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

Keras functional APIs – linking the layers

In the functional model, we must create and define an input layer, which specifies the shape of the input data. The input layer takes a shape argument that is a tuple, which indicates the dimensionality of the input data. When the input data is one-dimensional (for example, for a multilayer perceptron), the shape must leave space for the shape of the mini-batch size, which is determined while splitting the data when training the network. The shape tuple is always defined with an open last dimension when the input is a one-dimensional example (32).

How to do it...

In the following code, we define the first layer:

from keras.layers import Input
visible = Input(shape=(32,))

We connect the layers together:

visible = Input(shape=(32,))
hidden = Dense(32)(visible)



Model class

Here we can use the Model class to create the model instance, as shown in the following snippet:

from keras.models import Model
from keras.layers import Input
from keras.layers import...