Merge pull request #21 from anshuman23/dev

Added Keras example

examples/keras-example/mnist_keras.py

@@ -0,0 +1,73 @@
+'''Trains a simple deep NN on the MNIST dataset.
+Gets to 98.40% test accuracy after 20 epochs
+(there is *a lot* of margin for parameter tuning).
+2 seconds per epoch on a K520 GPU.
+'''
+
+from __future__ import print_function
+import keras
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers import Dense, Dropout
+from keras.optimizers import RMSprop
+from tensorflow.python.framework.graph_util import convert_variables_to_constants
+from keras import backend
+import tensorflow as tf
+
+def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
+    graph = session.graph
+    with graph.as_default():
+        freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
+        output_names = output_names or []
+        output_names += [v.op.name for v in tf.global_variables()]
+        input_graph_def = graph.as_graph_def()
+        if clear_devices:
+            for node in input_graph_def.node:
+                node.device = ""
+        frozen_graph = convert_variables_to_constants(session, input_graph_def, output_names, freeze_var_names)
+        return frozen_graph
+
+batch_size = 128
+num_classes = 10
+epochs = 20
+
+# the data, split between train and test sets
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+x_train = x_train.reshape(60000, 784)
+x_test = x_test.reshape(10000, 784)
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
+print(x_train.shape[0], 'train samples')
+print(x_test.shape[0], 'test samples')
+
+# convert class vectors to binary class matrices
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
+
+model = Sequential()
+model.add(Dense(512, activation='relu', input_shape=(784,), name="input"))
+model.add(Dropout(0.2))
+model.add(Dense(512, activation='relu'))
+model.add(Dropout(0.2))
+model.add(Dense(num_classes, activation='softmax', name="output"))
+
+model.summary()
+
+model.compile(loss='categorical_crossentropy',
+              optimizer=RMSprop(),
+              metrics=['accuracy'])
+
+history = model.fit(x_train, y_train,
+                    batch_size=batch_size,
+                    epochs=epochs,
+                    verbose=1,
+                    validation_data=(x_test, y_test))
+score = model.evaluate(x_test, y_test, verbose=0)
+print('Test loss:', score[0])
+print('Test accuracy:', score[1])
+
+frozen_graph = freeze_session(backend.get_session(), output_names=[out.op.name for out in model.outputs])
+tf.train.write_graph(frozen_graph, "./", "mnist_mlp.pb", as_text=False)