_backward =
+        this->net_->bottom_need_backward()[layer_id];
+    if (layer_name == "data") {
+      ASSERT_EQ(need_backward.size(), 0);
+      EXPECT_FALSE(layer_need_backward[layer_id]);
+    } else if (layer_name == "innerproduct") {
+      ASSERT_EQ(need_backward.size(), 1);
+      EXPECT_TRUE(need_backward[0]);  // data
+      EXPECT_TRUE(layer_need_backward[layer_id]);
+    } else if (layer_name == "loss") {
+      ASSERT_EQ(need_backward.size(), 2);
+      EXPECT_TRUE(need_backward[0]);   // innerproduct
+      EXPECT_FALSE(need_backward[1]);  // label
+      EXPECT_TRUE(layer_need_backward[layer_id]);
+    } else {
+      LOG(FATAL) << "Unknown layer: " << layer_name;
+    }
+  }
+}
+
+TYPED_TEST(NetTest, TestAllInOneNetTrain) {
+  vector<string> stages;
+  stages.push_back("train");
+  this->InitAllInOneNet(caffe::TRAIN, 0, &stages);
+  bool found_data = false;
+  bool found_loss = false;
+  for (int i = 0; i < this->net_->layers().size(); ++i) {
+    const string& layer_name = this->net_->layer_names()[i];
+    if (layer_name == "train-data") {
+      found_data = true;
+    } else if (layer_name == "loss") {
+      found_loss = true;
+    } else {
+      ASSERT_NE(layer_name, "val-data");
+      ASSERT_NE(layer_name, "deploy-data");
+    }
+  }
+  ASSERT_TRUE(found_data);
+  ASSERT_TRUE(found_loss);
+}
+
+TYPED_TEST(NetTest, TestAllInOneNetVal) {
+  vector<string> stages;
+  stages.push_back("val");
+  this->InitAllInOneNet(caffe::TEST, 0, &stages);
+  bool found_data = false;
+  bool found_loss = false;
+  for (int i = 0; i < this->net_->layers().size(); ++i) {
+    const string& layer_name = this->net_->layer_names()[i];
+    if (layer_name == "val-data") {
+      found_data = true;
+    } else if (layer_name == "loss") {
+      found_loss = true;
+    } else {
+      ASSERT_NE(layer_name, "train-data");
+      ASSERT_NE(layer_name, "deploy-data");
+    }
+  }
+  ASSERT_TRUE(found_data);
+  ASSERT_TRUE(found_loss);
+}
+
+TYPED_TEST(NetTest, TestAllInOneNetDeploy) {
+  vector<string> stages;
+  stages.push_back("deploy");
+  this->InitAllInOneNet(caffe::TEST, 0, &stages);
+  bool found_data = false;
+  for (int i = 0; i < this->net_->layers().size(); ++i) {
+    const string& layer_name = this->net_->layer_names()[i];
+    if (layer_name == "deploy-data") {
+      found_data = true;
+    } else {
+      ASSERT_NE(layer_name, "train-data");
+      ASSERT_NE(layer_name, "val-data");
+      ASSERT_NE(layer_name, "loss");
+    }
+  }
+  ASSERT_TRUE(found_data);
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index dd591f7d..180871a2 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -394,6 +394,26 @@ TYPED_TEST(NeuronLayerTest, TestExpGradient) {
   this->TestExpGradient(kBase, kScale, kShift);
 }
 
