Unsupervised Learning with Generative Adversarial Networks (GANs)

Learn how to implement a GAN for unsupervised learning in Python using TensorFlow

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Generative Adversarial Networks (GANs) are a type of deep learning algorithm that are designed to generate new data instances that are similar to existing data. These networks consist of two parts — a generator network and a discriminator network. The generator network generates new data instances, while the discriminator network assesses the generated data and decides if it is similar to the existing data or not.

Let’s look at how we can implement a GAN for unsupervised learning in Python using TensorFlow. This is a rather long tutorial, so let’s get started.

To start, we’ll import the necessary libraries and set up the TensorFlow environment.

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

tf.compat.v1.disable_eager_execution()

Next, we’ll define the generator network. This network takes as input a random noise vector and generates an output that is intended to resemble the existing data. In our example, we’ll use a simple feedforward neural network with one hidden layer.

def generator_network(z, reuse=False):
    with…