model.py

import os
import csv
import cv2
import numpy as np
from scipy import ndimage

# read driving log from csv file
lines = []
with open('/opt/carnd_p3/data/driving_log.csv') as csvfile:
    reader = csv.reader(csvfile)
    next(reader, None)
    for line in reader:
        lines.append(line)
        
import sklearn        
from sklearn.model_selection import train_test_split
train_samples, validation_samples = train_test_split(lines, test_size=0.2)

def generator(samples, batch_size=32):
    num_samples = len(samples)
    while 1: # Loop forever so the generator never terminates
        np.random.shuffle(samples)
        for offset in range(0, num_samples, batch_size):
            batch_samples = samples[offset:offset+batch_size]

            images = []
            angles = []
            for batch_sample in batch_samples:
                current_path = '/opt/carnd_p3/data/' + batch_sample[0]
                center_image = ndimage.imread(current_path)       
                center_angle = float(batch_sample[3])
                images.append(center_image)
                angles.append(center_angle)
                images.append(cv2.flip(center_image,1))
                angles.append(center_angle*-1)

            # trim image to only see section with road
            X_train = np.array(images)
            y_train = np.array(angles)
            yield sklearn.utils.shuffle(X_train, y_train)
            
# Set our batch size
batch_size = 32

# compile and train the model using the generator function
train_generator = generator(train_samples, batch_size=batch_size)
validation_generator = generator(validation_samples, batch_size=batch_size)

# creat model using keras
from keras.models import Sequential, Model
from keras.layers import Flatten, Dense, Lambda, MaxPooling2D, 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=((60,20),(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))

# train the model
model.compile(loss='mse', optimizer='adam')
history_object = model.fit_generator(train_generator, \
            steps_per_epoch=np.ceil(len(train_samples)/batch_size), \
            validation_data=validation_generator, \
            validation_steps=np.ceil(len(validation_samples)/batch_size), \
            epochs=4, verbose=1)

# save the model
model.save('model.h5')

import matplotlib.pyplot as plt

# plot the training and validation loss for each epoch
plt.plot(history_object.history['loss'])
plt.plot(history_object.history['val_loss'])
plt.title('model mean squared error loss')
plt.ylabel('mean squared error loss')
plt.xlabel('epoch')
plt.legend(['training set', 'validation set'], loc='upper right')
plt.show()

exit()