# tf.keras.activations.sigmoid

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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/activations.py#L388-L418) |

Sigmoid activation function, `sigmoid(x) = 1 / (1 + exp(-x))`.

#### View aliases


**Compat aliases for migration**

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

\`tf.compat.v1.keras.activations.sigmoid\`

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    tf.keras.activations.sigmoid(
        x
    )

Applies the sigmoid activation function. For small values (\<-5),
`sigmoid` returns a value close to zero, and for large values (\>5)
the result of the function gets close to 1.

Sigmoid is equivalent to a 2-element Softmax, where the second element is
assumed to be zero. The sigmoid function always returns a value between
0 and 1.

#### Example:

    a = tf.constant([-20, -1.0, 0.0, 1.0, 20], dtype = tf.float32)
    b = tf.keras.activations.sigmoid(a)
    b.numpy()
    array([2.0611537e-09, 2.6894143e-01, 5.0000000e-01, 7.3105860e-01,
             1.0000000e+00], dtype=float32)

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| ## Args ||
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| `x` | Input tensor. |

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| ## Returns ||
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| Tensor with the sigmoid activation: `1 / (1 + exp(-x))`. ||

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