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| <div class="title">AlexNet< ITensorType, TensorType, SubTensorType, Accessor, ActivationLayerFunction, ConvolutionLayerFunction, FullyConnectedLayerFunction, NormalizationLayerFunction, PoolingLayerFunction, SoftmaxLayerFunction, dt, fixed_point_position > Class Template Reference</div> </div> |
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| |
| <p><a class="el" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml" title="AlexNet model object. ">AlexNet</a> model object. |
| <a href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#details">More...</a></p> |
| |
| <p><code>#include <<a class="el" href="model__objects_2_alex_net_8h_source.xhtml">AlexNet.h</a>></code></p> |
| <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:a38a530655b80667542e46842c3ed8989"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a38a530655b80667542e46842c3ed8989">AlexNet</a> ()</td></tr> |
| <tr class="separator:a38a530655b80667542e46842c3ed8989"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a807107674868efa870ffe6fa1ad10f83"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a807107674868efa870ffe6fa1ad10f83">init_weights</a> (unsigned int batches, bool reshaped_weights=false)</td></tr> |
| <tr class="separator:a807107674868efa870ffe6fa1ad10f83"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a7740c7ab195c03ac140f1f75f633470f"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a7740c7ab195c03ac140f1f75f633470f">build</a> ()</td></tr> |
| <tr class="separator:a7740c7ab195c03ac140f1f75f633470f"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:acaefe811b78a2fdc4a0dba0c4029c3ef"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#acaefe811b78a2fdc4a0dba0c4029c3ef">allocate</a> ()</td></tr> |
| <tr class="separator:acaefe811b78a2fdc4a0dba0c4029c3ef"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a3b778cda9ac3fad08e7217edbcb942e0"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a3b778cda9ac3fad08e7217edbcb942e0">fill_random</a> ()</td></tr> |
| <tr class="memdesc:a3b778cda9ac3fad08e7217edbcb942e0"><td class="mdescLeft"> </td><td class="mdescRight">Fills the trainable parameters and input with random data. <a href="#a3b778cda9ac3fad08e7217edbcb942e0">More...</a><br /></td></tr> |
| <tr class="separator:a3b778cda9ac3fad08e7217edbcb942e0"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a1466ef70729f3c8b5da5ebfec3f53f26"><td class="memItemLeft" align="right" valign="top">std::vector< unsigned int > </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a1466ef70729f3c8b5da5ebfec3f53f26">get_classifications</a> ()</td></tr> |
| <tr class="memdesc:a1466ef70729f3c8b5da5ebfec3f53f26"><td class="mdescLeft"> </td><td class="mdescRight">Get the classification results. <a href="#a1466ef70729f3c8b5da5ebfec3f53f26">More...</a><br /></td></tr> |
| <tr class="separator:a1466ef70729f3c8b5da5ebfec3f53f26"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:ac8bb3912a3ce86b15842e79d0b421204"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#ac8bb3912a3ce86b15842e79d0b421204">clear</a> ()</td></tr> |
| <tr class="memdesc:ac8bb3912a3ce86b15842e79d0b421204"><td class="mdescLeft"> </td><td class="mdescRight">Clear all allocated memory from the tensor objects. <a href="#ac8bb3912a3ce86b15842e79d0b421204">More...</a><br /></td></tr> |
| <tr class="separator:ac8bb3912a3ce86b15842e79d0b421204"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a13a43e6d814de94978c515cb084873b1"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a13a43e6d814de94978c515cb084873b1">run</a> ()</td></tr> |
| <tr class="memdesc:a13a43e6d814de94978c515cb084873b1"><td class="mdescLeft"> </td><td class="mdescRight">Runs the model. <a href="#a13a43e6d814de94978c515cb084873b1">More...</a><br /></td></tr> |
| <tr class="separator:a13a43e6d814de94978c515cb084873b1"><td class="memSeparator" colspan="2"> </td></tr> |
| </table> |
| <a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2> |
| <div class="textblock"><h3>template<typename ITensorType, typename TensorType, typename SubTensorType, typename Accessor, typename ActivationLayerFunction, typename ConvolutionLayerFunction, typename FullyConnectedLayerFunction, typename NormalizationLayerFunction, typename PoolingLayerFunction, typename SoftmaxLayerFunction, DataType dt = DataType::F32, int fixed_point_position = 4><br /> |
| class arm_compute::test::model_objects::AlexNet< ITensorType, TensorType, SubTensorType, Accessor, ActivationLayerFunction, ConvolutionLayerFunction, FullyConnectedLayerFunction, NormalizationLayerFunction, PoolingLayerFunction, SoftmaxLayerFunction, dt, fixed_point_position ></h3> |
| |
| <p><a class="el" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml" title="AlexNet model object. ">AlexNet</a> model object. </p> |
| |
| <p>Definition at line <a class="el" href="model__objects_2_alex_net_8h_source.xhtml#l00054">54</a> of file <a class="el" href="model__objects_2_alex_net_8h_source.xhtml">AlexNet.h</a>.</p> |
| </div><h2 class="groupheader">Constructor & Destructor Documentation</h2> |
| <a class="anchor" id="a38a530655b80667542e46842c3ed8989"></a> |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
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| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml">AlexNet</a> </td> |
| <td>(</td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">inline</span></span> </td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Definition at line <a class="el" href="model__objects_2_alex_net_8h_source.xhtml#l00057">57</a> of file <a class="el" href="model__objects_2_alex_net_8h_source.xhtml">AlexNet.h</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  : _batches(1), _reshaped_weights(<span class="keyword">false</span>)</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  {</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  }</div></div><!-- fragment --> |
| </div> |
| </div> |
| <h2 class="groupheader">Member Function Documentation</h2> |
| <a class="anchor" id="acaefe811b78a2fdc4a0dba0c4029c3ef"></a> |
| <div class="memitem"> |
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| <td class="memname">void allocate </td> |
| <td>(</td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">inline</span></span> </td> |
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| </div><div class="memdoc"> |
| |
| <p>Definition at line <a class="el" href="model__objects_2_alex_net_8h_source.xhtml#l00288">288</a> of file <a class="el" href="model__objects_2_alex_net_8h_source.xhtml">AlexNet.h</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  {</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  input.allocator()->allocate();</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  output.allocator()->allocate();</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &wi : w)</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  {</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <span class="keywordflow">if</span>(wi.get())</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  {</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  wi->allocator()->allocate();</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  }</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  }</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &bi : b)</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  {</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="keywordflow">if</span>(bi.get())</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  {</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  bi->allocator()->allocate();</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  }</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  }</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keywordflow">if</span>(_reshaped_weights)</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  {</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keyword">dynamic_cast<</span>TensorType *<span