PyTorch Shape Mismatch Error Debugger
Paste any PyTorch shape mismatch RuntimeError and get a plain English explanation of what went wrong, which tensors don't match, and how to fix it.
Built by Michael Lip
Frequently Asked Questions
What causes shape mismatch errors in PyTorch?
Shape mismatches occur when two tensors in an operation have incompatible dimensions. Common causes: wrong in_features in Linear layers, missing Flatten before FC layers, incorrect reshape/view dimensions, or tensors from different batch sizes.
How do I debug shape errors in PyTorch?
1) Add print(tensor.shape) before the failing line. 2) Use HeyTensor's Chain Mode to trace shapes through your network. 3) Check that in_features matches the actual output of the previous layer. 4) Verify reshape/view dimensions multiply to the correct total.
What does 'expected input batch_size X to match target batch_size Y' mean?
Your model output and labels have different batch sizes. This often happens when you accidentally reshape a tensor and change the batch dimension. Check that your data loader returns matching batches and no layer modifies dimension 0.
About This Tool
This tool is part of HeyTensor, a free suite of PyTorch and deep learning utilities. All calculations run entirely in your browser — no data is sent to any server. The source code is open on GitHub.
Contact
HeyTensor is built and maintained by Michael Lip. For questions or feedback, email [email protected].