model.py

import cv2
import numpy as np
from sklearn.utils import shuffle
from keras.models import Sequential, load_model
from keras.layers import Dense, Flatten, Dropout, Lambda, Cropping2D, Convolution2D

"""
Load training and validation data sets generated by 'load_data.py'
"""
X_train = np.load('X_train.npy')
X_valid = np.load('X_valid.npy')
y_train = np.load('y_train.npy')
y_valid = np.load('y_valid.npy')

"""
Helper function
"""
def data_generator(X_samples, y_samples, batch_size=32):
	"""
	Generator for loading batches of image data into memory
	"""
	num_samples = len(X_samples)
	while 1:
		X_samples, y_samples = shuffle(X_samples, y_samples)

		for offset in range(0, num_samples, batch_size):
			X_batch = X_samples[offset:offset+batch_size]
			y_batch = y_samples[offset:offset+batch_size]
			images, steering_angles = [], []

			for X_sample, y_sample in zip(X_batch, y_batch):
				centercam_image = cv2.imread(X_sample) # BGR image
				centercam_image = cv2.cvtColor(centercam_image, cv2.COLOR_BGR2RGB)
				steering_angle = float(y_sample)

				# Add base image/steering angle to batch
				images.append(centercam_image)
				steering_angles.append(steering_angle)

				# Augment data by horizontal flipping
				images.append(np.fliplr(centercam_image))
				steering_angles.append(-1.0*steering_angle)

			X_out = np.array(images)
			y_out = np.array(steering_angles)
			yield shuffle(X_out, y_out)


"""
Set number of epochs and data generators for training and validation
"""
EPOCHS = 10

train_generator = data_generator(X_train, y_train, batch_size=32)
validation_generator = data_generator(X_valid, y_valid, batch_size=32)

"""
Keras model architecture similar to Nvidia paper 'End to End Learning for Self-Driving Cars'
"""
model = Sequential()
model.add(Cropping2D(cropping=((50,20), (0,0)), input_shape=(160,320,3)))
model.add(Lambda(lambda x: (x / 255.0) - 0.5 ))
model.add(Convolution2D(nb_filter=24, nb_row=5, nb_col=5, subsample=(2, 2), activation="relu"))
model.add(Convolution2D(nb_filter=36, nb_row=5, nb_col=5, subsample=(2, 2), activation="relu"))
model.add(Convolution2D(nb_filter=48, nb_row=5, nb_col=5, subsample=(2, 2), activation="relu"))
model.add(Convolution2D(nb_filter=64, nb_row=3, nb_col=3, activation="relu"))
model.add(Convolution2D(nb_filter=64, nb_row=3, nb_col=3, activation="relu"))
model.add(Flatten())
model.add(Dense(100, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(50, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(10, activation="relu"))
model.add(Dense(1))

"""
Compile model using mean-squared error and ADAM optimizer with default learning rate parameters.
Run model fit training using generators and save model and loss history results.
"""
model.compile(loss='mse', optimizer='adam')
trained_history = model.fit_generator(train_generator, 
									samples_per_epoch=(2*len(X_train)),
									validation_data=validation_generator,
									nb_val_samples=(2*len(X_valid)),
									nb_epoch=EPOCHS)

model.summary()
model.save('model.h5')
np.save("trained_history.npy", trained_history.history)
print('Model and history saved.')