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Debugging Machine Learning Models

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The main issues with debugging Machine Learning Models are:

  • Even if the code is totally correct, things can go wrong (incorrect data, poorly chosen hyper parameters, etc...)
  • Training takes a long time, so catching and reproducing a bug is slow.

Debugging tips​

  • Start with a simpler model (less layers, less data, etc...)
  • Add features one step at a time and check each time that performance stays consistent.
  • Use data visualisation to inspect behaviour

Common problems, possible causes and solutions​

Be sure to know what a Loss function is.

The loss never goes down.​

  • Make sure your data can predict the output
  • Make sure that the layers of your network are well-suited to your task (using a softmax layer for classification)
  • Tweak your hyper-parameters, your learning rate might be too small

The loss quickly oscillates​

  • Your learning rate might be too high

The loss suddently goes up​

  • Your data might not be homogeneous. Clean your dataset and train with random sampling.

The memory usage is too high​

  • Reduce the batch size.

Source:

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