![]() If you want to treat this range as continues, you end up with an infinite amount of samples. ![]() With the exemption of the horizontal flip (which doubles the number of samples), all the remaining augmentation techniques consist of a range of possible operations. Keras is using an online data-augmentation process, where every single image is augmented at the start of every epoch (they are probably processed in batches, but the point is that it happens ones per epoch). E.g., if I have a training directory with 2000 images, will the data augmentation create more than 2000 observations to train with? How do I know/control how many observations are developed?ĭata augmentation is used to artificially increase the number of samples in the training set (because small datasets are more vulnerable to over-fitting). What I want to know is how the ImageDataGenerator() is working. Train_generator = train_datagen.flow_from_directory(train_dir, ![]() Test_datagen = ImageDataGenerator(rescale=1./255) ![]() I have a question about what the following code does (appearing on pg 141 of the book): train_datagen = ImageDataGenerator(rescale=1./255, I'm reading through Francois Chollet's "Deep Learning with Python" and was recently introduced to a concept I had never encountered before in my statistics studies.
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