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

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Table of Contents (17 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
Packt Upsell
Packt Upsell
Contributors
Contributors
Preface
Preface
Free Chapter
1
Keras Installation
Keras Installation
Introduction
Installing Keras on Ubuntu 16.04
Installing Keras with Jupyter Notebook in a Docker image
Installing Keras on Ubuntu 16.04 with GPU enabled
2
Working with Keras Datasets and Models
Working with Keras Datasets and Models
Introduction
CIFAR-10 dataset
CIFAR-100 dataset
MNIST dataset
Load data from a CSV file
Models in Keras – getting started
Sequential models
Shared layer models
Keras functional APIs
Keras functional APIs – linking the layers
Image classification using Keras functional APIs
3
Data Preprocessing, Optimization, and Visualization
Data Preprocessing, Optimization, and Visualization
Feature standardization of image data
Sequence padding
Model visualization
Optimization 
Common code for samples
Optimization with stochastic gradient descent
Optimization with Adam
Optimization with AdaDelta
Optimization with RMSProp
4
Classification Using Different Keras Layers
Classification Using Different Keras Layers
Introduction
Classification for breast cancer
Classification for spam detection
5
Implementing Convolutional Neural Networks
Implementing Convolutional Neural Networks
Introduction
Cervical cancer classification
Digit recognition
6
Generative Adversarial Networks
Generative Adversarial Networks
Introduction
Basic GAN
Boundary seeking GAN
DCGAN
7
Recurrent Neural Networks
Recurrent Neural Networks
Introduction
Simple RNNs for time series data
LSTM networks for time series data
Time series forecasting with LSTM
Sequence to sequence learning for the same length output with LSTM
8
Natural Language Processing Using Keras Models
Natural Language Processing Using Keras Models
Introduction
Word embedding
Sentiment analysis
9
Text Summarization Using Keras Models
Text Summarization Using Keras Models
Introduction
Text summarization for reviews
10
Reinforcement Learning
Reinforcement Learning
Introduction
The CartPole game with Keras
Dueling DQN to play Cartpole 
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Index
Index

Model visualization

For simpler models, a simple model summary is sufficient, but for more complex topologies, Keras provides a way to visualize the model. It is a layer on top of the graphviz library.

Getting ready

Please make sure graphviz is installed:

sudo apt-get install graphviz

Also, install pydot, which is needed in the underlying implementation:

sudo pip install pydot

How to do it...

Let's take a look at an example where we create a simple model and call plot_model on it.

The plot_model() function in Keras creates a plot of the neural network. This function takes the following arguments:

  • model: (required) The model that is to be plotted
  • to_file: (required) The name of the file to save the plot
  • show_shapes: (optional, defaults to False) Boolean to show the output shapes of each layer
  • show_layer_names: (optional, defaults to True) Boolean to show the name for each layer

The following sections show how plot_model can be used.

Code listing

The following code creates a Sequential model with two Dense...

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