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TensorFlow-Slim

TF-Slim is a lightweight library for defining, training and evaluating models in TensorFlow. It enables defining complex networks quickly and concisely while keeping a model's architecture transparent and its hyperparameters explicit.

[TOC]

Teaser

As a demonstration of the simplicity of using TF-Slim, compare the simplicity of the code necessary for defining the entire [VGG] (http://www.robots.ox.ac.uk/~vgg/research/very_deep/) network using TF-Slim to the lengthy and verbose nature of defining just the first three layers (out of 16) using native tensorflow:

# VGG16 in TF-Slim.
def vgg16(inputs):
  with slim.arg_scope([slim.ops.conv2d, slim.ops.fc], stddev=0.01, weight_decay=0.0005):
    net = slim.ops.repeat_op(2, inputs, slim.ops.conv2d, 64, [3, 3], scope='conv1')
    net = slim.ops.max_pool(net, [2, 2], scope='pool1')
    net = slim.ops.repeat_op(2, net, slim.ops.conv2d, 128, [3, 3], scope='conv2')
    net = slim.ops.max_pool(net, [2, 2], scope='pool2')
    net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 256, [3, 3], scope='conv3')
    net = slim.ops.max_pool(net, [2, 2], scope='pool3')
    net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 512, [3, 3], scope='conv4')
    net = slim.ops.max_pool(net, [2, 2], scope='pool4')
    net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 512, [3, 3], scope='conv5')
    net = slim.ops.max_pool(net, [2, 2], scope='pool5')
    net = slim.ops.flatten(net, scope='flatten5')
    net = slim.ops.fc(net, 4096, scope='fc6')
    net = slim.ops.dropout(net, 0.5, scope='dropout6')
    net = slim.ops.fc(net, 4096, scope='fc7')
    net = slim.ops.dropout(net, 0.5, scope='dropout7')
    net = slim.ops.fc(net, 1000, activation=None, scope='fc8')
  return net

# Layers 1-3 (out of 16) of VGG16 in native tensorflow.
def vgg16(inputs):
  with tf.name_scope('conv1_1') as scope:
    kernel = tf.Variable(tf.truncated_normal([3, 3, 3, 64], dtype=tf.float32, stddev=1e-1), name='weights')
    conv = tf.nn.conv2d(inputs, kernel, [1, 1, 1, 1], padding='SAME')
    biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32), trainable=True, name='biases')
    bias = tf.nn.bias_add(conv, biases)
    conv1 = tf.nn.relu(bias, name=scope)
  with tf.name_scope('conv1_2') as scope:
    kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 64], dtype=tf.float32, stddev=1e-1), name='weights')
    conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
    biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32), trainable=True, name='biases')
    bias = tf.nn.bias_add(conv, biases)
    conv1 = tf.nn.relu(bias, name=scope)
  with tf.name_scope('pool1')
    pool1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID', name='pool1')

Why TF-Slim?

TF-Slim offers several advantages over just the built-in tensorflow libraries:

  • Allows one to define models much more compactly by eliminating boilerplate code. This is accomplished through the use of argument scoping and numerous high level operations. These tools increase readability and maintainability, reduce the likelihood of an error from copy-and-pasting hyperparameter values and simplifies hyperparameter tuning.
  • Makes developing models simple by providing commonly used [loss functions] (losses.py)
  • Provides a concise definition of [Inception v3] (http://arxiv.org/abs/1512.00567) network architecture ready to be used out-of-the-box or subsumed into new models.

Additionally TF-Slim was designed with several principles in mind:

  • The various modules of TF-Slim (scopes, variables, ops, losses) are independent. This flexibility allows users to pick and choose components of TF-Slim completely à la carte.
  • TF-Slim is written using a Functional Programming style. That means it's super-lightweight and can be used right alongside any of TensorFlow's native operations.
  • Makes re-using network architectures easy. This allows users to build new networks on top of existing ones as well as fine-tuning pre-trained models on new tasks.

What are the various components of TF-Slim?

TF-Slim is composed of several parts which were designed to exist independently. These include:

  • scopes.py: provides a new scope named arg_scope that allows a user to define default arguments for specific operations within that scope.
  • variables.py: provides convenience wrappers for variable creation and manipulation.
  • ops.py: provides high level operations for building models using tensorflow.
  • losses.py: contains commonly used loss functions.

