How to do perform data augmentation using keras?

This recipe helps you do perform data augmentation using keras

Recipe Objective

Performing image Data augmentation using keras.

Data Augmentation is used to increase the size of available data, Data Augmentation is an artificial technique. Image Data augmentation is the most common type of Data Augmentation.

Step 1- Import Libraries


from keras.datasets import mnist
from keras.preprocessing.image import ImageDataGenerator

Step 2- Load and preprocess the dataset

We will preprocess the datset according to our model.


(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape((X_train.shape[0], 28, 28, 1))
X_test = X_test.reshape((X_test.shape[0], 28, 28, 1))
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')

Step 3- Instantiate the DataGenerator.

We will instantiate the DataGenerator, defining with some parameters

imgdata = ImageDataGenerator(horizontal_flip=True, vertical_flip=False)
imgdata.fit(X_train)

Step 4- Print the model


print(imgdata)

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