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 <div class="title">AlexNet.h</div>  </div>
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 <a href="model__objects_2_alex_net_8h.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Copyright (c) 2017 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="preprocessor">#ifndef __ARM_COMPUTE_TEST_MODEL_OBJECTS_ALEXNET_H__</span></div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;<span class="preprocessor">#define __ARM_COMPUTE_TEST_MODEL_OBJECTS_ALEXNET_H__</span></div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;</div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_tensor_library_8h.xhtml">TensorLibrary.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;<span class="preprocessor">#include &quot;Utils.h&quot;</span></div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;</div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;<span class="preprocessor">#include &lt;memory&gt;</span></div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;</div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a>;</div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1test.xhtml">arm_compute::test</a>;</div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;</div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;{</div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;<span class="keyword">namespace </span>test</div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;{</div><div class="line"><a name="l00039"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1model__objects.xhtml">   39</a></span>&#160;<span class="keyword">namespace </span>model_objects</div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;{</div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> ITensorType,</div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;          <span class="keyword">typename</span> TensorType,</div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;          <span class="keyword">typename</span> SubTensorType,</div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;          <span class="keyword">typename</span> Accessor,</div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;          <span class="keyword">typename</span> ActivationLayerFunction,</div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;          <span class="keyword">typename</span> ConvolutionLayerFunction,</div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;          <span class="keyword">typename</span> FullyConnectedLayerFunction,</div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;          <span class="keyword">typename</span> NormalizationLayerFunction,</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;          <span class="keyword">typename</span> PoolingLayerFunction,</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;          <span class="keyword">typename</span> SoftmaxLayerFunction,</div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;          <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dt                   = <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>,</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;          <span class="keywordtype">int</span>      fixed_point_position = 4&gt;</div><div class="line"><a name="l00054"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml">   54</a></span>&#160;<span class="keyword">class </span><a class="code" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml">AlexNet</a></div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;{</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00057"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a38a530655b80667542e46842c3ed8989">   57</a></span>&#160;    <a class="code" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a38a530655b80667542e46842c3ed8989">AlexNet</a>()</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;        : _batches(1), _reshaped_weights(false)</div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;    {</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;    }</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;</div><div class="line"><a name="l00062"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a807107674868efa870ffe6fa1ad10f83">   62</a></span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a807107674868efa870ffe6fa1ad10f83">init_weights</a>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batches, <span class="keywordtype">bool</span> reshaped_weights = <span class="keyword">false</span>)</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;    {</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;        _batches          = batches;</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;        _reshaped_weights = reshaped_weights;</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;        <span class="comment">// Initialize weights and biases</span></div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;        <span class="keywordflow">if</span>(!_reshaped_weights)</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;        {</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;            <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;wi : w)</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;            {</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;                wi = std::unique_ptr&lt;TensorType&gt;(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;            }</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;            <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;bi : b)</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;            {</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;                bi = std::unique_ptr&lt;TensorType&gt;(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;            }</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;            w[0]-&gt;allocator()-&gt;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>(11<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 11<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 96<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, dt, fixed_point_position));</div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160; 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           b[3]-&gt;allocator()-&gt;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>(384<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, dt, fixed_point_position));</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;            w[4]-&gt;allocator()-&gt;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>(3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 192<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 256<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, dt, fixed_point_position));</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;            b[4]-&gt;allocator()-&gt;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>(256<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, dt, fixed_point_position));</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;            w[5]-&gt;allocator()-&gt;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>(9216<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, dt, fixed_point_position));</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;            b[5]-&gt;allocator()-&gt;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>(4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, dt, fixed_point_position));</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;            w[6]-&gt;allocator()-&gt;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>(4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, dt, fixed_point_position));</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;            b[6]-&gt;allocator()-&gt;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>(4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, dt, fixed_point_position));</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;            w[7]-&gt;allocator()-&gt;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>(4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1000<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, dt, fixed_point_position));</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;            b[7]-&gt;allocator()-&gt;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>(1000<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, dt, fixed_point_position));</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;            w21 = std::unique_ptr&lt;SubTensorType&gt;(<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>(5<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 5<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 48<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 128<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), <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>&#160;            w22 = std::unique_ptr&lt;SubTensorType&gt;(<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>(5<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 5<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 48<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 