tf.keras.layers.experimental.preprocessing.PreprocessingLayer

Base class for Preprocessing Layers.

Inherits From: Layer, Module

tf.keras.layers.experimental.preprocessing.PreprocessingLayer(
    **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.

is_adapted Whether the layer has been fit to data already.

Methods

adapt

View source

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

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 tf.data.Dataset.map, you should call map again on the input tf.data.Dataset 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.Normalization(
    axis=None)
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])
array([0., 1., 2.], dtype=float32)

tf.data.Dataset example with multiple adapts:

layer = tf.keras.layers.Normalization(
    axis=None)
layer.adapt([0, 2])
input_ds = tf.data.Dataset.range(3)
normalized_ds = input_ds.map(layer)
list(normalized_ds.as_numpy_iterator())
[array([-1.], dtype=float32),
 array([0.], dtype=float32),
 array([1.], dtype=float32)]
layer.adapt([-1, 1])
normalized_ds = input_ds.map(layer) # Re-map over the input dataset.
list(normalized_ds.as_numpy_iterator())
[array([0.], dtype=float32),
 array([1.], dtype=float32),
 array([2.], dtype=float32)]

adapt() is meant only as a single machine utility to compute layer state. To analyze a dataset that cannot fit on a single machine, see Tensorflow Transform for a multi-machine, map-reduce solution.

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.

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.