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<a href="#pub-methods">Public Member Functions</a> </div>
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<div class="title">GCSoftmaxLayer Class Reference</div> </div>
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<p>Basic function to compute a SoftmaxLayer.
<a href="classarm__compute_1_1_g_c_softmax_layer.xhtml#details">More...</a></p>
<p><code>#include &lt;<a class="el" href="_g_c_softmax_layer_8h_source.xhtml">GCSoftmaxLayer.h</a>&gt;</code></p>
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Collaboration diagram for GCSoftmaxLayer:</div>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a4c3d471a20099610ef1d617dbaf4b2d6"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_g_c_softmax_layer.xhtml#a4c3d471a20099610ef1d617dbaf4b2d6">GCSoftmaxLayer</a> (std::shared_ptr&lt; <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">IMemoryManager</a> &gt; memory_manager=nullptr)</td></tr>
<tr class="memdesc:a4c3d471a20099610ef1d617dbaf4b2d6"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructor. <a href="#a4c3d471a20099610ef1d617dbaf4b2d6">More...</a><br /></td></tr>
<tr class="separator:a4c3d471a20099610ef1d617dbaf4b2d6"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a87e0f86361e9b6d237464d91ce76fbfa"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_g_c_softmax_layer.xhtml#a87e0f86361e9b6d237464d91ce76fbfa">configure</a> (const <a class="el" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *input, <a class="el" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *output, float beta=1.0f, size_t axis=1)</td></tr>
<tr class="memdesc:a87e0f86361e9b6d237464d91ce76fbfa"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the input and output tensors. <a href="#a87e0f86361e9b6d237464d91ce76fbfa">More...</a><br /></td></tr>
<tr class="separator:a87e0f86361e9b6d237464d91ce76fbfa"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad1717410afd0be936c6213a63c8005fb"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_g_c_softmax_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a> () override</td></tr>
<tr class="memdesc:ad1717410afd0be936c6213a63c8005fb"><td class="mdescLeft">&#160;</td><td class="mdescRight">Run the kernels contained in the function. <a href="#ad1717410afd0be936c6213a63c8005fb">More...</a><br /></td></tr>
<tr class="separator:ad1717410afd0be936c6213a63c8005fb"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pub_methods_classarm__compute_1_1_i_function"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classarm__compute_1_1_i_function')"><img src="closed.png" alt="-"/>&#160;Public Member Functions inherited from <a class="el" href="classarm__compute_1_1_i_function.xhtml">IFunction</a></td></tr>
<tr class="memitem:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_i_function.xhtml#ab921ecc3f3f6ae2b4bd61f3e1998d8c4">~IFunction</a> ()=default</td></tr>
<tr class="memdesc:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destructor. <a href="classarm__compute_1_1_i_function.xhtml#ab921ecc3f3f6ae2b4bd61f3e1998d8c4">More...</a><br /></td></tr>
<tr class="separator:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a820f7291c24155a2980512fae45aac26 inherit pub_methods_classarm__compute_1_1_i_function"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_i_function.xhtml#a820f7291c24155a2980512fae45aac26">prepare</a> ()</td></tr>
<tr class="memdesc:a820f7291c24155a2980512fae45aac26 inherit pub_methods_classarm__compute_1_1_i_function"><td class="mdescLeft">&#160;</td><td class="mdescRight">Prepare the function for executing. <a href="classarm__compute_1_1_i_function.xhtml#a820f7291c24155a2980512fae45aac26">More...</a><br /></td></tr>
<tr class="separator:a820f7291c24155a2980512fae45aac26 inherit pub_methods_classarm__compute_1_1_i_function"><td class="memSeparator" colspan="2">&#160;</td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Basic function to compute a SoftmaxLayer. </p>
<p>Softmax is calculated by : </p><p class="formulaDsp">
\[ out = exp(x - max(x)) / sum(exp(x - max(x))) \]
</p>
<p>This function runs the following kernels:</p><ol type="1">
<li><a class="el" href="classarm__compute_1_1_g_c_logits1_d_max_kernel.xhtml">GCLogits1DMaxKernel</a></li>
<li><a class="el" href="classarm__compute_1_1_g_c_logits1_d_shift_exp_sum_kernel.xhtml">GCLogits1DShiftExpSumKernel</a></li>
<li><a class="el" href="classarm__compute_1_1_g_c_logits1_d_norm_kernel.xhtml">GCLogits1DNormKernel</a> </li>
</ol>
<p class="definition">Definition at line <a class="el" href="_g_c_softmax_layer_8h_source.xhtml#l00046">46</a> of file <a class="el" href="_g_c_softmax_layer_8h_source.xhtml">GCSoftmaxLayer.h</a>.</p>
</div><h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
<a id="a4c3d471a20099610ef1d617dbaf4b2d6"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a4c3d471a20099610ef1d617dbaf4b2d6">&#9670;&nbsp;</a></span>GCSoftmaxLayer()</h2>
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<tr>
<td class="memname"><a class="el" href="classarm__compute_1_1_g_c_softmax_layer.xhtml">GCSoftmaxLayer</a> </td>
<td>(</td>
<td class="paramtype">std::shared_ptr&lt; <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">IMemoryManager</a> &gt;&#160;</td>
<td class="paramname"><em>memory_manager</em> = <code>nullptr</code></td><td>)</td>
<td></td>
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</div><div class="memdoc">
<p>Constructor. </p>
<p class="definition">Definition at line <a class="el" href="_g_c_softmax_layer_8cpp_source.xhtml#l00032">32</a> of file <a class="el" href="_g_c_softmax_layer_8cpp_source.xhtml">GCSoftmaxLayer.cpp</a>.</p>
<div class="fragment"><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160; : _memory_group(std::move(memory_manager)), _max_kernel(), _shift_exp_sum_kernel(), _norm_kernel(), _max(), _sum(), _tmp()</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160;{</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;}</div></div><!-- fragment -->
</div>
</div>
<h2 class="groupheader">Member Function Documentation</h2>
<a id="a87e0f86361e9b6d237464d91ce76fbfa"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a87e0f86361e9b6d237464d91ce76fbfa">&#9670;&nbsp;</a></span>configure()</h2>
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<td class="memname">void configure </td>
<td>(</td>
<td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *&#160;</td>
<td class="paramname"><em>input</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *&#160;</td>
<td class="paramname"><em>output</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float&#160;</td>
<td class="paramname"><em>beta</em> = <code>1.0f</code>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">size_t&#160;</td>
<td class="paramname"><em>axis</em> = <code>1</code>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
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<p>Set the input and output tensors. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Source tensor. Data types supported: F16/F32 </td></tr>
<tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>Destination tensor. Data types supported: same as <code>input</code> </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">beta</td><td>(Optional) A scaling factor for the exponent. Only beta = 1 is supported </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">axis</td><td>(Optional) Reduction axis. It has the purpose of squashing the first <code>axis</code> dimensions together. For instance, given a [4x4x4x4] image, when <code>axis</code> is 2, the Softmax reduction will be applied on each of the [4x4] planes of the input image.</td></tr>
</table>
</dd>
</dl>
<dl class="section note"><dt>Note</dt><dd>The value of <code>axis</code> must be always 1 for GLES </dd></dl>
<p class="definition">Definition at line <a class="el" href="_g_c_softmax_layer_8cpp_source.xhtml#l00037">37</a> of file <a class="el" href="_g_c_softmax_layer_8cpp_source.xhtml">GCSoftmaxLayer.cpp</a>.</p>
