Exemplos de set_up_optimizer em Python

Exemplo n.º 1

def TrainNet():
    if args.train_data_dir:
        assert os.path.exists(args.train_data_dir)
        print("Loading data from {}".format(args.train_data_dir))
        (labels, images) = ofrecord_util.load_imagenet_for_training(args)

    else:
        print("Loading synthetic data.")
        (labels, images) = ofrecord_util.load_synthetic(args)
    logits = model_dict[args.model](images, args)
    if args.label_smoothing > 0:
        one_hot_labels = label_smoothing(labels, args.num_classes, args.label_smoothing, logits.dtype)
        loss = flow.nn.softmax_cross_entropy_with_logits(one_hot_labels, logits, name="softmax_loss")
    else:
        loss = flow.nn.sparse_softmax_cross_entropy_with_logits(labels, logits, name="softmax_loss")

    if not args.use_fp16:
        loss = flow.math.reduce_mean(loss)
    flow.losses.add_loss(loss)
    predictions = flow.nn.softmax(logits)
    outputs = {"loss": loss, "predictions": predictions, "labels": labels}

    # set up warmup,learning rate and optimizer
    optimizer_util.set_up_optimizer(loss, args)
    return outputs

Exemplo n.º 2

Arquivo: of_cnn_train_val.py Projeto: ruizewang/Oneflow-Model-Compression-1

        print("Loading synthetic data.")
        (labels, images) = ofrecord_util.load_synthetic(args)
    logits = model_dict[args.model](images, args)
    if args.label_smoothing > 0:
        one_hot_labels = label_smoothing(labels, args.num_classes, args.label_smoothing, logits.dtype)
        loss = flow.nn.softmax_cross_entropy_with_logits(one_hot_labels, logits, name="softmax_loss")
    else:
        loss = flow.nn.sparse_softmax_cross_entropy_with_logits(labels, logits, name="softmax_loss")

    if not args.use_fp16:
        loss = flow.math.reduce_mean(loss)
    predictions = flow.nn.softmax(logits)
    outputs = {"loss": loss, "predictions": predictions, "labels": labels}

    # set up warmup,learning rate and optimizer
    optimizer_util.set_up_optimizer(loss, args)
    return outputs


@flow.global_function("predict", get_val_config(args))
def InferenceNet():
    if args.val_data_dir:
        assert os.path.exists(args.val_data_dir)
        print("Loading data from {}".format(args.val_data_dir))
        (labels, images) = ofrecord_util.load_imagenet_for_validation(args)

    else:
        print("Loading synthetic data.")
        (labels, images) = ofrecord_util.load_synthetic(args)

    logits = model_dict[args.model](images, args)