class="keyword">></span>(w21.get())->allocator()->allocate();</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keyword">dynamic_cast<</span>TensorType *<span class="keyword">></span>(w22.get())->allocator()->allocate();</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keyword">dynamic_cast<</span>TensorType *<span class="keyword">></span>(w41.get())->allocator()->allocate();</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="keyword">dynamic_cast<</span>TensorType *<span class="keyword">></span>(w42.get())->allocator()->allocate();</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keyword">dynamic_cast<</span>TensorType *<span class="keyword">></span>(w51.get())->allocator()->allocate();</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keyword">dynamic_cast<</span>TensorType *<span class="keyword">></span>(w52.get())->allocator()->allocate();</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  }</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  conv1_out.allocator()->allocate();</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  act1_out.allocator()->allocate();</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  norm1_out.allocator()->allocate();</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  pool1_out.allocator()->allocate();</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  conv2_out.allocator()->allocate();</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  act2_out.allocator()->allocate();</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  norm2_out.allocator()->allocate();</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  pool2_out.allocator()->allocate();</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  conv3_out.allocator()->allocate();</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  act3_out.allocator()->allocate();</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  conv4_out.allocator()->allocate();</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  act4_out.allocator()->allocate();</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  conv5_out.allocator()->allocate();</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  act5_out.allocator()->allocate();</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  pool5_out.allocator()->allocate();</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  fc6_out.allocator()->allocate();</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  act6_out.allocator()->allocate();</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  fc7_out.allocator()->allocate();</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  act7_out.allocator()->allocate();</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  fc8_out.allocator()->allocate();</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  }</div></div><!-- fragment --> |
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| <td class="memname">void build </td> |
| <td>(</td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">inline</span></span> </td> |
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| |
| <p>Definition at line <a class="el" href="model__objects_2_alex_net_8h_source.xhtml#l00166">166</a> of file <a class="el" href="model__objects_2_alex_net_8h_source.xhtml">AlexNet.h</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  {</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  input.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(227U, 227U, 3U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  output.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1000U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> </div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="comment">// Initialize intermediate tensors</span></div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="comment">// Layer 1</span></div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  conv1_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(55U, 55U, 96U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  act1_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(55U, 55U, 96U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  norm1_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(55U, 55U, 96U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  pool1_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(27U, 27U, 96U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  pool11_out = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(&pool1_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(27U, 27U, 48U, _batches), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>()));</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  pool12_out = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(&pool1_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(27U, 27U, 48U, _batches), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0, 48)));</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="comment">// Layer 2</span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  conv2_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(27U, 27U, 256U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  conv21_out = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(&conv2_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(27U, 27U, 128U, _batches), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>()));</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  conv22_out = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(&conv2_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(27U, 27U, 128U, _batches), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0, 128)));</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  act2_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(27U, 27U, 256U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  norm2_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(27U, 27U, 256U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  pool2_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13U, 13U, 256U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="comment">// Layer 3</span></div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  conv3_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13U, 13U, 384U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  act3_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13U, 13U, 384U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  act31_out = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(&act3_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13U, 13U, 192U, _batches), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>()));</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  act32_out = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(&act3_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13U, 13U, 192U, _batches), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0, 192)));</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="comment">// Layer 4</span></div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  conv4_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13U, 13U, 384U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  conv41_out = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(&conv4_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13U, 13U, 192U, _batches), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>()));</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  conv42_out = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(&conv4_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13U, 13U, 192U, _batches), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0, 192)));</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  act4_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13U, 13U, 384U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  act41_out = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(&act4_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13U, 13U, 192U, _batches), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>()));</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  act42_out = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(&act4_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13U, 13U, 192U, _batches), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0, 192)));</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="comment">// Layer 5</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  conv5_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13U, 13U, 256U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  conv51_out = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(&conv5_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13U, 13U, 128U, _batches), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>()));</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  conv52_out = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(&conv5_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13U, 13U, 128U, _batches), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0, 128)));</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  act5_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13U, 13U, 256U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  pool5_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(6U, 6U, 256U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="comment">// Layer 6</span></div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  fc6_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4096U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  act6_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4096U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="comment">// Layer 7</span></div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  fc7_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4096U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  act7_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4096U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="comment">// Layer 8</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  fc8_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1000U, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span> </div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="comment">// Allocate layers</span></div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  {</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="comment">// Layer 1</span></div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  conv1 = std::unique_ptr<ConvolutionLayerFunction>(<span class="keyword">new</span> ConvolutionLayerFunction());</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  act1 = std::unique_ptr<ActivationLayerFunction>(<span class="keyword">new</span> ActivationLayerFunction());</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  norm1 = std::unique_ptr<NormalizationLayerFunction>(<span class="keyword">new</span> NormalizationLayerFunction());</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  pool1 = std::unique_ptr<PoolingLayerFunction>(<span class="keyword">new</span> PoolingLayerFunction());</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="comment">// Layer 2</span></div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  conv21 = std::unique_ptr<ConvolutionLayerFunction>(<span class="keyword">new</span> ConvolutionLayerFunction());</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  conv22 = std::unique_ptr<ConvolutionLayerFunction>(<span class="keyword">new</span> ConvolutionLayerFunction());</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  act2 = std::unique_ptr<ActivationLayerFunction>(<span class="keyword">new</span> ActivationLayerFunction());</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  norm2 = std::unique_ptr<NormalizationLayerFunction>(<span class="keyword">new</span> NormalizationLayerFunction());</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  pool2 = std::unique_ptr<PoolingLayerFunction>(<span class="keyword">new</span> PoolingLayerFunction());</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="comment">// Layer 3</span></div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  conv3 = std::unique_ptr<ConvolutionLayerFunction>(<span class="keyword">new</span> ConvolutionLayerFunction());</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  act3 = std::unique_ptr<ActivationLayerFunction>(<span class="keyword">new</span> ActivationLayerFunction());</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="comment">// Layer 4</span></div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  conv41 = std::unique_ptr<ConvolutionLayerFunction>(<span class="keyword">new</span> ConvolutionLayerFunction());</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  conv42 = std::unique_ptr<ConvolutionLayerFunction>(<span class="keyword">new</span> ConvolutionLayerFunction());</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  act4 = std::unique_ptr<ActivationLayerFunction>(<span class="keyword">new</span> ActivationLayerFunction());</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="comment">// Layer 5</span></div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  conv51 = std::unique_ptr<ConvolutionLayerFunction>(<span class="keyword">new</span> ConvolutionLayerFunction());</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  conv52 = std::unique_ptr<ConvolutionLayerFunction>(<span class="keyword">new</span> ConvolutionLayerFunction());</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  act5 = std::unique_ptr<ActivationLayerFunction>(<span class="keyword">new</span> ActivationLayerFunction());</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  pool5 = std::unique_ptr<PoolingLayerFunction>(<span class="keyword">new</span> PoolingLayerFunction());</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="comment">// Layer 6</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  fc6 = std::unique_ptr<FullyConnectedLayerFunction>(<span class="keyword">new</span> FullyConnectedLayerFunction());</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  act6 = std::unique_ptr<ActivationLayerFunction>(<span class="keyword">new</span> ActivationLayerFunction());</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="comment">// Layer 7</span></div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  fc7 = std::unique_ptr<FullyConnectedLayerFunction>(<span class="keyword">new</span> FullyConnectedLayerFunction());</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  act7 = std::unique_ptr<ActivationLayerFunction>(<span class="keyword">new</span> ActivationLayerFunction());</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="comment">// Layer 8</span></div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  fc8 = std::unique_ptr<FullyConnectedLayerFunction>(<span class="keyword">new</span> FullyConnectedLayerFunction());</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="comment">// Softmax</span></div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  smx = std::unique_ptr<SoftmaxLayerFunction>(<span class="keyword">new</span> SoftmaxLayerFunction());</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  }</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span> </div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="comment">// Configure Layers</span></div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  {</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="comment">// Layer 1</span></div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  conv1->configure(&input, w[0].<span class="keyword">get</span>(), b[0].<span class="keyword">get</span>(), &conv1_out, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(4, 4, 0, 0), <a class="code" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a>(_reshaped_weights, 11U));</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  act1->configure(&conv1_out, &act1_out, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>));</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  norm1->configure(&act1_out, &norm1_out, <a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml">NormalizationLayerInfo</a>(<a class="code" href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59caa980fef040549733973683b1a868f96e5">NormType::CROSS_MAP</a>, 5, 0.0001f, 0.75f));</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  pool1->configure(&norm1_out, &pool1_out, <a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 3, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)));</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="comment">// Layer 2</span></div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  conv21->configure(pool11_out.get(), w21.get(), b21.get(), conv21_out.get(), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 2, 2), <a class="code" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a>(_reshaped_weights, 5U));</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  conv22->configure(pool12_out.get(), w22.get(), b22.get(), conv22_out.get(), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 2, 2), <a class="code" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a>(_reshaped_weights, 5U));</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  act2->configure(&conv2_out, &act2_out, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>));</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  norm2->configure(&act2_out, &norm2_out, <a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml">NormalizationLayerInfo</a>(<a class="code" href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59caa980fef040549733973683b1a868f96e5">NormType::CROSS_MAP</a>, 5, 0.0001f, 0.75f));</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  pool2->configure(&norm2_out, &pool2_out, <a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 3, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)));</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="comment">// Layer 3</span></div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  conv3->configure(&pool2_out, w[2].<span class="keyword">get</span>(), b[2].<span class="keyword">get</span>(), &conv3_out, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), <a class="code" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a>(_reshaped_weights, 3U));</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  act3->configure(&conv3_out, &act3_out, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>));</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="comment">// Layer 4</span></div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  conv41->configure(act31_out.get(), w41.get(), b41.get(), conv41_out.get(), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), <a class="code" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a>(_reshaped_weights, 3U));</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  conv42->configure(act32_out.get(), w42.get(), b42.get(), conv42_out.get(), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), <a class="code" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a>(_reshaped_weights, 3U));</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  act4->configure(&conv4_out, &act4_out, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>));</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="comment">// Layer 5</span></div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  conv51->configure(act41_out.get(), w51.get(), b51.get(), conv51_out.get(), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), <a class="code" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a>(_reshaped_weights, 3U));</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  conv52->configure(act42_out.get(), w52.get(), b52.get(), conv52_out.get(), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), <a class="code" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a>(_reshaped_weights, 3U));</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  act5->configure(&conv5_out, &act5_out, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>));</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  pool5->configure(&act5_out, &pool5_out, <a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 3, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)));</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="comment">// Layer 6</span></div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  fc6->configure(&pool5_out, w[5].<span class="keyword">get</span>(), b[5].<span class="keyword">get</span>(), &fc6_out, <span class="keyword">true</span>, _reshaped_weights);</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  act6->configure(&fc6_out, &act6_out, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>));</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="comment">// Layer 7</span></div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  fc7->configure(&act6_out, w[6].<span class="keyword">get</span>(), b[6].<span class="keyword">get</span>(), &fc7_out, <span class="keyword">true</span>, _reshaped_weights);</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  act7->configure(&fc7_out, &act7_out, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>));</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <span class="comment">// Layer 8</span></div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  fc8->configure(&act7_out, w[7].<span class="keyword">get</span>(), b[7].<span class="keyword">get</span>(), &fc8_out, <span class="keyword">true</span>, _reshaped_weights);</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="comment">// Softmax</span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  smx->configure(&fc8_out, &output);</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  }</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  }</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#l00038">TensorShape.h:38</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">arm_compute::ActivationLayerInfo::ActivationFunction::RELU</a></div><div class="ttdoc">Rectifier. </div></div> |
| <div class="ttc" id="classarm__compute_1_1_normalization_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_normalization_layer_info.xhtml">arm_compute::NormalizationLayerInfo</a></div><div class="ttdoc">Normalization Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00506">Types.h:506</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml">arm_compute::ActivationLayerInfo</a></div><div class="ttdoc">Activation Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00458">Types.h:458</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_weights_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_weights_info.xhtml">arm_compute::WeightsInfo</a></div><div class="ttdoc">Convolution Layer Weights Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00563">Types.h:563</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_coordinates_xhtml"><div class="ttname"><a href="classarm__compute_1_1_coordinates.xhtml">arm_compute::Coordinates</a></div><div class="ttdoc">Coordinates of an item. </div><div class="ttdef"><b>Definition:</b> <a href="_coordinates_8h_source.xhtml#l00037">Coordinates.h:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00386">Types.h:386</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&#39;s metadata. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00042">TensorInfo.h:42</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5"><div class="ttname"><a href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">arm_compute::NonLinearFilterFunction::MAX</a></div><div class="ttdoc">Non linear dilate. </div></div> |
| <div class="ttc" id="classarm__compute_1_1_pooling_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pooling_layer_info.xhtml">arm_compute::PoolingLayerInfo</a></div><div class="ttdoc">Pooling Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00425">Types.h:425</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad4bb8dabdbf8ad75e34220cc666b59caa980fef040549733973683b1a868f96e5"><div class="ttname"><a href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59caa980fef040549733973683b1a868f96e5">arm_compute::NormType::CROSS_MAP</a></div><div class="ttdoc">Normalization applied cross maps. </div></div> |