Defining Models

Models can be succinctly defined using TF-Slim by combining its variables, operations and scopes. Each of these elements are defined below.

Variables

Creating Variables in native tensorflow requires either a predefined value or an initialization mechanism (random, normally distributed). Furthermore, if a variable needs to be created on a specific device, such as a GPU, the specification must be made explicit. To alleviate the code required for variable creation, TF-Slim provides a set of thin wrapper functions in variables.py which allow callers to easily define variables.

For example, to create a weight variable, initialize it using a truncated normal distribution, regularize it with an l2_loss and place it on the CPU, one need only declare the following:

weights = variables.variable('weights',
                             shape=[10, 10, 3 , 3],
                             initializer=tf.truncated_normal_initializer(stddev=0.1),
                             regularizer=lambda t: losses.l2_loss(t, weight=0.05),
                             device='/cpu:0')

In addition to the functionality provided by tf.Variable, slim.variables keeps track of the variables created by slim.ops to define a model, which allows one to distinguish variables that belong to the model versus other variables.

# Get all the variables defined by the model.
model_variables = slim.variables.get_variables()

# Get all the variables with the same given name, i.e. 'weights', 'biases'.
weights = slim.variables.get_variables_by_name('weights')
biases = slim.variables.get_variables_by_name('biases')

# Get all the variables in VARIABLES_TO_RESTORE collection.
variables_to_restore = tf.get_collection(slim.variables.VARIABLES_TO_RESTORE)


weights = variables.variable('weights',
                             shape=[10, 10, 3 , 3],
                             initializer=tf.truncated_normal_initializer(stddev=0.1),
                             regularizer=lambda t: losses.l2_loss(t, weight=0.05),
                             device='/cpu:0')

Operations (Layers)

While the set of TensorFlow operations is quite extensive, builders of neural networks typically think of models in terms of "layers". A layer, such as a Convolutional Layer, a Fully Connected Layer or a BatchNorm Layer are more abstract than a single TensorFlow operation and typically involve many such operations. For example, a Convolutional Layer in a neural network is built using several steps:

  1. Creating the weight variables
  2. Creating the bias variables
  3. Convolving the weights with the input from the previous layer
  4. Adding the biases to the result of the convolution.

In python code this can be rather laborious:

input = ...
with tf.name_scope('conv1_1') as scope:
  kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 128], dtype=tf.float32,
                                           stddev=1e-1), name='weights')
  conv = tf.nn.conv2d(input, kernel, [1, 1, 1, 1], padding='SAME')
  biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),
                       trainable=True, name='biases')
  bias = tf.nn.bias_add(conv, biases)
  conv1 = tf.nn.relu(bias, name=scope)

To alleviate the need to duplicate this code repeatedly, TF-Slim provides a number of convenient operations defined at the (more abstract) level of neural network layers. For example, compare the code above to an invocation of the TF-Slim code:

input = ...
net = slim.ops.conv2d(input, [3, 3], 128, scope='conv1_1')

TF-Slim provides numerous operations used in building neural networks which roughly correspond to such layers. These include:

Layer TF-Slim Op
Convolutional Layer ops.conv2d
Fully Connected Layer ops.fc
BatchNorm layer ops.batch_norm
Max Pooling Layer ops.max_pool
Avg Pooling Layer ops.avg_pool
Dropout Layer ops.dropout

ops.py also includes operations that are not really "layers" per se, but are often used to manipulate hidden unit representations during inference:

Operation TF-Slim Op
Flatten ops.flatten

TF-Slim also provides a meta-operation called repeat_op that allows one to repeatedly perform the same operation. Consider the following snippet from the VGG network whose layers perform several convolutions in a row between pooling layers:

net = ...
net = slim.ops.conv2d(net, 256, [3, 3], scope='conv3_1')
net = slim.ops.conv2d(net, 256, [3, 3], scope='conv3_2')
net = slim.ops.conv2d(net, 256, [3, 3], scope='conv3_3')
net = slim.ops.max_pool(net, [2, 2], scope='pool3')

