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Let’s consider Figure 2 ( left) of a normal distribution with zero mean and unit variance. This type of data augmentation increases the generalizability of our networks. Right: Adding a small amount of random “jitter” to the distribution. A simple data augmentation example Figure 2: Left: A sample of 250 data points that follow a normal distribution exactly. Given that our network is constantly seeing new, slightly modified versions of the input data, the network is able to learn more robust features.Īt testing time we do not apply data augmentation and simply evaluate our trained network on the unmodified testing data - in most cases, you’ll see an increase in testing accuracy, perhaps at the expense of a slight dip in training accuracy. Our goal when applying data augmentation is to increase the generalizability of the model. What is data augmentation?ĭata augmentation encompasses a wide range of techniques used to generate “new” training samples from the original ones by applying random jitters and perturbations (but at the same time ensuring that the class labels of the data are not changed).
#Keras data augmentation rotation how to#
Combining dataset generation and in-place augmentationįrom there I’ll teach you how to apply data augmentation to your own datasets (using all three methods) using Keras’ ImageDataGenerator class.In-place/on-the-fly data augmentation (most common).Dataset generation and data expansion via data augmentation (less common).I’ll then cover the three types of data augmentation you’ll see when training deep neural networks: We’ll start this tutorial with a discussion of data augmentation and why we use it. Update: This blog post is now TensorFlow 2+ compatible!
#Keras data augmentation rotation code#
Looking for the source code to this post? Jump Right To The Downloads Section Keras ImageDataGenerator and Data Augmentation To learn more about data augmentation, including using Keras’ ImageDataGenerator class, just keep reading!
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Accepting a batch of images used for training.While the word “augment” means to make something “greater” or “increase” something (in this case, data), the Keras ImageDataGenerator class actually works by: Only 5% of respondents answered this trick question “correctly” (at least if you’re using Keras’ ImageDataGenerator class).Īgain, it’s a trick question so that’s not exactly a fair assessment, but here’s the deal: Here are the results: Figure 1: My twitter poll on the concept of Data Augmentation. The question was simple - data augmentation does which of the following? Knowing that I was going to write a tutorial on data augmentation, two weekends ago I decided to have some fun and purposely post a semi-trick question on my Twitter feed. I’ll also dispel common confusions surrounding what data augmentation is, why we use data augmentation, and what it does/does not do. In today’s tutorial, you will learn how to use Keras’ ImageDataGenerator class to perform data augmentation.
#Keras data augmentation rotation download#
Click here to download the source code to this post