Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual mlpgrad Daniel@0: Daniel@0: Daniel@0: Daniel@0:

mlpgrad Daniel@0:

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Daniel@0: Purpose Daniel@0:

Daniel@0: Evaluate gradient of error function for 2-layer network. Daniel@0: Daniel@0:

Daniel@0: Synopsis Daniel@0:

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Daniel@0: 
Daniel@0: g = mlpgrad(net, x, t)
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Daniel@0: Description Daniel@0:

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

[g, gdata, gprior] = mlpgrad(net, x, t) also returns separately Daniel@0: the data and prior contributions to the gradient. In the case of Daniel@0: multiple groups in the prior, gprior is a matrix with a row Daniel@0: for each group and a column for each weight parameter. Daniel@0: Daniel@0:

Daniel@0: See Also Daniel@0:

Daniel@0: mlp, mlppak, mlpunpak, mlpfwd, mlperr, mlpbkp

Daniel@0: Pages: Daniel@0: Index Daniel@0:

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