DATA GENERATOR KERAS EXAMPLE HOW TO
DATA GENERATOR KERAS EXAMPLE UPDATE
DATA GENERATOR KERAS EXAMPLE CODE

You can perform feature standardization by setting the featurewise_center and featurewise_std_normalization arguments to True on the ImageDataGenerator class. This is called feature standardization and mirrors the type of standardization often performed for each column in a tabular dataset. It is also possible to standardize pixel values across the entire dataset. You can learn more about the Keras image data generator API in the Keras documentation.Įxample MNIST images Feature Standardization Instead of calling the fit() function on our model, we must call the fit_generator() function and pass in the data generator and the desired length of an epoch as well as the total number of epochs on which to train.įit_generator(datagen, samples_per_epoch=len(train), epochs=100) X_batch, y_batch = datagen.flow(train, train, batch_size=32)įinally we can make use of the data generator. We can configure the batch size and prepare the data generator and get batches of images by calling the flow() function. The data generator itself is in fact an iterator, returning batches of image samples when requested.

You can do this by calling the fit() function on the data generator and pass it your training dataset. This will calculate any statistics required to actually perform the transforms to your image data. This reduces your memory overhead, but adds some additional time cost during model training.Īfter you have created and configured your ImageDataGenerator, you must fit it on your data. Rather than performing the operations on your entire image dataset in memory, the API is designed to be iterated by the deep learning model fitting process, creating augmented image data for you just-in-time.

Random rotation, shifts, shear and flips.Īn augmented image generator can be created as follows:įrom import ImageDataGenerator Keras provides the ImageDataGenerator class that defines the configuration for image data preparation and augmentation. Like the rest of Keras, the image augmentation API is simple and powerful.

DATA GENERATOR KERAS EXAMPLE HOW TO

How to Configure and Use Image Data Augmentation Keras Image Augmentation API

DATA GENERATOR KERAS EXAMPLE UPDATE

Update Jul/2022: Updated for TensorFlow 2.x API with workaround on the feature standardization issueįor an extended tutorial on the ImageDataGenerator for image data augmentation, see: Update Sep/2019: Updated for Keras 2.2.5 API. Update Mar/2017: Updated for Keras 2.0.2, TensorFlow 1.0.1 and Theano 0.9.0. Update Jan/2017: Updated for Keras 1.2.0 and TensorFlow 0.12.1. Update Oct/2016: Updated for Keras 1.1.0, TensorFlow 0.10.0 and scikit-learn v0.18. Update Aug/2016: The examples in this post were updated for the latest Keras API.

DATA GENERATOR KERAS EXAMPLE CODE

Kick-start your project with my new book Deep Learning With Python, including step-by-step tutorials and the Python source code files for all examples. How to save augmented image data to disk. How to augment data with random rotations, shifts and flips. How to perform ZCA whitening of your images.

In this post you will discover how to use data preparation and data augmentation with your image datasets when developing and evaluating deep learning models in Python with Keras.Ībout the image augmentation API provide by Keras and how to use it with your models. Increasingly data augmentation is also required on more complex object recognition tasks. Data preparation is required when working with neural network and deep learning models.

Data generator keras example