def main(epochs, enable_function, buffer_size, batch_size, mode, growth_rate, output_classes, depth_of_model=None, num_of_blocks=None, num_layers_in_each_block=None, data_format='channels_last', bottleneck=True, compression=0.5, weight_decay=1e-4, dropout_rate=0., pool_initial=False, include_top=True, train_mode='custom_loop', data_dir=None): train_obj = Train(epochs, enable_function) train_dataset, test_dataset = create_dataset(buffer_size, batch_size, data_format, data_dir) model = densenet.DenseNet(mode, growth_rate, output_classes, depth_of_model, num_of_blocks, num_layers_in_each_block, data_format, bottleneck, compression, weight_decay, dropout_rate, pool_initial, include_top) print('Training...') if train_mode == 'custom_loop': return train_obj.custom_loop(train_dataset, test_dataset, model) elif train_mode == 'keras_fit': return train_obj.keras_fit(train_dataset, test_dataset, model)
def test_one_epoch_with_keras_fit(self): epochs = 1 enable_function = True depth_of_model = 7 growth_rate = 2 num_of_blocks = 3 output_classes = 10 mode = 'from_depth' data_format = 'channels_last' train_dataset = create_sample_dataset(batch_size=1) test_dataset = create_sample_dataset(batch_size=1) train_obj = train.Train(epochs, enable_function) model = densenet.DenseNet(mode, growth_rate, output_classes, depth_of_model, num_of_blocks, data_format) train_obj.keras_fit(train_dataset, test_dataset, model)