Google Git
Sign in
android / platform / external / ComputeLibrary / e8521c234 / . / documentation / _n_e_o_n_2_l_s_t_m_layer_quantized_8cpp_source.xhtml
blob: 8afbe39a4dee6dc866fb9c5e688404068069f365 [file] [log] [blame]
<!-- HTML header for doxygen 1.8.15-->
<!-- Remember to use version doxygen 1.8.15 +-->
<!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 1.8.15"/>
<meta name="robots" content="NOINDEX, NOFOLLOW" /> <!-- Prevent indexing by search engines -->
<title>Compute Library: tests/validation/NEON/LSTMLayerQuantized.cpp 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">
/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&amp;dn=gpl-2.0.txt GPL-v2 */
$(document).ready(initResizable);
/* @license-end */</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/x-mathjax-config">
MathJax.Hub.Config({
extensions: ["tex2jax.js"],
jax: ["input/TeX","output/HTML-CSS"],
});
</script><script type="text/javascript" async="async" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
<link href="stylesheet.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;">
<img alt="Compute Library" src="https://raw.githubusercontent.com/ARM-software/ComputeLibrary/gh-pages/ACL_logo.png" style="max-width: 100%;margin-top: 15px;margin-left: 10px"/>
<td style="padding-left: 0.5em;">
<div id="projectname">
&#160;<span id="projectnumber">20.02.1</span>
</div>
</td>
</tr>
</tbody>
</table>
</div>
<!-- end header part -->
<!-- Generated by Doxygen 1.8.15 -->
<script type="text/javascript">
/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&amp;dn=gpl-2.0.txt GPL-v2 */
var searchBox = new SearchBox("searchBox", "search",false,'Search');
/* @license-end */
</script>
<script type="text/javascript" src="menudata.js"></script>
<script type="text/javascript" src="menu.js"></script>
<script type="text/javascript">
/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&amp;dn=gpl-2.0.txt GPL-v2 */
$(function() {
initMenu('',true,false,'search.php','Search');
$(document).ready(function() { init_search(); });
});
/* @license-end */</script>
<div id="main-nav"></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">
/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&amp;dn=gpl-2.0.txt GPL-v2 */
$(document).ready(function(){initNavTree('_n_e_o_n_2_l_s_t_m_layer_quantized_8cpp_source.xhtml','');});
/* @license-end */
</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">LSTMLayerQuantized.cpp</div> </div>
</div><!--header-->
<div class="contents">
<a href="_n_e_o_n_2_l_s_t_m_layer_quantized_8cpp.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) 2019 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">#include &quot;<a class="code" href="_n_e_l_s_t_m_layer_quantized_8h.xhtml">arm_compute/runtime/NEON/functions/NELSTMLayerQuantized.h</a>&quot;</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_accessor_8h.xhtml">tests/NEON/Accessor.h</a>&quot;</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_padding_calculator_8h.xhtml">tests/PaddingCalculator.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="tests_2_utils_8h.xhtml">tests/Utils.h</a>&quot;</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;<span class="preprocessor">#include &quot;tests/datasets/LSTMLayerDataset.h&quot;</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_asserts_8h.xhtml">tests/framework/Asserts.h</a>&quot;</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="tests_2framework_2_macros_8h.xhtml">tests/framework/Macros.h</a>&quot;</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_datasets_8h.xhtml">tests/framework/datasets/Datasets.h</a>&quot;</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_validation_8h.xhtml">tests/validation/Validation.h</a>&quot;</span></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="preprocessor">#include &lt;vector&gt;</span></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><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></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>test</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160;{</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160;<span class="keyword">namespace </span>validation</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160;{</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160;<span class="keyword">namespace</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160;{</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160;<span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(Tensor &amp;tensor, <span class="keyword">const</span> std::vector&lt;T&gt; &amp;v)</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160;{</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; <span class="comment">// Import memory accounting for padding</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; TensorShape t_shape = tensor.info()-&gt;tensor_shape();</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; Window window;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; window.use_tensor_dimensions(t_shape);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; Iterator out(&amp;tensor, window);</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; <a class="code" href="namespacearm__compute.xhtml#a5002bf7ec46d52971f9526e94172cfee">execute_window_loop</a>(window, [&amp;](<span class="keyword">const</span> Coordinates &amp; <span class="keywordtype">id</span>)</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; {</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; *reinterpret_cast&lt;T *&gt;(out.ptr()) = v[<a class="code" href="namespacearm__compute_1_1test.xhtml#a9be4cb7e6ee20063a4a10bc3abb750b9">coord2index</a>(t_shape, <span class="keywordtype">id</span>)];</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; },</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; out);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160;}</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160;</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160;<span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(SimpleTensor&lt;T&gt; &amp;tensor, <span class="keyword">const</span> std::vector&lt;T&gt; &amp;v)</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160;{</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; std::memcpy(tensor.data(), v.data(), <span class="keyword">sizeof</span>(T) * v.size());</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160;}</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160;<span class="comment"></span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160;<span class="comment">/** Tolerance for quantized asymmetric operations */</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160;<span class="preprocessor">#if defined(__aarch64__)</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160;constexpr AbsoluteTolerance&lt;int16_t&gt; tolerance_qsymm16(0);</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160;<span class="preprocessor">#else // defined(__aarch64__)</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160;constexpr AbsoluteTolerance&lt;int16_t&gt; tolerance_qsymm16(1);</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160;<span class="preprocessor">#endif // defined(__aarch64__)</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160;</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160;} <span class="comment">// namespace</span></div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8f65156abdd90180036790221cfc915f">TEST_SUITE</a>(NEON)</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8f65156abdd90180036790221cfc915f">TEST_SUITE</a>(LSTMLayerQuantized)</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;<span class="comment">// *INDENT-OFF*</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160;<span class="comment">// clang-format off</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8f65156abdd90180036790221cfc915f">TEST_SUITE</a>(IntegrationTestCase)</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8f65156abdd90180036790221cfc915f">TEST_SUITE</a>(MultSmallerEq1)</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a963c5b8a3279186004d6a7e97a1b87e6">TEST_CASE</a>(RunSmall, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfab829534c7b40afdbd3c3ffea05202a97">framework::DatasetMode::PRECOMMIT</a>)</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160;{</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> batch_size = 2;</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6bb1fa96fb01419887f07ecd236c8cd4">input_size</a> = 2;</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">output_size</a> = 4;</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160;</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; QuantizationInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aee13fc8e41cf0443bbeab64e23eee1ec">qasymm</a>(1.f / 128.f, 128);</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; QuantizationInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>(1.f / 128.f, 128);</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; QuantizationInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a20c95b3935143dddbf37176fa0562685">qsymm_3</a>(8.f / 32768.f, 0);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; QuantizationInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a66b9829d1b2750c8f7bd6dad961cdc62">qsymm_4</a>(16.f / 32768.f, 0);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160;</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59e3cf27216b7361c71cbd2830ec4c2">input_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6bb1fa96fb01419887f07ecd236c8cd4">input_size</a>, batch_size };</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af607b7b3379e1f6a6d4f4cbcceaaec94">input_weights_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6bb1fa96fb01419887f07ecd236c8cd4">input_size</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">output_size</a> };</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a14f78505947c1a1bd529fca96241a771">recurrent_weights_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">output_size</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">output_size</a> };</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">output_size</a>, batch_size};</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a79820c7442073b8eb22fb3eaef6fd6ba">bias_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">output_size</a> };</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="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a93550027861552f1292b652d85b27b7c">input_to_input_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af607b7b3379e1f6a6d4f4cbcceaaec94">input_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3b793c410cba57a1395184692a018356">input_to_forget_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af607b7b3379e1f6a6d4f4cbcceaaec94">input_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac547a66fe26967afb94760061ee0d0d1">input_to_cell_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af607b7b3379e1f6a6d4f4cbcceaaec94">input_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ace4dd633420fa8d8aa71f60ff730f01f">input_to_output_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af607b7b3379e1f6a6d4f4cbcceaaec94">input_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3586fa7cce76c57e1cce83b2add420de">recurrent_to_input_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a14f78505947c1a1bd529fca96241a771">recurrent_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac62dfdcc14798598d953342789c9927e">recurrent_to_forget_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a14f78505947c1a1bd529fca96241a771">recurrent_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2236dfe2a3fc5fa4e125348829cbeb2">recurrent_to_cell_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a14f78505947c1a1bd529fca96241a771">recurrent_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aab02df8a9ee45153f2fd76e934407fbd">recurrent_to_output_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a14f78505947c1a1bd529fca96241a771">recurrent_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5061c02d0093b981703ab63fa7ddb13e">input_gate_bias</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a79820c7442073b8eb22fb3eaef6fd6ba">bias_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a55daaf57fb833fc416d779c28f7a3c85">forget_gate_bias</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a79820c7442073b8eb22fb3eaef6fd6ba">bias_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a69aaf7df811f8c7750cae785ea6f9719">cell_gate_bias</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a79820c7442073b8eb22fb3eaef6fd6ba">bias_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a507bd7e4d98cb3e45d3e820d8bac422a">output_gate_bias</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a79820c7442073b8eb22fb3eaef6fd6ba">bias_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160;</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; <span class="comment">// LSTM input</span></div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59e3cf27216b7361c71cbd2830ec4c2">input_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aee13fc8e41cf0443bbeab64e23eee1ec">qasymm</a>);</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160;</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; <span class="comment">// LSTM output state</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aee13fc8e41cf0443bbeab64e23eee1ec">qasymm</a>);</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; <span class="comment">// LSTM cell state</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a34e65b7d309d497fc82a9515166cde38">cell_state</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a66b9829d1b2750c8f7bd6dad961cdc62">qsymm_4</a>);</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; NELSTMLayerQuantized <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a0dad1220a6a2995196ead0bd79956bbc">lstmq</a>;</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; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a0dad1220a6a2995196ead0bd79956bbc">lstmq</a>.<a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ab90e9ae19db4dbc4f316851b03402bfa">configure</a>(&amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a93550027861552f1292b652d85b27b7c">input_to_input_weights</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3b793c410cba57a1395184692a018356">input_to_forget_weights</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac547a66fe26967afb94760061ee0d0d1">input_to_cell_weights</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ace4dd633420fa8d8aa71f60ff730f01f">input_to_output_weights</a>,</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3586fa7cce76c57e1cce83b2add420de">recurrent_to_input_weights</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac62dfdcc14798598d953342789c9927e">recurrent_to_forget_weights</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2236dfe2a3fc5fa4e125348829cbeb2">recurrent_to_cell_weights</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aab02df8a9ee45153f2fd76e934407fbd">recurrent_to_output_weights</a>,</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5061c02d0093b981703ab63fa7ddb13e">input_gate_bias</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a55daaf57fb833fc416d779c28f7a3c85">forget_gate_bias</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a69aaf7df811f8c7750cae785ea6f9719">cell_gate_bias</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a507bd7e4d98cb3e45d3e820d8bac422a">output_gate_bias</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a34e65b7d309d497fc82a9515166cde38">cell_state</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a34e65b7d309d497fc82a9515166cde38">cell_state</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>);</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160;</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a93550027861552f1292b652d85b27b7c">input_to_input_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3b793c410cba57a1395184692a018356">input_to_forget_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac547a66fe26967afb94760061ee0d0d1">input_to_cell_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ace4dd633420fa8d8aa71f60ff730f01f">input_to_output_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3586fa7cce76c57e1cce83b2add420de">recurrent_to_input_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac62dfdcc14798598d953342789c9927e">recurrent_to_forget_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2236dfe2a3fc5fa4e125348829cbeb2">recurrent_to_cell_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aab02df8a9ee45153f2fd76e934407fbd">recurrent_to_output_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5061c02d0093b981703ab63fa7ddb13e">input_gate_bias</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a55daaf57fb833fc416d779c28f7a3c85">forget_gate_bias</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a69aaf7df811f8c7750cae785ea6f9719">cell_gate_bias</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a507bd7e4d98cb3e45d3e820d8bac422a">output_gate_bias</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a34e65b7d309d497fc82a9515166cde38">cell_state</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>.allocator()-&gt;allocate();</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; <span class="comment">// Fill weights and biases</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a93550027861552f1292b652d85b27b7c">input_to_input_weights</a>, std::vector&lt;uint8_t&gt;{ 47, 168,</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; 66, 239,</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; 6, 42,</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; 237, 236 });</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160;</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3b793c410cba57a1395184692a018356">input_to_forget_weights</a>, std::vector&lt;uint8_t&gt; { 204, 193,</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; 148, 59,</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; 113, 17,</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; 66, 197 });</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="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac547a66fe26967afb94760061ee0d0d1">input_to_cell_weights</a>, std::vector&lt;uint8_t&gt; { 172, 101,</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; 184, 209,</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; 165, 82,</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; 108, 209 });</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ace4dd633420fa8d8aa71f60ff730f01f">input_to_output_weights</a>, std::vector&lt;uint8_t&gt; { 203, 244,</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; 219, 114,</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; 130, 16,</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; 163, 222 });</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; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3586fa7cce76c57e1cce83b2add420de">recurrent_to_input_weights</a>, std::vector&lt;uint8_t&gt; { 162, 168, 7, 95,</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; 91, 155, 108, 216,</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160; 255, 100, 48, 188,</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; 58, 37, 186, 147 });</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160;</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac62dfdcc14798598d953342789c9927e">recurrent_to_forget_weights</a>, std::vector&lt;uint8_t&gt; { 46, 58, 47, 170,</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; 246, 96, 12, 99,</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; 68, 23, 186, 161,</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; 237, 164, 89, 6 });</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; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2236dfe2a3fc5fa4e125348829cbeb2">recurrent_to_cell_weights</a>, std::vector&lt;uint8_t&gt; { 234, 99, 71, 206,</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; 205, 159, 64, 253,</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; 191, 148, 116, 8,</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160; 209, 136, 59, 138 });</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; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aab02df8a9ee45153f2fd76e934407fbd">recurrent_to_output_weights</a>, std::vector&lt;uint8_t&gt; { 23, 241, 137, 36,</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; 206, 5, 227, 56,</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; 254, 176, 231, 47,</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; 18, 201, 161, 11 });</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160;</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5061c02d0093b981703ab63fa7ddb13e">input_gate_bias</a>, std::vector&lt;int&gt; {-103038, 30525, 115255, -38154 });</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a55daaf57fb833fc416d779c28f7a3c85">forget_gate_bias</a>, std::vector&lt;int&gt; { -23428, 126970, 116806, 46307 });</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a69aaf7df811f8c7750cae785ea6f9719">cell_gate_bias</a>, std::vector&lt;int&gt; { 128006, 69949, -42808, 42568 });</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a507bd7e4d98cb3e45d3e820d8bac422a">output_gate_bias</a>, std::vector&lt;int&gt; { -67066, -53607, 47233, 7300 });</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160;</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160; SimpleTensor&lt;uint8_t&gt; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aee13fc8e41cf0443bbeab64e23eee1ec">qasymm</a>);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160;</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160; <span class="comment">// Initialize state</span></div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>, std::vector&lt;uint8_t&gt; { 128, 128, 128, 128,</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; 128, 128, 128, 128 });</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a34e65b7d309d497fc82a9515166cde38">cell_state</a>, std::vector&lt;int16_t&gt; { 0, 0, 0, 0,</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; 0, 0, 0, 0 });</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160;</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; <span class="comment">// First input</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, std::vector&lt;uint8_t&gt; { 106, 193,</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; 155, 150 });</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160;</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>, std::vector&lt;uint8_t&gt; { 128, 130, 36, 134,</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160; 128, 131, 35, 133 });</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160;</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a0dad1220a6a2995196ead0bd79956bbc">lstmq</a>.