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<html><head><title>Netlab Reference Manual mlpbkp</title></head><body><H1> mlpbkp</H1><h2>Purpose</h2>Backpropagate gradient of error function for 2-layer network.<p><h2>Synopsis</h2><PRE>g = mlpbkp(net, x, z, deltas)</PRE><p><h2>Description</h2><CODE>g = mlpbkp(net, x, z, deltas)</CODE> takes a network data structure<CODE>net</CODE> together with a matrix <CODE>x</CODE> of input vectors, a matrix <CODE>z</CODE> of hidden unit activations, and a matrix <CODE>deltas</CODE> of the gradient of the error function with respect to the values of theoutput units (i.e. the summed inputs to the output units, before theactivation function is applied). The return value is the gradient<CODE>g</CODE> of the error function with respect to the networkweights. Each row of <CODE>x</CODE> corresponds to one input vector.<p>This function is provided so that the common backpropagation algorithmcan be used by multi-layer perceptron network models to computegradients for mixture density networks as well as standard errorfunctions.<p><h2>See Also</h2><CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE>, <CODE><a href="mlpderiv.htm">mlpderiv</a></CODE>, <CODE><a href="mdngrad.htm">mdngrad</a></CODE><hr><b>Pages:</b><a href="index.htm">Index</a><hr><p>Copyright (c) Ian T Nabney (1996-9)</body></html>⌨️ 快捷键说明
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