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PYTORCH COMMON MISTAKES - How To Save Time 🕒

7/30/2020Updated 3/3/2026

Excerpt

OUTLINE: 0:00 - Introduction 0:21 - 1. Didn't overfit batch 2:45 - 2. Forgot toggle train/eval 4:47 - 3. Forgot .zero_grad() 6:15 - 4. Softmax when using CrossEntropy 8:09 - 5. Bias term with BatchNorm 9:54 - 6. Using view as permute 12:10 - 7. Incorrect Data Augmentation 14:19 - 8. Not Shuffling Data 15:28 - 9. Not Normalizing Data 17:28 - 10. Not Clipping Gradients

Source URL

https://www.youtube.com/watch?v=O2wJ3tkc-TU

Related Pain Points

Common PyTorch training mistakes cause silent model degradation

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Developers frequently make subtle implementation errors in PyTorch training loops — such as forgetting .zero_grad(), not toggling train/eval mode, or applying softmax before CrossEntropyLoss — that silently degrade model quality without raising errors. These mistakes are hard to detect and can waste significant compute time before being caught.

dxPyTorch