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

Making a model more robust with data augmentation

A popular method to boost the performance of a network for computer vision tasks is to add data augmentation. By using data augmentation during training time, you can increase the size of the training set. As a consequence, you make your model more robust to slight variations in the data. In Chapter 7, Computer Vision, we demonstrated some data augmentation techniques. In the following recipe, we will be using Keras and its ImageDataGenerator for data augmentation.

How to do it...

  1. We start by importing all libraries as usual:
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping, TensorBoard, ModelCheckpoint

from keras.datasets import cifar10
  1. We load and pre-process the training and validation data as follows:
(X_train, y_train...