Sigmoid Activation Function

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Sigmoid or Logistic Activation Function





Sigmoid Activation Function translates the output to the range (0;1). 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. It is non Zero Centric.

Execute the Following Code


Advantages:
  • It is mostly used in the output layer for binary classification.
  • But there are other activation function perform more effectively than Sigmoid Function
Disadvantages:
  • The exp( ) function is computationally expensive.
  • The problem of vanishing gradients
  • Not useful for the regression tasks as well.

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