And we display the image as a reference of what our input is before data augmentation.
#KERAS IMAGE GENERATOR DOWNLOAD#
Next we download a sample image from the Github repository, save it locally and load it into memory. import numpy as npįrom import load_img, img_to_arrayįrom import ImageDataGenerator We start by importing ImageDataGenerator and util functions to load the data.
#KERAS IMAGE GENERATOR CODE#
You can follow the tutorial by running the code in an ipython notebook on Colab for free, with zero setup! This allows you to play around with the parameters and explore what we discuss here in more detail.Ĭlick the link on the top of the post to either open the notebook in Google Colab or download it to run on your machine.įirst we will need some setup code. This would be a good point to look at the Colab notebook. For more details, have a look at the Keras documentation for the ImageDataGenerator class. It allows you to specify the augmentation parameters, which we will go over in the next steps. In Keras, there's an easy way to do data augmentation with the class .ImageDataGenerator. After all, we want to vary the augmented data every time it is shown to the model! While it can be done, it is usually not practical to store the augmented data on disk. It is often used when the training data is limited and as a way of preventing overfitting.ĭata augmentation is usually done on the fly when training a model. Data augmentation is a technique to increase the variation in a dataset by applying transformations to the original data.
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