def segment_normal_distribution_shift_flip_brightness_shadow_reg():
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)
# fine tune every part of training data so that make it meat std distrubtion
allocator = AngleSegmentRecordAllocator(
data_set,
AngleSegment((-1.5, -0.5), 10), # big sharp left
AngleSegment((-0.5, -0.25), 14), # sharp left
AngleSegment((-0.25, -0.249),
3), # sharp turn left (zero right camera)
AngleSegment((-0.249, -0.1), 10), # big turn left
AngleSegment((-0.1, 0), 11), # straight left
AngleSegment((0, 0.001), 4), # straight zero center camera
AngleSegment((0.001, 0.1), 11), # straight right
AngleSegment((0.1, 0.25), 10), # big turn right
AngleSegment((0.25, 0.251), 3), # sharp turn right (zero left camera)
AngleSegment((0.251, 0.5), 14), # sharp right
AngleSegment((0.5, 1.5), 10) # big sharp right
)
# a pipe line with shift -> flip -> brightness -> shadow augment processes
augment = pipe_line_generators(
shift_image_generator(angle_offset_pre_pixel=0.002), flip_generator,
brightness_image_generator(0.35), shadow_generator)
data_generator = DataGenerator(allocator.allocate, augment)
model = nvidia_with_regularizer(input_shape=data_set.output_shape(),
dropout=0.2)
Trainer(model,
learning_rate=0.0001,
epoch=45,
multi_process=use_multi_process,
custom_name=inspect.stack()[0][3]).fit_generator(
data_generator.generate(batch_size=256))
def segment_normal_distribution_flip_brightness_shadow_reg():
data_set_train, data_set_val = create_real_dataset(
filter_method=drive_record_filter_include_all)
# fine tune every part of training data so that make it meat std distrubtion
allocator_train = AngleSegmentRecordAllocator(
data_set_train,
AngleSegment((-1.5, -0.5), 10), # big sharp left
AngleSegment((-0.5, -0.25), 14), # sharp left
AngleSegment((-0.25, -0.249),
0.5), # sharp turn left (zero right camera)
AngleSegment((-0.249, -0.1), 12), # big turn left
AngleSegment((-0.1, 0), 13), # straight left
AngleSegment((0, 0.001), 1), # straight zero center camera
AngleSegment((0.001, 0.1), 13), # straight right
AngleSegment((0.1, 0.25), 12), # big turn right
AngleSegment((0.25, 0.251),
0.5), # sharp turn right (zero left camera)
AngleSegment((0.251, 0.5), 14), # sharp right
AngleSegment((0.5, 1.5), 10) # big sharp right
)
allocator_val = AngleSegmentRecordAllocator(
data_set_val,
AngleSegment((-1.5, -0.5), 10), # big sharp left
AngleSegment((-0.5, -0.25), 14), # sharp left
AngleSegment((-0.25, -0.249),
0.5), # sharp turn left (zero right camera)
AngleSegment((-0.249, -0.1), 12), # big turn left
AngleSegment((-0.1, 0), 13), # straight left
AngleSegment((0, 0.001), 1), # straight zero center camera
AngleSegment((0.001, 0.1), 13), # straight right
AngleSegment((0.1, 0.25), 12), # big turn right
AngleSegment((0.25, 0.251),
0.5), # sharp turn right (zero left camera)
AngleSegment((0.251, 0.5), 14), # sharp right
AngleSegment((0.5, 1.5), 10) # big sharp right
)
# a pipe line with shift -> flip -> brightness -> shadow augment processes
data_generator_train = DataGenerator(
allocator_train.allocate,
pipe_line_generators(flip_random_generator,
brightness_image_generator(0.35),
shadow_generator))
data_generator_val = DataGenerator(
allocator_val.allocate,
pipe_line_generators(flip_random_generator,
brightness_image_generator(0.35),
shadow_generator))
model = nvidia_with_regularizer(input_shape=data_set_train.output_shape(),
dropout=0.2)
Trainer(model,
learning_rate=0.0001,
epoch=450,
multi_process=use_multi_process,
custom_name="noshift").fit_generator(
data_generator_train.generate(batch_size=256),
data_generator_val.generate(batch_size=256))