+TYPED_TEST(NeuronLayerTest, TestExpLayerWithShift) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Test default base of "-1" -- should actually set base := e,
+  // with a non-zero shift
+  const Dtype kBase = -1;
+  const Dtype kScale = 1;
+  const Dtype kShift = 1;
+  this->TestExpForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpGradientWithShift) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Test default base of "-1" -- should actually set base := e,
+  // with a non-zero shift
+  const Dtype kBase = -1;
+  const Dtype kScale = 1;
+  const Dtype kShift = 1;
+  this->TestExpGradient(kBase, kScale, kShift);
+}
+
 TYPED_TEST(NeuronLayerTest, TestExpLayerBase2) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kBase = 2;
@@ -771,16 +791,19 @@ TYPED_TEST(NeuronLayerTest, TestPReLUInPlace) {
   ip2.Backward(blob_middle_vec_2, propagate_down, blob_bottom_vec_2);
   // Check numbers
   for (int s = 0; s < blob_bottom_2->count(); ++s) {
-    EXPECT_EQ(this->blob_bottom_->cpu_diff()[s], blob_bottom_2->cpu_diff()[s]);
+    EXPECT_FLOAT_EQ(this->blob_bottom_->cpu_diff()[s],
+        blob_bottom_2->cpu_diff()[s]);
   }
   for (int s = 0; s < ip.blobs()[0]->count(); ++s) {
-    EXPECT_EQ(ip.blobs()[0]->cpu_diff()[s], ip2.blobs()[0]->cpu_diff()[s]);
+    EXPECT_FLOAT_EQ(ip.blobs()[0]->cpu_diff()[s],
+        ip2.blobs()[0]->cpu_diff()[s]);
   }
   for (int s = 0; s < ip.blobs()[1]->count(); ++s) {
-    EXPECT_EQ(ip.blobs()[1]->cpu_diff()[s], ip2.blobs()[1]->cpu_diff()[s]);
+    EXPECT_FLOAT_EQ(ip.blobs()[1]->cpu_diff()[s],
+        ip2.blobs()[1]->cpu_diff()[s]);
   }
   for (int s = 0; s < prelu.blobs()[0]->count(); ++s) {
-    EXPECT_EQ(prelu.blobs()[0]->cpu_diff()[s],
+    EXPECT_FLOAT_EQ(prelu.blobs()[0]->cpu_diff()[s],
         prelu2.blobs()[0]->cpu_diff()[s]);
   }
 }
diff --git a/src/caffe/test/test_rnn_layer.cpp b/src/caffe/test/test_rnn_layer.cpp
new file mode 100644
index 00000000..dd8952d6
--- /dev/null
+++ b/src/caffe/test/test_rnn_layer.cpp
@@ -0,0 +1,217 @@
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/rnn_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class RNNLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  RNNLayerTest() : num_output_(7) {
+    blob_bottom_vec_.push_back(&blob_bottom_);
+    blob_bottom_vec_.push_back(&blob_bottom_cont_);
+    blob_top_vec_.push_back(&blob_top_);
+
+    ReshapeBlobs(1, 3);
+
+    layer_param_.mutable_recurrent_param()->set_num_output(num_output_);
+    FillerParameter* weight_filler =
+        layer_param_.mutable_recurrent_param()->mutable_weight_filler();
+    weight_filler->set_type("gaussian");
+    weight_filler->set_std(0.2);
+    FillerParameter* bias_filler =
+        layer_param_.mutable_recurrent_param()->mutable_bias_filler();
+    bias_filler->set_type("gaussian");
+    bias_filler->set_std(0.1);
+
+    layer_param_.set_phase(TEST);
+  }
+
+  void ReshapeBlobs(int num_timesteps, int num_instances) {
+    blob_bottom_.Reshape(num_timesteps, num_instances, 3, 2);
+    blob_bottom_static_.Reshape(num_instances, 2, 3, 4);
+    vector<int> shape(2);
+    shape[0] = num_timesteps;
+    shape[1] = num_instances;
+    blob_bottom_cont_.Reshape(shape);
+
+    FillerParameter filler_param;
+    filler_param.set_min(-1);
+    filler_param.set_max(1);
+    UniformFiller<Dtype> filler(filler_param);
+    filler.Fill(&blob_bottom_);
+  }
+
+  int num_output_;
+  LayerParameter layer_param_;
+  Blob<Dtype> blob_bottom_;
+  Blob<Dtype> blob_bottom_cont_;
+  Blob<Dtype> blob_bottom_static_;
+  Blob<Dtype> blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(RNNLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(RNNLayerTest, TestSetUp) {
+  typedef typename TypeParam::Dtype Dtype;
+  RNNLayer<Dtype> layer(this->layer_param_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  vector<int> expected_top_shape = this->blob_bottom_.shape();
+  expected_top_shape.resize(3);
+  expected_top_shape[2] = this->num_output_;
+  EXPECT_TRUE(this->blob_top_.shape() == expected_top_shape);
+}
+
+TYPED_TEST(RNNLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  const int kNumTimesteps = 3;
+  const int num = this->blob_bottom_.shape(1);
+  this->ReshapeBlobs(kNumTimesteps, num);
+
+  // Fill the cont blob with <0, 1, 1, ..., 1>,
+  // indicating a sequence that begins at the first timestep
+  // then continues for the rest of the sequence.
+  for (int t = 0; t < kNumTimesteps; ++t) {
+    for (int n = 0; n < num; ++n) {
+      this->blob_bottom_cont_.mutable_cpu_data()[t * num + n] = t > 0;
+    }
+  }
+
+  // Process the full sequence in a single batch.
+  FillerParameter filler_param;
+  filler_param.set_mean(0);
+  filler_param.set_std(1);
+  GaussianFiller<Dtype> sequence_filler(filler_param);
+  sequence_filler.Fill(&this->blob_bottom_);
+  shared_ptr<RNNLayer<Dtype> > layer(new RNNLayer<Dtype>(this->layer_param_));
+  Caffe::set_random_seed(1701);
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  LOG(INFO) << "Calling forward for full sequence RNN";
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  // Copy the inputs and outputs to reuse/check them later.
+  Blob<Dtype> bottom_copy(this->blob_bottom_.shape());
+  bottom_copy.CopyFrom(this->blob_bottom_);
+  Blob<Dtype> top_copy(this->blob_top_.shape());
+  top_copy.CopyFrom(this->blob_top_);
+
+  // Process the batch one timestep at a time;
+  // check that we get the same result.
+  this->ReshapeBlobs(1, num);
+  layer.reset(new RNNLayer<Dtype>(this->layer_param_));
+  Caffe::set_random_seed(1701);
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  const int bottom_count = this->blob_bottom_.count();
+  const int top_count = this->blob_top_.count();
+  const Dtype kEpsilon = 1e-5;
+  for (int t = 0; t < kNumTimesteps; ++t) {
+    caffe_copy(bottom_count, bottom_copy.cpu_data() + t * bottom_count,
+               this->blob_bottom_.mutable_cpu_data());
+    for (int n = 0; n < num; ++n) {
+      this->blob_bottom_cont_.mutable_cpu_data()[n] = t > 0;
+    }
+    LOG(INFO) << "Calling forward for RNN timestep " << t;
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    for (int i = 0; i < top_count; ++i) {
+      ASSERT_LT(t * top_count + i, top_copy.count());
+      EXPECT_NEAR(this->blob_top_.cpu_data()[i],
+                  top_copy.cpu_data()[t * top_count + i], kEpsilon)
+         << "t = " << t << "; i = " << i;
+    }
+  }
+
+  // Process the batch one timestep at a time with all cont blobs set to 0.
+  // Check that we get a different result, except in the first timestep.
+  Caffe::set_random_seed(1701);
+  layer.reset(new RNNLayer<Dtype>(this->layer_param_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int t = 0; t < kNumTimesteps; ++t) {
+    caffe_copy(bottom_count, bottom_copy.cpu_data() + t * bottom_count,
+               this->blob_bottom_.mutable_cpu_data());
+    for (int n = 0; n < num; ++n) {
+      this->blob_bottom_cont_.mutable_cpu_data()[n] = 0;
+    }
+    LOG(INFO) << "Calling forward for RNN timestep " << t;
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    for (int i = 0; i < top_count; ++i) {
+      if (t == 0) {
+        EXPECT_NEAR(this->blob_top_.cpu_data()[i],
+                    top_copy.cpu_data()[t * top_count + i], kEpsilon)
+           << "t = " << t << "; i = " << i;
+      } else {
+        EXPECT_NE(this->blob_top_.cpu_data()[i],
+                  top_copy.cpu_data()[t * top_count + i])
+           << "t = " << t << "; i = " << i;
+      }
+    }
+  }
+}
+
+TYPED_TEST(RNNLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  RNNLayer<Dtype> layer(this->layer_param_);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(RNNLayerTest, TestGradientNonZeroCont) {
+  typedef typename TypeParam::Dtype Dtype;
+  RNNLayer<Dtype> layer(this->layer_param_);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  for (int i = 0; i < this->blob_bottom_cont_.count(); ++i) {
+    this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;
+  }
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(RNNLayerTest, TestGradientNonZeroContBufferSize2) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->ReshapeBlobs(2, 2);
+  // fill the values
+  FillerParameter filler_param;
+  UniformFiller<Dtype> filler(filler_param);
+  filler.Fill(&this->blob_bottom_);
+  RNNLayer<Dtype> layer(this->layer_param_);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  for (int i = 0; i < this->blob_bottom_cont_.count(); ++i) {
+    this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;
+  }
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(RNNLayerTest, TestGradientNonZeroContBufferSize2WithStaticInput) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->ReshapeBlobs(2, 2);
+  FillerParameter filler_param;
+  UniformFiller<Dtype> filler(filler_param);
+  filler.Fill(&this->blob_bottom_);
+  filler.Fill(&this->blob_bottom_static_);
+  this->blob_bottom_vec_.push_back(&this->blob_bottom_static_);
+  RNNLayer<Dtype> layer(this->layer_param_);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  for (int i = 0; i < this->blob_bottom_cont_.count(); ++i) {
+    this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;
+  }
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 2);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
index 5dfd7656..1bd5f937 100644
--- a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
@@ -116,5 +116,33 @@ TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestGradient) {
       this->blob_top_vec_, 0);
 }
 
+TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestIgnoreGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  FillerParameter data_filler_param;
+  data_filler_param.set_std(1);
+  GaussianFiller<Dtype> data_filler(data_filler_param);
+  data_filler.Fill(this->blob_bottom_data_);
+  LayerParameter layer_param;
+  LossParameter* loss_param = layer_param.mutable_loss_param();
+  loss_param->set_ignore_label(-1);
+  Dtype* target = this->blob_bottom_targets_->mutable_cpu_data();
+  const int count = this->blob_bottom_targets_->count();
+  // Ignore half of targets, then check that diff of this half is zero,
+  // while the other half is nonzero.
+  caffe_set(count / 2, Dtype(-1), target);
+  SigmoidCrossEntropyLossLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  vector<bool> propagate_down(2);
+  propagate_down[0] = true;
+  propagate_down[1] = false;
+  layer.Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  const Dtype* diff = this->blob_bottom_data_->cpu_diff();
+  for (int i = 0; i < count / 2; ++i) {
+    EXPECT_FLOAT_EQ(diff[i], 0.);
+    EXPECT_NE(diff[i + count / 2], 0.);
+  }
+}
+
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index ba2ccbb2..00714212 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -886,67 +886,6 @@ TEST_F(SplitLayerInsertionTest, TestInsertionTwoTop) {
   this->RunInsertionTest(input_proto, expected_output_proto);
 }
 
