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Artificial Neural Networks made easy with the FANN library

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28 Aug 2013CPOL24 min read 194.3K   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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>&#13;	The mean square error value is calculated while the ANN is being trained. Some functions are implemented, to use and manipulate this error value. The
	<A
HREF="r577.html"
><CODE
CLASS="function"
>fann_get_MSE</CODE
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> function returns the error value and the
	<A
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> resets the error value. The following explains how the mean square error
	value is calculated, to give an idea of the value's ability to reveal the quality of the training.
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>&#13;	If <SPAN
CLASS="emphasis"
><I
CLASS="emphasis"
>d</I
></SPAN
> is the desired output of an output neuron and <SPAN
CLASS="emphasis"
><I
CLASS="emphasis"
>y</I
></SPAN
> is the actual output of the neuron, the square error is
	(d - y) squared. If two output neurons exists, then the mean square error for these two neurons is the average of the two square errors.
      </P
><P
>&#13;	When training with the <A
HREF="r806.html"
><CODE
CLASS="function"
>fann_train_on_file</CODE
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> function, an error value is printed. This
	error value is the mean square error for all the training data. Meaning that it is the average of all the square errors in each of the training pairs.
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