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author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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+++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mlpbkp.htm	Tue Feb 10 15:05:51 2015 +0000
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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 the
+output units (i.e. the summed inputs to the output units, before the
+activation function is applied). The return value is the gradient
+<CODE>g</CODE> of the error function with respect to the network
+weights. Each row of <CODE>x</CODE> corresponds to one input vector.
+
+<p>This function is provided so that the common backpropagation algorithm
+can be used by multi-layer perceptron network models to compute
+gradients for mixture density networks as well as standard error
+functions.
+
+<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>
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