This clear duplication of code can be removed via a standard loop:

net = ...
for i in range(3):
  net = slim.ops.conv2d(net, 256, [3, 3], scope='conv3_' % (i+1))
net = slim.ops.max_pool(net, [2, 2], scope='pool3')

While this does reduce the amount of duplication, it can be made even cleaner by using the RepeatOp:

net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 256, [3, 3], scope='conv3')
net = slim.ops.max_pool(net, [2, 2], scope='pool2')

Notice that the RepeatOp not only applies the same argument in-line, it also is smart enough to unroll the scopes such that the scopes assigned to each subsequent call of ops.conv2d is appended with an underscore and iteration number. More concretely, the scopes in the example above would be 'conv3_1', 'conv3_2' and 'conv3_3'.

Scopes

In addition to the types of scope mechanisms in TensorFlow ([name_scope] (https://www.tensorflow.org/api_docs/python/framework.html#name_scope), [variable_scope] (https://www.tensorflow.org/api_docs/python/state_ops.html#variable_scope), TF-Slim adds a new scoping mechanism called "argument scope" or [arg_scope] (scopes.py). This new scope allows a user to specify one or more operations and a set of arguments which will be passed to each of the operations defined in the arg_scope. This functionality is best illustrated by example. Consider the following code snippet:

net = slim.ops.conv2d(inputs, 64, [11, 11], 4, padding='SAME', stddev=0.01, weight_decay=0.0005, scope='conv1')
net = slim.ops.conv2d(net, 128, [11, 11], padding='VALID', stddev=0.01, weight_decay=0.0005, scope='conv2')
net = slim.ops.conv2d(net, 256, [11, 11], padding='SAME', stddev=0.01, weight_decay=0.0005, scope='conv3')

It should be clear that these three Convolution layers share many of the same hyperparameters. Two have the same padding, all three have the same weight_decay and standard deviation of its weights. Not only do the duplicated values make the code more difficult to read, it also adds the addition burder to the writer of needing to doublecheck that all of the values are identical in each step. One solution would be to specify default values using variables:

padding='SAME'
stddev=0.01
weight_decay=0.0005
net = slim.ops.conv2d(inputs, 64, [11, 11], 4, padding=padding, stddev=stddev, weight_decay=weight_decay, scope='conv1')
net = slim.ops.conv2d(net, 128, [11, 11], padding='VALID', stddev=stddev, weight_decay=weight_decay, scope='conv2')
net = slim.ops.conv2d(net, 256, [11, 11], padding=padding, stddev=stddev, weight_decay=weight_decay, scope='conv3')

This solution ensures that all three convolutions share the exact same variable values but doesn't reduce the code clutter. By using an arg_scope, we can both ensure that each layer uses the same values and simplify the code:

  with slim.arg_scope([slim.ops.conv2d], padding='SAME', stddev=0.01, weight_decay=0.0005):
    net = slim.ops.conv2d(inputs, 64, [11, 11], scope='conv1')
    net = slim.ops.conv2d(net, 128, [11, 11], padding='VALID', scope='conv2')
    net = slim.ops.conv2d(net, 256, [11, 11], scope='conv3')

As the example illustrates, the use of arg_scope makes the code cleaner, simpler and easier to maintain. Notice that while argument values are specifed in the arg_scope, they can be overwritten locally. In particular, while the padding argument has been set to 'SAME', the second convolution overrides it with the value of 'VALID'.

One can also nest arg_scopes and use multiple operations in the same scope. For example:

with arg_scope([slim.ops.conv2d, slim.ops.fc], stddev=0.01, weight_decay=0.0005):
  with arg_scope([slim.ops.conv2d], padding='SAME'), slim.arg_scope([slim.ops.fc], bias=1.0):
    net = slim.ops.conv2d(inputs, 64, [11, 11], 4, padding='VALID', scope='conv1')
    net = slim.ops.conv2d(net, 256, [5, 5], stddev=0.03, scope='conv2')
    net = slim.ops.flatten(net)
    net = slim.ops.fc(net, 1000, activation=None, scope='fc')

In this example, the first arg_scope applies the same stddev and weight_decay arguments to the conv2d and fc ops in its scope. In the second arg_scope, additional default arguments to conv2d only are specified.

