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

Adding Long Short-Term Memory (LSTM)

One limitation of a simple RNN is that it only accounts for the direct inputs around the current input. In many applications, and specifically language, one needs to understand the context of the sentence in a larger part as well. This is why LSTM has played an role in applying Deep Learning to unstructured data types such as text. An LSTM unit has an input, forget, and output gate, as is shown in Figure 4.2:

Figure 4.2: Example flow in an LSTM unit

In the following recipe, we will be classifying reviews from the IMDB dataset using the Keras framework.

How to do it...

  1. Let's start with the libraries as follows:
import numpy as np

from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import LSTM

from keras.datasets import imdb
  1. We will be using the IMDB dataset from Keras; load the data with the following code:
n_words = 1000