Always keep an eye on tensor shapes when working with PyTorch. During development and debugging, frequently inspect tensors using the .shape attribute to ensure data is flowing through your model as expected. If your network is not training properly, there are a few common issues to check. Make sure you are clearing gradients in the training loop with optimizer.zero_grad(). If you are running a validation phase, remember to switch the model to evaluation mode using model.eval() and then back to training mode with model.train().
Another frequent source of confusion involves CUDA-related errors, such as RuntimeError: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #1 'mat1'. This error indicates a device mismatch: one tensor is on the GPU while the other is on the CPU. PyTorch operations require all tensors involved to be on the same device. To avoid this issue, ensure that both the model and all relevant tensors are consistently moved to either the CPU or the GPU using .to(device), where device is set to "cpu" or "cuda".