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

Using gated recurrent units (GRUs)

Another type of unit used in RNNs is gated recurrent units (GRUs). These units are actually simpler than LSTM units, because they only have two gates: update and reset. The update gate determines the memory and the reset gate combines the memory with the current input. The flow of data is made visual in the following figure:

Figure 4.3: Example flow in a GRU unit

In this recipe, we will show how to incorporate a GRU into an RNN architecture to classify text with Keras.

How to do it...

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

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 GRU
from keras.callbacks import EarlyStopping

from keras.datasets import imdbimport numpy as np
import pandas as pd

from keras.preprocessing import sequence
from keras.models import Sequential