In addition to arg_scope, TF-Slim provides several decorators that wrap the use of tensorflow arg scopes. These include @AddArgScope, @AddNameScope, @AddVariableScope, @AddOpScope and @AddVariableOpScope. To illustrate their use, consider the following example.

def MyNewOp(inputs):
  varA = ...
  varB = ...
  outputs = tf.multiply(varA, inputs) + varB
  return outputs

In this example, the user has created a new op which creates two variables. To ensure that these variables exist within a certain variable scope (to avoid collisions with variables with the same name), in standard TF, the op must be called within a variable scope:

inputs = ...
with tf.variable_scope('layer1'):
  outputs = MyNewOp(inputs)

As an alternative, one can use TF-Slim's decorators to decorate the function and simplify the call:

@AddVariableScope
def MyNewOp(inputs):
  ...
  return outputs


inputs = ...
outputs = MyNewOp('layer1')

The @AddVariableScope decorater simply applies the tf.variable_scope scoping to the called function taking "layer1" as its argument. This allows the code to be written more concisely.

Losses

The loss function defines a quantity that we want to minimize. For classification problems, this is typically the cross entropy between the true (one-hot) distribution and the predicted probability distribution across classes. For regression problems, this is often the sum-of-squares differences between the predicted and true values.

Certain models, such as multi-task learning models, require the use of multiple loss functions simultaneously. In other words, the loss function ultimatey being minimized is the sum of various other loss functions. For example, consider a model that predicts both the type of scene in an image as well as the depth from the camera of each pixel. This model's loss function would be the sum of the classification loss and depth prediction loss.

TF-Slim provides an easy-to-use mechanism for defining and keeping track of loss functions via the losses.py module. Consider the simple case where we want to train the VGG network:

# Load the images and labels.
images, labels = ...

# Create the model.
predictions =  ...

# Define the loss functions and get the total loss.
loss = losses.cross_entropy_loss(predictions, labels)

In this example, we start by creating the model (using TF-Slim's VGG implementation), and add the standard classification loss. Now, lets turn to the case where we have a multi-task model that produces multiple outputs:

# Load the images and labels.
images, scene_labels, depth_labels = ...

# Create the model.
scene_predictions, depth_predictions = CreateMultiTaskModel(images)

# Define the loss functions and get the total loss.
classification_loss = slim.losses.cross_entropy_loss(scene_predictions, scene_labels)
sum_of_squares_loss = slim.losses.l2loss(depth_predictions - depth_labels)

# The following two lines have the same effect:
total_loss1 = classification_loss + sum_of_squares_loss
total_loss2 = tf.get_collection(slim.losses.LOSSES_COLLECTION)

In this example, we have two losses which we add by calling losses.cross_entropy_loss and losses.l2loss. We can obtain the total loss by adding them together (total_loss1) or by calling losses.GetTotalLoss(). How did this work? When you create a loss function via TF-Slim, TF-Slim adds the loss to a special TensorFlow collection of loss functions. This enables you to either manage the total loss manually, or allow TF-Slim to manage them for you.

What if you want to let TF-Slim manage the losses for you but have a custom loss function? losses.py also has a function that adds this loss to TF-Slims collection. For example:

# Load the images and labels.
images, scene_labels, depth_labels, pose_labels = ...

# Create the model.
scene_predictions, depth_predictions, pose_predictions = CreateMultiTaskModel(images)

# Define the loss functions and get the total loss.
classification_loss = slim.losses.cross_entropy_loss(scene_predictions, scene_labels)
sum_of_squares_loss = slim.losses.l2loss(depth_predictions - depth_labels)
pose_loss = MyCustomLossFunction(pose_predictions, pose_labels)
tf.add_to_collection(slim.losses.LOSSES_COLLECTION, pose_loss) # Letting TF-Slim know about the additional loss.

# The following two lines have the same effect:
total_loss1 = classification_loss + sum_of_squares_loss + pose_loss
total_loss2 = losses.GetTotalLoss()

In this example, we can again either produce the total loss function manually or let TF-Slim know about the additional loss and let TF-Slim handle the losses.