128<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), <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>&#160;            b21 = std::unique_ptr&lt;SubTensorType&gt;(<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>(128<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), <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>&#160;            b22 = std::unique_ptr&lt;SubTensorType&gt;(<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>(128<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), <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>&#160;</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;            w41 = std::unique_ptr&lt;SubTensorType&gt;(<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>(3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 192<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 192<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), <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>&#160;            w42 = std::unique_ptr&lt;SubTensorType&gt;(<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>(3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 192<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 192<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), <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>&#160;            b41 = std::unique_ptr&lt;SubTensorType&gt;(<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>(192<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), <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>&#160;            b42 = std::unique_ptr&lt;SubTensorType&gt;(<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>(192<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), <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>&#160;</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;            w51 = std::unique_ptr&lt;SubTensorType&gt;(<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>(3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 192<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 128<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), <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>&#160;            w52 = std::unique_ptr&lt;SubTensorType&gt;(<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>(3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 192<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 128<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), <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>&#160;            b51 = std::unique_ptr&lt;SubTensorType&gt;(<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>(128<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), <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>&#160;            b52 = std::unique_ptr&lt;SubTensorType&gt;(<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>(128<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), <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>&#160;        }</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;        <span class="keywordflow">else</span></div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;        {</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;            <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>&#160;</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;            <span class="comment">// Create tensor for the reshaped weights</span></div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;            w[0]            = std::unique_ptr&lt;TensorType&gt;(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;            <span class="keyword">auto</span> w21_tensor = std::unique_ptr&lt;TensorType&gt;(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;            <span class="keyword">auto</span> w22_tensor = std::unique_ptr&lt;TensorType&gt;(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;            w[2]            = std::unique_ptr&lt;TensorType&gt;(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;            <span class="keyword">auto</span> w41_tensor = std::unique_ptr&lt;TensorType&gt;(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;            <span class="keyword">auto</span> w42_tensor = std::unique_ptr&lt;TensorType&gt;(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;            <span class="keyword">auto</span> w51_tensor = std::unique_ptr&lt;TensorType&gt;(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;            <span class="keyword">auto</span> w52_tensor = std::unique_ptr&lt;TensorType&gt;(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;            w[0]-&gt;allocator()-&gt;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>(366<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> * dt_size, 96<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;            w21_tensor-&gt;allocator()-&gt;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>(1248<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> * dt_size, 128<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;            w22_tensor-&gt;allocator()-&gt;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>(1248<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> * dt_size, 128<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;            w[2]-&gt;allocator()-&gt;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>(2560<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> * dt_size, 384<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;            w41_tensor-&gt;allocator()-&gt;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>(1920<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> * dt_size, 192<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;            w42_tensor-&gt;allocator()-&gt;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>(1920<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> * dt_size, 192<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;            w51_tensor-&gt;allocator()-&gt;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>(1920<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> * dt_size, 128<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;            w52_tensor-&gt;allocator()-&gt;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>(1920<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> * dt_size, 128<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;            w21 = std::move(w21_tensor);</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;            w22 = std::move(w22_tensor);</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;            w41 = std::move(w41_tensor);</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;            w42 = std::move(w42_tensor);</div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;            w51 = std::move(w51_tensor);</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;            w52 = std::move(w52_tensor);</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;            w[5] = std::unique_ptr&lt;TensorType&gt;(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;            w[6] = std::unique_ptr&lt;TensorType&gt;(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;            w[7] = std::unique_ptr&lt;TensorType&gt;(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;            b[5] = std::unique_ptr&lt;TensorType&gt;(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;            b[6] = std::unique_ptr&lt;TensorType&gt;(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;            b[7] = std::unique_ptr&lt;TensorType&gt;(<span class="keyword">new</span> TensorType());</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;            b[5]-&gt;allocator()-&gt;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>(4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, dt, fixed_point_position));</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;            b[6]-&gt;allocator()-&gt;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>(4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, dt, fixed_point_position));</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;            b[7]-&gt;allocator()-&gt;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>(1000<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, dt, fixed_point_position));</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;</div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;            <span class="keywordflow">if</span>(_batches &gt; 1)</div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;            {</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;                w[5]-&gt;allocator()-&gt;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>(9216<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> * dt_size, 4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;                w[6]-&gt;allocator()-&gt;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>(4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> * dt_size, 4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;                w[7]-&gt;allocator()-&gt;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>(4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> * dt_size, 1000<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> / dt_size), 