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

Implementing an autoencoder

For autoencoders, we use a network architecture, as shown in the following figure. In the first couple of layers, we decrease the number of hidden units. Halfway, we start increasing the number of hidden units again until the number of hidden units is the same as the number of input variables. The middle hidden layer can be seen as an encoded variant of the inputs, where the output determines the quality of the encoded variant:

Figure 2.13: Autoencoder network with three hidden layers, with m < n

In the next recipe, we will implement an in Keras to decode Street View House Numbers (SVHN) from 32 x 32 images to 32 floating numbers. We can determine the quality of the encoder by decoding back to 32 x 32 and comparing the images.

How to do it...

  1. Import the necessary libraries with the following code:
import numpy as np
from matplotlib import pyplot as plt

from keras.models import Sequential
from keras.layers.core import Dense
from keras.optimizers...