Tensorflow

This is the final part in our series on Generative Adversarial Networks (GAN). We will write our training script and look at how to run the GAN. We will also take a look at the results we get out. Can you tell the difference between the real and generated faces?

Now that we’re able to import images into our network, we really need to build the GAN iteself. This tuorial will build the GAN class including the methods needed to create the generator and discriminator. We’ll also be looking at some of the data functions needed to make this work.

We’re ready to code! In Part 1 we looked at how GANs work and Part 2 showed how to get the data ready. In this Part, we will begin creating the functions that handle the image data including some pre-procesing and data normalisation.

This tutorial will provide the data that we will use when training our Generative Adversarial Networks. It will also take an overview on the structure of the necessary code for creating a GAN and provide some skeleton code which we can work on in the next post. If you’re not up to speed on GANs, please do read the brief introduction in Part 1 of this series on Generative Adversarial Networks.

We’ve looked at the principles behind how a CNN works, but how do we actually implement this in Python? This tutorial will look at the basic idea behind Google’s TensorFlow: an efficient way to build a CNN using purpose-build Python libraries.