Code – the GAN network

The GAN network combines the discriminator and generator from previous recipes into a conditional adversarial configuration for training.

Getting ready

Keep track of the fact that you remembered to add gan.py to your working directory:

├── docker
│   ├── build.sh
│   ├── clean.sh
│   └── Dockerfile
├── README.md
├── run.sh
└── src
|   ├── generator.py
|   ├── gan.py

How to do it...

The GAN network in this case is arguably the easiest part to implement—we're simply going to link up our networks so they can train together:

1. Import all of the libraries we need to use for this class:

#!/usr/bin/env python3
import sys
import numpy as np
from keras.models import Sequential, Model
from keras.layers import Input
from keras.optimizers import Adam, SGD
from keras.utils import plot_model

 

2. Implement the init class with the Adam optimizer and then an array of model_inputs and model_outputs:

class GAN(object):
    def __init__(self, model_inputs=[],model_outputs=[]):
        self.inputs ...