How do I handle gradients exploding when training a Keras LSTM model on text sequences

Question

Can i know How do I handle gradients exploding when training a Keras LSTM model on text sequences?

Answer 1

Use Gradient Clipping, Layer Normalization, or Batch Normalization to stabilize LSTM training and prevent exploding gradients.

Here is the code snippet you can refer to:

In the above code, we are using the following approaches:

• Gradient Clipping (clipnorm or clipvalue): Prevents extreme gradient values.
• Layer Normalization (LayerNormalization): Stabilizes activations inside LSTM cells.
• Batch Normalization (BatchNormalization): Normalizes inputs to control variance.
• Smaller Learning Rate: Reduces large weight updates causing instability.
• Use return_sequences=True in LSTM: Helps distribute gradient flow in deep networks.

Hence, applying gradient clipping and normalization techniques effectively prevents exploding gradients when training LSTM models on text sequences.