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1. To create a custom layer, simply subclass keras.layers.Layer, create the layer state in __init__() (or alternatively, in build(input_shape) if you need to know the input shape to create the state), and define the layer's computation in call().pic.twitter.com/VO3R8pS9wq
2. To implement a fully-featured training loop (that supports distributed training, compilation, and callbacks out of the box), subclass keras.Model and define the training logic in train_step. Metrics that you want to reset across epochs should be listed in the metrics property.pic.twitter.com/gSnfTgkQhc
This is arguably one of the features in #Keras, I can drool over 
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