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| <td class="memname">void clear </td> |
| <td>(</td> |
| <td class="paramname"></td><td>)</td> |
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| </td> |
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| <p>Clear all allocated memory from the tensor objects. </p> |
| |
| <p>Definition at line <a class="el" href="model__objects_2_alex_net_8h_source.xhtml#l00406">406</a> of file <a class="el" href="model__objects_2_alex_net_8h_source.xhtml">AlexNet.h</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  {</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  conv1.reset();</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  act1.reset();</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  norm1.reset();</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  pool1.reset();</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  conv21.reset();</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  conv22.reset();</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  act2.reset();</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  norm2.reset();</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  pool2.reset();</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  conv3.reset();</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  act3.reset();</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  conv41.reset();</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  conv42.reset();</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  act4.reset();</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  conv51.reset();</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  conv52.reset();</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  act5.reset();</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  pool5.reset();</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  fc6.reset();</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  act6.reset();</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  fc7.reset();</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  act7.reset();</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  fc8.reset();</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  smx.reset();</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span> </div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="comment">// Free allocations</span></div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  input.allocator()->free();</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  output.allocator()->free();</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &wi : w)</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  {</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  wi.reset();</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  }</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &bi : b)</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  {</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  bi.reset();</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  }</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span> </div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  w21.reset();</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  w22.reset();</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  b21.reset();</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  b21.reset();</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  w41.reset();</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  w42.reset();</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  b41.reset();</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  b42.reset();</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  w51.reset();</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  w52.reset();</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  b51.reset();</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  b52.reset();</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> </div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  conv1_out.allocator()->free();</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  act1_out.allocator()->free();</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  norm1_out.allocator()->free();</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  pool1_out.allocator()->free();</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  conv2_out.allocator()->free();</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  act2_out.allocator()->free();</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  norm2_out.allocator()->free();</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  pool2_out.allocator()->free();</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  conv3_out.allocator()->free();</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  act3_out.allocator()->free();</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  conv4_out.allocator()->free();</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  act4_out.allocator()->free();</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  conv5_out.allocator()->free();</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  act5_out.allocator()->free();</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  pool5_out.allocator()->free();</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  fc6_out.allocator()->free();</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  act6_out.allocator()->free();</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  fc7_out.allocator()->free();</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  act7_out.allocator()->free();</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  fc8_out.allocator()->free();</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  }</div></div><!-- fragment --> |
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| <td class="memname">void fill_random </td> |
| <td>(</td> |
| <td class="paramname"></td><td>)</td> |
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| <p>Fills the trainable parameters and input with random data. </p> |
| |
| <p>Definition at line <a class="el" href="model__objects_2_alex_net_8h_source.xhtml#l00338">338</a> of file <a class="el" href="model__objects_2_alex_net_8h_source.xhtml">AlexNet.h</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  {</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>->fill_tensor_uniform(Accessor(input), 0);</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keywordflow">if</span>(!_reshaped_weights)</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  {</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < w.size(); ++i)</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  {</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>->fill_tensor_uniform(Accessor(*w[i]), i + 1);</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>->fill_tensor_uniform(Accessor(*b[i]), i + 10);</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  }</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  }</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  {</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>->fill_tensor_uniform(Accessor(*w[0]), 1);</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>->fill_tensor_uniform(Accessor(*w[2]), 2);</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span> </div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>->fill_tensor_uniform(Accessor(*w[5]), 3);</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>->fill_tensor_uniform(Accessor(*b[5]), 4);</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>->fill_tensor_uniform(Accessor(*w[6]), 5);</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>->fill_tensor_uniform(Accessor(*b[6]), 6);</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>->fill_tensor_uniform(Accessor(*w[7]), 7);</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>->fill_tensor_uniform(Accessor(*b[7]), 8);</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span> </div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w21.get())), 9);</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w22.get())), 10);</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w41.get())), 11);</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w42.get())), 12);</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w51.get())), 13);</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w52.get())), 14);</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  }</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  }</div><div class="ttc" id="namespacearm__compute_1_1test_xhtml_a4ced6442a379a75e8a6c4be093fb666b"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">arm_compute::test::library</a></div><div class="ttdeci">std::unique_ptr< TensorLibrary > library</div><div class="ttdef"><b>Definition:</b> <a href="benchmark_2main_8cpp_source.xhtml#l00050">main.cpp:50</a></div></div> |
