Help protect the Great Barrier Reef with TensorFlow on Kaggle

tf.keras.activations.sigmoid

Sigmoid activation function.

Applies the sigmoid activation function. The sigmoid function is defined as 1 divided by (1 + exp(-x)). It's curve is like an "S" and is like a smoothed version of the Heaviside (Unit Step Function) function. For small values (<-5) the sigmoid returns a value close to zero and for larger 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.

For example:

````a = tf.constant([-20, -1.0, 0.0, 1.0, 20], dtype = tf.float32)`
`b = tf.keras.activations.sigmoid(a)`
`b.numpy() >= 0.0`
`array([ True,  True,  True,  True,  True])`
```

`x` Input tensor.

Tensor with the sigmoid activation: `(1.0 / (1.0 + exp(-x)))`. Tensor will be of same shape and dtype of input `x`.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]