Loss Functions Guide
Compare PyTorch loss functions: CrossEntropyLoss, MSELoss, BCELoss, and more. Formulas, when to use, code examples, and common pitfalls for each loss function.
Built by Michael Lip
Frequently Asked Questions
Which loss function should I use?
Multi-class classification: CrossEntropyLoss (no Softmax needed, it's built in). Binary classification: BCEWithLogitsLoss. Regression: MSELoss (L2) or L1Loss (more robust to outliers). Regression with outliers: HuberLoss.
Why does CrossEntropyLoss include Softmax?
PyTorch's CrossEntropyLoss combines LogSoftmax + NLLLoss for numerical stability. Do NOT apply Softmax before CrossEntropyLoss, you'll get wrong gradients. Your model's final layer should output raw logits.
What is the difference between BCE and CrossEntropy?
BCELoss is for binary classification (one output per sample, 0 or 1). CrossEntropyLoss is for multi-class classification (one-of-N classes). For multi-label classification (multiple labels per sample), use BCEWithLogitsLoss.
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].
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