Expected Input Size Error Debugger
Debug PyTorch expected input size errors. Fix "Expected N-dimensional input" and "expected input batch_size" RuntimeErrors with step-by-step guidance.
Built by Michael Lip
Frequently Asked Questions
What does Expected 4-dimensional input mean?
Conv2d and BatchNorm2d expect 4D input: (batch, channels, height, width). If you pass a 3D tensor (single image without batch), you get this error. Fix: tensor.unsqueeze(0).
What does Expected input batch_size to match target mean?
Model output and labels have different batch sizes. Usually happens when data is incorrectly split or reshape changes the batch dimension.
Why does my model expect different channels?
Conv2d's in_channels must match input channels. Grayscale=1, RGB=3, RGBA=4. If loading grayscale as RGB, use transforms.Grayscale() or change in_channels.
How to debug systematically?
(1) Print input.shape before failing layer. (2) Check expected dimensionality: Conv2d→4D, Linear→2D+, LSTM→3D. (3) Compare in_channels/in_features with actual input. (4) Use Chain Mode to trace shapes.
Is this tool free?
Yes. All HeyTensor tools are free, run in your browser, and require no signup.
About This Tool
Part of HeyTensor. All calculations run in your browser. Source code on GitHub.
Contact
Built by Michael Lip. Email [email protected].