tf.keras.layers.experimental.preprocessing.PreprocessingLayer

Base class for Preprocessing Layers.

Inherits From: Layer, Module

tf.keras.layers.experimental.preprocessing.PreprocessingLayer(
    stateful=False, streaming=True, **kwargs
)

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.

stateful Whether the layer contains state that needs to be adapted via PreprocessingLayer.adapt.
streaming Whether a layer can be adapted multiple times without resetting the state of the layer.
is_adapted Whether the layer has been fit to data already.

Methods

adapt

View source

adapt(
    data, batch_size=None, steps=None, reset_state=True
)

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

Arguments
data The data to train on. It can be passed either as a tf.data Dataset, or as a numpy array.
batch_size Integer or None. Number of samples per state update. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of datasets, generators, or keras.utils.Sequence instances (since they generate batches).
steps Integer or None. Total number of steps (batches of samples) When training with input tensors such as TensorFlow data tensors, the default None is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined. If x is a tf.data dataset, and 'steps' is None, the epoch will run until the input dataset is exhausted. When passing an infinitely repeating dataset, you must specify the steps argument. This argument is not supported with array inputs.
reset_state Optional argument specifying whether to clear the state of the layer at the start of the call to adapt, or whether to start from the existing state. This argument may not be relevant to all preprocessing layers: a subclass of PreprocessingLayer may choose to throw if 'reset_state' is set to False.

compile

View source

compile(
    run_eagerly=None, steps_per_execution=None
)

Configures the layer for adapt.

Arguments
run_eagerly Bool. Defaults to False. If True, this Model's logic will not be wrapped in a tf.function. Recommended to leave this as None unless your Model cannot be run inside a tf.function. steps_per_execution: Int. Defaults to 1. The number of batches to run during each tf.function call. Running multiple batches inside a single tf.function call can greatly improve performance on TPUs or small models with a large Python overhead.

finalize_state

View source

finalize_state()

Finalize the statistics for the preprocessing layer.

This method is called at the end of adapt. This method handles any one-time operations that should occur after all data has been seen.

make_adapt_function

View source

make_adapt_function()

Creates a function to execute one step of adapt.

This method can be overridden to support custom adapt logic. This method is called by PreprocessingLayer.adapt.

Typically, this method directly controls tf.function settings, and delegates the actual state update logic to PreprocessingLayer.update_state.

This function is cached the first time PreprocessingLayer.adapt is called. The cache is cleared whenever PreprocessingLayer.compile is called.

Returns
Function. The function created by this method should accept a tf.data.Iterator, retrieve a batch, and update the state of the layer.

merge_state

View source

merge_state(
    layers
)

Merge the statistics of multiple preprocessing layers.

This layer will contain the merged state.

Arguments
layers Layers whose statistics should be merge with the statistics of this layer.

reset_state

View source

reset_state()

Resets the statistics of the preprocessing layer.

update_state

View source

update_state(
    data
)

Accumulates statistics for the preprocessing layer.

Arguments
data A mini-batch of inputs to the layer.