# tf.keras.layers.Activation

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/layers/core/activation.py#L25-L67) |

Applies an activation function to an output.

Inherits From: [`Layer`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/layers/Layer), [`Module`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Module)

#### View aliases


**Compat aliases for migration**

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

\`tf.compat.v1.keras.layers.Activation\`

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    tf.keras.layers.Activation(
        activation, **kwargs
    )

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| ## Args ||
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| `activation` | Activation function, such as [`tf.nn.relu`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/nn/relu), or string name of built-in activation function, such as "relu". |

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#### Usage:

    layer = tf.keras.layers.Activation('relu')
    output = layer([-3.0, -1.0, 0.0, 2.0])
    list(output.numpy())
    [0.0, 0.0, 0.0, 2.0]
    layer = tf.keras.layers.Activation(tf.nn.relu)
    output = layer([-3.0, -1.0, 0.0, 2.0])
    list(output.numpy())
    [0.0, 0.0, 0.0, 2.0]

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

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| ## Output shape ||
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| Same shape as input. ||

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