def raw_data_centre_left_right_image_crop():
# crop_images=True was the only difference
data_set = DriveDataSet.from_csv(
"datasets/udacity-sample-track-1/driving_log.csv",
crop_images=True,
all_cameras_images=True,
filter_method=drive_record_filter_include_all)
allocator = RecordRandomAllocator(data_set)
generator = image_itself
data_generator = DataGenerator(allocator.allocate, generator)
model = nvidia(input_shape=data_set.output_shape(), dropout=0.5)
Trainer(model,
learning_rate=0.0001,
epoch=10,
custom_name=inspect.stack()[0][3]).fit_generator(
data_generator.generate(batch_size=128))
def raw_data_centre_left_right_crop_shift_flip():
data_set = DriveDataSet.from_csv(
"datasets/udacity-sample-track-1/driving_log.csv",
crop_images=True,
all_cameras_images=True,
filter_method=drive_record_filter_include_all)
allocator = RecordRandomAllocator(data_set)
# shift_image_generator was the only difference
generator = pipe_line_generators(
shift_image_generator(angle_offset_pre_pixel=0.002), flip_generator)
data_generator = DataGenerator(allocator.allocate, generator)
model = nvidia(input_shape=data_set.output_shape(), dropout=0.5)
Trainer(model,
learning_rate=0.0001,
epoch=20,
multi_process=use_multi_process,
custom_name=inspect.stack()[0][3]).fit_generator(
data_generator.generate(batch_size=128))
def raw_data_centre_image_no_dropout():
# Create DriveDataSet from csv file, you can specify crop image, using all cameras and which data will included in
data_set = DriveDataSet.from_csv(
"datasets/udacity-sample-track-1/driving_log.csv",
crop_images=False,
all_cameras_images=False,
filter_method=drive_record_filter_include_all)
# What the data distribution will be, below example just randomly return data from data set, so that the
# distribution will be same with what original data set have
allocator = RecordRandomAllocator(data_set)
# what's the data augment pipe line have, this have no pipe line, just the image itself
augment = image_itself
# connect allocator and augment together
data_generator = DataGenerator(allocator.allocate, augment)
# create the model
model = nvidia(input_shape=data_set.output_shape(), dropout=0.0)
# put everthing together, start a real Keras training process with fit_generator
Trainer(model,
learning_rate=0.0001,
epoch=10,
custom_name=inspect.stack()[0][3]).fit_generator(
data_generator.generate(batch_size=128))