Unit normalization layer.
tf.keras.layers.UnitNormalization(
    axis=-1, **kwargs
)
  Normalize a batch of inputs so that each input in the batch has a L2 norm equal to 1 (across the axes specified in axis).
data = tf.constant(np.arange(6).reshape(2, 3), dtype=tf.float32) normalized_data = tf.keras.layers.UnitNormalization()(data) print(tf.reduce_sum(normalized_data[0, :] ** 2).numpy()) 1.0
| Args | |
|---|---|
| axis | Integer or list/tuple. The axis or axes to normalize across. Typically this is the features axis or axes. The left-out axes are typically the batch axis or axes. Defaults to -1, the last dimension in the input. | 
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Code samples licensed under the Apache 2.0 License.
    https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/keras/layers/UnitNormalization