View source on GitHub |
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
@classmethod
from_config( config )
Instantiates a weight constraint from a configuration dictionary.
Example:
constraint = UnitNorm()
config = constraint.get_config()
constraint = UnitNorm.from_config(config)
Args | |
---|---|
config
|
A Python dictionary, the output of get_config .
|
Returns | |
---|---|
A tf.keras.constraints.Constraint instance.
|
get_config
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. |