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

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...