-TEST_F(SplitLayerInsertionTest, TestInputInsertion) {
-  const string& input_proto =
-      "name: 'TestNetwork' "
-      "input: 'data' "
-      "input_dim: 10 "
-      "input_dim: 3 "
-      "input_dim: 227 "
-      "input_dim: 227 "
-      "layer { "
-      "  name: 'innerprod1' "
-      "  type: 'InnerProduct' "
-      "  bottom: 'data' "
-      "  top: 'innerprod1' "
-      "} "
-      "layer { "
-      "  name: 'innerprod2' "
-      "  type: 'InnerProduct' "
-      "  bottom: 'data' "
-      "  top: 'innerprod2' "
-      "} "
-      "layer { "
-      "  name: 'loss' "
-      "  type: 'EuclideanLoss' "
-      "  bottom: 'innerprod1' "
-      "  bottom: 'innerprod2' "
-      "} ";
-  const string& expected_output_proto =
-      "name: 'TestNetwork' "
-      "input: 'data' "
-      "input_dim: 10 "
-      "input_dim: 3 "
-      "input_dim: 227 "
-      "input_dim: 227 "
-      "layer { "
-      "  name: 'data_input_0_split' "
-      "  type: 'Split' "
-      "  bottom: 'data' "
-      "  top: 'data_input_0_split_0' "
-      "  top: 'data_input_0_split_1' "
-      "} "
-      "layer { "
-      "  name: 'innerprod1' "
-      "  type: 'InnerProduct' "
-      "  bottom: 'data_input_0_split_0' "
-      "  top: 'innerprod1' "
-      "} "
-      "layer { "
-      "  name: 'innerprod2' "
-      "  type: 'InnerProduct' "
-      "  bottom: 'data_input_0_split_1' "
-      "  top: 'innerprod2' "
-      "} "
-      "layer { "
-      "  name: 'loss' "
-      "  type: 'EuclideanLoss' "
-      "  bottom: 'innerprod1' "
-      "  bottom: 'innerprod2' "
-      "} ";
-  this->RunInsertionTest(input_proto, expected_output_proto);
-}
-
 TEST_F(SplitLayerInsertionTest, TestWithInPlace) {
   const string& input_proto =
       "name: 'TestNetwork' "
diff --git a/src/caffe/util/benchmark.cpp b/src/caffe/util/benchmark.cpp
index 1d269c35..d994225f 100644
--- a/src/caffe/util/benchmark.cpp
+++ b/src/caffe/util/benchmark.cpp
@@ -44,7 +44,6 @@ void Timer::Stop() {
     if (Caffe::mode() == Caffe::GPU) {
 #ifndef CPU_ONLY
       CUDA_CHECK(cudaEventRecord(stop_gpu_, 0));
-      CUDA_CHECK(cudaEventSynchronize(stop_gpu_));
 #else
       NO_GPU;
 #endif
@@ -66,6 +65,7 @@ float Timer::MicroSeconds() {
   }
   if (Caffe::mode() == Caffe::GPU) {
 #ifndef CPU_ONLY
+    CUDA_CHECK(cudaEventSynchronize(stop_gpu_));
     CUDA_CHECK(cudaEventElapsedTime(&elapsed_milliseconds_, start_gpu_,
                                     stop_gpu_));
     // Cuda only measure milliseconds
@@ -89,6 +89,7 @@ float Timer::MilliSeconds() {
   }
   if (Caffe::mode() == Caffe::GPU) {
 #ifndef CPU_ONLY
+    CUDA_CHECK(cudaEventSynchronize(stop_gpu_));
     CUDA_CHECK(cudaEventElapsedTime(&elapsed_milliseconds_, start_gpu_,
                                     stop_gpu_));
 #else
diff --git a/src/caffe/util/blocking_queue.cpp b/src/caffe/util/blocking_queue.cpp
index 847dc0de..0afb3c5e 100644
--- a/src/caffe/util/blocking_queue.cpp
+++ b/src/caffe/util/blocking_queue.cpp
@@ -1,7 +1,6 @@
 #include <boost/thread.hpp>
 #include <string>
 
-#include "caffe/data_reader.hpp"
 #include "caffe/layers/base_data_layer.hpp"
 #include "caffe/parallel.hpp"
 #include "caffe/util/blocking_queue.hpp"
@@ -89,10 +88,6 @@ size_t BlockingQueue<T>::size() const {
 
 template class BlockingQueue<Batch<float>*>;
 template class BlockingQueue<Batch<double>*>;
-template class BlockingQueue<Datum*>;
-template class BlockingQueue<shared_ptr<DataReader::QueuePair> >;
-template class BlockingQueue<P2PSync<float>*>;
-template class BlockingQueue<P2PSync<double>*>;
 template class BlockingQueue<MultiBatch<float>*>;
 template class BlockingQueue<MultiBatch<double>*>;
 
diff --git a/src/caffe/util/db_lmdb.cpp b/src/caffe/util/db_lmdb.cpp
index 0bc82b53..491a9bd0 100644
--- a/src/caffe/util/db_lmdb.cpp
+++ b/src/caffe/util/db_lmdb.cpp
@@ -7,13 +7,10 @@
 
 namespace caffe { namespace db {
 
-const size_t LMDB_MAP_SIZE = 1099511627776;  // 1 TB
-
 void LMDB::Open(const string& source, Mode mode) {
   MDB_CHECK(mdb_env_create(&mdb_env_));
-  MDB_CHECK(mdb_env_set_mapsize(mdb_env_, LMDB_MAP_SIZE));
   if (mode == NEW) {
-    CHECK_EQ(mkdir(source.c_str(), 0744), 0) << "mkdir " << source << "failed";
+    CHECK_EQ(mkdir(source.c_str(), 0744), 0) << "mkdir " << source << " failed";
   }
   int flags = 0;
   if (mode == READ) {
@@ -35,7 +32,7 @@ void LMDB::Open(const string& source, Mode mode) {
     MDB_CHECK(rc);
   }
 #endif
-  LOG(INFO) << "Opened lmdb " << source;
+  LOG_IF(INFO, Caffe::root_solver()) << "Opened lmdb " << source;
 }
 
 LMDBCursor* LMDB::NewCursor() {
@@ -48,19 +45,67 @@ LMDBCursor* LMDB::NewCursor() {
 }
 
 LMDBTransaction* LMDB::NewTransaction() {
-  MDB_txn* mdb_txn;
-  MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, 0, &mdb_txn));
-  MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
-  return new LMDBTransaction(&mdb_dbi_, mdb_txn);
+  return new LMDBTransaction(mdb_env_);
 }
 
 void LMDBTransaction::Put(const string& key, const string& value) {
-  MDB_val mdb_key, mdb_value;
-  mdb_key.mv_data = const_cast<char*>(key.data());
-  mdb_key.mv_size = key.size();
-  mdb_value.mv_data = const_cast<char*>(value.data());
-  mdb_value.mv_size = value.size();
-  MDB_CHECK(mdb_put(mdb_txn_, *mdb_dbi_, &mdb_key, &mdb_value, 0));
+  keys.push_back(key);
+  values.push_back(value);
+}
+
+void LMDBTransaction::Commit() {
+  MDB_dbi mdb_dbi;
+  MDB_val mdb_key, mdb_data;
+  MDB_txn *mdb_txn;
+
+  // Initialize MDB variables
+  MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, 0, &mdb_txn));
+  MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi));
+
+  for (int i = 0; i < keys.size(); i++) {
+    mdb_key.mv_size = keys[i].size();
+    mdb_key.mv_data = const_cast<char*>(keys[i].data());
+    mdb_data.mv_size = values[i].size();
+    mdb_data.mv_data = const_cast<char*>(values[i].data());
+
+    // Add data to the transaction
+    int put_rc = mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0);
+    if (put_rc == MDB_MAP_FULL) {
+      // Out of memory - double the map size and retry
+      mdb_txn_abort(mdb_txn);
+      mdb_dbi_close(mdb_env_, mdb_dbi);
+      DoubleMapSize();
+      Commit();
+      return;
+    }
+    // May have failed for some other reason
+    MDB_CHECK(put_rc);
+  }
+
+  // Commit the transaction
+  int commit_rc = mdb_txn_commit(mdb_txn);
+  if (commit_rc == MDB_MAP_FULL) {
+    // Out of memory - double the map size and retry
+    mdb_dbi_close(mdb_env_, mdb_dbi);
+    DoubleMapSize();
+    Commit();
+    return;
+  }
+  // May have failed for some other reason
+  MDB_CHECK(commit_rc);
+
+  // Cleanup after successful commit
+  mdb_dbi_close(mdb_env_, mdb_dbi);
+  keys.clear();
+  values.clear();
+}
+
+void LMDBTransaction::DoubleMapSize() {
+  struct MDB_envinfo current_info;
+  MDB_CHECK(mdb_env_info(mdb_env_, &current_info));
+  size_t new_size = current_info.me_mapsize * 2;
+  DLOG(INFO) << "Doubling LMDB map size to " << (new_size>>20) << "MB ...";
+  MDB_CHECK(mdb_env_set_mapsize(mdb_env_, new_size));
 }
 
