# tf.keras.constraints.UnitNorm

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|---|---|
| [![](https://www.tensorflow.org/images/tf_logo_32px.png) TensorFlow 2 version](https://www.tensorflow.org/api_docs/python/tf/keras/constraints/UnitNorm) | [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/constraints.py#L91-L118) |

Constrains the weights incident to each hidden unit to have unit norm.

Inherits From: [`Constraint`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/constraints/Constraint)

#### View aliases


**Main aliases**

[`tf.keras.constraints.unit_norm`](https://www.tensorflow.org/api_docs/python/tf/keras/constraints/UnitNorm)
**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.keras.constraints.UnitNorm`](https://www.tensorflow.org/api_docs/python/tf/keras/constraints/UnitNorm), [`tf.compat.v1.keras.constraints.unit_norm`](https://www.tensorflow.org/api_docs/python/tf/keras/constraints/UnitNorm), \`tf.compat.v2.keras.constraints.UnitNorm\`, \`tf.compat.v2.keras.constraints.unit_norm\`

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    tf.keras.constraints.UnitNorm(
        axis=0
    )

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| ## Arguments ||
|---|---|
| `axis` | integer, axis along which to calculate weight norms. For instance, in a `Dense` layer the weight matrix has shape `(input_dim, output_dim)`, set `axis` to `0` to constrain each weight vector of length `(input_dim,)`. In a `Conv2D` layer with `data_format="channels_last"`, the weight tensor has shape `(rows, cols, input_depth, output_depth)`, set `axis` to `[0, 1, 2]` to constrain the weights of each filter tensor of size `(rows, cols, input_depth)`. |

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## Methods

### `get_config`

[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/constraints.py#L117-L118)

    get_config()

### `__call__`

[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/constraints.py#L111-L115)

    __call__(
        w
    )

Call self as a function.