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To implement my Binary CNN in keras, I had a dataset of ~~35000 images but only 700 is from one class and all the others are from the other class, so what I did: I get the 700 unique images from class 1 and get 700 images randomly from class 0 totalizing 1400 samples (700 each class). After I perform this augmentation in my set:

datagen = ImageDataGenerator(
    rotation_range=30,
    zoom_range=0.15,
    featurewise_std_normalization=True,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.15,
    horizontal_flip=True,
    fill_mode="nearest")

I did it right? or there's a better way to do?

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