clone.py

import csv
import cv2
import numpy as np

lines = []
with open('../data/driving_log.csv') as csvfile:
    reader = csv.reader(csvfile)
    for line in reader:
        lines.append(line)

images = []
measurements = []
for line in lines:
    for i in range(3):
        source_path = line[i]
        filename = source_path.split('/')[-1]
        current_path = '../data/IMG/' + filename
        image = cv2.imread(current_path)
        images.append(image)
        measurement = float(line[3])
        measurements.append(measurement)

augment_images , augment_measurements = [], []
for image, measurement in zip(images, measurements):
    augment_images.append(image)
    augment_images.append(cv2.flip(image,1))
    augment_measurements.append(measurement)
    augment_measurements.append(measurement * -1.0)

X_train = np.array(images)
y_train = np.array(measurements)

from keras.models import Sequential
from keras.layers import Flatten, Dense, Lambda, Cropping2D
from keras.layers.convolutional import Convolution2D

model = Sequential()
model.add(Lambda(lambda x : x / 255.0 - 0.5, input_shape=(160, 320, 3)))
model.add(Cropping2D(cropping=((70, 25), (0, 0))))
model.add(Convolution2D(24,5,5,subsample=(2,2),activation='relu'))
model.add(Convolution2D(36,5,5,subsample=(2,2),activation='relu'))
model.add(Convolution2D(48,5,5,subsample=(2,2),activation='relu'))
model.add(Convolution2D(64,3,3,activation='relu'))
model.add(Convolution2D(64,3,3,activation='relu'))
model.add(Flatten())
model.add(Dense(100))
model.add(Dense(50))
model.add(Dense(10))
model.add(Dense(1))

model.compile(loss='mse', optimizer='adam')
model.fit(X_train, y_train, validation_split=0.2, shuffle=True, nb_epoch=5)

model.save('model.h5')