Sigmoid activation function.

It is defined as: sigmoid(x) = 1 / (1 + exp(-x)).

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.

Usage

activation_sigmoid(x)

Arguments

x

Input tensor.

Value

A tensor, the result from applying the activation to the input tensor x.

See also

• https://keras.io/api/layers/activations#sigmoid-function

Other activations:
activation_elu()
activation_exponential()
activation_gelu()
activation_hard_sigmoid()
activation_leaky_relu()
activation_linear()
activation_log_softmax()
activation_mish()
activation_relu()
activation_relu6()
activation_selu()
activation_silu()
activation_softmax()
activation_softplus()
activation_softsign()
activation_tanh()