<a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae02c6fc90d9c60c634bfa258049eb46b">validate</a>(Accessor(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>, tolerance_qsymm16);</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>&#160;</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160; <span class="comment">// Second input</span></div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>, std::vector&lt;uint8_t&gt; { 128, 129, 12, 137,</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160; 128, 131, 10, 136 });</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a0dad1220a6a2995196ead0bd79956bbc">lstmq</a>.<a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae02c6fc90d9c60c634bfa258049eb46b">validate</a>(Accessor(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>, tolerance_qsymm16);</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160;</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>&#160; <span class="comment">// Third input</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>, std::vector&lt;uint8_t&gt; { 128, 129, 8, 140,</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>&#160; 128, 130, 6, 138 });</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a0dad1220a6a2995196ead0bd79956bbc">lstmq</a>.<a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae02c6fc90d9c60c634bfa258049eb46b">validate</a>(Accessor(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>, tolerance_qsymm16);</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160;}</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160;</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a963c5b8a3279186004d6a7e97a1b87e6">TEST_CASE</a>(RunLarge, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfab829534c7b40afdbd3c3ffea05202a97">framework::DatasetMode::PRECOMMIT</a>)</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>&#160;{</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> batch_size = 16;</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6bb1fa96fb01419887f07ecd236c8cd4">input_size</a> = 8;</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">output_size</a> = 8;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160;</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160;</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; QuantizationInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aee13fc8e41cf0443bbeab64e23eee1ec">qasymm</a>(1.f / 128.f, 128);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; QuantizationInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>(1.f / 128.f, 128);</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160; QuantizationInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a20c95b3935143dddbf37176fa0562685">qsymm_3</a>(8.f / 32768.f, 0);</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160; QuantizationInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a66b9829d1b2750c8f7bd6dad961cdc62">qsymm_4</a>(16.f / 32768.f, 0);</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160;</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>&#160; TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59e3cf27216b7361c71cbd2830ec4c2">input_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6bb1fa96fb01419887f07ecd236c8cd4">input_size</a>, batch_size };</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160; TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af607b7b3379e1f6a6d4f4cbcceaaec94">input_weights_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6bb1fa96fb01419887f07ecd236c8cd4">input_size</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">output_size</a> };</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160; TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a14f78505947c1a1bd529fca96241a771">recurrent_weights_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">output_size</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">output_size</a> };</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>&#160; TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">output_size</a>, batch_size};</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>&#160; TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a79820c7442073b8eb22fb3eaef6fd6ba">bias_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">output_size</a> };</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>&#160;</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a93550027861552f1292b652d85b27b7c">input_to_input_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af607b7b3379e1f6a6d4f4cbcceaaec94">input_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3b793c410cba57a1395184692a018356">input_to_forget_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af607b7b3379e1f6a6d4f4cbcceaaec94">input_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac547a66fe26967afb94760061ee0d0d1">input_to_cell_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af607b7b3379e1f6a6d4f4cbcceaaec94">input_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ace4dd633420fa8d8aa71f60ff730f01f">input_to_output_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af607b7b3379e1f6a6d4f4cbcceaaec94">input_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3586fa7cce76c57e1cce83b2add420de">recurrent_to_input_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a14f78505947c1a1bd529fca96241a771">recurrent_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac62dfdcc14798598d953342789c9927e">recurrent_to_forget_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a14f78505947c1a1bd529fca96241a771">recurrent_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2236dfe2a3fc5fa4e125348829cbeb2">recurrent_to_cell_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a14f78505947c1a1bd529fca96241a771">recurrent_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aab02df8a9ee45153f2fd76e934407fbd">recurrent_to_output_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a14f78505947c1a1bd529fca96241a771">recurrent_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5061c02d0093b981703ab63fa7ddb13e">input_gate_bias</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a79820c7442073b8eb22fb3eaef6fd6ba">bias_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a55daaf57fb833fc416d779c28f7a3c85">forget_gate_bias</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a79820c7442073b8eb22fb3eaef6fd6ba">bias_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a69aaf7df811f8c7750cae785ea6f9719">cell_gate_bias</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a79820c7442073b8eb22fb3eaef6fd6ba">bias_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a507bd7e4d98cb3e45d3e820d8bac422a">output_gate_bias</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a79820c7442073b8eb22fb3eaef6fd6ba">bias_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</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">// LSTM input</span></div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59e3cf27216b7361c71cbd2830ec4c2">input_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aee13fc8e41cf0443bbeab64e23eee1ec">qasymm</a>);</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>&#160;</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>&#160; <span class="comment">// LSTM output state</span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aee13fc8e41cf0443bbeab64e23eee1ec">qasymm</a>);</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>&#160;</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>&#160; <span class="comment">// LSTM cell state</span></div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a34e65b7d309d497fc82a9515166cde38">cell_state</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a66b9829d1b2750c8f7bd6dad961cdc62">qsymm_4</a>);</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>&#160;</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>&#160; NELSTMLayerQuantized <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a0dad1220a6a2995196ead0bd79956bbc">lstmq</a>;</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>&#160;</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a0dad1220a6a2995196ead0bd79956bbc">lstmq</a>.