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
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.
|
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Last updated 2021-08-16 UTC.
[null,null,["Last updated 2021-08-16 UTC."],[],[],null,["# tf.keras.constraints.RadialConstraint\n\n\u003cbr /\u003e\n\n|-----------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|\n| [TensorFlow 1 version](/versions/r1.15/api_docs/python/tf/keras/constraints/RadialConstraint) | [View source on GitHub](https://github.com/keras-team/keras/tree/master/keras/constraints.py#L225-L302) |\n\nConstrains `Conv2D` kernel weights to be the same for each radius.\n\nInherits From: [`Constraint`](../../../tf/keras/constraints/Constraint)\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.keras.constraints.radial_constraint`](https://www.tensorflow.org/api_docs/python/tf/keras/constraints/RadialConstraint)\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.keras.constraints.RadialConstraint`](https://www.tensorflow.org/api_docs/python/tf/keras/constraints/RadialConstraint), [`tf.compat.v1.keras.constraints.radial_constraint`](https://www.tensorflow.org/api_docs/python/tf/keras/constraints/RadialConstraint)\n\n\u003cbr /\u003e\n\nAlso available via the shortcut function\n[`tf.keras.constraints.radial_constraint`](../../../tf/keras/constraints/RadialConstraint).\n\nFor example, the desired output for the following 4-by-4 kernel: \n\n kernel = [[v_00, v_01, v_02, v_03],\n [v_10, v_11, v_12, v_13],\n [v_20, v_21, v_22, v_23],\n [v_30, v_31, v_32, v_33]]\n\nis this:: \n\n kernel = [[v_11, v_11, v_11, v_11],\n [v_11, v_33, v_33, v_11],\n [v_11, v_33, v_33, v_11],\n [v_11, v_11, v_11, v_11]]\n\nThis constraint can be applied to any `Conv2D` layer version, including\n`Conv2DTranspose` and `SeparableConv2D`, and with either `\"channels_last\"` or\n`\"channels_first\"` data format. The method assumes the weight tensor is of\nshape `(rows, cols, input_depth, output_depth)`.\n\nMethods\n-------\n\n### `get_config`\n\n[View source](https://github.com/keras-team/keras/tree/master/keras/constraints.py#L67-L76) \n\n get_config()\n\nReturns a Python dict of the object config.\n\nA constraint config is a Python dictionary (JSON-serializable) that can\nbe used to reinstantiate the same object.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| Python dict containing the configuration of the constraint object. ||\n\n\u003cbr /\u003e"]]
TensorFlow 1 version
View source on GitHub