How do I write custom activation functions for a VAE model

Question

Can you explain how to write custom activation functions for a VAE model using Python programming?

Answer 1

You can write custom activation functions for a Variational Autoencoder (VAE) by defining them as simple mathematical operations in PyTorch or TensorFlow and incorporating them into the model architecture. Here is the PyTorch code you can refer:

In the above code, we are using the following:

• Custom Activation: The custom_activation function combines sine and a scaled linear term for added flexibility.
• Model Integration: The custom activation is used in both the encoder and decoder layers.
• VAE Workflow: The encoder maps inputs to latent space, the decoder reconstructs inputs, and the reparameterization trick ensures smooth latent sampling.

Hence, by referring to the above, you can write custom activation functions for a VAE model.