What steps can I take to fix output degradation after training a generative model

Question

With Python programming, can you tell me What steps I can take to fix output degradation after training a generative model?

Answer 1

To fix output degradation after training a generative model, consider steps like adjusting the learning rate, adding regularization, using checkpointing, or improving data quality.

Here is the code reference you can refer to:

In the above code, we are using the following:

• Adjust Learning Rate: A low learning rate (e.g., 0.0001) can prevent overfitting and degradation.
• Regularization: Use L2 regularization (weight decay) to prevent overfitting and maintain model generalization.
• Checkpointing: Save the best-performing model during training to prevent degradation from later epochs.

Hence,  by referring to the above, you can fix output degradation after training a generative model