<a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ab90e9ae19db4dbc4f316851b03402bfa">configure</a>(&amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a93550027861552f1292b652d85b27b7c">input_to_input_weights</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3b793c410cba57a1395184692a018356">input_to_forget_weights</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac547a66fe26967afb94760061ee0d0d1">input_to_cell_weights</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ace4dd633420fa8d8aa71f60ff730f01f">input_to_output_weights</a>,</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>&#160; &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3586fa7cce76c57e1cce83b2add420de">recurrent_to_input_weights</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac62dfdcc14798598d953342789c9927e">recurrent_to_forget_weights</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2236dfe2a3fc5fa4e125348829cbeb2">recurrent_to_cell_weights</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aab02df8a9ee45153f2fd76e934407fbd">recurrent_to_output_weights</a>,</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>&#160; &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5061c02d0093b981703ab63fa7ddb13e">input_gate_bias</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a55daaf57fb833fc416d779c28f7a3c85">forget_gate_bias</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a69aaf7df811f8c7750cae785ea6f9719">cell_gate_bias</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a507bd7e4d98cb3e45d3e820d8bac422a">output_gate_bias</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a34e65b7d309d497fc82a9515166cde38">cell_state</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a34e65b7d309d497fc82a9515166cde38">cell_state</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>);</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>&#160;</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a93550027861552f1292b652d85b27b7c">input_to_input_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3b793c410cba57a1395184692a018356">input_to_forget_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac547a66fe26967afb94760061ee0d0d1">input_to_cell_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ace4dd633420fa8d8aa71f60ff730f01f">input_to_output_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3586fa7cce76c57e1cce83b2add420de">recurrent_to_input_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac62dfdcc14798598d953342789c9927e">recurrent_to_forget_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2236dfe2a3fc5fa4e125348829cbeb2">recurrent_to_cell_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aab02df8a9ee45153f2fd76e934407fbd">recurrent_to_output_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5061c02d0093b981703ab63fa7ddb13e">input_gate_bias</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a55daaf57fb833fc416d779c28f7a3c85">forget_gate_bias</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a69aaf7df811f8c7750cae785ea6f9719">cell_gate_bias</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a507bd7e4d98cb3e45d3e820d8bac422a">output_gate_bias</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a34e65b7d309d497fc82a9515166cde38">cell_state</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>&#160;</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>&#160; <span class="comment">// Fill weights and biases</span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a93550027861552f1292b652d85b27b7c">input_to_input_weights</a>, std::vector&lt;uint8_t&gt;{ 141, 89, 200, 180, 46, 50, 87, 128,</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>&#160; 149, 227, 177, 187, 212, 229, 54, 111,</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>&#160; 131, 116, 3, 58, 196, 26, 131, 255,</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>&#160; 22, 106, 216, 69, 239, 12, 232, 207,</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>&#160; 184, 56, 236, 172, 28, 143, 161, 124,</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>&#160; 255, 33, 197, 122, 47, 197, 26, 229,</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>&#160; 91, 79, 11, 160, 26, 80, 100, 36,</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>&#160; 248, 186, 97, 61, 125, 46, 14, 100, });</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>&#160;</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3b793c410cba57a1395184692a018356">input_to_forget_weights</a>, std::vector&lt;uint8_t&gt; { 237, 165, 141, 249, 72, 116, 36 , 115,</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>&#160; 234, 213, 85, 84, 59, 62, 150, 246,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>&#160; 182, 102, 158, 214, 182, 183, 94, 11,</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>&#160; 158, 192, 92, 189, 160, 219, 206, 249,</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>&#160; 88, 213, 193, 244, 151, 72, 129, 49,</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>&#160; 239, 83, 106, 9, 169, 187, 125, 171,</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>&#160; 32, 141, 126, 92, 13, 36, 224, 150,</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>&#160; 187, 250, 178, 169, 89, 214, 91, 173 });</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>&#160;</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac547a66fe26967afb94760061ee0d0d1">input_to_cell_weights</a>, std::vector&lt;uint8_t&gt; { 93, 103, 226, 139, 185, 252, 129, 171,</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>&#160; 159, 32, 25, 175, 224, 183, 165, 35,</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>&#160; 207, 69, 238, 228, 149, 214, 79, 6,</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>&#160; 5, 66, 102, 14, 19, 111, 36, 143,</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>&#160; 22, 85, 13, 78, 236, 121, 122, 77,</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>&#160; 249, 39, 88, 12, 205, 143, 93, 240,</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>&#160; 167, 89, 188, 50, 73, 69, 201, 251,</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>&#160; 59, 32, 203, 184, 139, 191, 199, 74});</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>&#160;</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ace4dd633420fa8d8aa71f60ff730f01f">input_to_output_weights</a>, std::vector&lt;uint8_t&gt; { 205, 7, 95, 104, 252, 143, 226, 73,</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>&#160; 229, 114, 152, 171, 221, 153, 73, 229,</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>&#160; 153, 165, 223, 239, 100, 38, 172, 211,</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>&#160; 226, 133, 239, 207, 116, 230, 170, 100,</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>&#160; 241, 95, 171, 124, 63, 115, 32, 127,</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>&#160; 141, 239, 53, 193, 201, 53, 104, 178,</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>&#160; 186, 212, 167, 107, 226, 230, 71, 213,</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>&#160; 148, 217, 19, 248, 233, 195, 183, 156 });</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>&#160;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3586fa7cce76c57e1cce83b2add420de">recurrent_to_input_weights</a>, std::vector&lt;uint8_t&gt; { 147, 112, 140, 103, 3, 255, 17, 49,</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>&#160; 84, 112, 144, 213, 138, 142, 112, 66,</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>&#160; 117, 30, 101, 35, 25, 132, 211, 229,</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>&#160; 183, 208, 102, 16, 38, 85, 101, 152,</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>&#160; 226, 83, 132, 22, 161, 110, 157, 129,</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>&#160; 184, 63, 168, 42, 220, 126, 209, 157,</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>&#160; 5, 88, 243, 83, 249, 19, 226, 209,</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>&#160; 173, 96, 185, 77, 146, 227, 238, 136 });</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>&#160;</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>&#160;</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac62dfdcc14798598d953342789c9927e">recurrent_to_forget_weights</a>, std::vector&lt;uint8_t&gt; { 52, 132, 92, 200, 213, 32, 213, 37,</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>&#160; 116, 142, 116, 180, 4, 172, 158, 143,</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>&#160; 110, 40, 99, 28, 221, 153, 133, 2,</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>&#160; 247, 144, 198, 100, 20, 15, 221, 196,</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>&#160; 159, 178, 188, 151, 171, 15, 25, 217,</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>&#160; 178, 109, 110, 118, 128, 39, 232, 234,</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>&#160; 184, 214, 177, 13, 56, 6, 28, 252,</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>&#160; 89, 187, 242, 59, 146, 111, 132, 129});</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>&#160;</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2236dfe2a3fc5fa4e125348829cbeb2">recurrent_to_cell_weights</a>, std::vector&lt;uint8_t&gt; { 70, 44, 137, 29, 36, 127, 1, 241,</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>&#160; 26, 241, 142, 114, 67, 181, 49, 57,</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>&#160; 131, 152, 175, 77, 23, 63, 37, 124,</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>&#160; 150, 113, 95, 103, 110, 201, 69, 97,</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>&#160; 196, 242, 62, 214, 66, 19, 45, 135,</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>&#160; 22, 168, 149, 104, 77, 101, 36, 68,</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>&#160; 170, 116, 222, 100, 109, 1, 154, 18,</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>&#160; 133, 215, 105, 93, 31, 57, 231, 112 });</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; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aab02df8a9ee45153f2fd76e934407fbd">recurrent_to_output_weights</a>, std::vector&lt;uint8_t&gt; { 45 , 181 , 220 , 219 , 49 , 63 , 49 , 129,</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>&#160; 7 , 166 , 104 , 114 , 83 , 40 , 1 , 195,</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>&#160; 245 , 142 , 82 , 232 , 104 , 245 , 82 , 196,</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>&#160; 111 , 56 , 156 , 9 , 141 , 240 , 180 , 148,</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>&#160; 247 , 198 , 234 , 137 , 13 , 210 , 161 , 192,</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>&#160; 196 , 59 , 233 , 184 , 142 , 187 , 140 , 166,</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>&#160; 2 , 95 , 152 , 46 , 71 , 46 , 113 , 32,</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>&#160; 175 , 229 , 86 , 87 , 62 , 93 , 74 , 130});</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>&#160;</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5061c02d0093b981703ab63fa7ddb13e">input_gate_bias</a>, std::vector&lt;int&gt; { -40040, -106916, -92315, -79123, 45160, -17954, 50962, -63758 });</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a55daaf57fb833fc416d779c28f7a3c85">forget_gate_bias</a>, std::vector&lt;int&gt; { -128514, 8463, -57831, 116977, 106547, -28132, -124557, 44941 });</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a69aaf7df811f8c7750cae785ea6f9719">cell_gate_bias</a>, std::vector&lt;int&gt; { 88388 , 123601, -116148, -13022, 21619, 48926, 57523, 39332 });</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a507bd7e4d98cb3e45d3e820d8bac422a">output_gate_bias</a>, std::vector&lt;int&gt; { 59485 , -33070, 21386, -100633, -115959, 125768, -56407, 24897 });</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>&#160;</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>&#160; SimpleTensor&lt;uint8_t&gt; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aee13fc8e41cf0443bbeab64e23eee1ec">qasymm</a>);</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>&#160;</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>&#160; <span class="comment">// Initialize state</span></div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>, std::vector&lt;uint8_t&gt; { 