Google Git
Sign in
android / platform / external / ComputeLibrary / f4a254c2745aeaab6f7276a675147d707002fe7a / . / documentation / model__objects_2_le_net5_8h_source.xhtml
blob: b46b8d024bf3f81a1d64e5d0d747149d03ef87d4 [file] [log] [blame]
<!-- HTML header for doxygen 1.8.9.1-->
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
<meta name="generator" content="Doxygen 1.8.11"/>
<meta name="robots" content="NOINDEX, NOFOLLOW" /> <!-- Prevent indexing by search engines -->
<title>Compute Library: tests/model_objects/LeNet5.h Source File</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="navtree.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="resize.js"></script>
<script type="text/javascript" src="navtreedata.js"></script>
<script type="text/javascript" src="navtree.js"></script>
<script type="text/javascript">
$(document).ready(initResizable);
$(window).load(resizeHeight);
</script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
<script type="text/javascript">
$(document).ready(function() { init_search(); });
</script>
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
extensions: ["tex2jax.js"],
jax: ["input/TeX","output/HTML-CSS"],
});
</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
<div id="titlearea">
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
<td style="padding-left: 0.5em;">
<div id="projectname">Compute Library
&#160;<span id="projectnumber">v17.06</span>
</div>
</td>
</tr>
</tbody>
</table>
</div>
<!-- end header part -->
<!-- Generated by Doxygen 1.8.11 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
<div id="navrow1" class="tabs">
<ul class="tablist">
<li><a href="index.xhtml"><span>Main&#160;Page</span></a></li>
<li><a href="pages.xhtml"><span>Related&#160;Pages</span></a></li>
<li><a href="namespaces.xhtml"><span>Namespaces</span></a></li>
<li><a href="annotated.xhtml"><span>Data&#160;Structures</span></a></li>
<li class="current"><a href="files.xhtml"><span>Files</span></a></li>
<li>
<div id="MSearchBox" class="MSearchBoxInactive">
<span class="left">
<img id="MSearchSelect" src="search/mag_sel.png"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
alt=""/>
<input type="text" id="MSearchField" value="Search" accesskey="S"
onfocus="searchBox.OnSearchFieldFocus(true)"
onblur="searchBox.OnSearchFieldFocus(false)"
onkeyup="searchBox.OnSearchFieldChange(event)"/>
</span><span class="right">
<a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
</span>
</div>
</li>
</ul>
</div>
<div id="navrow2" class="tabs2">
<ul class="tablist">
<li><a href="files.xhtml"><span>File&#160;List</span></a></li>
<li><a href="globals.xhtml"><span>Globals</span></a></li>
</ul>
</div>
</div><!-- top -->
<div id="side-nav" class="ui-resizable side-nav-resizable">
<div id="nav-tree">
<div id="nav-tree-contents">
<div id="nav-sync" class="sync"></div>
</div>
</div>
<div id="splitbar" style="-moz-user-select:none;"
class="ui-resizable-handle">
</div>
</div>
<script type="text/javascript">
$(document).ready(function(){initNavTree('model__objects_2_le_net5_8h_source.xhtml','');});
</script>
<div id="doc-content">
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
</div>
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
<iframe src="javascript:void(0)" frameborder="0"
name="MSearchResults" id="MSearchResults">
</iframe>
</div>
<div class="header">
<div class="headertitle">
<div class="title">LeNet5.h</div> </div>
</div><!--header-->
<div class="contents">
<a href="model__objects_2_le_net5_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_LENET5_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_LENET5_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"> 39</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> TensorType,</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; <span class="keyword">typename</span> Accessor,</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160; <span class="keyword">typename</span> ActivationLayerFunction,</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; <span class="keyword">typename</span> ConvolutionLayerFunction,</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; <span class="keyword">typename</span> FullyConnectedLayerFunction,</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160; <span class="keyword">typename</span> PoolingLayerFunction,</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; <span class="keyword">typename</span> SoftmaxLayerFunction&gt;</div><div class="line"><a name="l00049"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1model__objects_1_1_le_net5.xhtml"> 49</a></span>&#160;<span class="keyword">class </span><a class="code" href="classarm__compute_1_1test_1_1model__objects_1_1_le_net5.xhtml">LeNet5</a></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160;{</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00056"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1model__objects_1_1_le_net5.xhtml#a511458946c2bfea4a6f1642ac5c6f898"> 56</a></span>&#160; <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1model__objects_1_1_le_net5.xhtml#a511458946c2bfea4a6f1642ac5c6f898">build</a>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batches)</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; {</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; <span class="comment">// Initialize input, output, weights and biases</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</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>(28<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 28<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</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>(10<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; w[0].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>(5<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 5<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 20<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; b[0].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>(20<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; w[1].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>(5<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 5<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 20<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 50<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; b[1].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>(50<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; w[2].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>(800<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 500<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; b[2].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>(500<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; w[3].