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来自「一个功能强大的神经网络分析程序」· HTML 代码 · 共 211 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>fann_print_connections</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="Creation, Destruction, and Execution"HREF="c253.html#api.sec.create_destroy"><LINKREL="PREVIOUS"TITLE="fann_init_weights"HREF="r421.html"><LINKREL="NEXT"TITLE="Input/Output"HREF="x472.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="r421.html"ACCESSKEY="P">Prev</A></TD><TDWIDTH="80%"ALIGN="center"VALIGN="bottom"></TD><TDWIDTH="10%"ALIGN="right"VALIGN="bottom"><AHREF="x472.html"ACCESSKEY="N">Next</A></TD></TR></TABLE><HRALIGN="LEFT"WIDTH="100%"></DIV><H1><ANAME="api.fann_print_connections"></A>fann_print_connections</H1><DIVCLASS="refnamediv"><ANAME="AEN449"></A><H2>Name</H2>fann_print_connections -- Prints the connections of an ann.</DIV><DIVCLASS="refsect1"><ANAME="AEN452"></A><H2>Description</H2><codeclass="methodsynopsis"> <spanclass="type">void </span>fann_print_connections(<spanclass="methodparam"><spanclass="type">struct fann * </span><spanclass="parameter">ann</span></span>); </code><P> <CODECLASS="function">fann_print_connections</CODE> will print the connections of the ann in a compact matrix, for easy viewing of the internals of the ann. </P><P> The output from fann_print_connections on a small (2 2 1) network trained on the xor problem: <PRECLASS="literallayout"> Layer / Neuron 012345L 1 / N 3 ddb...L 1 / N 4 bbb...L 2 / N 6 ...cda </PRE> This network have five real neurons and two bias neurons. This gives a total of seven neurons named from 0 to 6. The connections between these neurons can be seen in the matrix. <CODECLASS="constant">"."</CODE> is a place where there is no connection, while a character tells how strong the connection is on a scale from a-z. The two real neurons in the hidden layer (neuron <CODECLASS="constant">3</CODE> and <CODECLASS="constant">4</CODE> in layer <CODECLASS="constant">1</CODE>) has connection from the three neurons in the previous layer as is visible in the first two lines. The output neuron (<CODECLASS="constant">6</CODE>) has connections form the three neurons in the hidden layer <CODECLASS="constant">3 - 5</CODE> as is visible in the last line. </P><P> To simplify the matrix output neurons is not visible as neurons that connections can come from, and input and bias neurons are not visible as neurons that connections can go to. </P><P>This function appears in FANN >= 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="r421.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="x472.html"ACCESSKEY="N">Next</A></TD></TR><TR><TDWIDTH="33%"ALIGN="left"VALIGN="top">fann_init_weights</TD><TDWIDTH="34%"ALIGN="center"VALIGN="top"><AHREF="c253.html#api.sec.create_destroy"ACCESSKEY="U">Up</A></TD><TDWIDTH="33%"ALIGN="right"VALIGN="top">Input/Output</TD></TR></TABLE></DIV></BODY></HTML>⌨️ 快捷键说明
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