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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>Fast Artificial Neural Network Library</TITLE><link href="../style.css" rel="stylesheet" type="text/css"><METANAME="GENERATOR"CONTENT="Modular DocBook HTML Stylesheet Version 1.79"><LINKREL="NEXT"TITLE="Introduction"HREF="c13.html"></HEAD><BODYCLASS="book"BGCOLOR="#FFFFFF"TEXT="#000000"LINK="#0000FF"VLINK="#840084"ALINK="#0000FF"><DIVCLASS="BOOK"><ANAME="AEN1"></A><DIVCLASS="TITLEPAGE"><H1CLASS="title"><ANAME="bookinfo">Fast Artificial Neural Network Library</A></H1><H3CLASS="author"><ANAME="AEN5"></A>Steffen Nissen</H3><H3CLASS="author"><ANAME="AEN8"></A>Evan Nemerson</H3><PCLASS="copyright">Copyright © 2004 </P><HR></DIV><DIVCLASS="TOC"><DL><DT><B>Table of Contents</B></DT><DT>1. <AHREF="c13.html">Introduction</A></DT><DD><DL><DT>1.1. <AHREF="c13.html#intro.dl">Getting FANN</A></DT><DT>1.2. <AHREF="x26.html">Installation</A></DT><DD><DL><DT>1.2.1. <AHREF="x26.html#intro.install.rpm">RPMs</A></DT><DT>1.2.2. <AHREF="x26.html#intro.install.deb">DEBs</A></DT><DT>1.2.3. <AHREF="x26.html#intro.install.win32">Windows</A></DT><DT>1.2.4. <AHREF="x26.html#intro.install.src">Compiling from source</A></DT></DL></DD><DT>1.3. <AHREF="x68.html">Getting Started</A></DT><DD><DL><DT>1.3.1. <AHREF="x68.html#intro.start.train">Training</A></DT><DT>1.3.2. <AHREF="x68.html#intro.start.execution">Execution</A></DT></DL></DD><DT>1.4. <AHREF="x100.html">Getting Help</A></DT></DL></DD><DT>2. <AHREF="c104.html">Advanced Usage</A></DT><DD><DL><DT>2.1. <AHREF="c104.html#adv.adj">Adjusting Parameters</A></DT><DT>2.2. <AHREF="x141.html">Network Design</A></DT><DT>2.3. <AHREF="x148.html">Understanding the Error Value</A></DT><DT>2.4. <AHREF="x161.html">Training and Testing</A></DT><DT>2.5. <AHREF="x181.html">Avoid Over-Fitting</A></DT><DT>2.6. <AHREF="x184.html">Adjusting Parameters During Training</A></DT></DL></DD><DT>3. <AHREF="c189.html">Fixed Point Usage</A></DT><DD><DL><DT>3.1. <AHREF="c189.html#fixed.train">Training a Fixed Point ANN</A></DT><DT>3.2. <AHREF="x203.html">Running a Fixed Point ANN</A></DT><DT>3.3. <AHREF="x217.html">Precision of a Fixed Point ANN</A></DT></DL></DD><DT>4. <AHREF="c225.html">Neural Network Theory</A></DT><DD><DL><DT>4.1. <AHREF="c225.html#theory.neural_networks">Neural Networks</A></DT><DT>4.2. <AHREF="x241.html">Artificial Neural Networks</A></DT><DT>4.3. <AHREF="x246.html">Training an ANN</A></DT></DL></DD><DT>5. <AHREF="c253.html">API Reference</A></DT><DD><DL><DT>5.1. <AHREF="c253.html#api.sec.create_destroy">Creation, Destruction, and Execution</A></DT><DD><DL><DT><AHREF="r258.html">fann_create</A> -- Create a new artificial neural network, and return a pointer to it.</DT><DT><AHREF="r285.html">fann_create_array</A> -- Create a new artificial neural network, and return a pointer to it.</DT><DT><AHREF="r315.html">fann_create_shortcut</A> -- Create a new artificial neural network with shortcut connections, and return a pointer to it.</DT><DT><AHREF="r339.html">fann_create_shortcut_array</A> -- Create a new artificial neural network with shortcut connections, and return a pointer to it.</DT><DT><AHREF="r361.html">fann_destroy</A> -- Destroy an ANN.</DT><DT><AHREF="r376.html">fann_run</A> -- Run (execute) an ANN.</DT><DT><AHREF="r396.html">fann_randomize_weights</A> -- Give each connection a random weight.</DT><DT><AHREF="r421.html">fann_init_weights</A> -- Initialize the weight of each connection.</DT><DT><AHREF="r448.html">fann_print_connections</A> -- Prints the connections of an ann.</DT></DL></DD><DT>5.2. <AHREF="x472.html">Input/Output</A></DT><DD><DL><DT><AHREF="r474.html">fann_save</A> -- Save an ANN to a file.</DT><DT><AHREF="r494.html">fann_save_to_fixed</A> -- Save an ANN to a fixed-point file.</DT><DT><AHREF="r519.html">fann_create_from_file</A> -- Load an ANN from a file.</DT></DL></DD><DT>5.3. <AHREF="x534.html">Training</A></DT><DD><DL><DT><AHREF="r536.html">fann_train</A> -- Train an ANN.