1, dt, fixed_point_position));</div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;            }</div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;            <span class="keywordflow">else</span></div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;            {</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;                w[5]-&gt;allocator()-&gt;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>(4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 9216<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, dt, fixed_point_position));</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;                w[6]-&gt;allocator()-&gt;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>(4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, dt, fixed_point_position));</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;                w[7]-&gt;allocator()-&gt;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>(1000<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, dt, fixed_point_position));</div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;            }</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;        }</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;    }</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;</div><div class="line"><a name="l00166"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a7740c7ab195c03ac140f1f75f633470f">  166</a></span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a7740c7ab195c03ac140f1f75f633470f">build</a>()</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;    {</div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;        input.allocator()-&gt;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>(227<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 227<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;        output.allocator()-&gt;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>(1000<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;        <span class="comment">// Initialize intermediate tensors</span></div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;        <span class="comment">// Layer 1</span></div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;        conv1_out.allocator()-&gt;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>(55<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 55<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 96<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;        act1_out.allocator()-&gt;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>(55<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 55<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 96<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;        norm1_out.allocator()-&gt;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>(55<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 55<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 96<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;        pool1_out.allocator()-&gt;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>(27<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 27<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 96<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;        pool11_out = std::unique_ptr&lt;SubTensorType&gt;(<span class="keyword">new</span> SubTensorType(&amp;pool1_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(27<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 27<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 48<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _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>&#160;        pool12_out = std::unique_ptr&lt;SubTensorType&gt;(<span class="keyword">new</span> SubTensorType(&amp;pool1_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(27<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 27<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 48<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _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>&#160;        <span class="comment">// Layer 2</span></div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;        conv2_out.allocator()-&gt;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>(27<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 27<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 256<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;        conv21_out = std::unique_ptr&lt;SubTensorType&gt;(<span class="keyword">new</span> SubTensorType(&amp;conv2_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(27<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 27<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 128<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _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>&#160;        conv22_out = std::unique_ptr&lt;SubTensorType&gt;(<span class="keyword">new</span> SubTensorType(&amp;conv2_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(27<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 27<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 128<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _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>&#160;        act2_out.allocator()-&gt;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>(27<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 27<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 256<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;        norm2_out.allocator()-&gt;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>(27<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 27<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 256<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;        pool2_out.allocator()-&gt;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>(13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 256<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;        <span class="comment">// Layer 3</span></div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;        conv3_out.allocator()-&gt;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>(13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 384<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;        act3_out.allocator()-&gt;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>(13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 384<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;        act31_out = std::unique_ptr&lt;SubTensorType&gt;(<span class="keyword">new</span> SubTensorType(&amp;act3_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 192<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _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>&#160;        act32_out = std::unique_ptr&lt;SubTensorType&gt;(<span class="keyword">new</span> SubTensorType(&amp;act3_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 192<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _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>&#160;        <span class="comment">// Layer 4</span></div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;        conv4_out.allocator()-&gt;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>(13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 384<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;        conv41_out = std::unique_ptr&lt;SubTensorType&gt;(<span class="keyword">new</span> SubTensorType(&amp;conv4_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 192<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _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>&#160;        conv42_out = std::unique_ptr&lt;SubTensorType&gt;(<span class="keyword">new</span> SubTensorType(&amp;conv4_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 192<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _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>&#160;        act4_out.allocator()-&gt;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>(13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 384<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;        act41_out = std::unique_ptr&lt;SubTensorType&gt;(<span class="keyword">new</span> SubTensorType(&amp;act4_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 192<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _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>&#160;        act42_out = std::unique_ptr&lt;SubTensorType&gt;(<span class="keyword">new</span> SubTensorType(&amp;act4_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 192<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _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>&#160;        <span class="comment">// Layer 5</span></div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;        conv5_out.allocator()-&gt;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>(13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 256<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;        conv51_out = std::unique_ptr&lt;SubTensorType&gt;(<span class="keyword">new</span> SubTensorType(&amp;conv5_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 128<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _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>&#160;        