2.5 Reducing Overfitting

To implement data generation, we will use the ImageDataGenerator from Keras. We will first create a datagenerator object from our ImageDataGenerator, which will include all the augmentations we want to make. Next, we will build our CNN model (the function isn’t shown, but it should be the same as any CNN model function, such as the one in the previous chapter). Finally, we train on our generated images, however, there are a few twists - we need to use fit_generator instead of our regular model.fit function, and inside our fit function, we need to use datagenerator.flow(x_train, y_train) as our data.

import keras
from keras.preprocessing.image import ImageDataGenerator

#you should already have your x_train, y_train, and possibly x_test and y_test

datagenerator = ImageDataGenerator(
								#list of augmentations
								)

datagenerator.fit(x_train) #applies generation to x_train

#training on our model
model = build_model()

model.fit_generator(datagenerator.flow(x_train, y_train), 
										validation_data = (x_test, y_test))