error report for chapter3 code

[leopardbruce]

An error occurred when I ran below code in Chapter3, could anyone please help to figure it out?

`classifier = classifier.to(args.device)

loss_func = nn.BCEWithLogitsLoss() optimizer = optim.Adam(classifier.parameters(), lr=args.learning_rate) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer, mode='min', factor=0.5, patience=1)

train_state = make_train_state(args)

epoch_bar = tqdm.notebook.tqdm(desc='training routine', total=args.num_epochs, position=0)

dataset.set_split('train') train_bar = tqdm.notebook.tqdm(desc='split=train', total=dataset.get_num_batches(args.batch_size), position=1, leave=True) dataset.set_split('val') val_bar = tqdm.notebook.tqdm(desc='split=val', total=dataset.get_num_batches(args.batch_size), position=1, leave=True)

try: for epoch_index in range(args.num_epochs): train_state['epoch_index'] = epoch_index

    # Iterate over training dataset

    # setup: batch generator, set loss and acc to 0, set train mode on
    dataset.set_split('train')
    batch_generator = generate_batches(dataset, 
                                       batch_size=args.batch_size, 
                                       device=args.device)
    running_loss = 0.0
    running_acc = 0.0
    classifier.train()

    for batch_index, batch_dict in enumerate(batch_generator):
        # the training routine is these 5 steps:

        # --------------------------------------
        # step 1. zero the gradients
        optimizer.zero_grad()

        # step 2. compute the output
        y_pred = classifier(x_in=batch_dict['x_data'].float())

        # step 3. compute the loss
        loss = loss_func(y_pred, batch_dict['y_target'].float())
        loss_t = loss.item()
        running_loss += (loss_t - running_loss) / (batch_index + 1)

        # step 4. use loss to produce gradients
        loss.backward()

        # step 5. use optimizer to take gradient step
        optimizer.step()
        # -----------------------------------------
        # compute the accuracy
        acc_t = compute_accuracy(y_pred, batch_dict['y_target'])
        running_acc += (acc_t - running_acc) / (batch_index + 1)

        # update bar
        train_bar.set_postfix(loss=running_loss, 
                              acc=running_acc, 
                              epoch=epoch_index)
        train_bar.update()

    train_state['train_loss'].append(running_loss)
    train_state['train_acc'].append(running_acc)

    # Iterate over val dataset

    # setup: batch generator, set loss and acc to 0; set eval mode on
    dataset.set_split('val')
    batch_generator = generate_batches(dataset, 
                                       batch_size=args.batch_size, 
                                       device=args.device)
    running_loss = 0.
    running_acc = 0.
    classifier.eval()

    for batch_index, batch_dict in enumerate(batch_generator):

        # compute the output
        y_pred = classifier(x_in=batch_dict['x_data'].float())

        # compute the loss
        loss = loss_func(y_pred, batch_dict['y_target'].float())
        loss_t = loss.item()
        running_loss += (loss_t - running_loss) / (batch_index + 1)

        # compute the accuracy
        acc_t = compute_accuracy(y_pred, batch_dict['y_target'])
        running_acc += (acc_t - running_acc) / (batch_index + 1)

        val_bar.set_postfix(loss=running_loss, 
                            acc=running_acc, 
                            epoch=epoch_index)
        val_bar.update()

    train_state['val_loss'].append(running_loss)
    train_state['val_acc'].append(running_acc)

    train_state = update_train_state(args=args, 
                                     model=classifier,
                                     train_state=train_state)

    scheduler.step(train_state['val_loss'][-1])

    train_bar.n = 0
    val_bar.n = 0
    epoch_bar.update()

    if train_state['stop_early']:
        break

    train_bar.n = 0
    val_bar.n = 0
    epoch_bar.update()

except KeyboardInterrupt: print("Exiting loop")`

ValueError: num_samples should be a positive integer value, but got num_samples=0