# tf.keras.layers.PReLU

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|---|---|
| [![](https://www.tensorflow.org/images/tf_logo_32px.png) TensorFlow 1 version](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/layers/PReLU) | [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.0.0/tensorflow/python/keras/layers/advanced_activations.py#L72-L158) |

Parametric Rectified Linear Unit.

Inherits From: [`Layer`](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/Layer)

#### View aliases


**Compat aliases for migration**

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

[`tf.compat.v1.keras.layers.PReLU`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/PReLU)

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    tf.keras.layers.PReLU(
        alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None,
        shared_axes=None, **kwargs
    )

#### It follows:

`f(x) = alpha * x for x < 0`,
`f(x) = x for x >= 0`,
where `alpha` is a learned array with the same shape as x.

#### Input shape:

Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.

#### Output shape:

Same shape as the input.

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| ## Arguments ||
|---|---|
| `alpha_initializer` | Initializer function for the weights. |
| `alpha_regularizer` | Regularizer for the weights. |
| `alpha_constraint` | Constraint for the weights. |
| `shared_axes` | The axes along which to share learnable parameters for the activation function. For example, if the incoming feature maps are from a 2D convolution with output shape `(batch, height, width, channels)`, and you wish to share parameters across space so that each filter only has one set of parameters, set `shared_axes=[1, 2]`. |

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