def main():
# read csv files for driving log
lines = []
with open('./data/data/driving_log.csv') as csvfile:
reader = csv.reader(csvfile)
for line in reader:
lines.append(line)
correction = 0.2 # This is a parameter to tune
current_path = './data/data/'
# modify the image path
images = []
measurements = []
for line in lines[1:]:
img_centre = line[0]
img_left = line[1][1:] # get rid of the space at the beginning
img_right = line[2][1:] # get rid of the space at the beginning
# create adjusted steering meaurements for the side camera iamges
steering_centre = float(line[3])
for num, img in enumerate([img_centre, img_left, img_right]):
image = cv2.imread(current_path + img)
images.append(image)
images.append(cv2.flip(image, 1))
if num == 1:
steering = steering_centre + correction
elif num == 2:
steering = steering_centre - correction
elif num == 0:
steering = steering_centre
measurements.append(steering)
measurements.append(steering * (-1.0))
X_train = np.array(images)
y_train = np.array(measurements)
# Load the model
model = DenseNet(input_shape=(160, 320, 3),
depth=3 * 3 + 4,
nb_dense_block=3,
growth_rate=12,
bottleneck=True,
reduction=0.5,
weights=None,
cropping=((70, 25), (0, 0)),
norm_lambda=True,
activation=None,
classes=1)
model.compile(loss='mse', optimizer='adam')
model.fit(X_train, y_train, validation_split=0.2, shuffle=True, epochs=5)
model.save('./models/model_densenet.h5')
featurewise_std_normalization=False,
rotation_range=10,
width_shift_range=0.1,
height_shift_range=0.1,
zoom_range=.1,
horizontal_flip=True)
# model parameters/compilation
model = DenseNet(classes=num_classes,
input_shape=(64, 64, 1),
depth=40,
growth_rate=12,
bottleneck=True,
reduction=0.5)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
datasets = ['fer2013']
for dataset_name in datasets:
print('Training dataset:', dataset_name)
# callbacks
log_file_path = base_path + dataset_name + '_emotion_training.log'
csv_logger = CSVLogger(log_file_path, append=False)
early_stop = EarlyStopping('val_loss', patience=patience)
reduce_lr = ReduceLROnPlateau('val_loss',
factor=0.1,
patience=int(patience / 4),
verbose=1)
