What causes LSTM to result in NaN losses?

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There are several possible causes that can result in NaN losses in LSTM (Long Short-Term Memory) models:

1. Exploding gradients: In LSTM models, the gradients can become so large that they result in an "exploding gradient" problem. This can cause the gradients to become too large to be represented by the computer's floating point representation and result in NaN values.

2. Vanishing gradients: The opposite of exploding gradients, vanishing gradients occur when the gradients become too small and disappear, resulting in NaN values.

3. Division by zero: In some cases, the LSTM model may attempt to divide by zero, resulting in NaN values.

4. Invalid inputs: If the LSTM receives invalid inputs, such as infinity or NaN, the model may produce NaN outputs.

5. Inconsistent batch sizes: If the batch sizes used during training and validation are inconsistent, it can lead to NaN losses.

To avoid NaN losses, it is important to properly set the learning rate, use gradient clipping to prevent exploding gradients, use batch normalization, and check inputs for invalid values.