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Generative Adversarial Networks
CycleGAN in TensorFlow 2.0
In the last section of this chapter we will implement a CycleGAN in TensorFlow
2.0. The CycleGAN requires a special dataset, a paired dataset, from one domain
of images to another domain. So, besides the necessary modules, we will use
tensorflow_datasets as well:
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.losses import mean_squared_error, mean_absolute_
error
import os
import time
import matplotlib.pyplot as plt
import numpy as np
import tensorflow_datasets as tfds
TensorFlow's Dataset API contains a list of datasets. It has many paired datasets for
CycleGANs, such as horse to zebra, apples to oranges, and so on. You can access the
complete list here: https://www.tensorflow.org/datasets/catalog/cycle_gan.
For our code we will be using summer2winter_yosemite, which contains images
of Yosemite (USA) in summer (Dataset A) and winter (Dataset B). We will train the
CycleGAN to convert an input image of summer to winter and vice versa. Let us
load the data and get train and test images:
dataset, metadata = tfds.load('cycle_gan/summer2winter_yosemite',
with_info=True, as_supervised=True)
train_A, train_B = dataset['trainA'], dataset['trainB']
test_A, test_B = dataset['testA'], dataset['testB']
We need to set some hyperparameters:
BUFFER_SIZE = 1000
BATCH_SIZE = 1
IMG_WIDTH = 256
IMG_HEIGHT = 256
EPOCHS = 50
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