Book Image

Python Deep Learning Cookbook

By : Indra den Bakker
Book Image

Python Deep Learning Cookbook

By: Indra den Bakker

Overview of this book

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Understanding GANs

To start implementing GANs, we need to. It is hard to the quality of examples produced by GANs. A lower loss value doesn't always represent better quality. Often, for images, the only way to determine the quality is by visually inspecting the generated examples. We can than determine whether the generated images are realistic enough, more or less like a simple Turing test. In the following recipe, we will introduce GANs by using the well-known MNIST dataset and the Keras framework.

How to do it...

  1. We start by importing the necessary libraries, as follows:
import numpy as np
from keras.models import Sequential, Model
from keras.layers import Input, Dense, Activation, Flatten, Reshape
from keras.layers import Conv2D, Conv2DTranspose, UpSampling2D
from keras.layers import LeakyReLU, Dropout
from keras.layers import BatchNormalization
from keras.optimizers import Adam
from keras import initializers

from keras.datasets import mnist

import matplotlib.pyplot as plt
  1. By using Keras...