conv52_out = std::unique_ptr&lt;SubTensorType&gt;(<span class="keyword">new</span> SubTensorType(&amp;conv5_out, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 128<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _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>&#160;        act5_out.allocator()-&gt;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>(13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 13<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 256<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;        pool5_out.allocator()-&gt;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>(6<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 6<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 256<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;        <span class="comment">// Layer 6</span></div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;        fc6_out.allocator()-&gt;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>(4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;        act6_out.allocator()-&gt;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>(4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;        <span class="comment">// Layer 7</span></div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;        fc7_out.allocator()-&gt;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>(4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;        act7_out.allocator()-&gt;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>(4096<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;        <span class="comment">// Layer 8</span></div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;        fc8_out.allocator()-&gt;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>(1000<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, dt, fixed_point_position));</div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;        <span class="comment">// Allocate layers</span></div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;        {</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;            <span class="comment">// Layer 1</span></div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;            conv1 = std::unique_ptr&lt;ConvolutionLayerFunction&gt;(<span class="keyword">new</span> ConvolutionLayerFunction());</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;            act1  = std::unique_ptr&lt;ActivationLayerFunction&gt;(<span class="keyword">new</span> ActivationLayerFunction());</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;            norm1 = std::unique_ptr&lt;NormalizationLayerFunction&gt;(<span class="keyword">new</span> NormalizationLayerFunction());</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;            pool1 = std::unique_ptr&lt;PoolingLayerFunction&gt;(<span class="keyword">new</span> PoolingLayerFunction());</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;            <span class="comment">// Layer 2</span></div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;            conv21 = std::unique_ptr&lt;ConvolutionLayerFunction&gt;(<span class="keyword">new</span> ConvolutionLayerFunction());</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;            conv22 = std::unique_ptr&lt;ConvolutionLayerFunction&gt;(<span class="keyword">new</span> ConvolutionLayerFunction());</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;            act2   = std::unique_ptr&lt;ActivationLayerFunction&gt;(<span class="keyword">new</span> ActivationLayerFunction());</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;            norm2  = std::unique_ptr&lt;NormalizationLayerFunction&gt;(<span class="keyword">new</span> NormalizationLayerFunction());</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;            pool2  = std::unique_ptr&lt;PoolingLayerFunction&gt;(<span class="keyword">new</span> PoolingLayerFunction());</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;            <span class="comment">// Layer 3</span></div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;            conv3 = std::unique_ptr&lt;ConvolutionLayerFunction&gt;(<span class="keyword">new</span> ConvolutionLayerFunction());</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;            act3  = std::unique_ptr&lt;ActivationLayerFunction&gt;(<span class="keyword">new</span> ActivationLayerFunction());</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;            <span class="comment">// Layer 4</span></div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;            conv41 = std::unique_ptr&lt;ConvolutionLayerFunction&gt;(<span class="keyword">new</span> ConvolutionLayerFunction());</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;            conv42 = std::unique_ptr&lt;ConvolutionLayerFunction&gt;(<span class="keyword">new</span> ConvolutionLayerFunction());</div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;            act4   = std::unique_ptr&lt;ActivationLayerFunction&gt;(<span class="keyword">new</span> ActivationLayerFunction());</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;            <span class="comment">// Layer 5</span></div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;            conv51 = std::unique_ptr&lt;ConvolutionLayerFunction&gt;(<span class="keyword">new</span> ConvolutionLayerFunction());</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;            conv52 = std::unique_ptr&lt;ConvolutionLayerFunction&gt;(<span class="keyword">new</span> ConvolutionLayerFunction());</div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;            act5   = std::unique_ptr&lt;ActivationLayerFunction&gt;(<span class="keyword">new</span> ActivationLayerFunction());</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;            pool5  = std::unique_ptr&lt;PoolingLayerFunction&gt;(<span class="keyword">new</span> PoolingLayerFunction());</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;            <span class="comment">// Layer 6</span></div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;            fc6  = std::unique_ptr&lt;FullyConnectedLayerFunction&gt;(<span class="keyword">new</span> FullyConnectedLayerFunction());</div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;            act6 = std::unique_ptr&lt;ActivationLayerFunction&gt;(<span class="keyword">new</span> ActivationLayerFunction());</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;            <span class="comment">// Layer 7</span></div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;            fc7  = std::unique_ptr&lt;FullyConnectedLayerFunction&gt;(<span class="keyword">new</span> FullyConnectedLayerFunction());</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;            act7 = std::unique_ptr&lt;ActivationLayerFunction&gt;(<span class="keyword">new</span> ActivationLayerFunction());</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;            <span class="comment">// Layer 8</span></div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;            fc8 = std::unique_ptr&lt;FullyConnectedLayerFunction&gt;(<span class="keyword">new</span> FullyConnectedLayerFunction());</div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;            <span class="comment">// Softmax</span></div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;            smx = std::unique_ptr&lt;SoftmaxLayerFunction&gt;(<span class="keyword">new</span> SoftmaxLayerFunction());</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;        }</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;        <span class="comment">// Configure Layers</span></div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;        {</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;            <span class="comment">// Layer 1</span></div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;            conv1-&gt;configure(&amp;input, w[0].<span class="keyword">get</span>(), b[0].<span class="keyword">get</span>(), &amp;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, 11<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;            act1-&gt;configure(&amp;conv1_out, &amp;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>&#160;            norm1-&gt;configure(&amp;act1_out, &amp;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>&#160;            pool1-&gt;configure(&amp;norm1_out, &amp;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>&#160;            <span class="comment">// Layer 2</span></div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;            conv21-&gt;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, 5<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;            conv22-&gt;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, 5<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;            act2-&gt;configure(&amp;conv2_out, &amp;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>&#160;            norm2-&gt;configure(&amp;act2_out, &amp;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>&#160;            pool2-&gt;configure(&amp;norm2_out, &amp;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>&#160;            <span class="comment">// Layer 3</span></div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;            conv3-&gt;configure(&amp;pool2_out, w[2].<span class="keyword">get</span>(), b[2].<span class="keyword">get</span>(), &amp;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, 3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;            act3-&gt;configure(&amp;conv3_out, &amp;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>&#160;            <span class="comment">// Layer 4</span></div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;            conv41-&gt;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, 3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;            conv42-&gt;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, 3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;            act4-&gt;configure(&amp;conv4_out, &amp;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>&#160;            <span class="comment">// Layer 5</span></div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;            conv51-&gt;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, 3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;            conv52-&gt;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, 3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;            act5-&gt;configure(&amp;conv5_out, &amp;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>&#160;            pool5-&gt;configure(&amp;act5_out, &amp;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>&#160;            <span class="comment">// Layer 6</span></div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;            fc6-&gt;configure(&amp;pool5_out, w[5].<span class="keyword">get</span>(), b[5].<span class="keyword">get</span>(), &amp;fc6_out, <span class="keyword">true</span>, _reshaped_weights);</div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;            act6-&gt;configure(&amp;fc6_out, &amp;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>&#160;            <span class="comment">// Layer 7</span></div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;            fc7-&gt;configure(&amp;act6_out, w[6].<span class="keyword">get</span>(), b[6].<span class="keyword">get</span>(), &amp;fc7_out, <span class="keyword">true</span>, _reshaped_weights);</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;            act7-&gt;configure(&amp;fc7_out, &amp;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>&#160;            <span class="comment">// Layer 8</span></div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;            fc8-&gt;configure(&amp;act7_out, w[7].<span class="keyword">get</span>(), b[7].<span class="keyword">get</span>(), &amp;fc8_out, <span class="keyword">true</span>, _reshaped_weights);</div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;            <span class="comment">// Softmax</span></div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;            smx-&gt;configure(&amp;fc8_out, &amp;output);</div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;        }</div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;    }</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;</div><div class="line"><a name="l00288"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#acaefe811b78a2fdc4a0dba0c4029c3ef">  288</a></span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#acaefe811b78a2fdc4a0dba0c4029c3ef">allocate</a>()</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;    {</div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;        input.allocator()-&gt;allocate();</div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;        output.allocator()-&gt;allocate();</div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;        <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;wi : w)</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;        {</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;            <span class="keywordflow">if</span>(wi.get())</div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;            {</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;                wi-&gt;allocator()-&gt;allocate();</div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;            }</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;        }</div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;        <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;bi : b)</div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;        {</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;            <span class="keywordflow">if</span>(bi.get())</div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;            {</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;                bi-&gt;allocator()-&gt;allocate();</div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;            }</div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;        }</div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;        <span class="keywordflow">if</span>(_reshaped_weights)</div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;        {</div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;            <span class="keyword">dynamic_cast&lt;</span>TensorType *<span class="keyword">&gt;</span>(w21.get())-&gt;allocator()-&gt;allocate();</div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;            <span class="keyword">dynamic_cast&lt;</span>TensorType *<span class="keyword">&gt;</span>(w22.get())-&gt;allocator()-&gt;allocate();</div><div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;            <span class="keyword">dynamic_cast&lt;</span>TensorType *<span class="keyword">&gt;</span>(w41.get())-&gt;allocator()-&gt;allocate();</div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;            <span class="keyword">dynamic_cast&lt;</span>TensorType *<span class="keyword">&gt;</span>(w42.get())-&gt;allocator()-&gt;allocate();</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;            <span class="keyword">dynamic_cast&lt;</span>TensorType *<span class="keyword">&gt;</span>(w51.get())-&gt;allocator()-&gt;allocate();</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;            <span class="keyword">dynamic_cast&lt;</span>TensorType *<span class="keyword">&gt;</span>(w52.get())-&gt;allocator()-&gt;allocate();</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;        }</div><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;        conv1_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;        act1_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;        norm1_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;        pool1_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;        conv2_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;        act2_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;        norm2_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;        pool2_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;        conv3_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;        act3_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;        conv4_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;        act4_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;        conv5_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;        act5_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;        pool5_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;        fc6_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;        act6_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;        fc7_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;        act7_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;        fc8_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;    }</div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;</div><div class="line"><a name="l00338"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a3b778cda9ac3fad08e7217edbcb942e0">  338</a></span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a3b778cda9ac3fad08e7217edbcb942e0">fill_random</a>()</div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;    {</div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;        <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill_tensor_uniform(Accessor(input), 0);</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;        <span class="keywordflow">if</span>(!_reshaped_weights)</div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;        {</div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;            <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; w.size(); ++i)</div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;            {</div><div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;                <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill_tensor_uniform(Accessor(*w[i]), i + 1);</div><div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;                <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill_tensor_uniform(Accessor(*b[i]), i + 10);</div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;            }</div><div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;        }</div><div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;        <span class="keywordflow">else</span></div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;        {</div><div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill_tensor_uniform(Accessor(*w[0]), 1);</div><div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill_tensor_uniform(Accessor(*w[2]), 2);</div><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill_tensor_uniform(Accessor(*w[5]), 3);</div><div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill_tensor_uniform(Accessor(*b[5]), 4);</div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill_tensor_uniform(Accessor(*w[6]), 5);</div><div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill_tensor_uniform(Accessor(*b[6]), 6);</div><div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill_tensor_uniform(Accessor(*w[7]), 7);</div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill_tensor_uniform(Accessor(*b[7]), 8);</div><div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;</div><div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill_tensor_uniform(Accessor(*dynamic_cast&lt;TensorType *&gt;(w21.get())), 9);</div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill_tensor_uniform(Accessor(*dynamic_cast&lt;TensorType *&gt;(w22.get())), 10);</div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill_tensor_uniform(Accessor(*dynamic_cast&lt;TensorType *&gt;(w41.get())), 11);</div><div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill_tensor_uniform(Accessor(*dynamic_cast&lt;TensorType *&gt;(w42.get())), 12);</div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill_tensor_uniform(Accessor(*dynamic_cast&lt;TensorType *&gt;(w51.get())), 13);</div><div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill_tensor_uniform(Accessor(*dynamic_cast&lt;TensorType *&gt;(w52.get())), 14);</div><div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;        }</div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;    }</div><div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;</div><div class="line"><a name="l00374"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a1466ef70729f3c8b5da5ebfec3f53f26">  374</a></span>&#160;    std::vector&lt;unsigned int&gt; <a class="code" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a1466ef70729f3c8b5da5ebfec3f53f26">get_classifications</a>()</div><div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;    {</div><div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;        std::vector&lt;unsigned int&gt; classified_labels;</div><div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;        Accessor                  output_accessor(output);</div><div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;</div><div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;        <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>&#160;        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>&#160;        <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> d = 1; d &lt; output_accessor.shape().num_dimensions(); ++d)</div><div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;        {</div><div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;            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>&#160;        }</div><div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;</div><div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;        <a class="code" href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">execute_window_loop</a>(window, [&amp;](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> &amp; <span class="keywordtype">id</span>)</div><div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;        {</div><div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;            <span class="keywordtype">int</span>               max_idx = 0;</div><div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;            <span class="keywordtype">float</span>             val     = 0;</div><div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;            <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>&#160;            <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> l = 0; l &lt; output_accessor.shape().x(); ++l)</div><div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;            {</div><div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;                <span class="keywordtype">float</span> curr_val = <span class="keyword">reinterpret_cast&lt;</span><span class="keyword">const </span><span class="keywordtype">float</span> *<span class="keyword">&gt;</span>(out_ptr)[l];</div><div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;                <span class="keywordflow">if</span>(curr_val &gt; val)</div><div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;                {</div><div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;                    max_idx = l;</div><div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;                    val     = curr_val;</div><div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;                }</div><div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;            }</div><div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;            classified_labels.push_back(max_idx);</div><div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;        });</div><div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;        <span class="keywordflow">return</span> classified_labels;</div><div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;    }</div><div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;</div><div class="line"><a name="l00406"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#ac8bb3912a3ce86b15842e79d0b421204">  406</a></span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#ac8bb3912a3ce86b15842e79d0b421204">clear</a>()</div><div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;    {</div><div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;        conv1.reset();</div><div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;        act1.reset();</div><div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;        norm1.reset();</div><div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;        pool1.reset();</div><div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;        conv21.reset();</div><div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;        conv22.reset();</div><div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;        act2.reset();</div><div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;        norm2.reset();</div><div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;        pool2.reset();</div><div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;        conv3.reset();</div><div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;        act3.reset();</div><div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;        conv41.reset();</div><div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;        conv42.reset();</div><div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;        act4.reset();</div><div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;        conv51.reset();</div><div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;        conv52.reset();</div><div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;        act5.reset();</div><div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;        pool5.reset();</div><div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;        fc6.reset();</div><div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;        act6.reset();</div><div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;        fc7.reset();</div><div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;        act7.reset();</div><div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;        fc8.reset();</div><div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;        smx.reset();</div><div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;</div><div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;        <span class="comment">// Free allocations</span></div><div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;        input.allocator()-&gt;free();</div><div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;        output.allocator()-&gt;free();</div><div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;        <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;wi : w)</div><div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;        {</div><div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;            wi.reset();</div><div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;        }</div><div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;        <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;bi : b)</div><div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;        {</div><div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;            bi.reset();</div><div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;        }</div><div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;</div><div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;        w21.reset();</div><div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;        w22.reset();</div><div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;        b21.reset();</div><div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;        b21.reset();</div><div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;        w41.reset();</div><div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;        w42.reset();</div><div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;        b41.reset();</div><div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;        b42.reset();</div><div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;        w51.reset();</div><div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;        w52.reset();</div><div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;        b51.reset();</div><div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;        b52.reset();</div><div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;</div><div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;        conv1_out.allocator()-&gt;free();</div><div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;        act1_out.allocator()-&gt;free();</div><div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;        norm1_out.allocator()-&gt;free();</div><div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;        pool1_out.allocator()-&gt;free();</div><div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;        conv2_out.allocator()-&gt;free();</div><div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;        act2_out.allocator()-&gt;free();</div><div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;        norm2_out.allocator()-&gt;free();</div><div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;        pool2_out.allocator()-&gt;free();</div><div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;        conv3_out.allocator()-&gt;free();</div><div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;        act3_out.allocator()-&gt;free();</div><div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;        conv4_out.allocator()-&gt;free();</div><div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;        act4_out.allocator()-&gt;free();</div><div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;        conv5_out.allocator()-&gt;free();</div><div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;        act5_out.allocator()-&gt;free();</div><div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;        pool5_out.allocator()-&gt;free();</div><div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;        fc6_out.allocator()-&gt;free();</div><div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;        act6_out.allocator()-&gt;free();</div><div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;        fc7_out.allocator()-&gt;free();</div><div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;        act7_out.allocator()-&gt;free();</div><div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;        fc8_out.allocator()-&gt;free();</div><div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;    }</div><div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;</div><div class="line"><a name="l00481"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a13a43e6d814de94978c515cb084873b1">  481</a></span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a13a43e6d814de94978c515cb084873b1">run</a>()</div><div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;    {</div><div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;        <span class="comment">// Layer 1</span></div><div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;        conv1-&gt;run();</div><div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;        act1-&gt;run();</div><div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;        norm1-&gt;run();</div><div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;        pool1-&gt;run();</div><div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;        <span class="comment">// Layer 2</span></div><div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;        conv21-&gt;run();</div><div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;        conv22-&gt;run();</div><div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;        act2-&gt;run();</div><div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;        norm2-&gt;run();</div><div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;        pool2-&gt;run();</div><div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160;        <span class="comment">// Layer 3</span></div><div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;        conv3-&gt;run();</div><div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;        act3-&gt;run();</div><div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;        <span class="comment">// Layer 4</span></div><div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;        conv41-&gt;run();</div><div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;        conv42-&gt;run();</div><div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;        act4-&gt;run();</div><div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;        <span class="comment">// Layer 5</span></div><div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;        conv51-&gt;run();</div><div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;        conv52-&gt;run();</div><div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;        act5-&gt;run();</div><div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;        pool5-&gt;run();</div><div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;        <span class="comment">// Layer 6</span></div><div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;        fc6-&gt;run();</div><div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;        act6-&gt;run();</div><div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;        <span class="comment">// Layer 7</span></div><div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;        fc7-&gt;run();</div><div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;        act7-&gt;run();</div><div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;        <span class="comment">// Layer 8</span></div><div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;        fc8-&gt;run();</div><div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;        <span class="comment">// Softmax</span></div><div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;        smx-&gt;run();</div><div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;    }</div><div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;</div><div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> _batches;</div><div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;    <span class="keywordtype">bool</span>         _reshaped_weights;</div><div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;</div><div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;    std::unique_ptr&lt;ActivationLayerFunction&gt;     act1{ <span class="keyword">nullptr</span> }, act2{ <span class="keyword">nullptr</span> }, act3{ <span class="keyword">nullptr</span> }, act4{ <span class="keyword">nullptr</span> }, act5{ <span class="keyword">nullptr</span> }, act6{ <span class="keyword">nullptr</span> }, act7{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;    std::unique_ptr&lt;ConvolutionLayerFunction&gt;    conv1{ <span class="keyword">nullptr</span> }, conv21{ <span class="keyword">nullptr</span> }, conv22{ <span class="keyword">nullptr</span> }, conv3{ <span class="keyword">nullptr</span> }, conv41{ <span class="keyword">nullptr</span> }, conv42{ <span class="keyword">nullptr</span> }, conv51{ <span class="keyword">nullptr</span> }, conv52{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;    std::unique_ptr&lt;FullyConnectedLayerFunction&gt; fc6{ <span class="keyword">nullptr</span> }, fc7{ <span class="keyword">nullptr</span> }, fc8{};</div><div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;    std::unique_ptr&lt;NormalizationLayerFunction&gt;  norm1{ <span class="keyword">nullptr</span> }, norm2{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;    std::unique_ptr&lt;PoolingLayerFunction&gt;        pool1{ <span class="keyword">nullptr</span> }, pool2{ <span class="keyword">nullptr</span> }, pool5{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;    std::unique_ptr&lt;SoftmaxLayerFunction&gt;        smx{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;</div><div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;    TensorType input{}, output{};</div><div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;    std::array&lt;std::unique_ptr&lt;TensorType&gt;, 8&gt; w{}, b{};</div><div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;    std::unique_ptr&lt;ITensorType&gt; w21{ <span class="keyword">nullptr</span> }, w22{ <span class="keyword">nullptr</span> }, b21{ <span class="keyword">nullptr</span> }, b22{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;    std::unique_ptr&lt;ITensorType&gt; w41{ <span class="keyword">nullptr</span> }, w42{ <span class="keyword">nullptr</span> }, b41{ <span