Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
Table of Contents (28 chapters)
26
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27
Index

Deep Convolutional GANs

After discussing the basic concepts of adversarial training, we can apply them to a practical example of DCGANs. In fact, even if it's possible to use only dense layers (MLPs), as we want to work with images, it's preferable to employ convolutions and transpose convolutions to obtain the best results.

Example of DCGAN with TensorFlow

In this example, we want to build a DCGAN (proposed in Radford A., Metz L., Chintala S., Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, arXiv:1511.06434 [cs.LG]) with the Fashion-MNIST dataset (obtained through the TensorFlow/Keras helper function). As the training speed is not very high, we limit the number of samples to 5,000, but I suggest repeating the experiment with larger values. The first step is loading and normalizing (between -1 and 1) the dataset:

import tensorflow as tf
import numpy as np
nb_samples = 5000
(X_train, _), (_, _) = \
        tf.keras...