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.
Is this tool free?
Yes. All HeyTensor tools are free, run in your browser, and require no signup.
Does this work offline?
Once loaded, the tool runs entirely in your browser. No internet needed after the initial page load.
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 calculator inputs or results are 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].
TENSOR PREFLIGHT · DOWNLOADABLE PYTORCH TOOLKIT
Keep the fix. Catch the mismatch next time.
Trace a local forward pass, inspect tensor shapes in an HTML report, and turn a repair into a regression check. Includes eight broken-and-fixed workflows, source code and setup instructions.
Is this toolkit right for my problem?
Use the free calculator for a layer formula or a quick shape check.
Consider Tensor Preflight when you have a local PyTorch model and want a forward-pass report plus repeatable checks. Python 3.9+ and PyTorch 2.8+ are required. It does not run your model in this website or automatically repair it.
Open the sample report before buying to check the output format.
Python 3.9+ · PyTorch 2.8+ · Runs locally