How do you use TensorFlow s functional API to build a complex multi-layer Generative AI model

Question

With the help of Python programming, show how to use TensorFlow's functional API to build a complex, multilayer generative AI model?

Answer 1

You can use TensorFlow's functional API to build a complex multilayer generative AI model by referring to following:

In the code snippet above, we are using the Input Layer to define the shaper of the latent space vector, the Dense layer, and Reshape to expand the input and then reshape it into a 3D shape to serve as a base for upsampling, Conv2DTranspose Layer uses successive upsampling Layers that add spatial resolution, commonly used in generative models, Output Layer to produce a final image (for example 28 x 28 x 1 for grayscale images) with values scaled between -1 and 1 using tanh.

Hence, using TensoeFlow's functional API, you can create complex multi-layer generative AI models by building flexible neural network architectures such as those for GANs, Autoencoders, and VAEs.