def test_learning_rate_value(self): lr = -1.0 with pytest.raises(ValueError): dr.exponential_decay_lr(lr, decay_rate, total_step, step_per_epoch, decay_epoch) with pytest.raises(ValueError): dr.polynomial_decay_lr(lr, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
def test_exponential_decay(): lr1 = dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch) assert len(lr1) == total_step lr2 = dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True) assert len(lr2) == total_step
def test_total_step1(self): total_step1 = 2.0 with pytest.raises(ValueError): dr.exponential_decay_lr(learning_rate, decay_rate, total_step1, step_per_epoch, decay_epoch) with pytest.raises(ValueError): dr.cosine_decay_lr(min_lr, max_lr, total_step1, step_per_epoch, decay_epoch) with pytest.raises(ValueError): dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step1, step_per_epoch, decay_epoch, power)
def test_decay_epoch1(self): decay_epoch1 = 'm' with pytest.raises(TypeError): dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch1) with pytest.raises(TypeError): dr.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch1) with pytest.raises(TypeError): dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch1, power)
parser.add_argument('--device_id', type=int, default=0, help='device id of GPU. (Default: 0)') args = parser.parse_args() if args.device_target == "CPU": args.dataset_sink_mode = False context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id) network = Inceptionv3(cfg.num_classes) net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean", smooth_factor=cfg.label_smoothing_eps) ds_train = create_dataset(args.data_path, cfg.batch_size, cfg.epoch_size) step_per_epoch = ds_train.get_dataset_size() total_step = step_per_epoch * cfg.epoch_size lr = exponential_decay_lr(learning_rate=cfg.lr_init, decay_rate=cfg.lr_decay_rate, total_step=total_step, step_per_epoch=step_per_epoch, decay_epoch=cfg.lr_decay_epoch) net_opt = nn.RMSProp(network.trainable_params(), learning_rate=lr, decay=cfg.rmsprop_decay, momentum=cfg.rmsprop_momentum, epsilon=cfg.rmsprop_epsilon) time_cb = TimeMonitor(data_size=ds_train.get_dataset_size()) config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps, keep_checkpoint_max=cfg.keep_checkpoint_max) ckpoint_cb = ModelCheckpoint(prefix="checkpoint_inceptionv3", config=config_ck) # summary_cb = SummaryCollector(args.summary_path, # collect_freq=1, # keep_default_action=False, # collect_specified_data={'collect_graph': True}) model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) print("============== Starting Training ==============")
def test_decay_rate_value(self): rate = -1.0 with pytest.raises(ValueError): dr.exponential_decay_lr(learning_rate, rate, total_step, step_per_epoch, decay_epoch)
def test_decay_rate_type(self): rate = 'a' with pytest.raises(TypeError): dr.exponential_decay_lr(learning_rate, rate, total_step, step_per_epoch, decay_epoch)
def test_is_stair(self): is_stair = 1 with pytest.raises(TypeError): dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair)