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+# Using Python Layers in your Caffe models with DIGITS
+
+Caffe gives users the ability to [define custom layer types in Python](https://github.com/BVLC/caffe/pull/1703), instead of the usual C++/CUDA.
+This can be a very useful feature, but it is poorly documented and tricky to implement correctly.
+This walkthrough will show you how to get started with Python layers in DIGITS.
+
+> NOTE: This feature is included automatically if you are building Caffe with CMake or installing the deb package. If you are building with Make, you will need to uncomment "WITH_PYTHON_LAYER := 1" in your `Makefile.config` to enable it.
+
+## Adding Occlusions to MNIST
+
+For this example, we are going to train LeNet on the MNIST dataset, but we are going to add a Python layer which cuts out one fourth of the image before feeding it through the network.
+This simulates occluded data and should result in a model which is robust to occlusions.
+
+For example, ![before occlusion](image-before-occlusion.jpg) might become ![after occlusion](image-after-occlusion.jpg).
+
+### Create a Dataset
+
+First, follow [these instructions](../../docs/GettingStarted.md#creating-a-dataset) to create the MNIST dataset in DIGITS (if you haven't already).
+
+### Create a Python File
+
+Next, you'll want to create a Python file which contains the definition for your Python layer.
+Open up a text editor and create a new file with these contents.
+```python
+import caffe
+import random
+
+class BlankSquareLayer(caffe.Layer):
+
+    def setup(self, bottom, top):
+        assert len(bottom) == 1,            'requires a single layer.bottom'
+        assert bottom[0].data.ndim >= 3,    'requires image data'
+        assert len(top) == 1,               'requires a single layer.top'
+
+    def reshape(self, bottom, top):
+        # Copy shape from bottom
+        top[0].reshape(*bottom[0].data.shape)
+
+    def forward(self, bottom, top):
+        # Copy all of the data
+        top[0].data[...] = bottom[0].data[...]
+        # Then zero-out one fourth of the image
+        height = top[0].data.shape[-2]
+        width = top[0].data.shape[-1]
+        h_offset = random.randrange(height/2)
+        w_offset = random.randrange(width/2)
+        top[0].data[...,
+                h_offset:(h_offset + height/2),
+                w_offset:(w_offset + width/2),
+                ] = 0
+
+    def backward(self, top, propagate_down, bottom):
+        pass
+```
+Save the file. We'll upload it along with your model in the next section.
+
+### Create a Model
+
+> NOTE: If you've never created a model in DIGITS before, you might want to follow the walkthrough in the [Getting Started guide](../../docs/GettingStarted.md#training-a-model) before proceeding.
+
+1. Click on `New Model > Images > Classification` from the homepage.
+2. Select your MNIST dataset from the list of datasets.
+3. Click on `Use client side file` and select the Python file that you created earlier.
+4. Click on `LeNet` under `Standard Networks > Caffe`.
+5. Click on the `Customize` link which appears to the right.
+
+![create model](create-model.jpg)
+
+That will take us to a pane where we can customize LeNet to add our custom Python layer.
+We are going to insert our layer in between the `scale` layer and the `conv1` layer.
+Find those layers (a few lines from the top) and insert this snippet of prototxt in-between:
+```
+layer {
+  name: "blank_square"
+  type: "Python"
+  bottom: "scale"
+  top: "scale"
+  python_param {
+    module: "digits_python_layers"
+    layer: "BlankSquareLayer"
+  }
+}
+```
+When you click on `Visualize`, you should see this:
+
+![network visualization](network-visualization.jpg)
+
+Then simply give your model a name and click `Create`.
+You should see your model training session start.
+If you're paying attention, you'll notice that this model reaches a lower accuracy than the default LeNet network. Why is that?
+
+### Testing the Model
+
+Now for the fun part.
+Find an image in the MNIST test set and upload it to `Test a single image` (at the bottom of the page).
+Don't forget to click on `Show  visualizations and statistics`!
+The original image is displayed on the top left, next to the the predicted class.
+In the `Visualization` column, you'll see the result of subtracting the mean image as the `data` activation.
+Just below it, you'll see the result of down-scaling the image from `[0 - 255]` to `[-1 - 1]`.
+You'll also see that a random fourth of the image has been removed - that's thanks to our Python layer!
+
+![lenet activations](lenet-activations.jpg)
+
+> NOTE: The second activation is displayed as a colorful heatmap, even though the underlying data is still single-channel and could be displayed as a grayscale image. The "data" activation is treated as a special case and all other activations are converted to heatmaps.
+
+Congratulations, you have completed this tutorial!
+
+### Extra credit
+
+For extra credit, try to change your Python layer to crop out two smaller squares instead of one big one:
+
+![two small squares](two-small-squares.jpg)
+
+Or try adding the layer to AlexNet instead of LeNet:
+
+![alexnet activations](alexnet-activations.jpg)
+
+### Discussion
+
+If you actually wanted to build an occlusion-resistant model, you would only want to add occlusions to the training data - not to the validation or test data.
+To do that, use this prototxt snippet instead to exclude the layer from the validation/test phases:
+```
+layer {
+  name: "blank_square"
+  type: "Python"
+  bottom: "scale"
+  top: "scale"
+  python_param {
+    module: "digits_python_layers"
+    layer: "BlankSquareLayer"
+  }
+  include {
+    phase: TRAIN
+  }
+}
+```
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