 }  // namespace db
diff --git a/src/caffe/util/hdf5.cpp b/src/caffe/util/hdf5.cpp
index 7730e76a..ed737429 100644
--- a/src/caffe/util/hdf5.cpp
+++ b/src/caffe/util/hdf5.cpp
@@ -9,7 +9,7 @@ namespace caffe {
 template <typename Dtype>
 void hdf5_load_nd_dataset_helper(
     hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
-    Blob<Dtype>* blob) {
+    Blob<Dtype>* blob, bool reshape) {
   // Verify that the dataset exists.
   CHECK(H5LTfind_dataset(file_id, dataset_name_))
       << "Failed to find HDF5 dataset " << dataset_name_;
@@ -29,10 +29,10 @@ void hdf5_load_nd_dataset_helper(
   CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name_;
   switch (class_) {
   case H5T_FLOAT:
-    LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_FLOAT";
+    { LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_FLOAT"; }
     break;
   case H5T_INTEGER:
-    LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_INTEGER";
+    { LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_INTEGER"; }
     break;
   case H5T_TIME:
     LOG(FATAL) << "Unsupported datatype class: H5T_TIME";
@@ -56,17 +56,38 @@ void hdf5_load_nd_dataset_helper(
     LOG(FATAL) << "Datatype class unknown";
   }
 
+
   vector<int> blob_dims(dims.size());
   for (int i = 0; i < dims.size(); ++i) {
     blob_dims[i] = dims[i];
   }
-  blob->Reshape(blob_dims);
+
+  if (reshape) {
+    blob->Reshape(blob_dims);
+  } else {
+    if (blob_dims != blob->shape()) {
+      // create shape string for error message
+      ostringstream stream;
+      int count = 1;
+      for (int i = 0; i < blob_dims.size(); ++i) {
+        stream << blob_dims[i] << " ";
+        count = count * blob_dims[i];
+      }
+      stream << "(" << count << ")";
+      string source_shape_string = stream.str();
+
+      CHECK(blob_dims == blob->shape()) << "Cannot load blob from hdf5; shape "
+            << "mismatch. Source shape is " << source_shape_string
+            << " target shape is " << blob->shape_string();
+    }
+  }
 }
 
 template <>
 void hdf5_load_nd_dataset<float>(hid_t file_id, const char* dataset_name_,
-        int min_dim, int max_dim, Blob<float>* blob) {
-  hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
+        int min_dim, int max_dim, Blob<float>* blob, bool reshape) {
+  hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob,
+                              reshape);
   herr_t status = H5LTread_dataset_float(
     file_id, dataset_name_, blob->mutable_cpu_data());
   CHECK_GE(status, 0) << "Failed to read float dataset " << dataset_name_;
@@ -74,8 +95,9 @@ void hdf5_load_nd_dataset<float>(hid_t file_id, const char* dataset_name_,
 
 template <>
 void hdf5_load_nd_dataset<double>(hid_t file_id, const char* dataset_name_,
-        int min_dim, int max_dim, Blob<double>* blob) {
-  hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
+        int min_dim, int max_dim, Blob<double>* blob, bool reshape) {
+  hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob,
+                              reshape);
   herr_t status = H5LTread_dataset_double(
     file_id, dataset_name_, blob->mutable_cpu_data());
   CHECK_GE(status, 0) << "Failed to read double dataset " << dataset_name_;
diff --git a/src/caffe/util/insert_splits.cpp b/src/caffe/util/insert_splits.cpp
index 475a2a9f..7a899c69 100644
--- a/src/caffe/util/insert_splits.cpp
+++ b/src/caffe/util/insert_splits.cpp
@@ -19,12 +19,6 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
   map<pair<int, int>, float> top_idx_to_loss_weight;
   map<pair<int, int>, int> top_idx_to_bottom_split_idx;
   map<int, string> layer_idx_to_layer_name;
-  layer_idx_to_layer_name[-1] = "input";
-  // Determine the number of times each blob is used as an input (bottom) blob.
-  for (int i = 0; i < param.input_size(); ++i) {
-    const string& blob_name = param.input(i);
-    blob_name_to_last_top_idx[blob_name] = make_pair(-1, i);
-  }
   for (int i = 0; i < param.layer_size(); ++i) {
     const LayerParameter& layer_param = param.layer(i);
     layer_idx_to_layer_name[i] = layer_param.name();
@@ -45,7 +39,7 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
       blob_name_to_last_top_idx[blob_name] = make_pair(i, j);
     }
     // A use of a top blob as a loss should be handled similarly to the use of
-    // a top blob as an input (bottom) blob to another layer.
+    // a top blob as a bottom blob to another layer.
     const int last_loss =
         std::min(layer_param.loss_weight_size(), layer_param.top_size());
     for (int j = 0; j < last_loss; ++j) {
@@ -57,19 +51,6 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
       }
     }
   }
-  // Create split layer for any input blobs used by other layer as bottom
-  // blobs more than once.
-  for (int i = 0; i < param.input_size(); ++i) {
-    const int split_count = top_idx_to_bottom_count[make_pair(-1, i)];
-    if (split_count > 1) {
-      const string& layer_name = layer_idx_to_layer_name[-1];
-      const string& blob_name = param.input(i);
-      LayerParameter* split_layer_param = param_split->add_layer();
-      const float kZeroLossWeight = 0;
-      ConfigureSplitLayer(layer_name, blob_name, i, split_count,
-          kZeroLossWeight, split_layer_param);
-    }
-  }
   for (int i = 0; i < param.layer_size(); ++i) {
     LayerParameter* layer_param = param_split->add_layer();
     layer_param->CopyFrom(param.layer(i));
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index 71c02274..59625bc0 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -196,6 +196,16 @@ void caffe_sqr<double>(const int n, const double* a, double* y) {
   vdSqr(n, a, y);
 }
 
+template <>
+void caffe_sqrt<float>(const int n, const float* a, float* y) {
+  vsSqrt(n, a, y);
+}
+
+template <>
+void caffe_sqrt<double>(const int n, const double* a, double* y) {
+  vdSqrt(n, a, y);
+}
+
 template <>
 void caffe_exp<float>(const int n, const float* a, float* y) {
   vsExp(n, a, y);
diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu
index 4c587537..314e6ba0 100644
--- a/src/caffe/util/math_functions.cu
+++ b/src/caffe/util/math_functions.cu
@@ -90,6 +90,26 @@ void caffe_gpu_scal<double>(const int N, const double alpha, double *X) {
   CUBLAS_CHECK(cublasDscal(Caffe::cublas_handle(), N, &alpha, X, 1));
 }
 
+template <>
+void caffe_gpu_scal<float>(const int N, const float alpha, float* X,
+                           cudaStream_t str) {
+  cudaStream_t initial_stream;
+  CUBLAS_CHECK(cublasGetStream(Caffe::cublas_handle(), &initial_stream));
+  CUBLAS_CHECK(cublasSetStream(Caffe::cublas_handle(), str));
+  CUBLAS_CHECK(cublasSscal(Caffe::cublas_handle(), N, &alpha, X, 1));
+  CUBLAS_CHECK(cublasSetStream(Caffe::cublas_handle(), initial_stream));
+}
+
+template <>
+void caffe_gpu_scal<double>(const int N, const double alpha, double* X,
+                            cudaStream_t str) {
+  cudaStream_t initial_stream;
+  CUBLAS_CHECK(cublasGetStream(Caffe::cublas_handle(), &initial_stream));
+  CUBLAS_CHECK(cublasSetStream(Caffe::cublas_handle(), str));
+  CUBLAS_CHECK(cublasDscal(Caffe::cublas_handle(), N, &alpha, X, 1));
+  CUBLAS_CHECK(cublasSetStream(Caffe::cublas_handle(), initial_stream));
+}
+
 template <>
 void caffe_gpu_axpby<float>(const int N, const float alpha, const float* X,
     const float beta, float* Y) {
@@ -367,6 +387,27 @@ void caffe_gpu_powx<double>(const int N, const double* a,
       N, a, alpha, y);
 }
 
