Monotonic Constrained NEURAL NETWORK
2 visualizaciones (últimos 30 días)
Mostrar comentarios más antiguos
I am new at the Neural Network field. Please be explicit. I have two input neurons, one output target and two hidden neurons. I want the derivative with respect to the inputs and the bias are positive. Is there a trick to constraint the Neural Network so that the derivative of the neural network's outputs with respect to the input variable is positive and the bias is positive. I believe I want that the products of the weights and the biases along all paths to be positive as long as the activation function is monotonically increasing .
1 comentario
Orion Wolfe
el 25 de Mzo. de 2016
Restrict all of the gains to be positive. This can be done by using a positive mapping on the gains. For example if the network is defined using tanh(w'*x+b) activation functions replace w with g(w) where g is R to R+ mapping g(w) = log(1+e^w) is one such mapping and the activation function become tanh(g(w)*x+b)
Respuestas (1)
Greg Heath
el 8 de Jul. de 2015
If the derivative of the target with respect to the input is positive, just design a good net with as few hidden nodes as possible. It may take a lot of trials.
0 comentarios
Ver también
Categorías
Más información sobre Define Shallow Neural Network Architectures en Help Center y File Exchange.
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!