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

Applying a 1D CNN to text

So far, we've applied to image data. As in the introduction, CNNs can also be applied to other types of input data. In the following recipe, we will show how you can apply a CNN to textual data. More specifically, we will use the structure of CNNs to classify text. Unlike images, which are 2D, text has 1D input data. Therefore, we will be using 1D convolutional layers in our next recipe. The Keras framework makes it really easy to pre-process the input data.

How to do it...

  1. Let's start with importing 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 Conv1D, GlobalMaxPooling1D
from keras.callbacks import EarlyStopping
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
(X_train, y_train), (X_test, y_test)...