How can the reduction of the VAE training loss function to nan be explained?

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There could be multiple reasons for the reduction of the VAE training loss function to nan, some of them are:

1. Vanishing gradient: During the training process, if the gradient for a particular weight becomes too small, it can lead to a failure in weight updates during backpropagation, resulting in a nan loss value.

2. Invalid input: If the input data contains invalid or superfluous values, it can result in nan values.

3. Overfitting: If the model is overfitted to the training data, the VAE loss value may become too small and may eventually result in a nan value.

4. Numerical instability: VAEs involve complex math operations, such as log probability calculations, which can cause numerical instability during training and result in nan values.

5. Incorrect model architecture or hyperparameters: If the model architecture or hyperparameters are not correctly set, it can lead to nan values during training, particularly during validation.