Debugging ML Models

Notes on debugging ML models, primarilly CNNs.

Debugging

• Train on a single example and see if it overfits.
  • If it doesn't overfit, there may be an issue with your code.
  • You can try increasing the capacity (e.g number of filters or number of nodes in FC) 2-4x.
  • Check that your loss is implemented correctly and taken against the correct ground truth image.
• Dump all inputs and outputs into TensorBoard. You may have an unexpected input or output somewhere.
• If it looks like it is underfitting (e.g. if the training output and validation output are both blurry):
  • Train for 4x as long until the training loss and validation loss both flatten.
  • Increase or decrease the learning rate one magnitude.
  • Make sure the batch size is a multiple of 2. Try increasing it to get more stable gradient updates or decreasing it to get faster iterations.
• Try disabling any tricks you have like dropout.
• Make sure there is no activation on the final layer.