Putting the Pieces Together

By combining TF-Slim Variables, Operations and scopes, we can write a normally very complex network with very few lines of code. For example, the entire [VGG] (https://www.robots.ox.ac.uk/~vgg/research/very_deep/) architecture can be defined with just the following snippet:

with arg_scope([slim.ops.conv2d, slim.ops.fc], stddev=0.01, weight_decay=0.0005):
  net = slim.ops.repeat_op(2, inputs, slim.ops.conv2d, 64, [3, 3], scope='conv1')
  net = slim.ops.max_pool(net, [2, 2], scope='pool1')
  net = slim.ops.repeat_op(2, net, slim.ops.conv2d, 128, [3, 3], scope='conv2')
  net = slim.ops.max_pool(net, [2, 2], scope='pool2')
  net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 256, [3, 3], scope='conv3')
  net = slim.ops.max_pool(net, [2, 2], scope='pool3')
  net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 512, [3, 3], scope='conv4')
  net = slim.ops.max_pool(net, [2, 2], scope='pool4')
  net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 512, [3, 3], scope='conv5')
  net = slim.ops.max_pool(net, [2, 2], scope='pool5')
  net = slim.ops.flatten(net, scope='flatten5')
  net = slim.ops.fc(net, 4096, scope='fc6')
  net = slim.ops.dropout(net, 0.5, scope='dropout6')
  net = slim.ops.fc(net, 4096, scope='fc7')
  net = slim.ops.dropout(net, 0.5, scope='dropout7')
  net = slim.ops.fc(net, 1000, activation=None, scope='fc8')
return net

Re-using previously defined network architectures and pre-trained models.

Brief Recap on Restoring Variables from a Checkpoint

After a model has been trained, it can be restored using tf.train.Saver() which restores Variables from a given checkpoint. For many cases, tf.train.Saver() provides a simple mechanism to restore all or just a few variables.

# Create some variables.
v1 = tf.Variable(..., name="v1")
v2 = tf.Variable(..., name="v2")
...
# Add ops to restore all the variables.
restorer = tf.train.Saver()

# Add ops to restore some variables.
restorer = tf.train.Saver([v1, v2])

# Later, launch the model, use the saver to restore variables from disk, and
# do some work with the model.
with tf.Session() as sess:
  # Restore variables from disk.
  restorer.restore(sess, "/tmp/model.ckpt")
  print("Model restored.")
  # Do some work with the model
  ...

See [Restoring Variables] (https://www.tensorflow.org/versions/r0.7/how_tos/variables/index.html#restoring-variables) and [Choosing which Variables to Save and Restore] (https://www.tensorflow.org/versions/r0.7/how_tos/variables/index.html#choosing-which-variables-to-save-and-restore) sections of the [Variables] (https://www.tensorflow.org/versions/r0.7/how_tos/variables/index.html) page for more details.

Using slim.variables to Track which Variables need to be Restored

It is often desirable to fine-tune a pre-trained model on an entirely new dataset or even a new task. In these situations, one must specify which layers of the model should be reused (and consequently loaded from a checkpoint) and which layers are new. Indicating which variables or layers should be restored is a process that quickly becomes cumbersome when done manually.

To help keep track of which variables to restore, slim.variables provides a restore argument when creating each Variable. By default, all variables are marked as restore=True, which results in all variables defined by the model being restored.

# Create some variables.
v1 = slim.variables.variable(name="v1", ..., restore=False)
v2 = slim.variables.variable(name="v2", ...) # By default restore=True
...
# Get list of variables to restore (which contains only 'v2')
variables_to_restore = tf.get_collection(slim.variables.VARIABLES_TO_RESTORE)
restorer = tf.train.Saver(variables_to_restore)
with tf.Session() as sess:
  # Restore variables from disk.
  restorer.restore(sess, "/tmp/model.ckpt")
  print("Model restored.")
  # Do some work with the model
  ...

Additionally, every layer in slim.ops that creates slim.variables (such as slim.ops.conv2d, slim.ops.fc, slim.ops.batch_norm) also has a restore argument which controls whether the variables created by that layer should be restored or not.

