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Sigmoid activation function.

Source: R/activations.R
activation_sigmoid.Rd

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

Other activations:
activation_celu()
activation_elu()
activation_exponential()
activation_gelu()
activation_glu()
activation_hard_shrink()
activation_hard_sigmoid()
activation_hard_tanh()
activation_leaky_relu()
activation_linear()
activation_log_sigmoid()
activation_log_softmax()
activation_mish()
activation_relu()
activation_relu6()
activation_selu()
activation_silu()
activation_soft_shrink()
activation_softmax()
activation_softplus()
activation_softsign()
activation_sparse_plus()
activation_sparse_sigmoid()
activation_sparsemax()
activation_squareplus()
activation_tanh()
activation_tanh_shrink()
activation_threshold()