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networks/small.py

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+from __future__ import absolute_import
+
+# tag::small_network[]
+from keras.layers.core import Dense, Activation, Flatten
+from keras.layers.convolutional import Conv2D, ZeroPadding2D
+
+
+def layers(input_shape):
+    return [
+        ZeroPadding2D(padding=3, input_shape=input_shape, data_format='channels_first'),  # <1>
+        Conv2D(48, (7, 7), data_format='channels_first'),
+        Activation('relu'),
+
+        ZeroPadding2D(padding=2, data_format='channels_first'),  # <2>
+        Conv2D(32, (5, 5), data_format='channels_first'),
+        Activation('relu'),
+
+        ZeroPadding2D(padding=2, data_format='channels_first'),
+        Conv2D(32, (5, 5), data_format='channels_first'),
+        Activation('relu'),
+
+        ZeroPadding2D(padding=2, data_format='channels_first'),
+        Conv2D(32, (5, 5), data_format='channels_first'),
+        Activation('relu'),
+
+        Flatten(),
+        Dense(512),
+        Activation('relu'),
+    ]
+
+# <1> We use zero padding layers to enlarge input images.
+# <2> By using `channels_first` we specify that the input plane dimension for our features comes first.
+# end::small_network[]