('height_shift_range', 0.1), ('shear_range', 0.), ('zoom_range', 0.1),
('channel_shift_range', 0.), ('fill_mode', 'constant'), ('cval', 0.),
('cvalMask', 0), ('horizontal_flip', True), ('vertical_flip', True),
('rescale', None), ('spline_warp', True), ('warp_sigma', 0.1),
('warp_grid_size', 3), ('crop_size', None)))
train_kwargs = OrderedDict((
# data
('num_classes', 1),
('batch_size', 40),
('val_batch_size', 200),
('num_epochs', 40),
('max_patience', 50),
# optimizer
('optimizer', 'RMSprop'), # 'RMSprop', 'nadam', 'adam', 'sgd'
('learning_rate', 0.0001),
# other
('show_model', False),
('save_every', 10), # Save predictions every x epochs
('mask_to_liver', False),
('liver_only', False)))
train_kwargs['num_outputs'] = model_kwargs['num_outputs']
run(general_settings=general_settings,
model_kwargs=model_kwargs,
data_gen_kwargs=data_gen_kwargs,
data_augmentation_kwargs=data_augmentation_kwargs,
train_kwargs=train_kwargs)
('height_shift_range', 0.1), ('shear_range', 0.), ('zoom_range', 0.1),
('channel_shift_range', 0.), ('fill_mode', 'constant'), ('cval', 0.),
('cvalMask', 0), ('horizontal_flip', True), ('vertical_flip', True),
('rescale', None), ('spline_warp', True), ('warp_sigma', 0.1),
('warp_grid_size', 3), ('crop_size', None)))
train_kwargs = OrderedDict((
# data
('num_classes', 1),
('batch_size', 40),
('val_batch_size', 200),
('num_epochs', 20),
('max_patience', 50),
# optimizer
('optimizer', 'RMSprop'), # 'RMSprop', 'nadam', 'adam', 'sgd'
('learning_rate', 0.0001),
# other
('show_model', False),
('save_every', 10), # Save predictions every x epochs
))
train_kwargs['num_outputs'] = model_kwargs['num_outputs']
run(general_settings=general_settings,
model_kwargs=model_kwargs,
data_gen_kwargs=data_gen_kwargs,
data_augmentation_kwargs=data_augmentation_kwargs,
train_kwargs=train_kwargs,
two_levels=True)
