tf.keras.layers.experimental.preprocessing.Normalization

Feature-wise normalization of the data.

Inherits From: PreprocessingLayer, Layer, Module

Used in the notebooks

Used in the guide Used in the tutorials

This layer will coerce its 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.

What happens in adapt: Compute mean and variance of the data and store them as the layer's weights. adapt should be called before fit, evaluate, or predict.

axis Integer or tuple of integers, the axis or axes that should be "kept". These axes are not be summed over when calculating the normalization statistics. By default the last axis, the features axis is kept and any space or time axes are summed. Each element in the the axes that are kept is normalized independently. If axis is set to 'None', the layer will perform scalar normalization (dividing the input by a single scalar value). The batch axis, 0, is always summed over (axis=0 is not allowed).
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 the mean and variance by analyzing the dataset in adapt.

adapt_data = np.array([[1.], [2.], [3.], [4.], [5.]], dtype=np.float32)
input_data = np.array([[1.], [2.], [3.]], np.float32)
layer = Normalization()
layer.adapt(adapt_data)
layer(input_data)