<div class="fragment"><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160;{</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160; <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(beta, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>);</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160;</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; <a class="code" href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(input, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(beta != 1.0f);</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a> != 1, <span class="stringliteral">&quot;Axis must be 1 for GLES&quot;</span>);</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160;</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; <span class="comment">// Create intermediate tensors shapes</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; _tmp.<a class="code" href="classarm__compute_1_1_g_c_tensor.xhtml#a44d1d7d909047fe63f5f6c11a9849986">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>(), input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#ad7829ae79223ab87f9da4c0bd7d229ba">num_channels</a>(), input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160;</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a> = input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.set(0, 1);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> tensor_info_max_sum(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>, input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#ad7829ae79223ab87f9da4c0bd7d229ba">num_channels</a>(), input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; _max.<a class="code" href="classarm__compute_1_1_g_c_tensor.xhtml#a44d1d7d909047fe63f5f6c11a9849986">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(tensor_info_max_sum);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; _sum.<a class="code" href="classarm__compute_1_1_g_c_tensor.xhtml#a44d1d7d909047fe63f5f6c11a9849986">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(tensor_info_max_sum);</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160;</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; <span class="comment">// Manage intermediate buffers</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_tmp);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_max);</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_sum);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; <span class="comment">// Configure Kernels</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; _max_kernel.<a class="code" href="classarm__compute_1_1_g_c_logits1_d_max_kernel.xhtml#aa029e9740bc43eb3301316be76be3b7e">configure</a>(input, &amp;_max);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; _shift_exp_sum_kernel.<a class="code" href="classarm__compute_1_1_g_c_logits1_d_shift_exp_sum_kernel.xhtml#a9b315185b36dd900eb209d005d630310">configure</a>(input, &amp;_max, &amp;_tmp, &amp;_sum);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; _norm_kernel.<a class="code" href="classarm__compute_1_1_g_c_logits1_d_norm_kernel.xhtml#adeaa28cbd0f25f372fca145853a763ad">configure</a>(&amp;_tmp, &amp;_sum, output);</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; <span class="comment">// Allocate intermediate buffers</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; _tmp.<a class="code" href="classarm__compute_1_1_g_c_tensor.xhtml#a44d1d7d909047fe63f5f6c11a9849986">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa8a4946cd749d482dd996874d295af85">allocate</a>();</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; _max.<a class="code" href="classarm__compute_1_1_g_c_tensor.xhtml#a44d1d7d909047fe63f5f6c11a9849986">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa8a4946cd749d482dd996874d295af85">allocate</a>();</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; _sum.<a class="code" href="classarm__compute_1_1_g_c_tensor.xhtml#a44d1d7d909047fe63f5f6c11a9849986">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa8a4946cd749d482dd996874d295af85">allocate</a>();</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a45cde9abb508c62d67c3bb2b9bf566a5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">arm_compute::test::validation::shape</a></div><div class="ttdeci">shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_absolute_difference_8cpp_source.xhtml#l00097">AbsoluteDifference.cpp:97</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7cfb31af63202568efef5214acfbf3ba"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">arm_compute::ITensorInfo::data_type</a></div><div class="ttdeci">virtual DataType data_type() const =0</div><div class="ttdoc">Data type used for each element of the tensor.</div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel</div></div>
