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model.py
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model.py
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import csv
import cv2
import numpy as np
from random import shuffle
import sklearn
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Flatten, Dense, Lambda, Dropout, Cropping2D
from keras.layers.convolutional import Convolution2D
from keras.layers.pooling import MaxPooling2D
from keras.models import Model
import matplotlib.pyplot as plt
driving_log_path = 'my_data/driving_log.csv'
image_path = 'my_data/IMG/'
lines = []
with open(driving_log_path) as csvfile:
reader = csv.reader(csvfile)
for line in reader:
lines.append(line)
def generator(samples, batch_size=32):
steering_correction = 0.25
num_samples = len(samples)
while 1: # Loop forever so the generator never terminates
shuffle(samples)
for offset in range(0, num_samples, batch_size):
batch_samples = samples[offset:offset+batch_size]
images = []
measurements = []
for batch_sample in batch_samples:
for camera_index in range(3):
source_path = batch_sample[camera_index]
filename = source_path.split('/')[-1]
current_path = image_path + filename
image = cv2.imread(current_path)
if image is None:
continue
# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
images.append(image)
try:
measurement = float(batch_sample[3])
except ValueError:
print("Measurement Error: ", batch_sample[3])
measurements.append(measurement)
measurements.append(measurement + steering_correction)
measurements.append(measurement - steering_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(measurement * -1.0)
# trim image to only see section with road
X_train = np.array(augmented_images)
y_train = np.array(augmented_measurements)
yield sklearn.utils.shuffle(X_train, y_train)
train_samples, validation_samples = train_test_split(lines, test_size=0.2)
# compile and train the model using the generator function
train_generator = generator(train_samples, batch_size=32)
validation_generator = generator(validation_samples, batch_size=32)
model = Sequential()
# model.add(Lambda(lambda x: (x / 127.5) - 1.0, input_shape=(160, 320, 3), output_shape=(160, 320, 3)))
model.add(Lambda(lambda x: (x / 255.0) - 0.5, input_shape=(160, 320, 3), output_shape=(160, 320, 3)))
model.add(Cropping2D(cropping=((70, 25), (0, 0))))
model.add(Convolution2D(24, (5, 5), strides=(2, 2), activation="relu"))
model.add(Dropout(0.4))
model.add(Convolution2D(36, (5, 5), strides=(2, 2), activation="relu"))
model.add(Dropout(0.4))
model.add(Convolution2D(48, (5, 5), strides=(2, 2), activation="relu"))
model.add(Dropout(0.4))
model.add(Convolution2D(64, (3, 3), strides=(1, 1), activation="relu"))
model.add(Dropout(0.4))
model.add(Convolution2D(64, (3, 3), strides=(1, 1), activation="relu"))
model.add(Dropout(0.4))
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_generator(train_generator, samples_per_epoch=len(train_samples), validation_data=validation_generator,
# nb_val_samples=len(validation_samples), nb_epoch=3)
batch_size = 32
history_object = model.fit_generator(train_generator, steps_per_epoch=len(train_samples)/batch_size,
validation_data=validation_generator,
validation_steps=len(validation_samples)/batch_size, epochs=3, verbose=1)
# print the keys contained in the history object
print(history_object.history.keys())
# 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()
model.save('model.h5')