wolffd@0: <html> wolffd@0: <head> wolffd@0: <title> wolffd@0: Netlab Reference Manual demtrain wolffd@0: </title> wolffd@0: </head> wolffd@0: <body> wolffd@0: <H1> demtrain wolffd@0: </H1> wolffd@0: <h2> wolffd@0: Purpose wolffd@0: </h2> wolffd@0: Demonstrate training of MLP network. wolffd@0: wolffd@0: <p><h2> wolffd@0: Synopsis wolffd@0: </h2> wolffd@0: <PRE> wolffd@0: demtrain</PRE> wolffd@0: wolffd@0: wolffd@0: <p><h2> wolffd@0: Description wolffd@0: </h2> wolffd@0: <CODE>demtrain</CODE> brings up a simple GUI to show the training of wolffd@0: an MLP network on classification and regression problems. The user wolffd@0: should load in a dataset (which should be in Netlab format: see wolffd@0: <CODE>datread</CODE>), select the output activation function, the wolffd@0: number of cycles and hidden units and then wolffd@0: train the network. The scaled conjugate gradient algorithm is used. wolffd@0: A graph shows the evolution of the error: the value is shown wolffd@0: <CODE>max(ceil(iterations / 50), 5)</CODE> cycles. wolffd@0: wolffd@0: <p>Once the network is trained, it is saved to the file <CODE>mlptrain.net</CODE>. wolffd@0: The results can then be viewed as a confusion matrix (for classification wolffd@0: problems) or a plot of output versus target (for regression problems). wolffd@0: wolffd@0: <p><h2> wolffd@0: See Also wolffd@0: </h2> wolffd@0: <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> wolffd@0: <b>Pages:</b> wolffd@0: <a href="index.htm">Index</a> wolffd@0: <hr> wolffd@0: <p>Copyright (c) Ian T Nabney (1996-9) wolffd@0: wolffd@0: wolffd@0: </body> wolffd@0: </html>