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<a href="#func-members">Functions</a> </div>
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<div class="title">Softmax Functions<div class="ingroups"><a class="el" href="group__groupNN.html">Neural Network Functions</a></div></div> </div>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
Functions</h2></td></tr>
<tr class="memitem:ga1cacd8b84b8363079311987d0016ebe5"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="group__Softmax.html#ga1cacd8b84b8363079311987d0016ebe5">arm_softmax_q15</a> (const q15_t *vec_in, const uint16_t dim_vec, q15_t *p_out)</td></tr>
<tr class="memdesc:ga1cacd8b84b8363079311987d0016ebe5"><td class="mdescLeft"> </td><td class="mdescRight">Q15 softmax function. <a href="#ga1cacd8b84b8363079311987d0016ebe5">More...</a><br/></td></tr>
<tr class="separator:ga1cacd8b84b8363079311987d0016ebe5"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ga89aff212a97a3cf32d9d7ddf11a8f43e"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="group__Softmax.html#ga89aff212a97a3cf32d9d7ddf11a8f43e">arm_softmax_q7</a> (const q7_t *vec_in, const uint16_t dim_vec, q7_t *p_out)</td></tr>
<tr class="memdesc:ga89aff212a97a3cf32d9d7ddf11a8f43e"><td class="mdescLeft"> </td><td class="mdescRight">Q7 softmax function. <a href="#ga89aff212a97a3cf32d9d7ddf11a8f43e">More...</a><br/></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Description</h2>
<p>EXP(2) based softmax function </p>
<h2 class="groupheader">Function Documentation</h2>
<a class="anchor" id="ga1cacd8b84b8363079311987d0016ebe5"></a>
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<td class="memname">void arm_softmax_q15 </td>
<td>(</td>
<td class="paramtype">const q15_t * </td>
<td class="paramname"><em>vec_in</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const uint16_t </td>
<td class="paramname"><em>dim_vec</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">q15_t * </td>
<td class="paramname"><em>p_out</em> </td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramdir">[in]</td><td class="paramname">vec_in</td><td>pointer to input vector </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">dim_vec</td><td>input vector dimention </td></tr>
<tr><td class="paramdir">[out]</td><td class="paramname">p_out</td><td>pointer to output vector </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>none.</dd></dl>
<p>Here, instead of typical e based softmax, we use 2-based softmax, i.e.,:</p>
<p>y_i = 2^(x_i) / sum(2^x_j)</p>
<p>The relative output will be different here. But mathematically, the gradient will be the same with a log(2) scaling factor. </p>
</div>
</div>
<a class="anchor" id="ga89aff212a97a3cf32d9d7ddf11a8f43e"></a>
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<td class="memname">void arm_softmax_q7 </td>
<td>(</td>
<td class="paramtype">const q7_t * </td>
<td class="paramname"><em>vec_in</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const uint16_t </td>
<td class="paramname"><em>dim_vec</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">q7_t * </td>
<td class="paramname"><em>p_out</em> </td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramdir">[in]</td><td class="paramname">vec_in</td><td>pointer to input vector </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">dim_vec</td><td>input vector dimention </td></tr>
<tr><td class="paramdir">[out]</td><td class="paramname">p_out</td><td>pointer to output vector </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>none.</dd></dl>
<p>Here, instead of typical natural logarithm e based softmax, we use 2-based softmax here, i.e.,:</p>
<p>y_i = 2^(x_i) / sum(2^x_j)</p>
<p>The relative output will be different here. But mathematically, the gradient will be the same with a log(2) scaling factor. </p>
<p>Referenced by <a class="el" href="arm__nnexamples__cifar10_8cpp.html#ae66f6b31b5ad750f1fe042a706a4e3d4">main()</a>.</p>
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