</DT><DT><AHREF="r557.html">fann_test</A> -- Tests an ANN.</DT><DT><AHREF="r577.html">fann_get_MSE</A> -- Return the mean square error of an ANN.</DT><DT><AHREF="r593.html">fann_reset_MSE</A> -- Reset the mean square error of an ANN.</DT></DL></DD><DT>5.4. <AHREF="x609.html">Training Data</A></DT><DD><DL><DT><AHREF="r611.html">fann_read_train_from_file</A> -- Read training data from a file.</DT><DT><AHREF="r629.html">fann_save_train</A> -- Save training data.</DT><DT><AHREF="r648.html">fann_save_train_to_fixed</A> -- Save training data as fixed point.</DT><DT><AHREF="r670.html">fann_destroy_train</A> -- Destroy training data.</DT><DT><AHREF="r685.html">fann_train_epoch</A> -- Trains one epoch.</DT><DT><AHREF="r709.html">fann_test_data</A> -- Calculates the mean square error for a set of data.</DT><DT><AHREF="r726.html">fann_train_on_data</A> -- Train an ANN.</DT><DT><AHREF="r761.html">fann_train_on_data_callback</A> -- Train an ANN.</DT><DT><AHREF="r806.html">fann_train_on_file</A> -- Train an ANN.</DT><DT><AHREF="r841.html">fann_train_on_file_callback</A> -- Train an ANN.</DT><DT><AHREF="r886.html">fann_shuffle_train_data</A> -- Shuffle the training data.</DT><DT><AHREF="r902.html">fann_merge_train_data</A> -- Merge two sets of training data.</DT><DT><AHREF="r922.html">fann_duplicate_train_data</A> -- Copies a set of training data.</DT></DL></DD><DT>5.5. <AHREF="x938.html">Options</A></DT><DD><DL><DT><AHREF="r940.html">fann_print_parameters</A> -- Prints all of the parameters and options of the ANN.</DT><DT><AHREF="r954.html">fann_get_training_algorithm</A> -- Retrieve training algorithm from a network.</DT><DT><AHREF="r972.html">fann_set_training_algorithm</A> -- Set a network's training algorithm.</DT><DT><AHREF="r993.html">fann_get_learning_rate</A> -- Retrieve learning rate from a network.</DT><DT><AHREF="r1007.html">fann_set_learning_rate</A> -- Set a network's learning rate.</DT><DT><AHREF="r1024.html">fann_get_activation_function_hidden</A> -- Get the activation function used in the hidden layers.</DT><DT><AHREF="r1040.html">fann_set_activation_function_hidden</A> -- Set the activation function for the hidden layers.</DT><DT><AHREF="r1060.html">fann_get_activation_function_output</A> -- Get the activation function of the output layer.</DT><DT><AHREF="r1076.html">fann_set_activation_function_output</A> -- Set the activation function for the output layer.</DT><DT><AHREF="r1096.html">fann_get_activation_steepness_hidden</A> -- Retrieve the steepness of the activation function of the hidden layers.</DT><DT><AHREF="r1112.html">fann_set_activation_steepness_hidden</A> -- Set the steepness of the activation function of the hidden layers.</DT><DT><AHREF="r1133.html">fann_get_activation_steepness_output</A> -- Retrieve the steepness of the activation function of the output layer.</DT><DT><AHREF="r1149.html">fann_set_activation_steepness_output</A> -- Set the steepness of the activation function of the output layer.</DT><DT><AHREF="r1170.html">fann_set_train_error_function</A> -- Sets the training error function to be used.</DT><DT><AHREF="r1191.html">fann_get_train_error_function</A> -- Gets the training error function to be used.</DT><DT><AHREF="r1209.html">fann_get_quickprop_decay</A> -- Get the decay parameter used by the quickprop training.</DT><DT><AHREF="r1224.html">fann_set_quickprop_decay</A> -- Set the decay parameter used by the quickprop training.</DT><DT><AHREF="r1242.html">fann_get_quickprop_mu</A> -- Get the mu factor used by quickprop training.</DT><DT><AHREF="r1257.html">fann_set_quickprop_mu</A> -- Set the mu factor used by quickprop training.</DT><DT><AHREF="r1275.html">fann_get_rprop_increase_factor</A> -- Get the increase factor used by RPROP training.</DT><DT><AHREF="r1290.html">fann_set_rprop_increase_factor</A> -- Get the increase factor used by RPROP training.</DT><DT><AHREF="r1308.html">fann_get_rprop_decrease_factor</A> -- Get the decrease factor used by RPROP training.</DT><DT⌨️ 快捷键说明
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