Can you write code for a Variational Autoencoder to generate images from the MNIST dataset

Question

With the help of Python programming, can you show the code for a Variational Autoencoder to generate images from the MNIST dataset?

Answer 1

You can refer to the code snippet below showing a concise implementation of a Variational Autoencoder (VAE) to generate images from the MNIST dataset using TensorFlow/Keras:

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

• Encoder: Maps input images to latent space (z_mean, z_log_var).
• Sampling Layer: Samples latent vectors using the reparameterization trick.
• Decoder: Reconstructs images from latent vectors.
• VAE Loss: Combines reconstruction loss and KL divergence.
• Training: Trains on MNIST and generates new images.

Hence, by referring to the above, you can write code for a Variational Autoencoder to generate images from the MNIST dataset.