![]() Height_shift_range=0. Width_shift_range=0.2, # randomly shift images horizontally (fraction of total width) Rotation_range=45, # randomly rotate images in the range (degrees, 0 to 180) In Tensorflow, we can make use of ImageDataGenerator (tf. ![]() ![]() Zca_whitening=False, # apply ZCA whitening In TensorFlow, data augmentation is accomplished using the ImageDataGenerator class. Samplewise_std_normalization=True, # divide each input by its std Samplewise_center=True, # set each sample mean to 0įeaturewise_std_normalization=True, # divide inputs by std of the dataset I'm confused when I add data augmentation should I get more data or the same data I tested my x_train length to confirm but I got the same length before augmentation and after augmentation is that correct or should I get the double of my data? print(len(x_train)) output : 5484Īfter augmentation : datagen = ImageDataGenerator(įeaturewise_center=True, # set input mean to 0 over the dataset
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