tf.keras.constraints.RadialConstraint

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

Inherits From: Constraint

View aliases

Main aliases

tf.keras.constraints.radial_constraint

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.constraints.RadialConstraint, tf.compat.v1.keras.constraints.radial_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

get_config

View source

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.

Returns
Python dict containing the configuration of the constraint object.