To implement early stopping in generative models, you can monitor a validation metric (e.g., loss or Inception Score) and stop training if the metric stops improving for a set number of epochs.
Here is the code snippet you can refer to:
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In the above code, we are using the following key points:
- Monitor Validation Loss: Track a metric (e.g., validation loss or Inception Score).
- Patience: Define how many epochs to wait for improvement before stopping.
- Stop Training: Stop training when no improvement is seen after the patience period.
Hence, this mechanism ensures that the model does not overtrain and saves computational resources.