fann-set-activation-function.xml

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<refentry xml:id="function.fann-set-activation-function" xmlns="http://docbook.org/ns/docbook" xmlns:xlink="http://www.w3.org/1999/xlink">
 <refnamediv>
  <refname>fann_set_activation_function</refname>
  <refpurpose>为已应用的神经元和层设置激活函数</refpurpose>
 </refnamediv>

 <refsect1 role="description">
  &reftitle.description;
  <methodsynopsis>
   <type>bool</type><methodname>fann_set_activation_function</methodname>
   <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
   <methodparam><type>int</type><parameter>activation_function</parameter></methodparam>
   <methodparam><type>int</type><parameter>layer</parameter></methodparam>
   <methodparam><type>int</type><parameter>neuron</parameter></methodparam>
  </methodsynopsis>
  <para>
   为层数为 <literal>layer</literal>，神经经元数为 <literal>neuron</literal> 的神经元设置激活函数，将输入层记为0.
  </para>
  <para>
   在输出层中的神经元设置激活函数是不可能的。
  </para>
  <para>
   在选择激活函数时，注意激活函数有不同的范围，这个很重要哦。
   <constant>FANN_SIGMOID</constant> 就是个例子。它的范围是0 -1，然而 <constant>FANN_SIGMOID_SYMMETRIC</constant> 的范围是-1 - 1， <constant>FANN_LINEAR</constant> 却是无限的。
  </para>
  <para>
   应用的激活函数应该是<link linkend="constants.fann-activation-funcs">激活函数</link>常量之一。
  </para>
  <para>
   返回值是<link linkend="constants.fann-train">激活函数</link>常量之一。
  </para>
 </refsect1>

 <refsect1 role="parameters">
  &reftitle.parameters;
  <variablelist>
   <varlistentry>
    <term><parameter>ann</parameter></term>
    <listitem>
     &fann.ann.description;
    </listitem>
   </varlistentry>
   <varlistentry>
    <term><parameter>activation_function</parameter></term>
    <listitem>
     <para>
      <link linkend="constants.fann-activation-funcs">激活函数</link>常量。
     </para>
    </listitem>
   </varlistentry>
   <varlistentry>
    <term><parameter>layer</parameter></term>
    <listitem>
     <para>
      层数。
     </para>
    </listitem>
   </varlistentry>
   <varlistentry>
    <term><parameter>neuron</parameter></term>
    <listitem>
     <para>
      神经元数。
     </para>
    </listitem>
   </varlistentry>
  </variablelist>
 </refsect1>

 <refsect1 role="returnvalues">
  &reftitle.returnvalues;
  &fann.return.bool;
 </refsect1>

 <refsect1 role="seealso">
  &reftitle.seealso;
  <para>
   <simplelist>
    <member><function>fann_set_activation_function_layer</function></member>
    <member><function>fann_set_activation_function_hidden</function></member>
    <member><function>fann_set_activation_function_output</function></member>
    <member><function>fann_set_activation_steepness</function></member>
    <member><function>fann_get_activation_function</function></member>
   </simplelist>
  </para>
 </refsect1>


</refentry>

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