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 stochastic gradient descent

Stochastic gradient descent (SGD), in contrast to batch gradient descent, performs a parameter update for each training example, x(i) and label y(i):

Θ = Θ - η∇Θj(Θ, x(i), y(i))

Getting ready

Make sure that the preceding common code list is added before the main code snippet in the following codes:

How to do it...

Create a sequential model with the appropriate network topology:

  • Input layer with shape (*, 784), and an output of (*, 512)
  • Hidden layer with an input (*, 512) and an output of (*, 512)
  • Output layer with the input dimension as (*, 512) and the output as (*, 10)

Let's look at the activation functions for each layer:

  • Layer 1 and Layer 1-relu
  • Layer 3-softmax
from keras.optimizers import SGD

y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dense(512, activation='relu...