128, 128, 128, 128, 128, 128, 128, 128,</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>&#160; 128, 128, 128, 128, 128, 128, 128, 128,</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>&#160; 128, 128, 128, 128, 128, 128, 128, 128,</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>&#160; 128, 128, 128, 128, 128, 128, 128, 128,</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>&#160; 128, 128, 128, 128, 128, 128, 128, 128,</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>&#160; 128, 128, 128, 128, 128, 128, 128, 128,</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>&#160; 128, 128, 128, 128, 128, 128, 128, 128,</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>&#160; 128, 128, 128, 128, 128, 128, 128, 128,</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>&#160; 128, 128, 128, 128, 128, 128, 128, 128,</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>&#160; 128, 128, 128, 128, 128, 128, 128, 128,</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>&#160; 128, 128, 128, 128, 128, 128, 128, 128,</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>&#160; 128, 128, 128, 128, 128, 128, 128, 128,</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>&#160; 128, 128, 128, 128, 128, 128, 128, 128,</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>&#160; 128, 128, 128, 128, 128, 128, 128, 128,</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>&#160; 128, 128, 128, 128, 128, 128, 128, 128,</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>&#160; 128, 128, 128, 128, 128, 128, 128, 128 });</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; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a34e65b7d309d497fc82a9515166cde38">cell_state</a>, std::vector&lt;int16_t&gt; { 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0});</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; <span class="comment">// First input</span></div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, std::vector&lt;uint8_t&gt; { 247, 203, 159, 131, 182, 114, 207, 195,</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>&#160; 48 , 61 , 154, 16, 80, 101, 116, 255,</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>&#160; 50 , 115 , 45, 186, 75, 212, 98, 48,</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>&#160; 88 , 146 , 24, 143, 218, 174, 203, 200,</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>&#160; 239 , 16 , 66, 136, 234, 54, 94, 51,</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>&#160; 101 , 128 , 220, 213, 164, 82, 137, 255,</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>&#160; 70 , 165 , 234, 220, 66, 35, 183, 206,</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>&#160; 39 , 57 , 180, 202, 23, 172, 224, 109,</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>&#160; 102 , 215 , 186, 82, 215, 147, 85, 187,</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>&#160; 96 , 249 , 59, 116, 150, 44, 167, 128,</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>&#160; 34 , 217 , 148, 193, 243, 38, 250, 208,</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>&#160; 112 , 130 , 208, 29, 16, 122, 20, 92,</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>&#160; 24 , 72 , 104, 29, 150, 233, 151, 19,</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>&#160; 158 , 192 , 254, 70, 73, 142, 106, 152,</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>&#160; 3 , 61 , 24, 135, 212, 9, 80, 234,</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>&#160; 147 , 246 , 83, 249, 49, 14, 68, 50});</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>&#160;</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>, std::vector&lt;uint8_t&gt; {131, 128, 128, 128, 128, 180, 129, 133,</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>&#160; 136, 128, 126, 128, 128, 173, 135, 130,</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>&#160; 160, 128, 128, 128, 128, 138, 132, 129,</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>&#160; 131, 128, 127, 128, 128, 169, 129, 131,</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>&#160; 133, 128, 128, 128, 128, 182, 130, 129,</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>&#160; 131, 128, 128, 128, 128, 163, 129, 130,</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>&#160; 131, 128, 128, 128, 128, 149, 132, 129,</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>&#160; 143, 128, 127, 128, 128, 150, 134, 131,</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>&#160; 134, 128, 128, 128, 128, 167, 130, 130,</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>&#160; 131, 128, 128, 128, 128, 152, 132, 129,</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>&#160; 128, 128, 128, 128, 128, 169, 130, 130,</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>&#160; 173, 128, 128, 128, 128, 148, 139, 130,</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>&#160; 152, 128, 128, 128, 128, 168, 139, 132,</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>&#160; 147, 128, 128, 128, 128, 161, 131, 132,</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>&#160; 130, 128, 128, 128, 128, 159, 134, 128,</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>&#160; 140, 128, 128, 128, 128, 133, 132, 128 });</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>&#160;</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a0dad1220a6a2995196ead0bd79956bbc">lstmq</a>.<a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae02c6fc90d9c60c634bfa258049eb46b">validate</a>(Accessor(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>, tolerance_qsymm16);</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; <span class="comment">// Second input</span></div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>, std::vector&lt;uint8_t&gt; { 130, 128, 128, 128, 128, 205, 129, 137,</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>&#160; 135, 128, 127, 128, 128, 190, 137, 132,</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>&#160; 160, 128, 128, 128, 128, 142, 133, 131,</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>&#160; 130, 128, 128, 128, 128, 185, 129, 133,</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>&#160; 132, 128, 128, 128, 128, 198, 131, 130,</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>&#160; 130, 128, 128, 128, 128, 178, 130, 131,</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>&#160; 131, 128, 128, 128, 128, 158, 132, 131,</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>&#160; 142, 128, 127, 128, 128, 158, 135, 134,</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>&#160; 133, 128, 128, 128, 128, 178, 131, 132,</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>&#160; 131, 128, 128, 128, 128, 160, 132, 130,</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>&#160; 128, 128, 128, 128, 128, 190, 131, 131,</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>&#160; 170, 128, 128, 128, 128, 157, 142, 131,</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>&#160; 149, 128, 128, 128, 128, 178, 142, 135,</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>&#160; 145, 128, 128, 128, 129, 173, 132, 135,</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>&#160; 129, 128, 128, 128, 128, 171, 134, 129,</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>&#160; 140, 128, 128, 128, 128, 135, 132, 129});</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a0dad1220a6a2995196ead0bd79956bbc">lstmq</a>.<a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae02c6fc90d9c60c634bfa258049eb46b">validate</a>(Accessor(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>, tolerance_qsymm16);</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;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ed31007ae463a3cec24a581f3651f6">TEST_SUITE_END</a>() <span class="comment">// MultSmallerEq1</span></div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>&#160;</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>&#160;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8f65156abdd90180036790221cfc915f">TEST_SUITE</a>(MultGreater1)</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>&#160;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a963c5b8a3279186004d6a7e97a1b87e6">TEST_CASE</a>(RunSmall, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::PRECOMMIT)</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>&#160;{</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>&#160; <span class="comment">//Input sequence length is 1</span></div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> batch_size = 2;</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6bb1fa96fb01419887f07ecd236c8cd4">input_size</a> = 2;</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">output_size</a> = 4;</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>&#160;</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>&#160; QuantizationInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aee13fc8e41cf0443bbeab64e23eee1ec">qasymm</a>(1.f / 128.f, 128);</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>&#160; QuantizationInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>(1.f / 16.f, 16);</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>&#160; QuantizationInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a20c95b3935143dddbf37176fa0562685">qsymm_3</a>(8.f / 32768.f, 0);</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>&#160; QuantizationInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a66b9829d1b2750c8f7bd6dad961cdc62">qsymm_4</a>(16.f / 32768.f, 0);</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>&#160;</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>&#160; TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59e3cf27216b7361c71cbd2830ec4c2">input_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6bb1fa96fb01419887f07ecd236c8cd4">input_size</a>, batch_size };</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>&#160; TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af607b7b3379e1f6a6d4f4cbcceaaec94">input_weights_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6bb1fa96fb01419887f07ecd236c8cd4">input_size</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">output_size</a> };</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>&#160; TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a14f78505947c1a1bd529fca96241a771">recurrent_weights_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">output_size</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">output_size</a> };</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>&#160; TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">output_size</a>, batch_size};</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>&#160; TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a79820c7442073b8eb22fb3eaef6fd6ba">bias_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">output_size</a> };</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; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a93550027861552f1292b652d85b27b7c">input_to_input_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af607b7b3379e1f6a6d4f4cbcceaaec94">input_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3b793c410cba57a1395184692a018356">input_to_forget_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af607b7b3379e1f6a6d4f4cbcceaaec94">input_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac547a66fe26967afb94760061ee0d0d1">input_to_cell_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af607b7b3379e1f6a6d4f4cbcceaaec94">input_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ace4dd633420fa8d8aa71f60ff730f01f">input_to_output_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af607b7b3379e1f6a6d4f4cbcceaaec94">input_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3586fa7cce76c57e1cce83b2add420de">recurrent_to_input_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a14f78505947c1a1bd529fca96241a771">recurrent_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac62dfdcc14798598d953342789c9927e">recurrent_to_forget_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a14f78505947c1a1bd529fca96241a771">recurrent_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2236dfe2a3fc5fa4e125348829cbeb2">recurrent_to_cell_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a14f78505947c1a1bd529fca96241a771">recurrent_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aab02df8a9ee45153f2fd76e934407fbd">recurrent_to_output_weights</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a14f78505947c1a1bd529fca96241a771">recurrent_weights_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">qweights</a>);</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5061c02d0093b981703ab63fa7ddb13e">input_gate_bias</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a79820c7442073b8eb22fb3eaef6fd6ba">bias_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a55daaf57fb833fc416d779c28f7a3c85">forget_gate_bias</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a79820c7442073b8eb22fb3eaef6fd6ba">bias_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a69aaf7df811f8c7750cae785ea6f9719">cell_gate_bias</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a79820c7442073b8eb22fb3eaef6fd6ba">bias_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a507bd7e4d98cb3e45d3e820d8bac422a">output_gate_bias</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a79820c7442073b8eb22fb3eaef6fd6ba">bias_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>&#160;</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>&#160; <span class="comment">// LSTM input</span></div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59e3cf27216b7361c71cbd2830ec4c2">input_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aee13fc8e41cf0443bbeab64e23eee1ec">qasymm</a>);</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>&#160;</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>&#160; <span class="comment">// LSTM output state</span></div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aee13fc8e41cf0443bbeab64e23eee1ec">qasymm</a>);</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>&#160;</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>&#160; <span class="comment">// LSTM cell state</span></div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>&#160; <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a34e65b7d309d497fc82a9515166cde38">cell_state</a> = create_tensor&lt;Tensor&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a66b9829d1b2750c8f7bd6dad961cdc62">qsymm_4</a>);</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>&#160;</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>&#160; NELSTMLayerQuantized <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a0dad1220a6a2995196ead0bd79956bbc">lstmq</a>;</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>&#160;</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a0dad1220a6a2995196ead0bd79956bbc">lstmq</a>.<a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ab90e9ae19db4dbc4f316851b03402bfa">configure</a>(&amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a93550027861552f1292b652d85b27b7c">input_to_input_weights</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3b793c410cba57a1395184692a018356">input_to_forget_weights</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac547a66fe26967afb94760061ee0d0d1">input_to_cell_weights</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ace4dd633420fa8d8aa71f60ff730f01f">input_to_output_weights</a>,</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>&#160; &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3586fa7cce76c57e1cce83b2add420de">recurrent_to_input_weights</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac62dfdcc14798598d953342789c9927e">recurrent_to_forget_weights</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2236dfe2a3fc5fa4e125348829cbeb2">recurrent_to_cell_weights</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aab02df8a9ee45153f2fd76e934407fbd">recurrent_to_output_weights</a>,</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>&#160; &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5061c02d0093b981703ab63fa7ddb13e">input_gate_bias</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a55daaf57fb833fc416d779c28f7a3c85">forget_gate_bias</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a69aaf7df811f8c7750cae785ea6f9719">cell_gate_bias</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a507bd7e4d98cb3e45d3e820d8bac422a">output_gate_bias</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a34e65b7d309d497fc82a9515166cde38">cell_state</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a34e65b7d309d497fc82a9515166cde38">cell_state</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>);</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>&#160;</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a93550027861552f1292b652d85b27b7c">input_to_input_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3b793c410cba57a1395184692a018356">input_to_forget_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac547a66fe26967afb94760061ee0d0d1">input_to_cell_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ace4dd633420fa8d8aa71f60ff730f01f">input_to_output_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3586fa7cce76c57e1cce83b2add420de">recurrent_to_input_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac62dfdcc14798598d953342789c9927e">recurrent_to_forget_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2236dfe2a3fc5fa4e125348829cbeb2">recurrent_to_cell_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aab02df8a9ee45153f2fd76e934407fbd">recurrent_to_output_weights</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5061c02d0093b981703ab63fa7ddb13e">input_gate_bias</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a55daaf57fb833fc416d779c28f7a3c85">forget_gate_bias</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a69aaf7df811f8c7750cae785ea6f9719">cell_gate_bias</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a507bd7e4d98cb3e45d3e820d8bac422a">output_gate_bias</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a34e65b7d309d497fc82a9515166cde38">cell_state</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>.allocator()-&gt;allocate();</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>&#160;</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>&#160; <span class="comment">// Fill weights and biases</span></div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a93550027861552f1292b652d85b27b7c">input_to_input_weights</a>, std::vector&lt;uint8_t&gt;{ 122, 130,</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>&#160; 124, 134,</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>&#160; 120, 122,</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>&#160; 134, 134 });</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; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3b793c410cba57a1395184692a018356">input_to_forget_weights</a>, std::vector&lt;uint8_t&gt; { 204, 193,</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>&#160; 148, 59,</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>&#160; 113, 17,</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>&#160; 66, 197 });</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>&#160;</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac547a66fe26967afb94760061ee0d0d1">input_to_cell_weights</a>, std::vector&lt;uint8_t&gt; { 172, 101,</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>&#160; 184, 209,</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>&#160; 165, 82,</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>&#160; 108, 209 });</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>&#160;</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ace4dd633420fa8d8aa71f60ff730f01f">input_to_output_weights</a>, std::vector&lt;uint8_t&gt; { 203, 244,</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>&#160; 219, 114,</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>&#160; 130, 16,</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>&#160; 163, 222 });</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; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3586fa7cce76c57e1cce83b2add420de">recurrent_to_input_weights</a>, std::vector&lt;uint8_t&gt; { 162, 168, 7, 95,</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>&#160; 91, 155, 108, 216,</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>&#160; 255, 100, 48, 188,</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>&#160; 58, 37, 186, 147 });</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>&#160;</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac62dfdcc14798598d953342789c9927e">recurrent_to_forget_weights</a>, std::vector&lt;uint8_t&gt; { 46, 58, 47, 170,</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>&#160; 246, 96, 12, 99,</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>&#160; 68, 23, 186, 161,</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>&#160; 237, 164, 89, 6 });</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>&#160;</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2236dfe2a3fc5fa4e125348829cbeb2">recurrent_to_cell_weights</a>, std::vector&lt;uint8_t&gt; { 234, 99, 71, 206,</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>&#160; 205, 159, 64, 253,</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>&#160; 191, 148, 116, 8,</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>&#160; 209, 136, 59, 138 });</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>&#160;</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aab02df8a9ee45153f2fd76e934407fbd">recurrent_to_output_weights</a>, std::vector&lt;uint8_t&gt; { 