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| <td class="memname">std::vector<unsigned int> get_classifications </td> |
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| <td class="paramname"></td><td>)</td> |
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| <p>Get the classification results. </p> |
| <dl class="section return"><dt>Returns</dt><dd><a class="el" href="struct_vector.xhtml" title="Structure to hold Vector information. ">Vector</a> containing the classified labels </dd></dl> |
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| <p>Definition at line <a class="el" href="model__objects_2_alex_net_8h_source.xhtml#l00374">374</a> of file <a class="el" href="model__objects_2_alex_net_8h_source.xhtml">AlexNet.h</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  {</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  std::vector<unsigned int> classified_labels;</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  Accessor output_accessor(output);</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span> </div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window;</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(0, 1, 1));</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> d = 1; d < output_accessor.shape().num_dimensions(); ++d)</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  {</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(d, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(0, output_accessor.shape()[d], 1));</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  }</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span> </div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <a class="code" href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">execute_window_loop</a>(window, [&](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> & <span class="keywordtype">id</span>)</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  {</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <span class="keywordtype">int</span> max_idx = 0;</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keywordtype">float</span> val = 0;</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="keyword">const</span> <span class="keywordtype">void</span> *<span class="keyword">const</span> out_ptr = output_accessor(<span class="keywordtype">id</span>);</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> l = 0; l < output_accessor.shape().x(); ++l)</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  {</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="keywordtype">float</span> curr_val = <span class="keyword">reinterpret_cast<</span><span class="keyword">const </span><span class="keywordtype">float</span> *<span class="keyword">></span>(out_ptr)[l];</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keywordflow">if</span>(curr_val > val)</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  {</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  max_idx = l;</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  val = curr_val;</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  }</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  }</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  classified_labels.push_back(max_idx);</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  });</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="keywordflow">return</span> classified_labels;</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  }</div><div class="ttc" id="classarm__compute_1_1_window_1_1_dimension_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window_1_1_dimension.xhtml">arm_compute::Window::Dimension</a></div><div class="ttdoc">Describe one of the image&#39;s dimensions with a start, end and step. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00068">Window.h:68</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_aa96e81276ee4f87ab386cd05a5539a7d"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">arm_compute::Window::DimX</a></div><div class="ttdeci">static constexpr size_t DimX</div><div class="ttdoc">Alias for dimension 0 also known as X dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00043">Window.h:43</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a6c0dcc38187027dcb89cd9724bc5a823"><div class="ttname"><a href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">arm_compute::execute_window_loop</a></div><div class="ttdeci">void execute_window_loop(const Window &w, L &&lambda_function, Ts &&...iterators)</div><div class="ttdoc">Iterate through the passed window, automatically adjusting the iterators and calling the lambda_funct...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00176">Helpers.inl:176</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_coordinates_xhtml"><div class="ttname"><a href="classarm__compute_1_1_coordinates.xhtml">arm_compute::Coordinates</a></div><div class="ttdoc">Coordinates of an item. </div><div class="ttdef"><b>Definition:</b> <a href="_coordinates_8h_source.xhtml#l00037">Coordinates.h:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_acd3d2bba51cb84d34dd7656ad2375a6e"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">arm_compute::Window::set</a></div><div class="ttdeci">void set(size_t dimension, const Dimension &dim)</div><div class="ttdoc">Set the values of a given dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00040">Window.inl:40</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml">arm_compute::Window</a></div><div class="ttdoc">Describe a multidimensional execution window. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00039">Window.h:39</a></div></div> |
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| <td class="memname">void init_weights </td> |
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| <td class="paramtype">bool </td> |
| <td class="paramname"><em>reshaped_weights</em> = <code>false</code> </td> |