<div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00337">Error.h:337</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_allocator_xhtml_af36143939a43fa124312e395975091ed"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">arm_compute::ITensorAllocator::init</a></div><div class="ttdeci">void init(const TensorInfo &amp;input, size_t alignment=0)</div><div class="ttdoc">Initialize a tensor based on the passed TensorInfo.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_allocator_8cpp_source.xhtml#l00038">ITensorAllocator.cpp:38</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::Format::F16</a></div><div class="ttdoc">1 channel, 1 F16 per channel</div></div>
<div class="ttc" id="_error_8h_xhtml_a6dc630a6ae9cc063b3924bcea8dee9d6"><div class="ttname"><a href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a></div><div class="ttdeci">#define ARM_COMPUTE_UNUSED(...)</div><div class="ttdoc">To avoid unused variables warnings.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00160">Error.h:160</a></div></div>
<div class="ttc" id="classarm__compute_1_1_memory_group_base_xhtml_ac1f67376afb7822f262a0174ef4a3104"><div class="ttname"><a href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">arm_compute::MemoryGroupBase::manage</a></div><div class="ttdeci">void manage(TensorType *obj)</div><div class="ttdoc">Sets a object to be managed by the given memory group.</div><div class="ttdef"><b>Definition:</b> <a href="_memory_group_base_8h_source.xhtml#l00102">MemoryGroupBase.h:102</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7c66505457d00ece3aa4b34cab80757d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">arm_compute::ITensorInfo::tensor_shape</a></div><div class="ttdeci">virtual const TensorShape &amp; tensor_shape() const =0</div><div class="ttdoc">Size for each dimension of the tensor.</div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a0e95dc1e53c361348314873b168ae237"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">arm_compute::ITensor::info</a></div><div class="ttdeci">virtual ITensorInfo * info() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_allocator_xhtml_aa8a4946cd749d482dd996874d295af85"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa8a4946cd749d482dd996874d295af85">arm_compute::ITensorAllocator::allocate</a></div><div class="ttdeci">virtual void allocate()=0</div><div class="ttdoc">Interface to be implemented by the child class to allocate the tensor.</div></div>
<div class="ttc" id="_validate_8h_xhtml_aadf5c9cff86327b96d88d04649d9715e"><div class="ttname"><a href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00789">Validate.h:789</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_accc088009d44c521706aa98d6387ee21"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">arm_compute::test::validation::axis</a></div><div class="ttdeci">axis</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_stack_layer_8cpp_source.xhtml#l00226">StackLayer.cpp:226</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00045">TensorInfo.h:45</a></div></div>
<div class="ttc" id="classarm__compute_1_1_g_c_logits1_d_max_kernel_xhtml_aa029e9740bc43eb3301316be76be3b7e"><div class="ttname"><a href="classarm__compute_1_1_g_c_logits1_d_max_kernel.xhtml#aa029e9740bc43eb3301316be76be3b7e">arm_compute::GCLogits1DMaxKernel::configure</a></div><div class="ttdeci">void configure(const IGCTensor *input, IGCTensor *output)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_softmax_layer_kernel_8cpp_source.xhtml#l00042">GCSoftmaxLayerKernel.cpp:42</a></div></div>
<div class="ttc" id="classarm__compute_1_1_g_c_logits1_d_shift_exp_sum_kernel_xhtml_a9b315185b36dd900eb209d005d630310"><div class="ttname"><a href="classarm__compute_1_1_g_c_logits1_d_shift_exp_sum_kernel.xhtml#a9b315185b36dd900eb209d005d630310">arm_compute::GCLogits1DShiftExpSumKernel::configure</a></div><div class="ttdeci">void configure(const IGCTensor *input, const IGCTensor *max, IGCTensor *output, IGCTensor *sum)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_softmax_layer_kernel_8cpp_source.xhtml#l00107">GCSoftmaxLayerKernel.cpp:107</a></div></div>
<div class="ttc" id="classarm__compute_1_1_g_c_tensor_xhtml_a44d1d7d909047fe63f5f6c11a9849986"><div class="ttname"><a href="classarm__compute_1_1_g_c_tensor.xhtml#a44d1d7d909047fe63f5f6c11a9849986">arm_compute::GCTensor::allocator</a></div><div class="ttdeci">ITensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_tensor_8cpp_source.xhtml#l00034">GCTensor.cpp:34</a></div></div>
