Machine Learning for Developers

Sigmoid or logistic function

A sigmoid or logistic function is the canonical activation function and is well-suited for calculating probabilities in classification properties. Firstly, let's prepare a function that will be used to graph all the transfer functions with their derivatives, from a common range of -2.0 to 2.0, which will allow us to see the main characteristics of them around the y axis. The classical formula for the sigmoid function is as follows:

    class Sigmoid(TransferFunction):  #Squash 0,1
        def getTransferFunction(x):
            return 1/(1+np.exp(-x))
        def getTransferFunctionDerivative(x):
        return x*(1-x)

    graphTransferFunction(Sigmoid)

Take a look at the following graph: