12/30/2023 0 Comments Keras datagenerator![]() 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). 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! 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! Learn how to apply data augmentation with Keras and the ImageDataGenerator class.Dispel any confusion you have surrounding data augmentation.Learn about three types of data augmentation.Inside the rest of today’s tutorial you will: I’ll help you clear up some of the confusion regarding data augmentation (and give you the context you need to successfully apply it). Technically, all the answers are correct - but the only way you know if a given definition of data augmentation is correct is via the context of its application. Instead, the ImageDataGenerator accepts the original data, randomly transforms it, and returns only the new, transformed data.īut remember how I said this was a trick question? It’s not taking the original data, randomly transforming it, and then returning both the original data and transformed data. ![]() That’s right - the Keras ImageDataGenerator class is not an “additive” operation. Training the CNN on this randomly transformed batch (i.e., the original data itself is not used for training).Replacing the original batch with the new, randomly transformed batch.Taking this batch and applying a series of random transformations to each image in the batch (including random rotation, resizing, shearing, etc.). ![]()
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