How to fix "NaN" related errors in Keras?

There are several ways to fix “NaN” related errors in Keras:

1. Check your data: Make sure your data does not contain any missing values, or NaNs. You can use pandas library to check the presence of NaN values.
2. Normalize your data: Scaling your data to a smaller range can help prevent exploding gradients and NaN errors.
3. Adjust your learning rate: Using an appropriate learning rate can help prevent NaN errors. Reduce the learning rate if the error is due to vanishing/exploding gradients.
4. Use regularizers: Adding L1 or L2 regularization to your model can help prevent overfitting, which may cause NaN errors.
5. Check your loss function: Make sure your loss function is appropriate for your problem. If you are using a custom loss function, check it carefully for possible errors.
6. Check your model architecture: Make sure your model architecture is appropriate for your problem. If your model is too complex for your data, it can lead to NaN errors.
7. Increase the batch size: Reducing the batch size can cause increased variance and contribute to NaN errors. Increasing the batch size can help prevent NaNs due to this reason.
8. Clip the gradients: The exploding gradient problem can result in NaNs, clipping the gradients during backpropagation can workaround this problem.

These are some of the common ways to fix NaN related errors in Keras or any machine learning framework.