TensorFlow 2 version | View source on GitHub |
Constrains Conv2D
kernel weights to be the same for each radius.
Inherits From: 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()
__call__
__call__(
w
)
Call self as a function.