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author Daniel Wolff
date Fri, 19 Aug 2016 13:07:06 +0200
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Daniel@0 1 <html>
Daniel@0 2 <head>
Daniel@0 3 <title>
Daniel@0 4 Netlab Reference Manual demtrain
Daniel@0 5 </title>
Daniel@0 6 </head>
Daniel@0 7 <body>
Daniel@0 8 <H1> demtrain
Daniel@0 9 </H1>
Daniel@0 10 <h2>
Daniel@0 11 Purpose
Daniel@0 12 </h2>
Daniel@0 13 Demonstrate training of MLP network.
Daniel@0 14
Daniel@0 15 <p><h2>
Daniel@0 16 Synopsis
Daniel@0 17 </h2>
Daniel@0 18 <PRE>
Daniel@0 19 demtrain</PRE>
Daniel@0 20
Daniel@0 21
Daniel@0 22 <p><h2>
Daniel@0 23 Description
Daniel@0 24 </h2>
Daniel@0 25 <CODE>demtrain</CODE> brings up a simple GUI to show the training of
Daniel@0 26 an MLP network on classification and regression problems. The user
Daniel@0 27 should load in a dataset (which should be in Netlab format: see
Daniel@0 28 <CODE>datread</CODE>), select the output activation function, the
Daniel@0 29 number of cycles and hidden units and then
Daniel@0 30 train the network. The scaled conjugate gradient algorithm is used.
Daniel@0 31 A graph shows the evolution of the error: the value is shown
Daniel@0 32 <CODE>max(ceil(iterations / 50), 5)</CODE> cycles.
Daniel@0 33
Daniel@0 34 <p>Once the network is trained, it is saved to the file <CODE>mlptrain.net</CODE>.
Daniel@0 35 The results can then be viewed as a confusion matrix (for classification
Daniel@0 36 problems) or a plot of output versus target (for regression problems).
Daniel@0 37
Daniel@0 38 <p><h2>
Daniel@0 39 See Also
Daniel@0 40 </h2>
Daniel@0 41 <CODE><a href="confmat.htm">confmat</a></CODE>, <CODE><a href="datread.htm">datread</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="netopt.htm">netopt</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE><hr>
Daniel@0 42 <b>Pages:</b>
Daniel@0 43 <a href="index.htm">Index</a>
Daniel@0 44 <hr>
Daniel@0 45 <p>Copyright (c) Ian T Nabney (1996-9)
Daniel@0 46
Daniel@0 47
Daniel@0 48 </body>
Daniel@0 49 </html>