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>fann_save_to_fixed</H1
><DIV
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><A
NAME="AEN495"
></A
><H2
>Name</H2
>fann_save_to_fixed -- Save an ANN to a fixed-point file.</DIV
><DIV
CLASS="refsect1"
><A
NAME="AEN498"
></A
><H2
>Description</H2
><code
class="methodsynopsis"
> <span
class="type"
>void </span
>fann_save_to_fixed(<span
class="methodparam"
><span
class="type"
>struct fann * </span
><span
class="parameter"
>ann</span
></span
><span
class="methodparam"
>, <span
class="type"
>const char * </span
><span
class="parameter"
>configuration_file</span
></span
>); </code
><P
> <CODE
CLASS="function"
>fann_save_to_fixed</CODE
> will attempt to save <VAR
CLASS="parameter"
>ann</VAR
> to the file located at
<VAR
CLASS="parameter"
>configuration_file</VAR
> as a fixed-point network.
</P
><P
> This is useful for training a network in floating points,
and then later executing it in fixed point.
</P
><P
> The function returns the bit position of the fix point, which
can be used to find out how accurate the fixed point network will be.
A high value indicates high precision, and a low value indicates low
precision.
</P
><P
> A negative value indicates very low precision, and a very
strong possibility for overflow.
(the actual fix point will be set to 0, since a negative
fix point does not make sense).
</P
><P
> Generally, a fix point lower than 6 is bad, and should be avoided.
The best way to avoid this, is to have less connections to each neuron,
or just less neurons in each layer.
</P
><P
> The fixed point use of this network is only intended for use on machines that
have no floating point processor, like an iPAQ. On normal computers the floating
point version is actually faster.
</P
><P
>This function appears in FANN >= 1.0.0.</P
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