tf.keras.activations.selu

TensorFlow 1 version

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

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tf.compat.v1.keras.activations.sigmoid

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.

For 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)

Arguments
`x`	Input tensor.

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

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tf.keras / activations / activations.selu

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For example:

Arguments

Returns