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Base class for Preprocessing Layers.

Inherits From: Layer, Module

Don't use this class directly: it's an abstract base class! You may be looking for one of the many built-in preprocessing layers instead.

Preprocessing layers are layers whose state gets computed before model training starts. They do not get updated during training. Most preprocessing layers implement an adapt() method for state computation.

The PreprocessingLayer class is the base class you would subclass to implement your own preprocessing layers.

is_adapted Whether the layer has been fit to data already.



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Fits the state of the preprocessing layer to the data being passed.

After calling adapt on a layer, a preprocessing layer's state will not update during training. In order to make preprocessing layers efficient in any distribution context, they are kept constant with respect to any compiled tf.Graphs that call the layer. This does not affect the layer use when adapting each layer only once, but if you adapt a layer multiple times you will need to take care to re-compile any compiled functions as follows:

  • If you are adding a preprocessing layer to a keras.Model, you need to call model.compile after each subsequent call to adapt.
  • If you are calling a preprocessing layer inside, you should call map again on the input after each adapt.
  • If you are using a tf.function directly which calls a preprocessing layer, you need to call tf.function again on your callable after each subsequent call to adapt.

tf.keras.Model example with multiple adapts:

layer = tf.keras.layers.experimental.preprocessing.Normalization(
layer.adapt([0, 2])
model = tf.keras.Sequential(layer)
model.predict([0, 1, 2])
array([-1.,  0.,  1.], dtype=float32)
layer.adapt([-1, 1])
model.compile() # This is needed to re-compile model.predict!
model.predict([0, 1, 2])