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

Optimization with RMSProp

In this recipe, we look at the code sample on how to optimize with RMSProp.

RMSprop is an (unpublished) adaptive learning rate method proposed by Geoff Hinton. RMSprop and AdaDelta were both developed independently around the same time, stemming from the need to resolve AdaGrad's radically diminishing learning rates. RMSprop is identical to the first update vector of AdaDelta that we derived earlier:

RMSprop divides the learning rate by an exponentially decaying average of squared gradients. It is suggested that γ to be set to 0.9, while a good default value for the learning rate is η is 0.001.

Getting ready

Import the relevant classes, methods, and so on, as specified in the preceding common code section.

How to do it...

Create a sequential model with the appropriate share:

from keras.optimizers import RMSprop
model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dense(512, activation='relu'))