model.py

import csv
import cv2
import numpy as np
from keras.models import Sequential, Model
from keras.layers import Flatten, Dense, Lambda
from keras.layers.convolutional import Cropping2D, Convolution2D
import matplotlib.pyplot as plt

lines = []
with open('./input/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 = './input/IMG/' + filename
        image = cv2.imread(current_path)
        images.append(image)
    correction = 0.2
    measurement = float(line[3])
    measurements.append(measurement)
    measurements.append(measurement+correction)
    measurements.append(measurement-correction)

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

X_train = np.array(augmented_images)
y_train = np.array(augmented_measurements)

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=3, verbose =1)

model.save('model.h5')