![]() batch_index = 0 def _len_ ( self ): # round up return ( self. _set_index_array () def on_epoch_end ( self ): self. samples def reset_index ( self ): """Reset the generator indexes array. flow_from_directory ( directory, target_size = ( img_height, img_width ), class_mode = "categorical", batch_size = batch_size, shuffle = True, subset = subset ) # Number of images across all classes in image directory. flow_from_directory ( directory, target_size = ( img_height, img_width ), class_mode = "categorical", batch_size = batch_size, shuffle = True, subset = subset ) # Second iterator yielding tuples of (x, y) self. alpha = alpha # First iterator yielding tuples of (x, y) self. Import numpy as np class MixupImageDataGenerator (): def _init_ ( self, generator, directory, batch_size, img_height, img_width, alpha = 0.2, subset = None ): """Constructor for mixup image data generator. It increases the robustness to the adversarial examples and stabilizes the training of generative adversarial networks.Īttempting to give mixup a spin? Let's implement an image data generator that reads images from files and works with Keras model.fit_generator() out of the box.It reduces the memorization of corrupt labels,.It makes decision boundaries transit linearly from class to class, providing a smoother estimate of uncertainty.Mixup is a data-agnostic data augmentation routine.While the traditional data augmentation like those provided in Keras ImageDataGenerator class consistently leads to improved generalization, the procedure is dataset-dependent, and thus requires the use of expert knowledge.īesides, data augmentation does not model the relation across examples of different classes. Α ∈ leads to improved performance, smaller α creates less mixup effect, whereas, for large α, mixup leads to underfitting.Īs you can see in the following graph, given a small α = 0.2, beta distribution samples more values closer to either 0 and 1, making the mixup result closer to either one of the two examples. ( x i y i ) and ( x j y j ) are two examples drawn at random from our training data, and λ ∈ , in practice, λ is randomly sampled from the beta distribution, i.e. B y forming a new example through weighted linear interpolation of two existing examples. The paper mixup : B EYOND E MPIRICAL R ISK M INIMIZATION offers an alternative to traditional image augmentation technique like zooming and rotation. Previously, we introduced a bag of tricks to improve image classification performance with convolutional networks in Keras, this time, we will take a closer look at the last trick called mixup.
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