How can you implement batch normalization layers in TensorFlow for stable GAN training

Question

With the help of Python programming, can you tell me how you can implement batch normalization layers in TensorFlow for stable GAN training?

Answer 1

In TensorFlow, you can implement batch normalization using the tf.keras.layers.BatchNormalization layer to stabilize GAN training. 

Here is the code example you can refer to:

In the above code snippet, we are using two components that are:

• Generator: Includes BatchNormalization after dense layers to stabilize training by normalizing activations.
• Discriminator: Excludes batch normalization to ensure stability when distinguishing real vs. fake data.

Hence, by referring to the above, you can implement batch normalization layers in TensorFlow for stable GAN training.

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