I’m going through a learning PyTorch tutorial by Daniel Bourke. I got part 3, where we start learning how to classify images, which we do in Google colab However, when training the model, I come up with the takes 1 positional argument but 2 were given error. I even looked at the source code that Daniel has available, copied it into my code section, and I got the same thing.

# Import tqdm for progress bar
from tqdm.auto import tqdm

# Set the seed and start the timer
torch.manual_seed(42)
train_time_start_on_cpu = timer()

# Set the number of epochs (we'll keep this small for faster training times)
epochs = 3

# Create training and testing loop
for epoch in tqdm(range(epochs)):
    print(f"Epoch: {epoch}n-------")
    ### Training
    train_loss = 0
    # Add a loop to loop through training batches
    for batch, (X, y) in enumerate(train_dataloader):
        model_0.train()
        # 1. Forward pass
        y_pred = model_0(X)

        # 2. Calculate loss (per batch)
        loss = loss_fn(y_pred, y)
        train_loss += loss # accumulatively add up the loss per epoch

        # 3. Optimizer zero grad
        optimizer.zero_grad()

        # 4. Loss backward
        loss.backward()

        # 5. Optimizer step
        optimizer.step()

        # Print out how many samples have been seen
        if batch % 400 == 0:
            print(f"Looked at {batch * len(X)}/{len(train_dataloader.dataset)} samples")

    # Divide total train loss by length of train dataloader (average loss per batch per epoch)
    train_loss /= len(train_dataloader)

    ### Testing
    # Setup variables for accumulatively adding up loss and accuracy
    test_loss, test_acc = 0, 0
    model_0.eval()
    with torch.inference_mode():
        for X, y in test_dataloader:
            # 1. Forward pass
            test_pred = model_0(X)

            # 2. Calculate loss (accumatively)
            test_loss += loss_fn(test_pred, y) # accumulatively add up the loss per epoch

            # 3. Calculate accuracy (preds need to be same as y_true)
            test_acc += accuracy_fn(y_true=y, y_pred=test_pred.argmax(dim=1))

        # Calculations on test metrics need to happen inside torch.inference_mode()
        # Divide total test loss by length of test dataloader (per batch)
        test_loss /= len(test_dataloader)

        # Divide total accuracy by length of test dataloader (per batch)
        test_acc /= len(test_dataloader)

    ## Print out what's happening
    print(f"nTrain loss: {train_loss:.5f} | Test loss: {test_loss:.5f}, Test acc: {test_acc:.2f}%n")

# Calculate training time
train_time_end_on_cpu = timer()
total_train_time_model_0 = print_train_time(start=train_time_start_on_cpu,
                                           end=train_time_end_on_cpu,
                                           device=str(next(model_0.parameters()).device))

The link to my Google Colab and the rest of my code if you need it: text

I looked at the online code that daniel has available and couldn’t find any difference. If someone could point me to where I’m making the error, please let me know.