23, 241, 137, 36,</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>&#160; 206, 5, 227, 56,</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>&#160; 254, 176, 231, 47,</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>&#160; 18, 201, 161, 11 });</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>&#160;</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5061c02d0093b981703ab63fa7ddb13e">input_gate_bias</a>, std::vector&lt;int&gt; {-103038, 30525, 115255, -38154 });</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a55daaf57fb833fc416d779c28f7a3c85">forget_gate_bias</a>, std::vector&lt;int&gt; { -23428, 126970, 116806, 46307 });</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a69aaf7df811f8c7750cae785ea6f9719">cell_gate_bias</a>, std::vector&lt;int&gt; { 128006, 69949, -42808, 42568 });</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a507bd7e4d98cb3e45d3e820d8bac422a">output_gate_bias</a>, std::vector&lt;int&gt; { -67066, -53607, 47233, 7300 });</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>&#160;</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>&#160; SimpleTensor&lt;uint8_t&gt; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aee13fc8e41cf0443bbeab64e23eee1ec">qasymm</a>);</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>&#160;</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>&#160; <span class="comment">// Initialize state</span></div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>, std::vector&lt;uint8_t&gt; { 128, 128, 128, 128,</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>&#160; 128, 128, 128, 128 });</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a34e65b7d309d497fc82a9515166cde38">cell_state</a>, std::vector&lt;int16_t&gt; { 0, 0, 0, 0,</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>&#160; 0, 0, 0, 0 });</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>&#160;</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>&#160; <span class="comment">// First input</span></div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, std::vector&lt;uint8_t&gt; { 106, 193,</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>&#160; 155, 150 });</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>&#160;</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>, std::vector&lt;uint8_t&gt; { 128, 128, 31, 128,</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>&#160; 128, 128, 31, 128 });</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>&#160;</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a0dad1220a6a2995196ead0bd79956bbc">lstmq</a>.<a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae02c6fc90d9c60c634bfa258049eb46b">validate</a>(Accessor(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>);</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>&#160;</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>&#160; <span class="comment">// Second input</span></div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>, std::vector&lt;uint8_t&gt; { 128, 128, 5, 128,</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>&#160; 128, 128, 5, 128 });</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a0dad1220a6a2995196ead0bd79956bbc">lstmq</a>.<a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae02c6fc90d9c60c634bfa258049eb46b">validate</a>(Accessor(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>);</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>&#160;</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>&#160; <span class="comment">// Third input</span></div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">fill_tensor</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>, std::vector&lt;uint8_t&gt; { 128, 128, 1, 128,</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>&#160; 128, 128, 1, 128, });</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a0dad1220a6a2995196ead0bd79956bbc">lstmq</a>.<a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae02c6fc90d9c60c634bfa258049eb46b">validate</a>(Accessor(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">output_state</a>), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">expected_output</a>);</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>&#160;}</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>&#160;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ed31007ae463a3cec24a581f3651f6">TEST_SUITE_END</a>() <span class="comment">// MultGreater1</span></div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>&#160;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ed31007ae463a3cec24a581f3651f6">TEST_SUITE_END</a>() <span class="comment">// IntegrationTestCase</span></div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>&#160;<span class="comment">// clang-format on</span></div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>&#160;<span class="comment">// *INDENT-ON*</span></div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>&#160;</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>&#160;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ed31007ae463a3cec24a581f3651f6">TEST_SUITE_END</a>() <span class="comment">// LSTMLayerQuantized</span></div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>&#160;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ed31007ae463a3cec24a581f3651f6">TEST_SUITE_END</a>() <span class="comment">// NEON</span></div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>&#160;} <span class="comment">// namespace validation</span></div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>&#160;} <span class="comment">// namespace test</span></div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>&#160;} <span class="comment">// namespace arm_compute</span></div><div class="ttc" id="_accessor_8h_xhtml"><div class="ttname"><a href="_accessor_8h.xhtml">Accessor.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a507bd7e4d98cb3e45d3e820d8bac422a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a507bd7e4d98cb3e45d3e820d8bac422a">arm_compute::test::validation::output_gate_bias</a></div><div class="ttdeci">auto output_gate_bias</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00484">LSTMLayerQuantized.cpp:484</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">arm_compute::DataType::QSYMM16</a></div><div class="ttdoc">quantized, symmetric fixed-point 16-bit number</div></div>
<div class="ttc" id="_padding_calculator_8h_xhtml"><div class="ttname"><a href="_padding_calculator_8h.xhtml">PaddingCalculator.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a55daaf57fb833fc416d779c28f7a3c85"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a55daaf57fb833fc416d779c28f7a3c85">arm_compute::test::validation::forget_gate_bias</a></div><div class="ttdeci">auto forget_gate_bias</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00482">LSTMLayerQuantized.cpp:482</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a93550027861552f1292b652d85b27b7c"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a93550027861552f1292b652d85b27b7c">arm_compute::test::validation::input_to_input_weights</a></div><div class="ttdeci">auto input_to_input_weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00473">LSTMLayerQuantized.cpp:473</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_add0cebf7f56d2fb24512082e80eb00dc"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#add0cebf7f56d2fb24512082e80eb00dc">arm_compute::test::validation::qweights</a></div><div class="ttdeci">QuantizationInfo qweights(1.f/16.f, 16)</div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ac62dfdcc14798598d953342789c9927e"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ac62dfdcc14798598d953342789c9927e">arm_compute::test::validation::recurrent_to_forget_weights</a></div><div class="ttdeci">auto recurrent_to_forget_weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00478">LSTMLayerQuantized.cpp:478</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8ad4a9123e325f198138c3ecf1993616"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8ad4a9123e325f198138c3ecf1993616">arm_compute::test::validation::expected_output</a></div><div class="ttdeci">SimpleTensor&lt; uint8_t &gt; expected_output(output_shape, DataType::QASYMM8, 1, qasymm)</div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a963c5b8a3279186004d6a7e97a1b87e6"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a963c5b8a3279186004d6a7e97a1b87e6">arm_compute::test::validation::TEST_CASE</a></div><div class="ttdeci">TEST_CASE(Configuration, framework::DatasetMode::ALL)</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_batch_concatenate_layer_8cpp_source.xhtml#l00086">BatchConcatenateLayer.cpp:86</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a14f78505947c1a1bd529fca96241a771"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a14f78505947c1a1bd529fca96241a771">arm_compute::test::validation::recurrent_weights_shape</a></div><div class="ttdeci">TensorShape recurrent_weights_shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00469">LSTMLayerQuantized.cpp:469</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a34e65b7d309d497fc82a9515166cde38"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a34e65b7d309d497fc82a9515166cde38">arm_compute::test::validation::cell_state</a></div><div class="ttdeci">auto cell_state</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00493">LSTMLayerQuantized.cpp:493</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1framework_xhtml_a7edf31944a6144ffd493d2f9a7bfb5cfab829534c7b40afdbd3c3ffea05202a97"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfab829534c7b40afdbd3c3ffea05202a97">arm_compute::test::framework::DatasetMode::PRECOMMIT</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2020 ARM Limited.</div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00024">00_introduction.dox:24</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_xhtml_a9be4cb7e6ee20063a4a10bc3abb750b9"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a9be4cb7e6ee20063a4a10bc3abb750b9">arm_compute::test::coord2index</a></div><div class="ttdeci">int coord2index(const TensorShape &amp;shape, const Coordinates &amp;coord)</div><div class="ttdoc">Linearise the given coordinate.</div><div class="ttdef"><b>Definition:</b> <a href="tests_2_utils_8h_source.xhtml#l00485">Utils.h:485</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized_xhtml_ab90e9ae19db4dbc4f316851b03402bfa"><div class="ttname"><a href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ab90e9ae19db4dbc4f316851b03402bfa">arm_compute::CLLSTMLayerQuantized::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, const ICLTensor *input_to_input_weights, const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights, const ICLTensor *recurrent_to_input_weights, const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights, const ICLTensor *input_gate_bias, const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias, ICLTensor *cell_state_in, const ICLTensor *output_state_in, ICLTensor *cell_state_out, ICLTensor *output_state_out)</div><div class="ttdoc">Initialize function's tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00058">CLLSTMLayerQuantized.cpp:58</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ae59e3cf27216b7361c71cbd2830ec4c2"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59e3cf27216b7361c71cbd2830ec4c2">arm_compute::test::validation::input_shape</a></div><div class="ttdeci">input_shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">ConvolutionLayer.cpp:182</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ac547a66fe26967afb94760061ee0d0d1"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ac547a66fe26967afb94760061ee0d0d1">arm_compute::test::validation::input_to_cell_weights</a></div><div class="ttdeci">auto input_to_cell_weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00475">LSTMLayerQuantized.cpp:475</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a20c95b3935143dddbf37176fa0562685"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a20c95b3935143dddbf37176fa0562685">arm_compute::test::validation::qsymm_3</a></div><div class="ttdeci">QuantizationInfo qsymm_3(8.f/32768.f, 0)</div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_af607b7b3379e1f6a6d4f4cbcceaaec94"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#af607b7b3379e1f6a6d4f4cbcceaaec94">arm_compute::test::validation::input_weights_shape</a></div><div class="ttdeci">TensorShape input_weights_shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00468">LSTMLayerQuantized.cpp:468</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8fcf2ddd9a1d58b1b280f5c0aed71845"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">arm_compute::test::validation::input</a></div><div class="ttdeci">auto input</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">LSTMLayerQuantized.cpp:487</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_aab02df8a9ee45153f2fd76e934407fbd"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#aab02df8a9ee45153f2fd76e934407fbd">arm_compute::test::validation::recurrent_to_output_weights</a></div><div class="ttdeci">auto recurrent_to_output_weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00480">LSTMLayerQuantized.cpp:480</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a79820c7442073b8eb22fb3eaef6fd6ba"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a79820c7442073b8eb22fb3eaef6fd6ba">arm_compute::test::validation::bias_shape</a></div><div class="ttdeci">bias_shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">ConvolutionLayer.cpp:182</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::Format::S32</a></div><div class="ttdoc">1 channel, 1 S32 per channel</div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ace4dd633420fa8d8aa71f60ff730f01f"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ace4dd633420fa8d8aa71f60ff730f01f">arm_compute::test::validation::input_to_output_weights</a></div><div class="ttdeci">auto input_to_output_weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00476">LSTMLayerQuantized.cpp:476</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1framework_xhtml_a7edf31944a6144ffd493d2f9a7bfb5cf"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">arm_compute::test::framework::DatasetMode</a></div><div class="ttdeci">DatasetMode</div><div class="ttdoc">Possible dataset modes.</div><div class="ttdef"><b>Definition:</b> <a href="_dataset_modes_8h_source.xhtml#l00040">DatasetModes.h:40</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a6a5eef7d8485a2b8c04bf9b4638a90e9"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a6a5eef7d8485a2b8c04bf9b4638a90e9">arm_compute::test::validation::fill_tensor</a></div><div class="ttdeci">fill_tensor(input_to_input_weights, std::vector&lt; uint8_t &gt;{ 122, 130, 124, 134, 120, 122, 134, 134 })</div></div>
<div class="ttc" id="_datasets_8h_xhtml"><div class="ttname"><a href="_datasets_8h.xhtml">Datasets.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a3586fa7cce76c57e1cce83b2add420de"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a3586fa7cce76c57e1cce83b2add420de">arm_compute::test::validation::recurrent_to_input_weights</a></div><div class="ttdeci">auto recurrent_to_input_weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00477">LSTMLayerQuantized.cpp:477</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::DataType::QASYMM8</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number unsigned</div></div>
<div class="ttc" id="_n_e_l_s_t_m_layer_quantized_8h_xhtml"><div class="ttname"><a href="_n_e_l_s_t_m_layer_quantized_8h.xhtml">NELSTMLayerQuantized.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a69aaf7df811f8c7750cae785ea6f9719"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a69aaf7df811f8c7750cae785ea6f9719">arm_compute::test::validation::cell_gate_bias</a></div><div class="ttdeci">auto cell_gate_bias</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00483">LSTMLayerQuantized.cpp:483</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ac2236dfe2a3fc5fa4e125348829cbeb2"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2236dfe2a3fc5fa4e125348829cbeb2">arm_compute::test::validation::recurrent_to_cell_weights</a></div><div class="ttdeci">auto recurrent_to_cell_weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00479">LSTMLayerQuantized.cpp:479</a></div></div>
<div class="ttc" id="tests_2framework_2_macros_8h_xhtml"><div class="ttname"><a href="tests_2framework_2_macros_8h.xhtml">Macros.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLLSTMLayerQuantized::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00465">CLLSTMLayerQuantized.cpp:465</a></div></div>
<div class="ttc" id="tests_2_utils_8h_xhtml"><div class="ttname"><a href="tests_2_utils_8h.xhtml">Utils.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a5061c02d0093b981703ab63fa7ddb13e"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a5061c02d0093b981703ab63fa7ddb13e">arm_compute::test::validation::input_gate_bias</a></div><div class="ttdeci">auto input_gate_bias</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00481">LSTMLayerQuantized.cpp:481</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ae02c6fc90d9c60c634bfa258049eb46b"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ae02c6fc90d9c60c634bfa258049eb46b">arm_compute::test::validation::validate</a></div><div class="ttdeci">validate(dst.info() -&gt;valid_region(), valid_region)</div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8e8cda25052994ca279b7ab21a8beda8"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8e8cda25052994ca279b7ab21a8beda8">arm_compute::test::validation::output_state</a></div><div class="ttdeci">auto output_state</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00490">LSTMLayerQuantized.cpp:490</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a6bb1fa96fb01419887f07ecd236c8cd4"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a6bb1fa96fb01419887f07ecd236c8cd4">arm_compute::test::validation::input_size</a></div><div class="ttdeci">const int input_size</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00459">LSTMLayerQuantized.cpp:459</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ac2ed31007ae463a3cec24a581f3651f6"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ed31007ae463a3cec24a581f3651f6">arm_compute::test::validation::TEST_SUITE_END</a></div><div class="ttdeci">TEST_SUITE_END() FIXTURE_DATA_TEST_CASE(RunSmall</div><div class="ttdoc">Input data sets.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_dequantization_layer_8cpp_source.xhtml#l00137">DequantizationLayer.cpp:137</a></div></div>
<div class="ttc" id="_validation_8h_xhtml"><div class="ttname"><a href="_validation_8h.xhtml">Validation.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ab1806bf0c5a41f674fb9d2dc6af644f5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">ConvolutionLayer.cpp:182</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_aee13fc8e41cf0443bbeab64e23eee1ec"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#aee13fc8e41cf0443bbeab64e23eee1ec">arm_compute::test::validation::qasymm</a></div><div class="ttdeci">QuantizationInfo qasymm(1.f/128.f, 128)</div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a5002bf7ec46d52971f9526e94172cfee"><div class="ttname"><a href="namespacearm__compute.xhtml#a5002bf7ec46d52971f9526e94172cfee">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#l00123">Helpers.inl:123</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a66b9829d1b2750c8f7bd6dad961cdc62"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a66b9829d1b2750c8f7bd6dad961cdc62">arm_compute::test::validation::qsymm_4</a></div><div class="ttdeci">QuantizationInfo qsymm_4(16.f/32768.f, 0)</div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a0dad1220a6a2995196ead0bd79956bbc"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a0dad1220a6a2995196ead0bd79956bbc">arm_compute::test::validation::lstmq</a></div><div class="ttdeci">CLLSTMLayerQuantized lstmq</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00495">LSTMLayerQuantized.cpp:495</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ad44f86834ae016bf696e8e664f39c136"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ad44f86834ae016bf696e8e664f39c136">arm_compute::test::validation::output_size</a></div><div class="ttdeci">const int output_size</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00460">LSTMLayerQuantized.cpp:460</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a3b793c410cba57a1395184692a018356"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a3b793c410cba57a1395184692a018356">arm_compute::test::validation::input_to_forget_weights</a></div><div class="ttdeci">auto input_to_forget_weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00474">LSTMLayerQuantized.cpp:474</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8f65156abdd90180036790221cfc915f"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8f65156abdd90180036790221cfc915f">arm_compute::test::validation::TEST_SUITE</a></div><div class="ttdeci">TEST_SUITE(U8_to_S8) DATA_TEST_CASE(Configuration</div></div>
<div class="ttc" id="_asserts_8h_xhtml"><div class="ttname"><a href="_asserts_8h.xhtml">Asserts.h</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_e7c7b16542faa38cb4655ff1750d3604.xhtml">validation</a></li><li class="navelem"><a class="el" href="dir_0538a82dae0483a21d0f1e9d7e0a45c6.xhtml">NEON</a></li><li class="navelem"><a class="el" href="_n_e_o_n_2_l_s_t_m_layer_quantized_8cpp.xhtml">LSTMLayerQuantized.cpp</a></li>
<li class="footer">Generated on Thu Mar 5 2020 16:07:05 for Compute Library by
<a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.15 </li>
</ul>
</div>
</body>
</html>