Copy with CANDLE module name

examples/mnist/mnist_baseline_keras2.py

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+import mnist
+import candle
+
+def initialize_parameters():
+
+    # Build benchmark object
+    mnist_common = mnist.MNIST(mnist.file_path,
+        'mnist_complex.txt',
+        'keras',
+        prog='mnist_mlp',
+        desc='MNIST example'
+    )
+
+    # Initialize parameters
+    gParameters = candle.finalize_parameters(mnist_common)
+
+    return gParameters
+
+def run(gParameters):
+    import keras
+    from keras.datasets import mnist
+    from keras.models import Sequential
+    from keras.layers import Dense, Dropout
+    from keras.optimizers import RMSprop
+
+    batch_size = gParameters['batch_size']
+    num_classes = 10
+    dense_layers = gParameters['dense']
+    dropout = gParameters['dropout']
+    epochs = gParameters['epochs']
+
+    activation = gParameters['activation']
+    optimizer = gParameters['optimizer']
+
+    # 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()
+    for i, layer_width in enumerate(dense_layers):
+        if i == 0:
+            model.add(Dense(layer_width, activation=activation, input_shape=(784,)))
+        else:
+            model.add(Dense(layer_width, activation=activation))
+        if dropout > 0:
+            model.add(Dropout(dropout))
+
+    model.add(Dense(num_classes, activation='softmax'))
+
+    model.summary()
+    # candle.model_plot(model, 'newname.png')
+
+    model.compile(loss='categorical_crossentropy',
+                optimizer=optimizer,
+                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])
+
+    candle.plot_history('test',history,'loss')
+
+    print(str(history))
+    return history
+
+if __name__ == '__main__':
+    gParams = initialize_parameters()
+    run(gParams)