TensorFlow 1 version
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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
get_config
get_config()