ANN Series 5 - Sigmoid Activation Function - First order derivative of sigmoid and tanh
Linear Activation Functions For classification problems, linear functions like the identity function can have Separating Plane to be linear hyperplane. With a single layer of ANN can only it can produce a line. A model composed of only linear functions, no matter how many layers it has, cannot learn non-linear boundaries because the composition of linear functions is itself a linear function. However, the introduction of non-linear activation functions between layers enables neural networks to learn non-linear separators. They are suitable for regression tasks, especially in the output layer for problems expecting a continuous output range. Sigmoid Functions Sigmoid Functions are mathematical function with S shaped curve(sigmoid curve) Types of Sigmoid functions 1. Logistic function Output between 0 and 1 f(x) = 1/(1+𝑒^(−𝑥) ) 2. Hyperbolic tangent Output between -1 and 1 f(x) = (𝑒^𝑥− 𝑒^(-𝑥 ))/(𝑒^𝑥+ 𝑒^(-𝑥) ) Effect of w and b on logistic function When we use the logistic ...