Writing A Custom Data Augmentation Layer in Keras

Writing A Custom Data Augmentation Layer in Keras

Subclass Layer, and implement call() with TensorFlow functions. Data augmentation can help an image ML model learn to handle variations of the image that are not in the training dataset

Data augmentation can help an image ML model learn to handle variations of the image that are not in the training dataset. For example, it is likely that photographs provided to an ML model (especially if these are photographs by amateur photographers) will vary quite considerably in terms of lighting. We can therefore increase the effective size of the training dataset and make the ML model more resilient if we augment the training dataset by randomly changing the brightness, contrast, saturation, etc. of the training images. While Keras has several built-in data augmentation layers (like RandomFlip), it doesn’t currently support changing the contrast and brightness. So, let’s implement one.

tensorflow machine-learning keras google-cloud-platform computer-vision

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