<div class="ttc" id="classarm__compute_1_1_g_c_logits1_d_norm_kernel_xhtml_adeaa28cbd0f25f372fca145853a763ad"><div class="ttname"><a href="classarm__compute_1_1_g_c_logits1_d_norm_kernel.xhtml#adeaa28cbd0f25f372fca145853a763ad">arm_compute::GCLogits1DNormKernel::configure</a></div><div class="ttdeci">void configure(const IGCTensor *input, const IGCTensor *sum, IGCTensor *output)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_softmax_layer_kernel_8cpp_source.xhtml#l00201">GCSoftmaxLayerKernel.cpp:201</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_ad7829ae79223ab87f9da4c0bd7d229ba"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#ad7829ae79223ab87f9da4c0bd7d229ba">arm_compute::ITensorInfo::num_channels</a></div><div class="ttdeci">virtual size_t num_channels() const =0</div><div class="ttdoc">The number of channels for each tensor element.</div></div>
<div class="ttc" id="_error_8h_xhtml_a5bbdcf574d3f5e412fa6a1117911e67b"><div class="ttname"><a href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond,...)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00328">Error.h:328</a></div></div>
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<p class="reference">References <a class="el" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa8a4946cd749d482dd996874d295af85">ITensorAllocator::allocate()</a>, <a class="el" href="_g_c_tensor_8cpp_source.xhtml#l00034">GCTensor::allocator()</a>, <a class="el" href="_error_8h_source.xhtml#l00337">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="_validate_8h_source.xhtml#l00789">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>, <a class="el" href="_error_8h_source.xhtml#l00328">ARM_COMPUTE_ERROR_ON_MSG</a>, <a class="el" href="_error_8h_source.xhtml#l00160">ARM_COMPUTE_UNUSED</a>, <a class="el" href="_n_e_o_n_2_stack_layer_8cpp_source.xhtml#l00226">arm_compute::test::validation::axis</a>, <a class="el" href="_g_c_softmax_layer_kernel_8cpp_source.xhtml#l00042">GCLogits1DMaxKernel::configure()</a>, <a class="el" href="_g_c_softmax_layer_kernel_8cpp_source.xhtml#l00107">GCLogits1DShiftExpSumKernel::configure()</a>, <a class="el" href="_g_c_softmax_layer_kernel_8cpp_source.xhtml#l00201">GCLogits1DNormKernel::configure()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">ITensorInfo::data_type()</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::F16</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">ITensor::info()</a>, <a class="el" href="_i_tensor_allocator_8cpp_source.xhtml#l00038">ITensorAllocator::init()</a>, <a class="el" href="_memory_group_base_8h_source.xhtml#l00102">MemoryGroupBase&lt; TensorType &gt;::manage()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#ad7829ae79223ab87f9da4c0bd7d229ba">ITensorInfo::num_channels()</a>, <a class="el" href="_c_l_2_absolute_difference_8cpp_source.xhtml#l00097">arm_compute::test::validation::shape</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
<p class="reference">Referenced by <a class="el" href="validation_2_g_l_e_s___c_o_m_p_u_t_e_2_softmax_layer_8cpp_source.xhtml#l00060">arm_compute::test::validation::DATA_TEST_CASE()</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#ad1717410afd0be936c6213a63c8005fb">&#9670;&nbsp;</a></span>run()</h2>
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<td class="memname">void run </td>
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<p>Run the kernels contained in the function. </p>
<p>For NEON kernels:</p><ul>
<li>Multi-threading is used for the kernels which are parallelisable.</li>
<li>By default std::thread::hardware_concurrency() threads are used.</li>
</ul>
<dl class="section note"><dt>Note</dt><dd><a class="el" href="classarm__compute_1_1_c_p_p_scheduler.xhtml#ae64eebaa07f4d2da6cc2ba538c3cb095">CPPScheduler::set_num_threads()</a> can be used to manually set the number of threads</dd></dl>
<p>For OpenCL kernels:</p><ul>
<li>All the kernels are enqueued on the queue associated with <a class="el" href="classarm__compute_1_1_c_l_scheduler.xhtml" title="Provides global access to a CL context and command queue.">CLScheduler</a>.</li>
<li>The queue is then flushed.</li>
