r2077.html

来自「一个功能强大的神经网络分析程序」· HTML 代码 · 共 206 行

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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN""http://www.w3.org/TR/html4/loose.dtd"><HTML><HEAD><TITLE>Training Error Functions</TITLE><link href="../style.css" rel="stylesheet" type="text/css"><METANAME="GENERATOR"CONTENT="Modular DocBook HTML Stylesheet Version 1.79"><LINKREL="HOME"TITLE="Fast Artificial Neural Network Library"HREF="index.html"><LINKREL="UP"TITLE="Constants"HREF="x1994.html"><LINKREL="PREVIOUS"TITLE="Activation Functions"HREF="r2030.html"><LINKREL="NEXT"TITLE="Error Codes"HREF="r2099.html"></HEAD><BODYCLASS="refentry"BGCOLOR="#FFFFFF"TEXT="#000000"LINK="#0000FF"VLINK="#840084"ALINK="#0000FF"><DIVCLASS="NAVHEADER"><TABLESUMMARY="Header navigation table"WIDTH="100%"BORDER="0"CELLPADDING="0"CELLSPACING="0"><TR><THCOLSPAN="3"ALIGN="center">Fast Artificial Neural Network Library</TH></TR><TR><TDWIDTH="10%"ALIGN="left"VALIGN="bottom"><AHREF="r2030.html"ACCESSKEY="P">Prev</A></TD><TDWIDTH="80%"ALIGN="center"VALIGN="bottom"></TD><TDWIDTH="10%"ALIGN="right"VALIGN="bottom"><AHREF="r2099.html"ACCESSKEY="N">Next</A></TD></TR></TABLE><HRALIGN="LEFT"WIDTH="100%"></DIV><H1><ANAME="api.sec.constants.errorfunc"></A>Training Error Functions</H1><DIVCLASS="refnamediv"><ANAME="AEN2078"></A><H2>Name</H2>Training Error Functions&nbsp;--&nbsp;Constants representing errors functions.</DIV><DIVCLASS="refsect1"><ANAME="AEN2081"></A><H2>Description</H2><P>&#13;	    These constants represent the error functions used when calculating the error during training.	  </P><P>&#13;	    The training error function used is chosen by the 	    <AHREF="r1170.html"><CODECLASS="function">fann_set_train_error_function</CODE></A> 	    function. The default training error function is <CODECLASS="constant">FANN_ERRORFUNC_TANH</CODE>.	  </P><P></P><DIVCLASS="variablelist"><P><B>Constants</B></P><DL><DT>FANN_ERRORFUNC_LINEAR</DT><DD><P>&#13;		  The basic linear error function which simply calculates the error as the difference		  between the real output and the desired output.		</P></DD><DT>FANN_ERRORFUNC_TANH</DT><DD><P>&#13;		  The tanh error function is an error function that makes large deviations 		  stand out, by altering the error value used when training the network.		  The idea behind this is that it is worse to have 1 output that misses the target		  by 100%, than having 10 outputs that misses the target by 10%.		</P><P>&#13;		  This is the default error function and it is usually better. It can however 		  give poor results with high learning rates.		</P></DD></DL></DIV></DIV><DIVCLASS="NAVFOOTER"><HRALIGN="LEFT"WIDTH="100%"><TABLESUMMARY="Footer navigation table"WIDTH="100%"BORDER="0"CELLPADDING="0"CELLSPACING="0"><TR><TDWIDTH="33%"ALIGN="left"VALIGN="top"><AHREF="r2030.html"ACCESSKEY="P">Prev</A></TD><TDWIDTH="34%"ALIGN="center"VALIGN="top"><AHREF="index.html"ACCESSKEY="H">Home</A></TD><TDWIDTH="33%"ALIGN="right"VALIGN="top"><AHREF="r2099.html"ACCESSKEY="N">Next</A></TD></TR><TR><TDWIDTH="33%"ALIGN="left"VALIGN="top">Activation Functions</TD><TDWIDTH="34%"ALIGN="center"VALIGN="top"><AHREF="x1994.html"ACCESSKEY="U">Up</A></TD><TDWIDTH="33%"ALIGN="right"VALIGN="top">Error Codes</TD></TR></TABLE></DIV></BODY></HTML>

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