def keras_nn(X_train, y_train):
"""
Constructs a neural network using keras and trains it.
The best final networks is saved in model.json and model.h5
Parameters
----------
X_train : numpy array
The images
y_train : numpy array
The angles
"""
model = Sequential()
# Further reduces the dimension of the image to 8x16
model.add(AveragePooling2D((2, 2), border_mode='valid', input_shape=(16, 32, 1)))
# Applies 2x2 convolution
model.add(Convolution2D(1, 2, 2, subsample=(1, 1)))
model.add(ELU())
# Max Pooling to reduce the dimensions. 2X4 used because it matches the aspect ratio of the input
model.add(MaxPooling2D((2, 4), border_mode='valid'))
# Droput - We only have 10 connections at this point, but it still improves performance. However it should be kept low, e.g. 0.5 doesn't work
model.add(Dropout(0.25))
model.add(Flatten())
# The final layer - outputs a float number (the steering angle)
model.add(Dense(1)) #
# Show a summary of the neural network
model.summary()
# Save the best model by validation mean squared error
checkpoint = ModelCheckpoint("model.h5", monitor='val_mean_squared_error', verbose=1, save_best_only=True, mode='min')
# Stop training when there is no improvment.
# This is to speed up training, the accuracy is not affected, because the checkpoint will pick-up the best model anyway
early_stop = EarlyStopping(monitor='val_mean_squared_error', min_delta=0.0001, patience=4, verbose=1, mode='min')
# Compile the model with Adam optimizer and monitor mean squared error
model.compile('adam', 'mean_squared_error', ['mean_squared_error'])
# Save the model to JSON
model_json = model.to_json()
with open("model.json", "w") as model_file:
model_file.write(model_json)
# Start training.
# nb_epoch should be a big number, there is early stopping callback anyway
# Data is split by keras to training and validation
history = model.fit(X_train, y_train, batch_size=32, nb_epoch=150, verbose=1, callbacks=[checkpoint, early_stop], validation_split=0.2, shuffle=True)
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