class="keyword">nullptr</span> }, b42{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;    std::unique_ptr&lt;ITensorType&gt; w51{ <span class="keyword">nullptr</span> }, w52{ <span class="keyword">nullptr</span> }, b51{ <span class="keyword">nullptr</span> }, b52{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;</div><div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;    TensorType conv1_out{}, act1_out{}, norm1_out{}, pool1_out{};</div><div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;    TensorType conv2_out{}, act2_out{}, pool2_out{}, norm2_out{};</div><div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;    TensorType conv3_out{}, act3_out{};</div><div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;    TensorType conv4_out{}, act4_out{};</div><div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;    TensorType conv5_out{}, act5_out{}, pool5_out{};</div><div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;    TensorType fc6_out{}, act6_out{};</div><div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;    TensorType fc7_out{}, act7_out{};</div><div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;    TensorType fc8_out{};</div><div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;</div><div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;    std::unique_ptr&lt;SubTensorType&gt; pool11_out{ <span class="keyword">nullptr</span> }, pool12_out{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;    std::unique_ptr&lt;SubTensorType&gt; conv21_out{ <span class="keyword">nullptr</span> }, conv22_out{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;    std::unique_ptr&lt;SubTensorType&gt; act31_out{ <span class="keyword">nullptr</span> }, act32_out{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160;    std::unique_ptr&lt;SubTensorType&gt; conv41_out{ <span class="keyword">nullptr</span> }, conv42_out{ <span class="keyword">nullptr</span> }, act41_out{ <span class="keyword">nullptr</span> }, act42_out{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;    std::unique_ptr&lt;SubTensorType&gt; conv51_out{ <span class="keyword">nullptr</span> }, conv52_out{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;};</div><div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160;} <span class="comment">// namespace model_objects</span></div><div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;} <span class="comment">// namespace test</span></div><div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;} <span class="comment">// namespace arm_compute</span></div><div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;<span class="preprocessor">#endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_ALEXNET_H__</span></div><div class="ttc" id="classarm__compute_1_1test_1_1model__objects_1_1_alex_net_xhtml_a807107674868efa870ffe6fa1ad10f83"><div class="ttname"><a href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a807107674868efa870ffe6fa1ad10f83">arm_compute::test::model_objects::AlexNet::init_weights</a></div><div class="ttdeci">void init_weights(unsigned int batches, bool reshaped_weights=false)</div><div class="ttdef"><b>Definition:</b> <a href="model__objects_2_alex_net_8h_source.xhtml#l00062">AlexNet.h:62</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00038">TensorShape.h:38</a></div></div>
 <div class="ttc" id="classarm__compute_1_1test_1_1model__objects_1_1_alex_net_xhtml_a38a530655b80667542e46842c3ed8989"><div class="ttname"><a href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a38a530655b80667542e46842c3ed8989">arm_compute::test::model_objects::AlexNet::AlexNet</a></div><div class="ttdeci">AlexNet()</div><div class="ttdef"><b>Definition:</b> <a href="model__objects_2_alex_net_8h_source.xhtml#l00057">AlexNet.h:57</a></div></div>
 <div class="ttc" id="namespacearm__compute_1_1test_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml">arm_compute::test</a></div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_g_e_m_m_8h_source.xhtml#l00039">GEMM.h:39</a></div></div>
 <div class="ttc" id="classarm__compute_1_1test_1_1model__objects_1_1_alex_net_xhtml_a3b778cda9ac3fad08e7217edbcb942e0"><div class="ttname"><a href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a3b778cda9ac3fad08e7217edbcb942e0">arm_compute::test::model_objects::AlexNet::fill_random</a></div><div class="ttdeci">void fill_random()</div><div class="ttdoc">Fills the trainable parameters and input with random data. </div><div class="ttdef"><b>Definition:</b> <a href="model__objects_2_alex_net_8h_source.xhtml#l00338">AlexNet.h:338</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="_tensor_library_8h_xhtml"><div class="ttname"><a href="_tensor_library_8h.xhtml">TensorLibrary.h</a></div></div>
 <div class="ttc" id="classarm__compute_1_1test_1_1model__objects_1_1_alex_net_xhtml_a13a43e6d814de94978c515cb084873b1"><div class="ttname"><a href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a13a43e6d814de94978c515cb084873b1">arm_compute::test::model_objects::AlexNet::run</a></div><div class="ttdeci">void run()</div><div class="ttdoc">Runs the model. </div><div class="ttdef"><b>Definition:</b> <a href="model__objects_2_alex_net_8h_source.xhtml#l00481">AlexNet.h:481</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F16 per channel </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_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&amp;#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_1test_1_1model__objects_1_1_alex_net_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml">arm_compute::test::model_objects::AlexNet</a></div><div class="ttdoc">AlexNet model object. </div><div class="ttdef"><b>Definition:</b> <a href="model__objects_2_alex_net_8h_source.xhtml#l00054">AlexNet.h:54</a></div></div>
 <div class="ttc" id="classarm__compute_1_1test_1_1model__objects_1_1_alex_net_xhtml_a1466ef70729f3c8b5da5ebfec3f53f26"><div class="ttname"><a href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a1466ef70729f3c8b5da5ebfec3f53f26">arm_compute::test::model_objects::AlexNet::get_classifications</a></div><div class="ttdeci">std::vector&lt; unsigned int &gt; get_classifications()</div><div class="ttdoc">Get the classification results. </div><div class="ttdef"><b>Definition:</b> <a href="model__objects_2_alex_net_8h_source.xhtml#l00374">AlexNet.h:374</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="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdef"><b>Definition:</b> <a href="01__library_8dox_source.xhtml#l00001">01_library.dox:1</a></div></div>
 <div class="ttc" id="classarm__compute_1_1test_1_1model__objects_1_1_alex_net_xhtml_acaefe811b78a2fdc4a0dba0c4029c3ef"><div class="ttname"><a href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#acaefe811b78a2fdc4a0dba0c4029c3ef">arm_compute::test::model_objects::AlexNet::allocate</a></div><div class="ttdeci">void allocate()</div><div class="ttdef"><b>Definition:</b> <a href="model__objects_2_alex_net_8h_source.xhtml#l00288">AlexNet.h:288</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_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_a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb"><div class="ttname"><a href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">arm_compute::Channel::U</a></div><div class="ttdoc">Cb/U channel. </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 &amp;w, L &amp;&amp;lambda_function, Ts &amp;&amp;...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="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_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_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 &amp;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="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&lt; TensorLibrary &gt; library</div><div class="ttdef"><b>Definition:</b> <a href="benchmark_2main_8cpp_source.xhtml#l00050">main.cpp:50</a></div></div>
 <div class="ttc" id="classarm__compute_1_1test_1_1model__objects_1_1_alex_net_xhtml_ac8bb3912a3ce86b15842e79d0b421204"><div class="ttname"><a href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#ac8bb3912a3ce86b15842e79d0b421204">arm_compute::test::model_objects::AlexNet::clear</a></div><div class="ttdeci">void clear()</div><div class="ttdoc">Clear all allocated memory from the tensor objects. </div><div class="ttdef"><b>Definition:</b> <a href="model__objects_2_alex_net_8h_source.xhtml#l00406">AlexNet.h:406</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&amp;#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="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdoc">Available data types. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00060">Types.h:60</a></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>
 <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>
 <div class="ttc" id="classarm__compute_1_1test_1_1model__objects_1_1_alex_net_xhtml_a7740c7ab195c03ac140f1f75f633470f"><div class="ttname"><a href="classarm__compute_1_1test_1_1model__objects_1_1_alex_net.xhtml#a7740c7ab195c03ac140f1f75f633470f">arm_compute::test::model_objects::AlexNet::build</a></div><div class="ttdeci">void build()</div><div class="ttdef"><b>Definition:</b> <a href="model__objects_2_alex_net_8h_source.xhtml#l00166">AlexNet.h:166</a></div></div>
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