Deep Learning with Keras
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Activation function — sigmoid

The sigmoid function is defined as follows:

As represented in the following graph, it has small output changes in (0, 1) when the input varies in . Mathematically, the function is continuous. A typical sigmoid function is represented in the following graph:

A neuron can use the sigmoid for computing the nonlinear function . Note that, if  is very large and positive, then , so , while if  is very large and negative  so . In other words, a neuron with sigmoid activation has a behavior similar to the perceptron, but the changes are gradual and output values, such as 0.5539 or 0.123191, are perfectly legitimate. In this sense, a sigmoid neuron can answer maybe.