📄 r685.html

📁 一个功能强大的神经网络分析程序
💻 HTML
字号:
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN""http://www.w3.org/TR/html4/loose.dtd"><HTML><HEAD><TITLE>fann_train_epoch</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="Training Data"HREF="x609.html"><LINKREL="PREVIOUS"TITLE="fann_destroy_train"HREF="r670.html"><LINKREL="NEXT"TITLE="fann_test_data"HREF="r709.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="r670.html"ACCESSKEY="P">Prev</A></TD><TDWIDTH="80%"ALIGN="center"VALIGN="bottom"></TD><TDWIDTH="10%"ALIGN="right"VALIGN="bottom"><AHREF="r709.html"ACCESSKEY="N">Next</A></TD></TR></TABLE><HRALIGN="LEFT"WIDTH="100%"></DIV><H1><ANAME="api.fann_train_epoch"></A>fann_train_epoch</H1><DIVCLASS="refnamediv"><ANAME="AEN686"></A><H2>Name</H2>fann_train_epoch&nbsp;--&nbsp;Trains one epoch.</DIV><DIVCLASS="refsect1"><ANAME="AEN689"></A><H2>Description</H2><codeclass="methodsynopsis">&#13;  <spanclass="type">float </span>fann_train_epoch(<spanclass="methodparam"><spanclass="type">struct fann * </span><spanclass="parameter">ann</span></span><spanclass="methodparam">, <spanclass="type">struct fann_train_data * </span><spanclass="parameter">data</span></span>);&#13;</code><P>&#13;	    Train one epoch with the training data stored in <CODECLASS="parameter">data</CODE>. One epoch is 	    where all of the training data is considered exactly once.	  </P><P>&#13;	    This function returns the MSE error as it is calculated either before or during the actual training.	    This is not the actual MSE after the training epoch, but since calculating this will require to go 	    through the entire training set once more, it is more than adequate to use this value during training.	  </P><P>&#13;	    The training algorithm used by this function is chosen by the 	    <AHREF="r972.html"><CODECLASS="function">fann_set_training_algorithm</CODE></A> 	    function. The default training algorithm is <AHREF="r1996.html"><CODECLASS="constant">FANN_TRAIN_RPROP</CODE></A>.	  </P><P>This function appears in FANN &#62;= 1.2.0.</P></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="r670.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="r709.html"ACCESSKEY="N">Next</A></TD></TR><TR><TDWIDTH="33%"ALIGN="left"VALIGN="top">fann_destroy_train</TD><TDWIDTH="34%"ALIGN="center"VALIGN="top"><AHREF="x609.html"ACCESSKEY="U">Up</A></TD><TDWIDTH="33%"ALIGN="right"VALIGN="top">fann_test_data</TD></TR></TABLE></DIV></BODY></HTML>
⌨️ 快捷键说明

复制代码 Ctrl + C
搜索代码 Ctrl + F
全屏模式 F11
切换主题 Ctrl + Shift + D
显示快捷键 ?
增大字号 Ctrl + =
减小字号 Ctrl + -