How do I measure model convergence during GAN training in TensorFlow

Question

With the help of code, can you explain how I measure model convergence during GAN training in TensorFlow?

Answer 1

To measure model convergence during GAN training in TensorFlow, you can track losses for the generator and discriminator and optionally use metrics like the Frechet Inception Distance (FID) for generated image quality. Here is the code snippet you can refer to:

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

• Track Losses:

  • Generator Loss: Should decrease over time as it learns to produce realistic samples.
  • Discriminator Loss: Should stabilize around 0.5 when the discriminator struggles to distinguish real from fake.

• Use Metrics:

  • Calculate FID to evaluate image quality and diversity during training.

• Plot Losses:

  • Visualize generator and discriminator losses to check for oscillations or divergence.

• Inspect Samples:

  • Periodically generate samples and visually inspect their quality.

Hence, by using the following, you can measure model convergence during GAN training in TensorFlow