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Debugging ML Models: Difference between revisions
→NaNs and Infs
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* Checking that the training data has no NaNs or Infs. | * Checking that the training data has no NaNs or Infs. | ||
* Checking that there are no divides anywhere in the code or that all divides are safe. | * Checking that there are no divides anywhere in the code or that all divides are safe. | ||
** See [https://www.tensorflow.org/api_docs/python/tf/math/divide_no_nan <code>tf.math.divide_no_nan</code>]. | |||
* Checking the gradients of trig functions in the code. | * Checking the gradients of trig functions in the code. | ||