+template <typename Dtype>
+__global__ void sqrt_kernel(const int n, const Dtype* a, Dtype* y) {
+  CUDA_KERNEL_LOOP(index, n) {
+    y[index] = sqrt(a[index]);
+  }
+}
+
+template <>
+void caffe_gpu_sqrt<float>(const int N, const float* a, float* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  sqrt_kernel<float><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, y);
+}
+
+template <>
+void caffe_gpu_sqrt<double>(const int N, const double* a, double* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  sqrt_kernel<double><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, y);
+}
+
 DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sign, y[index] = (Dtype(0) < x[index])
                                       - (x[index] < Dtype(0)));
 DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sgnbit, y[index] = signbit(x[index]));
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index ff3f8ffc..94771c8c 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -13,7 +13,9 @@
 namespace caffe {
 
 bool NetNeedsUpgrade(const NetParameter& net_param) {
-  return NetNeedsV0ToV1Upgrade(net_param) || NetNeedsV1ToV2Upgrade(net_param);
+  return NetNeedsV0ToV1Upgrade(net_param) || NetNeedsV1ToV2Upgrade(net_param)
+      || NetNeedsDataUpgrade(net_param) || NetNeedsInputUpgrade(net_param)
+      || NetNeedsBatchNormUpgrade(net_param);
 }
 
 bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
@@ -60,6 +62,24 @@ bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
                 << "V1LayerParameter";
     }
   }
+  // NetParameter uses old style input fields; try to upgrade it.
+  if (NetNeedsInputUpgrade(*param)) {
+    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+              << "input fields: " << param_file;
+    UpgradeNetInput(param);
+    LOG(INFO) << "Successfully upgraded file specified using deprecated "
+              << "input fields.";
+    LOG(WARNING) << "Note that future Caffe releases will only support "
+                 << "input layers and not input fields.";
+  }
+  // NetParameter uses old style batch norm layers; try to upgrade it.
+  if (NetNeedsBatchNormUpgrade(*param)) {
+    LOG(INFO) << "Attempting to upgrade batch norm layers using deprecated "
+              << "params: " << param_file;
+    UpgradeNetBatchNorm(param);
+    LOG(INFO) << "Successfully upgraded batch norm layers using deprecated "
+              << "params.";
+  }
   return success;
 }
 
@@ -645,12 +665,14 @@ void UpgradeNetDataTransformation(NetParameter* net_param) {
 }
 
 bool UpgradeV1Net(const NetParameter& v1_net_param, NetParameter* net_param) {
-  bool is_fully_compatible = true;
   if (v1_net_param.layer_size() > 0) {
-    LOG(ERROR) << "Input NetParameter to be upgraded already specifies 'layer' "
-               << "fields; these will be ignored for the upgrade.";
-    is_fully_compatible = false;
+    LOG(FATAL) << "Refusing to upgrade inconsistent NetParameter input; "
+        << "the definition includes both 'layer' and 'layers' fields. "
+        << "The current format defines 'layer' fields with string type like "
+        << "layer { type: 'Layer' ... } and not layers { type: LAYER ... }. "
+        << "Manually switch the definition to 'layer' format to continue.";
   }
+  bool is_fully_compatible = true;
   net_param->CopyFrom(v1_net_param);
   net_param->clear_layers();
   net_param->clear_layer();
@@ -937,6 +959,76 @@ const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type) {
   }
 }
 
+bool NetNeedsInputUpgrade(const NetParameter& net_param) {
+  return net_param.input_size() > 0;
+}
+
+void UpgradeNetInput(NetParameter* net_param) {
+  // Collect inputs and convert to Input layer definitions.
+  // If the NetParameter holds an input alone, without shape/dim, then
+  // it's a legacy caffemodel and simply stripping the input field is enough.
+  bool has_shape = net_param->input_shape_size() > 0;
+  bool has_dim = net_param->input_dim_size() > 0;
+  if (has_shape || has_dim) {
+    LayerParameter* layer_param = net_param->add_layer();
+    layer_param->set_name("input");
+    layer_param->set_type("Input");
+    InputParameter* input_param = layer_param->mutable_input_param();
+    // Convert input fields into a layer.
+    for (int i = 0; i < net_param->input_size(); ++i) {
+      layer_param->add_top(net_param->input(i));
+      if (has_shape) {
+        input_param->add_shape()->CopyFrom(net_param->input_shape(i));
+      } else {
+        // Turn legacy input dimensions into shape.
+        BlobShape* shape = input_param->add_shape();
+        int first_dim = i*4;
+        int last_dim = first_dim + 4;
+        for (int j = first_dim; j < last_dim; j++) {
+          shape->add_dim(net_param->input_dim(j));
+        }
+      }
+    }
+    // Swap input layer to beginning of net to satisfy layer dependencies.
+    for (int i = net_param->layer_size() - 1; i > 0; --i) {
+      net_param->mutable_layer(i-1)->Swap(net_param->mutable_layer(i));
+    }
+  }
+  // Clear inputs.
+  net_param->clear_input();
+  net_param->clear_input_shape();
+  net_param->clear_input_dim();
+}
+
+bool NetNeedsBatchNormUpgrade(const NetParameter& net_param) {
+  for (int i = 0; i < net_param.layer_size(); ++i) {
+    // Check if BatchNorm layers declare three parameters, as required by
+    // the previous BatchNorm layer definition.
+    if (net_param.layer(i).type() == "BatchNorm"
+        && net_param.layer(i).param_size() == 3) {
+      return true;
+    }
+  }
+  return false;
+}
+
+void UpgradeNetBatchNorm(NetParameter* net_param) {
+  for (int i = 0; i < net_param->layer_size(); ++i) {
+    // Check if BatchNorm layers declare three parameters, as required by
+    // the previous BatchNorm layer definition.
+    if (net_param->layer(i).type() == "BatchNorm"
+        && net_param->layer(i).param_size() == 3) {
+      // set lr_mult and decay_mult to zero. leave all other param intact.
+      for (int ip = 0; ip < net_param->layer(i).param_size(); ip++) {
+        ParamSpec* fixed_param_spec =
+          net_param->mutable_layer(i)->mutable_param(ip);
+        fixed_param_spec->set_lr_mult(0.f);
+        fixed_param_spec->set_decay_mult(0.f);
+      }
+    }
+  }
+}
+
 // Return true iff the solver contains any old solver_type specified as enums
 bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param) {
   if (solver_param.has_solver_type()) {
diff --git a/src/gtest/CMakeLists.txt b/src/gtest/CMakeLists.txt
index ef7ff7ed..e98254af 100644
--- a/src/gtest/CMakeLists.txt
+++ b/src/gtest/CMakeLists.txt
@@ -1,5 +1,8 @@
 add_library(gtest STATIC EXCLUDE_FROM_ALL gtest.h gtest-all.cpp)
 caffe_default_properties(gtest)
+target_include_directories(gtest PUBLIC ${Caffe_SRC_DIR})
+target_compile_definitions(gtest PUBLIC -DGTEST_USE_OWN_TR1_TUPLE)
+
 
 #add_library(gtest_main gtest_main.cc)
 #target_link_libraries(gtest_main gtest)
diff --git a/src/gtest/gtest-all.cpp b/src/gtest/gtest-all.cpp
index 92619741..81cdb578 100644
--- a/src/gtest/gtest-all.cpp
+++ b/src/gtest/gtest-all.cpp
@@ -2697,7 +2697,7 @@ AssertionResult IsHRESULTFailure(const char* expr, long hr) {  // NOLINT
 // Utility functions for encoding Unicode text (wide strings) in
 // UTF-8.
 
