tf.keras.regularizers.OrthogonalRegularizer

Regularizer that encourages input vectors to be orthogonal to each other.

Inherits From: Regularizer

tf.keras.regularizers.OrthogonalRegularizer(
    factor=0.01, mode='rows'
)

It can be applied to either the rows of a matrix (mode="rows") or its columns (mode="columns"). When applied to a Dense kernel of shape (input_dim, units), rows mode will seek to make the feature vectors (i.e. the basis of the output space) orthogonal to each other.

factor Float. The regularization factor. The regularization penalty will be proportional to factor times the mean of the dot products between the L2-normalized rows (if mode="rows", or columns if mode="columns") of the inputs, excluding the product of each row/column with itself. Defaults to 0.01.
mode String, one of {"rows", "columns"}. Defaults to "rows". In rows mode, the regularization effect seeks to make the rows of the input orthogonal to each other. In columns mode, it seeks to make the columns of the input orthogonal to each other.

Example:

regularizer = tf.keras.regularizers.OrthogonalRegularizer(factor=0.01)
layer = tf.keras.layers.Dense(units=4, kernel_regularizer=regularizer)

Methods

from_config

View source

@classmethod
from_config(
    config
)

Creates a regularizer from its config.

This method is the reverse of get_config, capable of instantiating the same regularizer from the config dictionary.

This method is used by Keras model_to_estimator, saving and loading models to HDF5 formats, Keras model cloning, some visualization utilities, and exporting models to and from JSON.

Args
config A Python dictionary, typically the output of get_config.

Returns
A regularizer instance.

get_config

View source

get_config()

Returns the config of the regularizer.

An regularizer config is a Python dictionary (serializable) containing all configuration parameters of the regularizer. The same regularizer can be reinstantiated later (without any saved state) from this configuration.

This method is optional if you are just training and executing models, exporting to and from SavedModels, or using weight checkpoints.

This method is required for Keras model_to_estimator, saving and loading models to HDF5 formats, Keras model cloning, some visualization utilities, and exporting models to and from JSON.

Returns
Python dictionary.

__call__

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__call__(
    inputs
)

Compute a regularization penalty from an input tensor.