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- # we create two instances with the same arguments
- data_gen_args = dict(featurewise_center=True,
- featurewise_std_normalization=True,
- rotation_range=90.,
- width_shift_range=0.1,
- height_shift_range=0.1,
- zoom_range=0.2)
- image_datagen = ImageDataGenerator(**data_gen_args)
- mask_datagen = ImageDataGenerator(**data_gen_args)
- # Provide the same seed and keyword arguments to the fit and flow methods
- seed = 1
- image_datagen.fit(images, augment=True, seed=seed)
- mask_datagen.fit(masks, augment=True, seed=seed)
- image_generator = image_datagen.flow_from_directory(
- 'data/images',
- class_mode=None,
- seed=seed)
- mask_generator = mask_datagen.flow_from_directory(
- 'data/masks',
- class_mode=None,
- seed=seed)
- # combine generators into one which yields image and masks
- train_generator = zip(image_generator, mask_generator)
- model.fit_generator(
- train_generator,
- steps_per_epoch=2000,
- epochs=50)
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