-// A Unicode code-point can have upto 21 bits, and is encoded in UTF-8
+// A Unicode code-point can have up to 21 bits, and is encoded in UTF-8
 // like this:
 //
 // Code-point length   Encoding
@@ -7550,7 +7550,7 @@ FilePath FilePath::RemoveExtension(const char* extension) const {
   return *this;
 }
 
-// Returns a pointer to the last occurence of a valid path separator in
+// Returns a pointer to the last occurrence of a valid path separator in
 // the FilePath. On Windows, for example, both '/' and '\' are valid path
 // separators. Returns NULL if no path separator was found.
 const char* FilePath::FindLastPathSeparator() const {
diff --git a/src/gtest/gtest.h b/src/gtest/gtest.h
index 3143bd67..124fb232 100644
--- a/src/gtest/gtest.h
+++ b/src/gtest/gtest.h
@@ -3395,7 +3395,7 @@ class GTEST_API_ FilePath {
 
   void Normalize();
 
-  // Returns a pointer to the last occurence of a valid path separator in
+  // Returns a pointer to the last occurrence of a valid path separator in
   // the FilePath. On Windows, for example, both '/' and '\' are valid path
   // separators. Returns NULL if no path separator was found.
   const char* FindLastPathSeparator() const;
diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt
index 02fbd5ca..37894505 100644
--- a/tools/CMakeLists.txt
+++ b/tools/CMakeLists.txt
@@ -25,5 +25,6 @@ foreach(source ${srcs})
   endif()
 
   # Install
-  install(TARGETS ${name} DESTINATION bin)
+  install(TARGETS ${name} DESTINATION ${CMAKE_INSTALL_BINDIR})
+
 endforeach(source)
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index ebe95d61..3587d8aa 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -33,7 +33,14 @@ DEFINE_string(gpu, "",
 DEFINE_string(solver, "",
     "The solver definition protocol buffer text file.");
 DEFINE_string(model, "",
-    "The model definition protocol buffer text file..");
+    "The model definition protocol buffer text file.");
+DEFINE_string(phase, "",
+    "Optional; network phase (TRAIN or TEST). Only used for 'time'.");
+DEFINE_int32(level, 0,
+    "Optional; network level.");
+DEFINE_string(stage, "",
+    "Optional; network stages (not to be confused with phase), "
+    "separated by ','.");
 DEFINE_string(snapshot, "",
     "Optional; the snapshot solver state to resume training.");
 DEFINE_string(weights, "",
@@ -101,6 +108,25 @@ static void get_gpus(vector<int>* gpus) {
   }
 }
 
+// Parse phase from flags
+caffe::Phase get_phase_from_flags(caffe::Phase default_value) {
+  if (FLAGS_phase == "")
+    return default_value;
+  if (FLAGS_phase == "TRAIN")
+    return caffe::TRAIN;
+  if (FLAGS_phase == "TEST")
+    return caffe::TEST;
+  LOG(FATAL) << "phase must be \"TRAIN\" or \"TEST\"";
+  return caffe::TRAIN;  // Avoid warning
+}
+
+// Parse stages from flags
+vector<string> get_stages_from_flags() {
+  vector<string> stages;
+  boost::split(stages, FLAGS_stage, boost::is_any_of(","));
+  return stages;
+}
+
 // caffe commands to call by
 //     caffe <command> <args>
 //
@@ -156,13 +182,20 @@ int train() {
   CHECK(!FLAGS_snapshot.size() || !FLAGS_weights.size())
       << "Give a snapshot to resume training or weights to finetune "
       "but not both.";
+  vector<string> stages = get_stages_from_flags();
 
   caffe::SolverParameter solver_param;
   caffe::ReadSolverParamsFromTextFileOrDie(FLAGS_solver, &solver_param);
 
+  solver_param.mutable_train_state()->set_level(FLAGS_level);
+  for (int i = 0; i < stages.size(); i++) {
+    solver_param.mutable_train_state()->add_stage(stages[i]);
+  }
+
   // If the gpus flag is not provided, allow the mode and device to be set
   // in the solver prototxt.
   if (FLAGS_gpu.size() == 0
+      && solver_param.has_solver_mode()
       && solver_param.solver_mode() == caffe::SolverParameter_SolverMode_GPU) {
       if (solver_param.has_device_id()) {
           FLAGS_gpu = "" +
@@ -212,11 +245,15 @@ int train() {
     CopyLayers(solver.get(), FLAGS_weights);
   }
 
+  LOG(INFO) << "Starting Optimization";
   if (gpus.size() > 1) {
-    caffe::P2PSync<float> sync(solver, NULL, solver->param());
-    sync.run(gpus);
+#ifdef USE_NCCL
+    caffe::NCCL<float> nccl(solver);
+    nccl.Run(gpus, FLAGS_snapshot.size() > 0 ? FLAGS_snapshot.c_str() : NULL);
+#else
+    LOG(FATAL) << "Multi-GPU execution not available - rebuild with USE_NCCL";
+#endif
   } else {
-    LOG(INFO) << "Starting Optimization";
     solver->Solve();
   }
   LOG(INFO) << "Optimization Done.";
@@ -229,6 +266,7 @@ RegisterBrewFunction(train);
 int test() {
   CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to score.";
   CHECK_GT(FLAGS_weights.size(), 0) << "Need model weights to score.";
+  vector<string> stages = get_stages_from_flags();
 
   // Set device id and mode
   vector<int> gpus;
@@ -247,18 +285,17 @@ int test() {
     Caffe::set_mode(Caffe::CPU);
   }
   // Instantiate the caffe net.
-  Net<float> caffe_net(FLAGS_model, caffe::TEST);
+  Net<float> caffe_net(FLAGS_model, caffe::TEST, FLAGS_level, &stages);
   caffe_net.CopyTrainedLayersFrom(FLAGS_weights);
   LOG(INFO) << "Running for " << FLAGS_iterations << " iterations.";
 
-  vector<Blob<float>* > bottom_vec;
   vector<int> test_score_output_id;
   vector<float> test_score;
   float loss = 0;
   for (int i = 0; i < FLAGS_iterations; ++i) {
     float iter_loss;
     const vector<Blob<float>*>& result =
-        caffe_net.Forward(bottom_vec, &iter_loss);
+        caffe_net.Forward(&iter_loss);
     loss += iter_loss;
     int idx = 0;
     for (int j = 0; j < result.size(); ++j) {
@@ -301,6 +338,8 @@ RegisterBrewFunction(test);
 // Time: benchmark the execution time of a model.
 int time() {
   CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to time.";
+  caffe::Phase phase = get_phase_from_flags(caffe::TRAIN);
+  vector<string> stages = get_stages_from_flags();
 
   // Set device id and mode
   vector<int> gpus;
@@ -314,7 +353,7 @@ int time() {
     Caffe::set_mode(Caffe::CPU);
   }
   // Instantiate the caffe net.
-  Net<float> caffe_net(FLAGS_model, caffe::TRAIN);
+  Net<float> caffe_net(FLAGS_model, phase, FLAGS_level, &stages);
 
   // Do a clean forward and backward pass, so that memory allocation are done
   // and future iterations will be more stable.
@@ -322,7 +361,7 @@ int time() {
   // Note that for the speed benchmark, we will assume that the network does
   // not take any input blobs.
   float initial_loss;
-  caffe_net.Forward(vector<Blob<float>*>(), &initial_loss);
+  caffe_net.Forward(&initial_loss);
   LOG(INFO) << "Initial loss: " << initial_loss;
   LOG(INFO) << "Performing Backward";
   caffe_net.Backward();
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index 2035d515..417f5e4c 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -22,9 +22,11 @@ DEFINE_string(backend, "lmdb",
         "The backend {leveldb, lmdb} containing the images");
 
 int main(int argc, char** argv) {
+#ifdef USE_OPENCV
   ::google::InitGoogleLogging(argv[0]);
+  // Print output to stderr (while still logging)
+  FLAGS_alsologtostderr = 1;
 
