Top Document: comp.ai.neuralnets FAQ, Part 2 of 7: Learning Previous Document: Why use activation functions? Next Document: What is a softmax activation function? See reader questions & answers on this topic!  Help others by sharing your knowledge The formula for the logistic activation function is often written as: netoutput = 1 / (1+exp(netinput)); But this formula can produce floatingpoint overflow in the exponential function if you program it in this simple form. To avoid overflow, you can do this: if (netinput < 45) netoutput = 0; else if (netinput > 45) netoutput = 1; else netoutput = 1 / (1+exp(netinput)); The constant 45 will work for double precision on all machines that I know of, but there may be some bizarre machines where it will require some adjustment. Other activation functions can be handled similarly. User Contributions:Comment about this article, ask questions, or add new information about this topic:Top Document: comp.ai.neuralnets FAQ, Part 2 of 7: Learning Previous Document: Why use activation functions? Next Document: What is a softmax activation function? Part1  Part2  Part3  Part4  Part5  Part6  Part7  Single Page [ Usenet FAQs  Web FAQs  Documents  RFC Index ] Send corrections/additions to the FAQ Maintainer: saswss@unx.sas.com (Warren Sarle)
Last Update March 27 2014 @ 02:11 PM
