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

Sequence to sequence learning for the same length output with LSTM

In this recipe, we will learn how to use LSTM to predict a value that is of the same or a slightly different length, such as subtraction of two numbers. 

Getting ready

Create a requirements.txt with Keras and six.moves dependencies. Import the relevant classes from keras, numpy, and six.moves as follows:

from __future__ import print_function
from keras.models import Sequential
from keras import layers
import numpy as np
import six.moves

In the next section, we will learn how to implement an LSTM network that can handle any three-digit subtraction.

How to do it…

  1. Create a character table that can handle encoding and decoding. This class has three methods, as follows:
    • __init__()
    • encode()
    • decode()
  1. The code is as follows:
class CharTable(object):
    def __init__(self, char):
        self.char = sorted(set(char))
        self.char_indices = dict((ch, i) for i, ch in enumerate(self.char))
        self.indices_char = dict((i, ch) for i, ch...