So I guess I need to do an argmax on my batch because right now every image in the batch has a 1000 class predictions.
If I understand correctly a batch of 8 should have 8 class predictions or 1 per image.
Do you know how I can achieve this with tensors?
 for image_batch, label_batch, path in train_dl:

    pred_batch = model(image_batch.to(device))
    #pred_batch.shape = torch.Size([8, 1000])
    #something here, to reshape to [8, 8]
    batch_loss = criterion(pred_batch, label_batch.to(device))

Sorry if the numbers keep changing, Its different runs but ultimately same issue