tf.keras.layers.Normalization

A preprocessing layer which normalizes continuous features.

Inherits From: PreprocessingLayer, Layer, Module

tf.keras.layers.Normalization(
    axis=-1, mean=None, variance=None, **kwargs
)

This layer will shift and scale inputs into a distribution centered around 0 with standard deviation 1. It accomplishes this by precomputing the mean and variance of the data, and calling (input - mean) / sqrt(var) at runtime.

The mean and variance values for the layer must be either supplied on construction or learned via adapt(). adapt() will compute the mean and variance of the data and store them as the layer's weights. adapt() should be called before fit(), evaluate(), or predict().

For an overview and full list of preprocessing layers, see the preprocessing guide.

axis Integer, tuple of integers, or None. The axis or axes that should have a separate mean and variance for each index in the shape. For example, if shape is (None, 5) and axis=1, the layer will track 5 separate mean and variance values for the last axis. If axis is set to None, the layer will normalize all elements in the input by a scalar mean and variance. Defaults to -1, where the last axis of the input is assumed to be a feature dimension and is normalized per index. Note that in the specific case of batched scalar inputs where the only axis is the batch axis, the default will normalize each index in the batch separately. In this case, consider passing axis=None.
mean The mean value(s) to use during normalization. The passed value(s) will be broadcast to the shape of the kept axes above; if the value(s) cannot be broadcast, an error will be raised when this layer's build() method is called.
variance The variance value(s) to use during normalization. The passed value(s) will be broadcast to the shape of the kept axes above; if the value(s) cannot be broadcast, an error will be raised when this layer's build() method is called.

Examples:

Calculate a global mean and variance by analyzing the dataset in adapt().

adapt_data = np.array([1., 2., 3., 4., 5.], dtype='float32')
input_data = np.array([1., 2., 3.], dtype='float32')
layer = tf.keras.layers.Normalization(axis=None)
layer.adapt(adapt_data)
layer(input_data)
<tf.Tensor: shape=(3,), dtype=float32, numpy=
array([-1.4142135, -0.70710677, 0.], dtype=float32)>

Calculate a mean and variance for each index on the last axis.

adapt_data = np.array([[0., 7., 4.],
                       [2., 9., 6.],
                       [0., 7., 4.],
                       [2., 9., 6.]], dtype='float32')
input_data = np.array([[0., 7., 4.]], dtype='float32')
layer = tf.keras.layers.Normalization(axis=-1)
layer.adapt(adapt_data)
layer(input_data)
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=
array([-1., -1., -1.], dtype=float32)>

Pass the mean and variance directly.

input_data = np.array([[1.], [2.], [3.]], dtype='float32')
layer = tf.keras.layers.Normalization(mean=3., variance=2.)
layer(input_data)
<tf.Tensor: shape=(3, 1), dtype=float32, numpy=
array([[-1.4142135 ],
       [-0.70710677],
       [ 0.        ]], dtype=float32)>

is_adapted Whether the layer has been fit to data already.

Methods

adapt

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

Computes the mean and variance of values in a dataset.

Calling adapt() on a Normalization layer is an alternative to passing in mean and variance arguments during layer construction. A Normalization layer should always either be adapted over a dataset or passed mean and variance.

During adapt(), the layer will compute a mean and variance separately for each position in each axis specified by the axis argument. To calculate a single mean and variance over the input data, simply pass axis=None.

In order to make Normalization efficient in any distribution context, the computed mean and variance are kept static with respect to any compiled tf.Graphs that call the layer. As a consequence, if the layer is adapted a second time, any models using the layer should be re-compiled. For more information see tf.keras.layers.experimental.preprocessing.PreprocessingLayer.adapt.

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

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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.

reset_state

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reset_state()

Resets the statistics of the preprocessing layer.

update_state

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update_state(
    data
)

Accumulates statistics for the preprocessing layer.

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