# Create a small network.
net = slim.ops.conv2d(images, 32, [7, 7], stride=2, scope='conv1')
net = slim.ops.conv2d(net, 64, [3, 3], scope='conv2')
net = slim.ops.conv2d(net, 128, [3, 3], scope='conv3')
net = slim.ops.max_pool(net, [3, 3], stride=2, scope='pool3')
net = slim.ops.flatten(net)
net = slim.ops.fc(net, 10, scope='logits', restore=False)
...

# VARIABLES_TO_RESTORE would contain the 'weights' and 'bias' defined by 'conv1'
# 'conv2' and 'conv3' but not the ones defined by 'logits'
variables_to_restore = tf.get_collection(slim.variables.VARIABLES_TO_RESTORE)

# Create a restorer that would restore only the needed variables.
restorer = tf.train.Saver(variables_to_restore)

# Create a saver that would save all the variables (including 'logits').
saver = tf.train.Saver()
with tf.Session() as sess:
  # Restore variables from disk.
  restorer.restore(sess, "/tmp/model.ckpt")
  print("Model restored.")

  # Do some work with the model
  ...
  saver.save(sess, "/tmp/new_model.ckpt")

Note: When restoring variables from a checkpoint, the Saver locates the variable names in a checkpoint file and maps them to variables in the current graph. Above, we created a saver by passing to it a list of variables. In this case, the names of the variables to locate in the checkpoint file were implicitly obtained from each provided variable's var.op.name.

This works well when the variable names in the checkpoint file match those in the graph. However, sometimes, we want to restore a model from a checkpoint whose variables have different names those in the current graph. In this case, we must provide the Saver a dictionary that maps from each checkpoint variable name to each graph variable. Consider the following example where the checkpoint variables names are obtained via a simple function:

# Assuming that 'conv1/weights' should be restored from 'vgg16/conv1/weights'
def name_in_checkpoint(var):
  return 'vgg16/' + var.op.name

# Assuming that 'conv1/weights' and 'conv1/bias' should be restored from 'conv1/params1' and 'conv1/params2'
def name_in_checkpoint(var):
  if "weights" in var.op.name:
    return var.op.name.replace("weights", "params1")
  if "bias" in var.op.name:
    return var.op.name.replace("bias", "params2")

variables_to_restore = tf.get_collection(slim.variables.VARIABLES_TO_RESTORE)
variables_to_restore = {name_in_checkpoint(var):var for var in variables_to_restore}
restorer = tf.train.Saver(variables_to_restore)
with tf.Session() as sess:
  # Restore variables from disk.
  restorer.restore(sess, "/tmp/model.ckpt")

Reusing the VGG16 network defined in TF-Slim on a different task, i.e. PASCAL-VOC.

Assuming one have already a pre-trained VGG16 model, one just need to replace the last layer fc8 with a new layer fc8_pascal and use restore=False.

def vgg16_pascal(inputs):
  with slim.arg_scope([slim.ops.conv2d, slim.ops.fc], stddev=0.01, weight_decay=0.0005):
    net = slim.ops.repeat_op(2, inputs, slim.ops.conv2d, 64, [3, 3], scope='conv1')
    net = slim.ops.max_pool(net, [2, 2], scope='pool1')
    net = slim.ops.repeat_op(2, net, slim.ops.conv2d, 128, [3, 3], scope='conv2')
    net = slim.ops.max_pool(net, [2, 2], scope='pool2')
    net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 256, [3, 3], scope='conv3')
    net = slim.ops.max_pool(net, [2, 2], scope='pool3')
    net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 512, [3, 3], scope='conv4')
    net = slim.ops.max_pool(net, [2, 2], scope='pool4')
    net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 512, [3, 3], scope='conv5')
    net = slim.ops.max_pool(net, [2, 2], scope='pool5')
    net = slim.ops.flatten(net, scope='flatten5')
    net = slim.ops.fc(net, 4096, scope='fc6')
    net = slim.ops.dropout(net, 0.5, scope='dropout6')
    net = slim.ops.fc(net, 4096, scope='fc7')
    net = slim.ops.dropout(net, 0.5, scope='dropout7')
    # To reuse vgg16 on PASCAL-VOC, just change the last layer.
    net = slim.ops.fc(net, 21, activation=None, scope='fc8_pascal', restore=False)
  return net

Authors

Sergio Guadarrama and Nathan Silberman