Book Image

Keras 2.x Projects

By : Giuseppe Ciaburro
Book Image

Keras 2.x Projects

By: Giuseppe Ciaburro

Overview of this book

Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more. By the end of this book, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems.
Table of Contents (13 chapters)

The Keras autoencoders model

As we said in the Autoencoders section, an autoencoder is a neural network whose purpose is to code its input into small dimensions and the result obtained to be able to reconstruct the input itself. Autoencoders are made up of the union of the following two subnets: encoder and decoder. To these functions is added another; it's a loss function calculated as the distance between the amount of information loss between the compressed representation of the data and the decompressed representation.

The encoder and the decoder will be differentiable with respect to the distance function, so the parameters of the encoding/decoding functions can be optimized to minimize the loss of reconstruction, using the gradient stochastic.

As we saw in Chapter 1, Getting Started with Keras, there are two types of models available in Keras:

  • Sequential model

  • Keras...