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| <p>Definition at line <a class="el" href="model__objects_2_alex_net_8h_source.xhtml#l00062">62</a> of file <a class="el" href="model__objects_2_alex_net_8h_source.xhtml">AlexNet.h</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  {</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  _batches = batches;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  _reshaped_weights = reshaped_weights;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="comment">// Initialize weights and biases</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keywordflow">if</span>(!_reshaped_weights)</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  {</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &wi : w)</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  {</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  wi = std::unique_ptr<TensorType>(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  }</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &bi : b)</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  {</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  bi = std::unique_ptr<TensorType>(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  }</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  w[0]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(11U, 11U, 3U, 96U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  b[0]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(96U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  w[1]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(5U, 5U, 48U, 256U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  b[1]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(256U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  w[2]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(3U, 3U, 256U, 384U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  b[2]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(384U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  w[3]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(3U, 3U, 192U, 384U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  b[3]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(384U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  w[4]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(3U, 3U, 192U, 256U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  b[4]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(256U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  w[5]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(9216U, 4096U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  b[5]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4096U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  w[6]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4096U, 4096U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  b[6]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4096U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  w[7]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4096U, 1000U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  b[7]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1000U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> </div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  w21 = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(w[1].<span class="keyword">get</span>(), <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(5U, 5U, 48U, 128U), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>()));</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  w22 = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(w[1].<span class="keyword">get</span>(), <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(5U, 5U, 48U, 128U), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0, 0, 128)));</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  b21 = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(b[1].<span class="keyword">get</span>(), <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(128U), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>()));</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  b22 = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(b[1].<span class="keyword">get</span>(), <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(128U), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(128)));</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> </div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  w41 = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(w[3].<span class="keyword">get</span>(), <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(3U, 3U, 192U, 192U), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>()));</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  w42 = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(w[3].<span class="keyword">get</span>(), <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(3U, 3U, 192U, 192U), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0, 0, 192)));</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  b41 = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(b[3].<span class="keyword">get</span>(), <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(192U), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>()));</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  b42 = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(b[3].<span class="keyword">get</span>(), <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(192U), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(192)));</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  w51 = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(w[4].<span class="keyword">get</span>(), <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(3U, 3U, 192U, 128U), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>()));</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  w52 = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(w[4].<span class="keyword">get</span>(), <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(3U, 3U, 192U, 128U), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0, 0, 128)));</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  b51 = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(b[4].<span class="keyword">get</span>(), <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(128U), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>()));</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  b52 = std::unique_ptr<SubTensorType>(<span class="keyword">new</span> SubTensorType(b[4].<span class="keyword">get</span>(), <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(128U), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(128)));</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  }</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  {</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dt_size = 16 / <a class="code" href="namespacearm__compute.xhtml#abb7e0f23a4f2e63f39433f158dad47ab">arm_compute::data_size_from_type</a>(dt);</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> </div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="comment">// Create tensor for the reshaped weights</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  w[0] = std::unique_ptr<TensorType>(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keyword">auto</span> w21_tensor = std::unique_ptr<TensorType>(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keyword">auto</span> w22_tensor = std::unique_ptr<TensorType>(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  w[2] = std::unique_ptr<TensorType>(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keyword">auto</span> w41_tensor = std::unique_ptr<TensorType>(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keyword">auto</span> w42_tensor = std::unique_ptr<TensorType>(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keyword">auto</span> w51_tensor = std::unique_ptr<TensorType>(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keyword">auto</span> w52_tensor = std::unique_ptr<TensorType>(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> </div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  w[0]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(366U * dt_size, 96U / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  w21_tensor->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1248U * dt_size, 128U / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  w22_tensor->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1248U * dt_size, 128U / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  w[2]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(2560U * dt_size, 384U / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  w41_tensor->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1920U * dt_size, 192U / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  w42_tensor->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1920U * dt_size, 192U / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  w51_tensor->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1920U * dt_size, 128U / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  w52_tensor->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1920U * dt_size, 128U / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> </div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  w21 = std::move(w21_tensor);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  w22 = std::move(w22_tensor);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  w41 = std::move(w41_tensor);</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  w42 = std::move(w42_tensor);</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  w51 = std::move(w51_tensor);</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  w52 = std::move(w52_tensor);</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> </div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  w[5] = std::unique_ptr<TensorType>(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  w[6] = std::unique_ptr<TensorType>(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  w[7] = std::unique_ptr<TensorType>(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  b[5] = std::unique_ptr<TensorType>(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  b[6] = std::unique_ptr<TensorType>(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  b[7] = std::unique_ptr<TensorType>(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> </div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  b[5]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4096U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  b[6]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4096U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  b[7]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1000U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> </div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordflow">if</span>(_batches > 1)</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  {</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  w[5]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(9216U * dt_size, 4096U / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  w[6]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4096U * dt_size, 4096U / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  w[7]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4096U * dt_size, 1000U / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  }</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  {</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  w[5]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4096U, 9216U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  w[6]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4096U, 4096U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  w[7]->allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1000U, 4096U), 1, dt, fixed_point_position));</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  }</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  }</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  }</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#l00038">TensorShape.h:38</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_coordinates_xhtml"><div class="ttname"><a href="classarm__compute_1_1_coordinates.xhtml">arm_compute::Coordinates</a></div><div class="ttdoc">Coordinates of an item. </div><div class="ttdef"><b>Definition:</b> <a href="_coordinates_8h_source.xhtml#l00037">Coordinates.h:37</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_abb7e0f23a4f2e63f39433f158dad47ab"><div class="ttname"><a href="namespacearm__compute.xhtml#abb7e0f23a4f2e63f39433f158dad47ab">arm_compute::data_size_from_type</a></div><div class="ttdeci">size_t data_size_from_type(DataType data_type)</div><div class="ttdoc">The size in bytes of the data type. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l00099">Utils.h:99</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&#39;s metadata. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00042">TensorInfo.h:42</a></div></div> |
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| <td class="memname">void run </td> |
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| <p>Runs the model. </p> |
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| <p>Definition at line <a class="el" href="model__objects_2_alex_net_8h_source.xhtml#l00481">481</a> of file <a class="el" href="model__objects_2_alex_net_8h_source.xhtml">AlexNet.h</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  {</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <span class="comment">// Layer 1</span></div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  conv1->run();</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  act1->run();</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  norm1->run();</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  pool1->run();</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <span class="comment">// Layer 2</span></div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  conv21->run();</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  conv22->run();</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  act2->run();</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  norm2->run();</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  pool2->run();</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <span class="comment">// Layer 3</span></div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  conv3->run();</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  act3->run();</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <span class="comment">// Layer 4</span></div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  conv41->run();</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  conv42->run();</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  act4->run();</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <span class="comment">// Layer 5</span></div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  conv51->run();</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  conv52->run();</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  act5->run();</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  pool5->run();</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  <span class="comment">// Layer 6</span></div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  fc6->run();</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  act6->run();</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <span class="comment">// Layer 7</span></div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  fc7->run();</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  act7->run();</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  <span class="comment">// Layer 8</span></div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  fc8->run();</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <span class="comment">// Softmax</span></div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  smx->run();</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  }</div></div><!-- fragment --> |
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| <hr/>The documentation for this class was generated from the following file:<ul> |
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| <li class="navelem"><a class="el" href="namespacearm__compute.xhtml">arm_compute</a></li><li class="navelem"><a class="el" href="namespacearm__compute_1_1test.xhtml">test</a></li><li class="navelem"><a class="el" href="namespacearm__compute_1_1test_1_1model__objects.xhtml">model_objects</a></li><li class="navelem"><a class="el" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml">AlexNet</a></li> |
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