-#ifdef USE_OPENCV
 #ifndef GFLAGS_GFLAGS_H_
   namespace gflags = google;
 #endif
@@ -65,7 +67,7 @@ int main(int argc, char** argv) {
   for (int i = 0; i < size_in_datum; ++i) {
     sum_blob.add_data(0.);
   }
-  LOG(INFO) << "Starting Iteration";
+  LOG(INFO) << "Starting iteration";
   while (cursor->valid()) {
     Datum datum;
     datum.ParseFromString(cursor->value());
@@ -114,7 +116,7 @@ int main(int argc, char** argv) {
     for (int i = 0; i < dim; ++i) {
       mean_values[c] += sum_blob.data(dim * c + i);
     }
-    LOG(INFO) << "mean_value channel [" << c << "]:" << mean_values[c] / dim;
+    LOG(INFO) << "mean_value channel [" << c << "]: " << mean_values[c] / dim;
   }
 #else
   LOG(FATAL) << "This tool requires OpenCV; compile with USE_OPENCV.";
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 9c52bfa0..90cdb15d 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -73,10 +73,13 @@ int main(int argc, char** argv) {
 
   std::ifstream infile(argv[2]);
   std::vector<std::pair<std::string, int> > lines;
-  std::string filename;
+  std::string line;
+  size_t pos;
   int label;
-  while (infile >> filename >> label) {
-    lines.push_back(std::make_pair(filename, label));
+  while (std::getline(infile, line)) {
+    pos = line.find_last_of(' ');
+    label = atoi(line.substr(pos + 1).c_str());
+    lines.push_back(std::make_pair(line.substr(0, pos), label));
   }
   if (FLAGS_shuffle) {
     // randomly shuffle data
diff --git a/tools/extra/extract_seconds.py b/tools/extra/extract_seconds.py
index 591a51f9..68af69a2 100755
--- a/tools/extra/extract_seconds.py
+++ b/tools/extra/extract_seconds.py
@@ -48,11 +48,19 @@ def extract_seconds(input_file, output_file):
     start_datetime = get_start_time(lines, log_created_year)
     assert start_datetime, 'Start time not found'
 
+    last_dt = start_datetime
     out = open(output_file, 'w')
     for line in lines:
         line = line.strip()
         if line.find('Iteration') != -1:
             dt = extract_datetime_from_line(line, log_created_year)
+
+            # if it's another year
+            if dt.month < last_dt.month:
+                log_created_year += 1
+                dt = extract_datetime_from_line(line, log_created_year)
+            last_dt = dt
+
             elapsed_seconds = (dt - start_datetime).total_seconds()
             out.write('%f\n' % elapsed_seconds)
     out.close()
diff --git a/tools/extra/parse_log.py b/tools/extra/parse_log.py
index 48f9bee0..4248e2b8 100755
--- a/tools/extra/parse_log.py
+++ b/tools/extra/parse_log.py
@@ -16,13 +16,10 @@
 
 def parse_log(path_to_log):
     """Parse log file
-    Returns (train_dict_list, train_dict_names, test_dict_list, test_dict_names)
+    Returns (train_dict_list, test_dict_list)
 
     train_dict_list and test_dict_list are lists of dicts that define the table
     rows
-
-    train_dict_names and test_dict_names are ordered tuples of the column names
-    for the two dict_lists
     """
 
     regex_iteration = re.compile('Iteration (\d+)')
@@ -41,6 +38,7 @@ def parse_log(path_to_log):
     logfile_year = extract_seconds.get_log_created_year(path_to_log)
     with open(path_to_log) as f:
         start_time = extract_seconds.get_start_time(f, logfile_year)
+        last_time = start_time
 
         for line in f:
             iteration_match = regex_iteration.search(line)
@@ -51,8 +49,19 @@ def parse_log(path_to_log):
                 # iteration
                 continue
 
-            time = extract_seconds.extract_datetime_from_line(line,
-                                                              logfile_year)
+            try:
+                time = extract_seconds.extract_datetime_from_line(line,
+                                                                  logfile_year)
+            except ValueError:
+                # Skip lines with bad formatting, for example when resuming solver
+                continue
+
+            # if it's another year
+            if time.month < last_time.month:
+                logfile_year += 1
+                time = extract_seconds.extract_datetime_from_line(line, logfile_year)
+            last_time = time
+
             seconds = (time - start_time).total_seconds()
 
             learning_rate_match = regex_learning_rate.search(line)
@@ -149,6 +158,11 @@ def write_csv(output_filename, dict_list, delimiter, verbose=False):
     """Write a CSV file
     """
 
+    if not dict_list:
+        if verbose:
+            print('Not writing %s; no lines to write' % output_filename)
+        return
+
     dialect = csv.excel
     dialect.delimiter = delimiter
 
@@ -189,7 +203,7 @@ def main():
     args = parse_args()
     train_dict_list, test_dict_list = parse_log(args.logfile_path)
     save_csv_files(args.logfile_path, args.output_dir, train_dict_list,
-                   test_dict_list, delimiter=args.delimiter)
+                   test_dict_list, delimiter=args.delimiter, verbose=args.verbose)
 
 
 if __name__ == '__main__':
diff --git a/tools/extra/parse_log.sh b/tools/extra/parse_log.sh
index 9892c897..122eb9e6 100755
--- a/tools/extra/parse_log.sh
+++ b/tools/extra/parse_log.sh
@@ -39,7 +39,7 @@ rm aux.txt aux0.txt aux1.txt aux2.txt aux3.txt aux4.txt
 grep '] Solving ' $1 > aux.txt
 grep ', loss = ' $1 >> aux.txt
 grep 'Iteration ' aux.txt | sed  's/.*Iteration \([[:digit:]]*\).*/\1/g' > aux0.txt
-grep ', loss = ' $1 | awk '{print $9}' > aux1.txt
+grep ', loss = ' $1 | awk -F = '{print $2}' > aux1.txt
 grep ', lr = ' $1 | awk '{print $9}' > aux2.txt
 
 # Extracting elapsed seconds
diff --git a/tools/extra/plot_log.gnuplot.example b/tools/extra/plot_log.gnuplot.example
index 748b96e6..02c68e1d 100644
--- a/tools/extra/plot_log.gnuplot.example
+++ b/tools/extra/plot_log.gnuplot.example
@@ -4,7 +4,7 @@
 # Be warned that the fields in the training log may change in the future.
 # You had better check the data files before designing your own plots.
 
-# Please generate the neccessary data files with 
+# Please generate the necessary data files with 
 # /path/to/caffe/tools/extra/parse_log.sh before plotting.
 # Example usage: 
 #     ./parse_log.sh mnist.log
diff --git a/tools/extra/plot_training_log.py.example b/tools/extra/plot_training_log.py.example
index 4d3ed0d1..8caca6b8 100755
--- a/tools/extra/plot_training_log.py.example
+++ b/tools/extra/plot_training_log.py.example
@@ -10,7 +10,8 @@ import matplotlib.legend as lgd
 import matplotlib.markers as mks
 
 def get_log_parsing_script():
-    dirname = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+    dirname = os.path.dirname(os.path.abspath(inspect.getfile(
+        inspect.currentframe())))
     return dirname + '/parse_log.sh'
 
 def get_log_file_suffix():
@@ -61,16 +62,17 @@ def get_data_file_type(chart_type):
     return data_file_type
 
 def get_data_file(chart_type, path_to_log):
-    return os.path.basename(path_to_log) + '.' + get_data_file_type(chart_type).lower()
+    return (os.path.basename(path_to_log) + '.' +
+            get_data_file_type(chart_type).lower())
 
 def get_field_descriptions(chart_type):
     description = get_chart_type_description(chart_type).split(
         get_chart_type_description_separator())
     y_axis_field = description[0]
     x_axis_field = description[1]
-    return x_axis_field, y_axis_field    
+    return x_axis_field, y_axis_field
 
