A popular method to boost the performance of a network for computer vision tasks is to add data augmentation. By using data augmentation during training time, you can increase the size of the training set. As a consequence, you make your model more robust to slight variations in the training data. In Chapter 7, Computer Vision, we demonstrated some data augmentation techniques. In the following recipe, we will be using Keras and its ImageDataGenerator for data augmentation.
Making a model more robust with data augmentation
How to do it...
- We start by importing all libraries as usual:
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation...
