Book Image

Python Deep Learning Cookbook

By : Indra den Bakker
Book Image

Python Deep Learning Cookbook

By: Indra den Bakker

Overview of this book

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Character-level text generation

RNNs are not only powerful to and classify text. RNNs can also be used to generate text. In it's simplest form, text is generated on character level. More specifically, the text is generated character per character. Before we can generate text, we need to train a decoder on full sentences. By including a GRU layer in our decoder, the model does not only depend on the previous input but does try to predict the next character based on the context around it. In the following recipe, we will demonstrate how to implement a character-level text generator with PyTorch. 

How to do it...

  1. Let's start with importing the libraries as follows:
import unidecode
import string
import random
import math

import torch
import torch.nn as nn
from torch.autograd import Variable
  1. As input and output, we can use any character:
all_characters = string.printable
input_size = len(all_characters)
output_size = input_size
  1. We will be using a dataset with speeches from Obama...