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Generative Adversarial Networks Projects

Generative Adversarial Networks Projects

By : Ahirwar
2.3 (3)
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Generative Adversarial Networks Projects

Generative Adversarial Networks Projects

2.3 (3)
By: Ahirwar

Overview of this book

Generative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the GAN domain. Generative Adversarial Network Projects begins by covering the concepts, tools, and libraries that you will use to build efficient projects. You will also use a variety of datasets for the different projects covered in the book. The level of complexity of the operations required increases with every chapter, helping you get to grips with using GANs. You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and you’ll gain an understanding of the architecture and functioning of generative models through their practical implementation. By the end of this book, you will be ready to build, train, and optimize your own end-to-end GAN models at work or in your own projects.
Table of Contents (11 chapters)
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A Keras implementation of pix2pix

As mentioned, pix2pix has two networks: a generator and a discriminator. The generator is inspired by the architecture of U-Net. Similarly, the discriminator network is inspired by the architecture of PatchGAN. We will implement both networks in the following sections.

Before starting to write the implementations, create a Python file main.py and import the essential modules as follows:

import os
import time

import h5py
import keras.backend as K
import matplotlib.pyplot as plt
import numpy as np
from cv2 import imwrite
from keras import Input, Model
from keras.layers import Convolution2D, LeakyReLU, BatchNormalization, UpSampling2D, Dropout, Activation, Flatten, Dense, Lambda, Reshape, concatenate
from keras.optimizers import Adam

The generator network

...

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