#Image data generator how to#
#Image data generator code#
#Image data generator series#
#Image data generator download#

Instead, to increase the generalizability of our classifier, we may first randomly jitter points along the distribution by adding some random values drawn from a random distribution ( right). Training a machine learning model on this data may result in us modeling the distribution exactly - however, in real-world applications, data rarely follows such a nice, neat distribution. 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).

#Image data generator 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!

#Image data generator 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!

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.

#Image data generator series#

Taking this batch and applying a series of random transformations to each image in the batch (including random rotation, resizing, shearing, etc.).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.

#Image data generator download#

Click here to download the source code to this post