tf.keras.constraints.RadialConstraint

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Constrains Conv2D kernel weights to be the same for each radius.

Inherits From: Constraint

Also available via the shortcut function tf.keras.constraints.radial_constraint.

For example, the desired output for the following 4-by-4 kernel:

    kernel = [[v_00, v_01, v_02, v_03],
              [v_10, v_11, v_12, v_13],
              [v_20, v_21, v_22, v_23],
              [v_30, v_31, v_32, v_33]]

is this::

    kernel = [[v_11, v_11, v_11, v_11],
              [v_11, v_33, v_33, v_11],
              [v_11, v_33, v_33, v_11],
              [v_11, v_11, v_11, v_11]]

This constraint can be applied to any Conv2D layer version, including Conv2DTranspose and SeparableConv2D, and with either "channels_last" or "channels_first" data format. The method assumes the weight tensor is of shape (rows, cols, input_depth, output_depth).

Methods

from_config

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@classmethod
from_config(
    config
)

Instantiates a weight constraint from a configuration dictionary.

Example:

constraint = UnitNorm()
config = constraint.get_config()
constraint = UnitNorm.from_config(config)

get_config

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get_config()

Returns a Python dict of the object config.

A constraint config is a Python dictionary (JSON-serializable) that can be used to reinstantiate the same object.