Book Image

Keras Deep Learning Cookbook

By : Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra
Book Image

Keras Deep Learning Cookbook

By: Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra

Overview of this book

Keras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy. The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks. By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learning
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell

Sequential models

A Sequential model can be created by passing a stack of layers to the constructor of a class called Sequential

How to do it...

Creating a basic Sequential mode involves specifying one or more layers.

Create a Sequential model

We will create a Sequential network with four layers.

  1. Layer 1 is a dense layer which has input_shape of (*, 784) and an output_shape of (*, 32)


A dense layer is a regular densely-connected neural network layer. A Dense layer implements the operation output = activation(dot(input, kernel) + bias), where activation is the element-wise activation function passed as the activation argument, kernel is a weights matrix created by the layer, and bias is a bias vector created by the layer. (This is only applicable if use_bias is True).

  1. Layer 2 is an activation layer with the tanh Activation functionapplies activation to the incoming tensor:

Activation can also be applied as a parameter to the dense layer: