Take a target distribution:

Define how to sample from that distribution:

Plot the distribution and a histogram of random samples:

Define a generator of scalar values:

Define a discriminator that outputs a score for a given scalar:

Create a NetGANOperator that embeds the generator and the discriminator to train them in an adversarial manner:

Create a data generator that yields latent random variables and sampled real data:

Sample from this data generator:

In order to monitor the training of NetGANOperator, it will be useful to make a function to watch generated samples:

Check the monitoring on a random initialization of NetGANOperator:

In this figure, the green curve is the target distribution, the red curve is the discriminator function and the orange histogram represents the distribution of generated fake numbers.

Train a generative adversarial network and watch live how it performs:

Watch the generation of the trained model:

Generate numbers and plot the histogram:

Take a target two-dimensional distribution:

Plot the distribution:

Define a generator of pairs of numbers:

Define a discriminator that outputs a score for a given pair of numbers:

Create a NetGANOperator that embeds the generator and the discriminator to train them in an adversarial manner:

Compute the losses and the generated pair of points on some data:

Write a function to visualize how the generator and the discriminator of NetGANOperator are performing:

With a random initialization, generated samples are random:

Train NetGANOperator:

After training, the generated samples fit the target distribution better:

Take images of the MNIST dataset:

Define a binary discriminator for images:

Define a generator for images of the same size:

Define a data generator that yields random seeds and sampled images of digits:

Define a function to monitor how good the generated images are:

Try the function on a randomly initialized NetGANOperator:

Train a NetGANOperator to generate images, watching generated images through the training process:

Extract the generator from the trained NetGANOperator and use it to generate new images of digits:

Get the images of two different digits:

Sample some random images:

Take a pretrained image classifier:

Define a binary discriminator from this pretrained classifier:

Define a generator that takes an image and generates another image of the same shape:

Define a Cycle-GAN architecture, using two instances of NetGANOperator and shared generators, adding cycle consistency losses to adversarial losses:

Define a function to visualize the generated images:

Watch the generated images on a random initialization:

Train the Cycle-GAN: