('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)