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<dl class="section note"><dt>Note</dt><dd>The function will not block until the kernels are executed. It is the user's responsibility to wait. </dd>
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Will call <a class="el" href="classarm__compute_1_1_i_function.xhtml#a820f7291c24155a2980512fae45aac26" title="Prepare the function for executing.">prepare()</a> on first run if hasn't been done </dd></dl>
<p>Implements <a class="el" href="classarm__compute_1_1_i_function.xhtml#a18954417d3124a8095783ea13dc6d00b">IFunction</a>.</p>
<p class="definition">Definition at line <a class="el" href="_g_c_softmax_layer_8cpp_source.xhtml#l00070">70</a> of file <a class="el" href="_g_c_softmax_layer_8cpp_source.xhtml">GCSoftmaxLayer.cpp</a>.</p>
<div class="fragment"><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160;{</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; <a class="code" href="classarm__compute_1_1_memory_group_resource_scope.xhtml">MemoryGroupResourceScope</a> scope_mg(_memory_group);</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160;</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a66a29e27a51a13250143981b0ee4ad19">dispatch</a>(_max_kernel, <span class="keyword">false</span>);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a2dcf87458fcfdfb5e9fdd369e0320d78">memory_barrier</a>();</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a66a29e27a51a13250143981b0ee4ad19">dispatch</a>(_shift_exp_sum_kernel, <span class="keyword">false</span>);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a2dcf87458fcfdfb5e9fdd369e0320d78">memory_barrier</a>();</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a66a29e27a51a13250143981b0ee4ad19">dispatch</a>(_norm_kernel);</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1_g_c_scheduler_xhtml_a66a29e27a51a13250143981b0ee4ad19"><div class="ttname"><a href="classarm__compute_1_1_g_c_scheduler.xhtml#a66a29e27a51a13250143981b0ee4ad19">arm_compute::GCScheduler::dispatch</a></div><div class="ttdeci">void dispatch(IGCKernel &amp;kernel, bool flush=true)</div><div class="ttdoc">Schedule the execution of the passed kernel if possible.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_scheduler_8cpp_source.xhtml#l00069">GCScheduler.cpp:69</a></div></div>
<div class="ttc" id="classarm__compute_1_1_g_c_scheduler_xhtml_a2dcf87458fcfdfb5e9fdd369e0320d78"><div class="ttname"><a href="classarm__compute_1_1_g_c_scheduler.xhtml#a2dcf87458fcfdfb5e9fdd369e0320d78">arm_compute::GCScheduler::memory_barrier</a></div><div class="ttdeci">void memory_barrier()</div><div class="ttdoc">Defines a barrier ordering memory transactions.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_scheduler_8cpp_source.xhtml#l00078">GCScheduler.cpp:78</a></div></div>
<div class="ttc" id="classarm__compute_1_1_g_c_scheduler_xhtml_a9c5f715748222ab9607cc52134b36b0b"><div class="ttname"><a href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">arm_compute::GCScheduler::get</a></div><div class="ttdeci">static GCScheduler &amp; get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_scheduler_8cpp_source.xhtml#l00062">GCScheduler.cpp:62</a></div></div>
<div class="ttc" id="classarm__compute_1_1_memory_group_resource_scope_xhtml"><div class="ttname"><a href="classarm__compute_1_1_memory_group_resource_scope.xhtml">arm_compute::MemoryGroupResourceScope</a></div><div class="ttdoc">Memory group resources scope handling class.</div><div class="ttdef"><b>Definition:</b> <a href="_i_memory_group_8h_source.xhtml#l00046">IMemoryGroup.h:46</a></div></div>
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<p class="reference">References <a class="el" href="_g_c_scheduler_8cpp_source.xhtml#l00069">GCScheduler::dispatch()</a>, <a class="el" href="_g_c_scheduler_8cpp_source.xhtml#l00062">GCScheduler::get()</a>, and <a class="el" href="_g_c_scheduler_8cpp_source.xhtml#l00078">GCScheduler::memory_barrier()</a>.</p>
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<hr/>The documentation for this class was generated from the following files:<ul>
<li>arm_compute/runtime/GLES_COMPUTE/functions/<a class="el" href="_g_c_softmax_layer_8h_source.xhtml">GCSoftmaxLayer.h</a></li>
<li>src/runtime/GLES_COMPUTE/functions/<a class="el" href="_g_c_softmax_layer_8cpp_source.xhtml">GCSoftmaxLayer.cpp</a></li>
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