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>(500<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 10<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; b[3].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>(10<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</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="comment">// Initialize intermediate tensors</span></div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; <span class="comment">// Layer 1</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</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>(24<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 24<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 20<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</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>(12<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 12<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 20<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; <span class="comment">// Layer 2</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</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>(8<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 8<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 50<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</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>(4<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 4<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 50<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; <span class="comment">// Layer 3</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; fc1_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>(500<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</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>(500<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; <span class="comment">// Layer 6</span></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; fc2_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>(10<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; <span class="comment">// Allocate layers</span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; {</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; <span class="comment">// Layer 1</span></div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; conv1 = std::unique_ptr&lt;ConvolutionLayerFunction&gt;(<span class="keyword">new</span> ConvolutionLayerFunction());</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; pool1 = std::unique_ptr&lt;PoolingLayerFunction&gt;(<span class="keyword">new</span> PoolingLayerFunction());</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; <span class="comment">// Layer 2</span></div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; conv2 = std::unique_ptr&lt;ConvolutionLayerFunction&gt;(<span class="keyword">new</span> ConvolutionLayerFunction());</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; pool2 = std::unique_ptr&lt;PoolingLayerFunction&gt;(<span class="keyword">new</span> PoolingLayerFunction());</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; <span class="comment">// Layer 3</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; fc1 = std::unique_ptr&lt;FullyConnectedLayerFunction&gt;(<span class="keyword">new</span> FullyConnectedLayerFunction());</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; act1 = std::unique_ptr&lt;ActivationLayerFunction&gt;(<span class="keyword">new</span> ActivationLayerFunction());</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; <span class="comment">// Layer 4</span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; fc2 = std::unique_ptr&lt;FullyConnectedLayerFunction&gt;(<span class="keyword">new</span> FullyConnectedLayerFunction());</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; <span class="comment">// Softmax</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; smx = std::unique_ptr&lt;SoftmaxLayerFunction&gt;(<span class="keyword">new</span> SoftmaxLayerFunction());</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; }</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; <span class="comment">// Configure Layers</span></div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; {</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; conv1-&gt;configure(&amp;input, &amp;w[0], &amp;b[0], &amp;conv1_out, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; pool1-&gt;configure(&amp;conv1_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>, 2, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)));</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; conv2-&gt;configure(&amp;pool1_out, &amp;w[1], &amp;b[1], &amp;conv2_out, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; pool2-&gt;configure(&amp;conv2_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>, 2, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)));</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; fc1-&gt;configure(&amp;pool2_out, &amp;w[2], &amp;b[2], &amp;fc1_out);</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; act1-&gt;configure(&amp;fc1_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="l00108"></a><span class="lineno"> 108</span>&#160; fc2-&gt;configure(&amp;act1_out, &amp;w[3], &amp;b[3], &amp;fc2_out);</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; smx-&gt;configure(&amp;fc2_out, &amp;output);</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; }</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="comment">// Allocate tensors</span></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; input.allocator()-&gt;allocate();</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; output.allocator()-&gt;allocate();</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;wi : w)</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; {</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; wi.allocator()-&gt;allocate();</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; }</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;bi : b)</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; {</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; bi.allocator()-&gt;allocate();</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; conv1_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; pool1_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; conv2_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; pool2_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; fc1_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; act1_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; fc2_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; }</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;</div><div class="line"><a name="l00135"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1model__objects_1_1_le_net5.xhtml#a3b778cda9ac3fad08e7217edbcb942e0"> 135</a></span>&#160; <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1model__objects_1_1_le_net5.xhtml#a3b778cda9ac3fad08e7217edbcb942e0">fill_random</a>()</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; {</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; std::uniform_real_distribution&lt;&gt; distribution(-1, 1);</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill(Accessor(input), distribution, 0);</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</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="l00140"></a><span class="lineno"> 140</span>&#160; {</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill(Accessor(w[i]), distribution, i + 1);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; <a class="code" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a>-&gt;fill(Accessor(b[i]), distribution, i + 10);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160; }</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; }</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160;</div><div class="line"><a name="l00150"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1model__objects_1_1_le_net5.xhtml#a1466ef70729f3c8b5da5ebfec3f53f26"> 150</a></span>&#160; std::vector&lt;unsigned int&gt; <a class="code" href="classarm__compute_1_1test_1_1model__objects_1_1_le_net5.xhtml#a1466ef70729f3c8b5da5ebfec3f53f26">get_classifications</a>()</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; {</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; std::vector&lt;unsigned int&gt; classified_labels;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; Accessor output_accessor(output);</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160;</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window;</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</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="l00157"></a><span class="lineno"> 157</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="l00158"></a><span class="lineno"> 158</span>&#160; {</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</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="l00160"></a><span class="lineno"> 160</span>&#160; }</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160;</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</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="l00163"></a><span class="lineno"> 163</span>&#160; {</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; <span class="keywordtype">int</span> max_idx = 0;</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; <span class="keywordtype">float</span> val = 0;</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</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="l00167"></a><span class="lineno"> 167</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="l00168"></a><span class="lineno"> 168</span>&#160; {</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</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="l00170"></a><span class="lineno"> 170</span>&#160; <span class="keywordflow">if</span>(curr_val &gt; val)</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; {</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; max_idx = l;</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; val = curr_val;</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; }</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; }</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; classified_labels.push_back(max_idx);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; });</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160; <span class="keywordflow">return</span> classified_labels;</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160; }</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160;</div><div class="line"><a name="l00182"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1model__objects_1_1_le_net5.xhtml#ac8bb3912a3ce86b15842e79d0b421204"> 182</a></span>&#160; <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1model__objects_1_1_le_net5.xhtml#ac8bb3912a3ce86b15842e79d0b421204">clear</a>()</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; {</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; conv1.reset();</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; pool1.reset();</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; conv2.reset();</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; pool2.reset();</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; fc1.reset();</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160; act1.reset();</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160; fc2.reset();</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160; smx.reset();</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160;</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160; input.allocator()-&gt;free();</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; output.allocator()-&gt;free();</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160; <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;wi : w)</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; {</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; wi.allocator()-&gt;free();</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; }</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;bi : b)</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; {</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160; bi.allocator()-&gt;free();</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; }</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160;</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160; conv1_out.allocator()-&gt;free();</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160; pool1_out.allocator()-&gt;free();</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160; conv2_out.allocator()-&gt;free();</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>&#160; pool2_out.allocator()-&gt;free();</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160; fc1_out.allocator()-&gt;free();</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; act1_out.allocator()-&gt;free();</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160; fc2_out.allocator()-&gt;free();</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160; }</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160;</div><div class="line"><a name="l00214"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1model__objects_1_1_le_net5.xhtml#a13a43e6d814de94978c515cb084873b1"> 214</a></span>&#160; <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1model__objects_1_1_le_net5.xhtml#a13a43e6d814de94978c515cb084873b1">run</a>()</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>&#160; {</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>&#160; <span class="comment">// Layer 1</span></div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160; conv1-&gt;run();</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>&#160; pool1-&gt;run();</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160; <span class="comment">// Layer 2</span></div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160; conv2-&gt;run();</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160; pool2-&gt;run();</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>&#160; <span class="comment">// Layer 3</span></div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; fc1-&gt;run();</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160; act1-&gt;run();</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160; <span class="comment">// Layer 4</span></div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160; fc2-&gt;run();</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160; <span class="comment">// Softmax</span></div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; smx-&gt;run();</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; }</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160;</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160; std::unique_ptr&lt;ActivationLayerFunction&gt; act1{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>&#160; std::unique_ptr&lt;ConvolutionLayerFunction&gt; conv1{ <span class="keyword">nullptr</span> }, conv2{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160; std::unique_ptr&lt;FullyConnectedLayerFunction&gt; fc1{ <span class="keyword">nullptr</span> }, fc2{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160; std::unique_ptr&lt;PoolingLayerFunction&gt; pool1{ <span class="keyword">nullptr</span> }, pool2{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>&#160; std::unique_ptr&lt;SoftmaxLayerFunction&gt; smx{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>&#160;</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>&#160; TensorType input{}, output{};</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>&#160; std::array&lt;TensorType, 4&gt; w{}, b{};</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>&#160;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160; TensorType conv1_out{}, pool1_out{};</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160; TensorType conv2_out{}, pool2_out{};</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160; TensorType fc1_out{}, act1_out{};</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>&#160; TensorType fc2_out{};</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>&#160;};</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>&#160;} <span class="comment">// namespace model_objects</span></div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>&#160;} <span class="comment">// namespace test</span></div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>&#160;} <span class="comment">// namespace arm_compute</span></div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>&#160;<span class="preprocessor">#endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_LENET5_H__</span></div><div class="ttc" id="classarm__compute_1_1test_1_1model__objects_1_1_le_net5_xhtml_ac8bb3912a3ce86b15842e79d0b421204"><div class="ttname"><a href="classarm__compute_1_1test_1_1model__objects_1_1_le_net5.xhtml#ac8bb3912a3ce86b15842e79d0b421204">arm_compute::test::model_objects::LeNet5::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_le_net5_8h_source.xhtml#l00182">LeNet5.h:182</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_le_net5_xhtml_a511458946c2bfea4a6f1642ac5c6f898"><div class="ttname"><a href="classarm__compute_1_1test_1_1model__objects_1_1_le_net5.xhtml#a511458946c2bfea4a6f1642ac5c6f898">arm_compute::test::model_objects::LeNet5::build</a></div><div class="ttdeci">void build(unsigned int batches)</div><div class="ttdoc">Initialize and build the model. </div><div class="ttdef"><b>Definition:</b> <a href="model__objects_2_le_net5_8h_source.xhtml#l00056">LeNet5.h:56</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_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="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_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_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_le_net5_xhtml_a1466ef70729f3c8b5da5ebfec3f53f26"><div class="ttname"><a href="classarm__compute_1_1test_1_1model__objects_1_1_le_net5.xhtml#a1466ef70729f3c8b5da5ebfec3f53f26">arm_compute::test::model_objects::LeNet5::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_le_net5_8h_source.xhtml#l00150">LeNet5.h:150</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="classarm__compute_1_1test_1_1model__objects_1_1_le_net5_xhtml_a13a43e6d814de94978c515cb084873b1"><div class="ttname"><a href="classarm__compute_1_1test_1_1model__objects_1_1_le_net5.xhtml#a13a43e6d814de94978c515cb084873b1">arm_compute::test::model_objects::LeNet5::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_le_net5_8h_source.xhtml#l00214">LeNet5.h:214</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1model__objects_1_1_le_net5_xhtml_a3b778cda9ac3fad08e7217edbcb942e0"><div class="ttname"><a href="classarm__compute_1_1test_1_1model__objects_1_1_le_net5.xhtml#a3b778cda9ac3fad08e7217edbcb942e0">arm_compute::test::model_objects::LeNet5::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_le_net5_8h_source.xhtml#l00135">LeNet5.h:135</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="classarm__compute_1_1test_1_1model__objects_1_1_le_net5_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1model__objects_1_1_le_net5.xhtml">arm_compute::test::model_objects::LeNet5</a></div><div class="ttdoc">Lenet5 model object. </div><div class="ttdef"><b>Definition:</b> <a href="model__objects_2_le_net5_8h_source.xhtml#l00049">LeNet5.h:49</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_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="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="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><!-- fragment --></div><!-- contents -->
</div><!-- doc-content -->
<!-- start footer part -->
<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
<ul>
<li class="navelem"><a class="el" href="dir_59425e443f801f1f2fd8bbe4959a3ccf.xhtml">tests</a></li><li class="navelem"><a class="el" href="dir_ba1dc693ed461bc4c6534d3b34c816d0.xhtml">model_objects</a></li><li class="navelem"><a class="el" href="model__objects_2_le_net5_8h.xhtml">LeNet5.h</a></li>
<li class="footer">Generated on Fri Jun 23 2017 15:44:33 for Compute Library by
<a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.11 </li>
</ul>
</div>
</body>
</html>