wolffd@0: wolffd@0: wolffd@0: wolffd@0: Netlab Reference Manual glmgrad wolffd@0: wolffd@0: wolffd@0: wolffd@0:

glmgrad wolffd@0:

wolffd@0:

wolffd@0: Purpose wolffd@0:

wolffd@0: Evaluate gradient of error function for generalized linear model. wolffd@0: wolffd@0:

wolffd@0: Synopsis wolffd@0:

wolffd@0:

wolffd@0: 
wolffd@0: g = glmgrad(net, x, t)
wolffd@0: [g, gdata, gprior] = glmgrad(net, x, t)
wolffd@0:

wolffd@0: wolffd@0: wolffd@0:

wolffd@0: Description wolffd@0:

wolffd@0: g = glmgrad(net, x, t) takes a generalized linear model wolffd@0: data structure net wolffd@0: together with a matrix x of input vectors and a matrix t wolffd@0: of target vectors, and evaluates the gradient g of the error wolffd@0: function with respect to the network weights. The error function wolffd@0: corresponds to the choice of output unit activation function. Each row wolffd@0: of x corresponds to one input vector and each row of t wolffd@0: corresponds to one target vector. wolffd@0: wolffd@0:

[g, gdata, gprior] = glmgrad(net, x, t) also returns separately wolffd@0: the data and prior contributions to the gradient. wolffd@0: wolffd@0:

wolffd@0: See Also wolffd@0:

wolffd@0: glm, glmpak, glmunpak, glmfwd, glmerr, glmtrain

wolffd@0: Pages: wolffd@0: Index wolffd@0:

wolffd@0: