Added deep_learning module

Author: mnielsen

code/deep_learning.py

@@ -0,0 +1,46 @@
+"""
+deep_learning
+~~~~~~~~~~~~~
+
+Module to do deep learning.  Most of the functionality needed is
+already in the ``backprop2`` and ``deep_autoencoder`` modules, but
+this adds convenience functions to help in doing things like unrolling
+deep autoencoders, and adding and training a classifier layer."""
+
+# My Libraries
+from backprop2 import Network
+from deep_autoencoder import DeepAutoencoder
+
+def unroll(deep_autoencoder):
+    """
+    Return a Network that contains the compression stage of the
+    ``deep_autoencoder``."""
+    net = Network(deep_autoencoder.layers)
+    net.weights = deep_autoencoder.weights[:len(deep_autoencoder.layers)-1]
+    net.biases = deep_autoencoder.biases[:len(deep_autoencoder.layers)-1]
+    return net
+
+def add_classifier_layer(net, num_outputs):
+    """
+    Return the Network ``net``, but with an extra layer containing
+    ``num_outputs`` neurons appended."""
+    net_classifier = Network(net.sizes+[num_outputs])
+    net_classifier.weights[:-1] = net.weights
+    net_classifier.biases[:-1] = net.biases
+    return net_classifier
+
+def SGD_final_layer(
+    self, training_data, epochs, mini_batch_size, eta, lmbda, 
+    test=False, test_inputs=None, actual_test_results=None):
+    """
+    Run SGD on the final layer of the Network ``self``.  Note that
+    ``training_data`` is the input to the whole Network, not the
+    encoded training data input to the final layer.
+    """
+    net = Network([self.sizes[-2], self.sizes[-1]])
+    encoded_training_data = self.feedforward(training_data, start=0, end=-1)
+    net.SGD(encoded_training_data, epochs, mini_batch_size, eta,
+            lmbda, test, test_inputs, actual_test_results)
+
+# Add the SGD_final_layer method to the Network class
+Network.SGD_final_layer = SGD_final_layer