How to make prediction equation with several input neuron in artificial neural network?Can we use the bias value, weight from ANN with MAtlab analysis?
Tanh or sigmoid functions are very useful for binary output problems. Well, in case of a polinomial output, which activation function do you supposed to use?
You can use the same activation functions for outputs greater than 2. The activation function will essentially turn on or off for each potential output in the list of possible outputs
Thank you sir! great explanation . . but would you explain me something about the Tanh(a) and Tanh(b) functions, why and how are they initialized to 1.72 and 0.67 respectively ? Is it must to initialize it? If yes, how?
Thanks! The short answer is that it is a heurisitic choice. This choice is discussed in a paper by LeCun, "Generalization and Network Design", 1989. It has to do with making the equations a little simpler, and the overall second derivative of the hyperbolic function is a pleasing (well, to some!) +1 to -1. This choice is thought to improve convergence of the learning process.
How to make prediction equation with several input neuron in artificial neural network?Can we use the bias value, weight from ANN with MAtlab analysis?
Can you pls provide the idea for preparation excel for preceptron neural networks
Not sure I understood the question, can you elaborate?
Tanh or sigmoid functions are very useful for binary output problems. Well, in case of a polinomial output, which activation function do you supposed to use?
You can use the same activation functions for outputs greater than 2. The activation function will essentially turn on or off for each potential output in the list of possible outputs
Great Explanation!
Thank you!
Thank you sir! great explanation . . but would you explain me something about the Tanh(a) and Tanh(b) functions, why and how are they initialized to 1.72 and 0.67 respectively ? Is it must to initialize it? If yes, how?
Thanks! The short answer is that it is a heurisitic choice. This choice is discussed in a paper by LeCun, "Generalization and Network Design", 1989. It has to do with making the equations a little simpler, and the overall second derivative of the hyperbolic function is a pleasing (well, to some!) +1 to -1. This choice is thought to improve convergence of the learning process.
Is the file available?
Thank you