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Artificial Neural Networks made easy with the FANN library

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28 Aug 2013CPOL24 min read 194.1K   10.6K   206  
Neural networks are typically associated with specialised applications, developed only by select groups of experts. This misconception has had a highly negative effect on its popularity. Hopefully, the FANN library will help fill this gap.
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>fann_get_activation_steepness_output</H1
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>Name</H2
>fann_get_activation_steepness_output&nbsp;--&nbsp;Retrieve the steepness of the activation function of the output layer.</DIV
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><code
class="methodsynopsis"
>&#13;  <span
class="type"
>fann_type </span
>fann_get_activation_steepness_output(<span
class="methodparam"
><span
class="type"
>struct fann * </span
><span
class="parameter"
>ann</span
></span
>);&#13;</code
><P
>Return the steepness of the activation function of the hidden layers.</P
><P
>&#13;	    The steepness defaults to 0.5 and a larger steepness will make the slope of the
	    activation function more steep, while a smaller steepness will make the slope less
	    steep. A large steepness is well suited for classification problems while a small
	    steepness is well suited for function approximation.
	  </P
><P
>This function appears in FANN &#62;= 1.2.0. and replaces the <CODE
CLASS="methodname"
>fann_get_activation_output_steepness</CODE
> function from FANN &#62;= 1.0.0.</P
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