-def get_field_indecies(x_axis_field, y_axis_field):    
+def get_field_indices(x_axis_field, y_axis_field):
     data_file_type = get_data_file_type(chart_type)
     fields = create_field_index()[0][data_file_type]
     return fields[x_axis_field], fields[y_axis_field]
@@ -88,9 +90,9 @@ def load_data(data_file, field_idx0, field_idx1):
 
 def random_marker():
     markers = mks.MarkerStyle.markers
-    num = len(markers.values())
+    num = len(markers.keys())
     idx = random.randint(0, num - 1)
-    return markers.values()[idx]
+    return markers.keys()[idx]
 
 def get_data_label(path_to_log):
     label = path_to_log[path_to_log.rfind('/')+1 : path_to_log.rfind(
@@ -111,7 +113,7 @@ def plot_chart(chart_type, path_to_png, path_to_log_list):
         os.system('%s %s' % (get_log_parsing_script(), path_to_log))
         data_file = get_data_file(chart_type, path_to_log)
         x_axis_field, y_axis_field = get_field_descriptions(chart_type)
-        x, y = get_field_indecies(x_axis_field, y_axis_field)
+        x, y = get_field_indices(x_axis_field, y_axis_field)
         data = load_data(data_file, x, y)
         ## TODO: more systematic color cycle for lines
         color = [random.random(), random.random(), random.random()]
@@ -124,22 +126,15 @@ def plot_chart(chart_type, path_to_png, path_to_log_list):
             plt.plot(data[0], data[1], label = label, color = color,
                      linewidth = linewidth)
         else:
-            ok = False
-            ## Some markers throw ValueError: Unrecognized marker style
-            while not ok:
-                try:
-                    marker = random_marker()
-                    plt.plot(data[0], data[1], label = label, color = color,
-                             marker = marker, linewidth = linewidth)
-                    ok = True
-                except:
-                    pass
+            marker = random_marker()
+            plt.plot(data[0], data[1], label = label, color = color,
+                     marker = marker, linewidth = linewidth)
     legend_loc = get_legend_loc(chart_type)
     plt.legend(loc = legend_loc, ncol = 1) # ajust ncol to fit the space
     plt.title(get_chart_type_description(chart_type))
     plt.xlabel(x_axis_field)
-    plt.ylabel(y_axis_field)  
-    plt.savefig(path_to_png)     
+    plt.ylabel(y_axis_field)
+    plt.savefig(path_to_png)
     plt.show()
 
 def print_help():
@@ -160,28 +155,30 @@ Supported chart types:""" % (len(get_supported_chart_types()) - 1,
     num = len(supported_chart_types)
     for i in xrange(num):
         print '    %d: %s' % (i, supported_chart_types[i])
-    exit
+    sys.exit()
 
 def is_valid_chart_type(chart_type):
     return chart_type >= 0 and chart_type < len(get_supported_chart_types())
-  
+
 if __name__ == '__main__':
     if len(sys.argv) < 4:
         print_help()
     else:
         chart_type = int(sys.argv[1])
         if not is_valid_chart_type(chart_type):
+            print '%s is not a valid chart type.' % chart_type
             print_help()
         path_to_png = sys.argv[2]
         if not path_to_png.endswith('.png'):
             print 'Path must ends with png' % path_to_png
-            exit            
+            sys.exit()
         path_to_logs = sys.argv[3:]
         for path_to_log in path_to_logs:
             if not os.path.exists(path_to_log):
                 print 'Path does not exist: %s' % path_to_log
-                exit
+                sys.exit()
             if not path_to_log.endswith(get_log_file_suffix()):
+                print 'Log file must end in %s.' % get_log_file_suffix()
                 print_help()
         ## plot_chart accpets multiple path_to_logs
         plot_chart(chart_type, path_to_png, path_to_logs)
diff --git a/tools/extra/resize_and_crop_images.py b/tools/extra/resize_and_crop_images.py
index c844f590..fd2c3134 100755
--- a/tools/extra/resize_and_crop_images.py
+++ b/tools/extra/resize_and_crop_images.py
@@ -101,7 +101,7 @@ def map(self, key, value):
         yield value, FLAGS.output_folder
 
 mapreducer.REGISTER_DEFAULT_MAPPER(ResizeCropImagesMapper)
-
+mapreducer.REGISTER_DEFAULT_REDUCER(mapreducer.NoPassReducer)
 mapreducer.REGISTER_DEFAULT_READER(mapreducer.FileReader)
 mapreducer.REGISTER_DEFAULT_WRITER(mapreducer.FileWriter)
  
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index d6562f98..51c791e4 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -130,13 +130,12 @@ int feature_extraction_pipeline(int argc, char** argv) {
     txns.push_back(txn);
   }
 
-  LOG(ERROR)<< "Extacting Features";
+  LOG(ERROR)<< "Extracting Features";
 
   Datum datum;
-  std::vector<Blob<float>*> input_vec;
   std::vector<int> image_indices(num_features, 0);
   for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {
-    feature_extraction_net->Forward(input_vec);
+    feature_extraction_net->Forward();
     for (int i = 0; i < num_features; ++i) {
       const boost::shared_ptr<Blob<Dtype> > feature_blob =
         feature_extraction_net->blob_by_name(blob_names[i]);
diff --git a/tools/upgrade_net_proto_binary.cpp b/tools/upgrade_net_proto_binary.cpp
index 8a0dd7af..ede07ecc 100644
--- a/tools/upgrade_net_proto_binary.cpp
+++ b/tools/upgrade_net_proto_binary.cpp
@@ -16,6 +16,7 @@ using std::ofstream;
 using namespace caffe;  // NOLINT(build/namespaces)
 
 int main(int argc, char** argv) {
+  FLAGS_alsologtostderr = 1;  // Print output to stderr (while still logging)
   ::google::InitGoogleLogging(argv[0]);
   if (argc != 3) {
     LOG(ERROR) << "Usage: "
@@ -39,11 +40,11 @@ int main(int argc, char** argv) {
                  << "see details above.";
     }
   } else {
-    LOG(ERROR) << "File already in V1 proto format: " << argv[1];
+    LOG(ERROR) << "File already in latest proto format: " << input_filename;
   }
 
   WriteProtoToBinaryFile(net_param, argv[2]);
 
-  LOG(ERROR) << "Wrote upgraded NetParameter binary proto to " << argv[2];
+  LOG(INFO) << "Wrote upgraded NetParameter binary proto to " << argv[2];
   return !success;
 }
diff --git a/tools/upgrade_net_proto_text.cpp b/tools/upgrade_net_proto_text.cpp
index 9200431b..617b48dc 100644
--- a/tools/upgrade_net_proto_text.cpp
+++ b/tools/upgrade_net_proto_text.cpp
@@ -16,6 +16,7 @@ using std::ofstream;
 using namespace caffe;  // NOLINT(build/namespaces)
 
 int main(int argc, char** argv) {
+  FLAGS_alsologtostderr = 1;  // Print output to stderr (while still logging)
   ::google::InitGoogleLogging(argv[0]);
   if (argc != 3) {
     LOG(ERROR) << "Usage: "
@@ -31,7 +32,6 @@ int main(int argc, char** argv) {
     return 2;
   }
   bool need_upgrade = NetNeedsUpgrade(net_param);
-  bool need_data_upgrade = NetNeedsDataUpgrade(net_param);
   bool success = true;
   if (need_upgrade) {
     success = UpgradeNetAsNeeded(input_filename, &net_param);
@@ -43,13 +43,9 @@ int main(int argc, char** argv) {
     LOG(ERROR) << "File already in latest proto format: " << input_filename;
   }
 
-  if (need_data_upgrade) {
-    UpgradeNetDataTransformation(&net_param);
-  }
-
   // Save new format prototxt.
   WriteProtoToTextFile(net_param, argv[2]);
 
-  LOG(ERROR) << "Wrote upgraded NetParameter text proto to " << argv[2];
+  LOG(INFO) << "Wrote upgraded NetParameter text proto to " << argv[2];
   return !success;
 }
diff --git a/tools/upgrade_solver_proto_text.cpp b/tools/upgrade_solver_proto_text.cpp
index 7130232a..ddff1ce6 100644
--- a/tools/upgrade_solver_proto_text.cpp
+++ b/tools/upgrade_solver_proto_text.cpp
@@ -16,6 +16,7 @@ using std::ofstream;
 using namespace caffe;  // NOLINT(build/namespaces)
 
 int main(int argc, char** argv) {
+  FLAGS_alsologtostderr = 1;  // Print output to stderr (while still logging)
   ::google::InitGoogleLogging(argv[0]);
   if (argc != 3) {
     LOG(ERROR) << "Usage: upgrade_solver_proto_text "
@@ -45,6 +46,6 @@ int main(int argc, char** argv) {
   // Save new format prototxt.
   WriteProtoToTextFile(solver_param, argv[2]);
 
-  LOG(ERROR) << "Wrote upgraded SolverParameter text proto to " << argv[2];
+  LOG(INFO) << "Wrote upgraded SolverParameter text proto to " << argv[2];
   return !success;
 }