Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual mlpevfwd Daniel@0: Daniel@0: Daniel@0: Daniel@0:

mlpevfwd Daniel@0:

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Daniel@0: Purpose Daniel@0:

Daniel@0: Forward propagation with evidence for MLP Daniel@0: Daniel@0:

Daniel@0: Synopsis Daniel@0:

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Daniel@0: [y, extra] = mlpevfwd(net, x, t, x_test)
Daniel@0: [y, extra, invhess] = mlpevfwd(net, x, t, x_test, invhess)
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Daniel@0: Description Daniel@0:

Daniel@0: y = mlpevfwd(net, x, t, x_test) takes a network data structure Daniel@0: net together with the input x and target t training data Daniel@0: and input test data x_test. Daniel@0: It returns the normal forward propagation through the network y Daniel@0: together with a matrix extra which consists of error bars (variance) Daniel@0: for a regression problem or moderated outputs for a classification problem. Daniel@0: The optional argument (and return value) Daniel@0: invhess is the inverse of the network Hessian Daniel@0: computed on the training data inputs and targets. Passing it in avoids Daniel@0: recomputing it, which can be a significant saving for large training sets. Daniel@0: Daniel@0:

Daniel@0: See Also Daniel@0:

Daniel@0: fevbayes
Daniel@0: Pages: Daniel@0: Index Daniel@0:
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Copyright (c) Ian T Nabney (1996-9) Daniel@0: Daniel@0: Daniel@0: Daniel@0: