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diff --git a/20.02/_lstm_test_impl_8cpp_source.xhtml b/20.02/_lstm_test_impl_8cpp_source.xhtml new file mode 100644 index 0000000000..f360f52161 --- /dev/null +++ b/20.02/_lstm_test_impl_8cpp_source.xhtml @@ -0,0 +1,197 @@ +<!-- Copyright (c) 2020 ARM Limited. --> +<!-- --> +<!-- SPDX-License-Identifier: MIT --> +<!-- --> +<!-- HTML header for doxygen 1.8.13--> +<!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 1.8.13"/> +<meta name="robots" content="NOINDEX, NOFOLLOW" /> +<meta name="viewport" content="width=device-width, initial-scale=1"/> +<title>ArmNN: src/backends/backendsCommon/test/layerTests/LstmTestImpl.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"> + $(document).ready(initResizable); +</script> +<link href="search/search.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="search/searchdata.js"></script> +<script type="text/javascript" src="search/search.js"></script> +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + extensions: ["tex2jax.js"], + jax: ["input/TeX","output/HTML-CSS"], +}); +</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> +<link href="doxygen.css" rel="stylesheet" type="text/css" /> +<link href="stylesheet.css" rel="stylesheet" type="text/css"/> +</head> +<body> +<div id="top"><!-- do not remove this div, it is closed by doxygen! --> +<div id="titlearea"> +<table cellspacing="0" cellpadding="0"> + <tbody> + <tr style="height: 56px;"> + <img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/> + <td style="padding-left: 0.5em;"> + <div id="projectname"> +  <span id="projectnumber">20.02</span> + </div> + </td> + </tr> + </tbody> +</table> +</div> +<!-- end header part --> +<!-- Generated by Doxygen 1.8.13 --> +<script type="text/javascript"> +var searchBox = new SearchBox("searchBox", "search",false,'Search'); +</script> +<script type="text/javascript" src="menudata.js"></script> +<script type="text/javascript" src="menu.js"></script> +<script type="text/javascript"> +$(function() { + initMenu('',true,false,'search.php','Search'); + $(document).ready(function() { init_search(); }); +}); +</script> +<div id="main-nav"></div> +</div><!-- top --> +<div id="side-nav" class="ui-resizable side-nav-resizable"> + <div id="nav-tree"> + <div id="nav-tree-contents"> + <div id="nav-sync" class="sync"></div> + </div> + </div> + <div id="splitbar" style="-moz-user-select:none;" + class="ui-resizable-handle"> + </div> +</div> +<script type="text/javascript"> +$(document).ready(function(){initNavTree('_lstm_test_impl_8cpp_source.xhtml','');}); +</script> +<div id="doc-content"> +<!-- window showing the filter options --> +<div id="MSearchSelectWindow" + onmouseover="return searchBox.OnSearchSelectShow()" + onmouseout="return searchBox.OnSearchSelectHide()" + onkeydown="return searchBox.OnSearchSelectKey(event)"> +</div> + +<!-- iframe showing the search results (closed by default) --> +<div id="MSearchResultsWindow"> +<iframe src="javascript:void(0)" frameborder="0" + name="MSearchResults" id="MSearchResults"> +</iframe> +</div> + +<div class="header"> + <div class="headertitle"> +<div class="title">LstmTestImpl.cpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_lstm_test_impl_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> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_lstm_test_impl_8hpp.xhtml">LstmTestImpl.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> </div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_cpu_tensor_handle_8hpp.xhtml">backendsCommon/CpuTensorHandle.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_tensor_copy_utils_8hpp.xhtml">backendsCommon/test/TensorCopyUtils.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <<a class="code" href="_workload_test_utils_8hpp.xhtml">backendsCommon/test/WorkloadTestUtils.hpp</a>></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> </div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <<a class="code" href="_decoders_8hpp.xhtml">reference/workloads/Decoders.hpp</a>></span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <<a class="code" href="_encoders_8hpp.xhtml">reference/workloads/Encoders.hpp</a>></span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <<a class="code" href="_lstm_utils_8hpp.xhtml">reference/workloads/LstmUtils.hpp</a>></span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> </div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#include <<a class="code" href="_tensor_helpers_8hpp.xhtml">test/TensorHelpers.hpp</a>></span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="preprocessor">#include <boost/multi_array.hpp></span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> </div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> {</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="keywordtype">void</span> LstmUtilsVectorBatchVectorAddTestImpl(</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  boost::multi_array<float, 1>& vec,</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  boost::multi_array<float, 2>& batchVec,</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  uint32_t vSize,</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  uint32_t nBatch,</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  boost::multi_array<float, 2>& expectedOutput )</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> {</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keywordtype">float</span> qScale = 0.0f;</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  int32_t qOffset = 0;</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo({nBatch, vSize}, ArmnnType, qScale, qOffset );</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> </div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="comment">// Make encoder and decoder</span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  std::unique_ptr<armnn::Decoder<float>> vecDecoder = armnn::MakeDecoder<float>(tensorInfo, vec.data());</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  std::unique_ptr<armnn::Decoder<float>> batchVecDecoder = armnn::MakeDecoder<float>(tensorInfo, batchVec.data());</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  std::unique_ptr<armnn::Encoder<float>> batchVecEncoder = armnn::MakeEncoder<float>(tensorInfo, batchVec.data());</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a>(*vecDecoder, vSize, *batchVecDecoder, nBatch, *batchVecEncoder);</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="comment">// check shape and compare values</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  BOOST_TEST(<a class="code" href="_tensor_helpers_8hpp.xhtml#a0b8fbb443d2cf34a41f6aaae934e3dcb">CompareTensors</a>(batchVec, expectedOutput));</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="comment">// check if iterator is back at start position</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  batchVecEncoder->Set(1.0f);</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  BOOST_TEST(batchVec[0][0] == 1.0f);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> }</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> </div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="keywordtype">void</span> LstmUtilsZeroVectorTestImpl(</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  boost::multi_array<float, 1>& input,</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  uint32_t vSize,</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  boost::multi_array<float, 1>& expectedOutput)</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> {</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordtype">float</span> qScale = 0.0f;</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  int32_t qOffset = 0;</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> </div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo({vSize}, ArmnnType, qScale, qOffset );</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> </div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="comment">// Make encoder for input</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  std::unique_ptr<armnn::Encoder<float>> outputEncoder = armnn::MakeEncoder<float>(tensorInfo, input.data());</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> </div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="comment">// call ZeroVector</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*outputEncoder, vSize);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="comment">// check shape and compare values</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  BOOST_TEST(<a class="code" href="_tensor_helpers_8hpp.xhtml#a0b8fbb443d2cf34a41f6aaae934e3dcb">CompareTensors</a>(input, expectedOutput));</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="comment">// check if iterator is back at start position</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  outputEncoder->Set(1.0f);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  BOOST_TEST(input[0] == 1.0f);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> </div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> }</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> <span class="keywordtype">void</span> LstmUtilsMeanStddevNormalizationTestImpl(</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  boost::multi_array<float, 2>& input,</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  uint32_t vSize,</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  uint32_t nBatch,</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  boost::multi_array<float, 2>& expectedOutput)</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> {</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordtype">float</span> qScale = 0.0f;</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  int32_t qOffset = 0;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo({nBatch, vSize}, ArmnnType, qScale, qOffset );</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="comment">// Make encoder and decoder for input</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  std::unique_ptr<armnn::Decoder<float>> inputDecoder = armnn::MakeDecoder<float>(tensorInfo, input.data());</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  std::unique_ptr<armnn::Encoder<float>> outputEncoder = armnn::MakeEncoder<float>(tensorInfo, input.data());</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> </div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a>(*inputDecoder, *outputEncoder, vSize, nBatch, 1e-8f);</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> </div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="comment">// check shape and compare values</span></div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  BOOST_TEST(<a class="code" href="_tensor_helpers_8hpp.xhtml#a0b8fbb443d2cf34a41f6aaae934e3dcb">CompareTensors</a>(input, expectedOutput));</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> </div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="comment">// check if iterator is back at start position</span></div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  outputEncoder->Set(1.0f);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  BOOST_TEST(input[0][0] == 1.0f);</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> }</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> <span class="keywordtype">void</span> LstmUtilsVectorBatchVectorCwiseProductTestImpl(</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  boost::multi_array<float, 1>& vec,</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  boost::multi_array<float, 2>& batchVec,</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  uint32_t vSize,</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  uint32_t nBatch,</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  boost::multi_array<float, 2>& expectedOutput)</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> {</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keywordtype">float</span> qScale = 0.0f;</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  int32_t qOffset = 0;</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo({nBatch, vSize}, ArmnnType, qScale, qOffset );</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> </div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="comment">// Make encoder and decoder</span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  std::unique_ptr<armnn::Decoder<float>> vecDecoder = armnn::MakeDecoder<float>(tensorInfo, vec.data());</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  std::unique_ptr<armnn::Decoder<float>> batchVecDecoder = armnn::MakeDecoder<float>(tensorInfo, batchVec.data());</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  std::unique_ptr<armnn::Encoder<float>> batchVecEncoder = armnn::MakeEncoder<float>(tensorInfo, batchVec.data());</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> </div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a>(*vecDecoder, vSize, *batchVecDecoder, nBatch, *batchVecEncoder);</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> </div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="comment">// check shape and compare values</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  BOOST_TEST(<a class="code" href="_tensor_helpers_8hpp.xhtml#a0b8fbb443d2cf34a41f6aaae934e3dcb">CompareTensors</a>(batchVec, expectedOutput));</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> </div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="comment">// check if iterator is back at start position</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  batchVecEncoder->Set(1.0f);</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  BOOST_TEST(batchVec[0][0] == 1.0f);</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> }</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> </div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> <span class="comment">// Lstm Layer tests:</span></div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> <span class="comment">// *********************************** //</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a></div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> LstmNoCifgNoPeepholeNoProjectionTestImpl(</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keyword">const</span> boost::multi_array<T, 2>& input,</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keyword">const</span> boost::multi_array<T, 2>& outputExpected,</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordtype">float</span> qScale = 0.0f,</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  int32_t qOffset = 0,</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> constantDataType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>)</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> {</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(input.shape()[0]);</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(input.shape()[1]);</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputExpected.shape()[1]);</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="comment">// cellSize and outputSize have the same size when there is no projection.</span></div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keywordtype">unsigned</span> numUnits = outputSize;</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> </div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({batchSize , inputSize}, ArmnnType, qScale, qOffset );</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateInTensorInfo({batchSize , numUnits}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateInTensorInfo({batchSize , outputSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> </div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> scratchBufferTensorInfo({batchSize, numUnits * 4}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateOutTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateOutTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> </div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> ret(outputTensorInfo);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> </div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  std::vector<float> inputVector;</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  inputVector.assign(input.data(), input.data() + (batchSize * inputSize));</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keyword">auto</span> inputTensor = MakeTensor<float,2>(inputTensorInfo, inputVector);</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> </div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keyword">auto</span> cellStateInTensor = MakeTensor<float,2>(cellStateInTensorInfo, cellStateInVector);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> </div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keyword">auto</span> outputStateInTensor = MakeTensor<float,2>(outputStateInTensorInfo, outputStateInVector);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> </div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  std::vector<float> scratchBufferVector(batchSize * numUnits * 4, 0.f);</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keyword">auto</span> scratchBufferTensor = MakeTensor<float,2>(scratchBufferTensorInfo, scratchBufferVector);</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> </div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  std::vector<float> outputStateOutVector(batchSize * outputSize, 0.f);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keyword">auto</span> outputStateOutTensor = MakeTensor<float,2>(outputStateOutTensorInfo, outputStateOutVector);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> </div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  std::vector<float> cellStateOutVector(batchSize * numUnits, 0.f);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keyword">auto</span> cellStateOutTensor = MakeTensor<float,2>(cellStateOutTensorInfo, cellStateOutVector);</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> </div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  std::vector<float> outputVector;</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize));</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  ret.outputExpected = MakeTensor<float, 2>(outputTensorInfo, outputVector);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(cellStateInTensorInfo);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputStateInTensorInfo);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> </div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  std::unique_ptr<armnn::ITensorHandle> scratchHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(scratchBufferTensorInfo);</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputStateOutTensorInfo);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(cellStateOutTensorInfo);</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> </div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> </div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml">armnn::LstmQueueDescriptor</a> data;</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> </div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> </div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchHandle.get());</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> </div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo4({numUnits}, constantDataType , qScale, qOffset);</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo8({numUnits, 2}, constantDataType, qScale, qOffset);</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset);</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> </div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keyword">auto</span> inputToInputWeights = MakeTensor<float, 2>(tensorInfo8, {-0.45018822f, -0.02338299f, -0.0870589f,</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  -0.34550029f, 0.04266912f, -0.15680569f,</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  -0.34856534f, 0.43890524f});</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span> </div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keyword">auto</span> inputToForgetWeights = MakeTensor<float, 2>(tensorInfo8, {0.09701663f, 0.20334584f, -0.50592935f,</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  -0.31343272f, -0.40032279f, 0.44781327f,</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  0.01387155f, -0.35593212f});</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> </div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="keyword">auto</span> inputToCellWeights = MakeTensor<float, 2>(tensorInfo8, {-0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f,</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  -0.20583314f, 0.44344562f, 0.22077113f,</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  -0.29909778f});</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> </div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keyword">auto</span> inputToOutputWeights = MakeTensor<float, 2>(tensorInfo8, {-0.25065863f, -0.28290087f, 0.04613829f,</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  0.40525138f, 0.44272184f, 0.03897077f,</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  -0.1556896f, 0.19487578f});</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> </div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keyword">auto</span> recurrentToInputWeights = MakeTensor<float, 2>(tensorInfo16, {-0.0063535f, -0.2042388f, 0.31454784f,</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  -0.35746509f, 0.28902304f, 0.08183324f,</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  -0.16555229f, 0.02286911f, -0.13566875f,</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  0.03034258f, 0.48091322f, -0.12528998f,</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  0.24077177f, -0.51332325f, -0.33502164f,</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  0.10629296f});</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span> </div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keyword">auto</span> recurrentToForgetWeights = MakeTensor<float, 2>(tensorInfo16, {-0.48684245f, -0.06655136f, 0.42224967f,</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  0.2112639f, 0.27654213f, 0.20864892f,</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  -0.07646349f, 0.45877004f, 0.00141793f,</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  -0.14609534f, 0.36447752f, 0.09196436f,</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  0.28053468f, 0.01560611f, -0.20127171f,</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  -0.01140004f});</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span> </div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keyword">auto</span> recurrentToCellWeights = MakeTensor<float, 2>(tensorInfo16, {-0.3407414f, 0.24443203f, -0.2078532f,</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  0.26320225f, 0.05695659f, -0.00123841f,</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  -0.4744786f, -0.35869038f, -0.06418842f,</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  -0.13502428f, -0.501764f, 0.22830659f,</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  -0.46367589f, 0.26016325f, -0.03894562f,</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  -0.16368064f});</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span> </div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keyword">auto</span> recurrentToOutputWeights = MakeTensor<float, 2>(tensorInfo16, {0.43385774f, -0.17194885f, 0.2718237f,</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  0.09215671f, 0.24107647f, -0.39835793f,</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  0.18212086f, 0.01301402f, 0.48572797f,</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  -0.50656658f, 0.20047462f, -0.20607421f,</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  -0.51818722f, -0.15390486f, 0.0468148f,</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  0.39922136f});</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> </div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="keyword">auto</span> cellToInputWeights = MakeTensor<float, 1>(tensorInfo4, {0., 0., 0., 0.});</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> </div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="keyword">auto</span> inputGateBias = MakeTensor<float, 1>(tensorInfo4, {0., 0., 0., 0.});</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span> </div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keyword">auto</span> forgetGateBias = MakeTensor<float, 1>(tensorInfo4, {1., 1., 1., 1.});</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> </div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="keyword">auto</span> cellBias = MakeTensor<float, 1>(tensorInfo4, {0., 0., 0., 0.});</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> </div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keyword">auto</span> outputGateBias = MakeTensor<float, 1>(tensorInfo4, {0., 0., 0., 0.});</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> </div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToInputWeightsTensor(tensorInfo8);</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToForgetWeightsTensor(tensorInfo8);</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToCellWeightsTensor(tensorInfo8);</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToOutputWeightsTensor(tensorInfo8);</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToInputWeightsTensor(tensorInfo16);</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToForgetWeightsTensor(tensorInfo16);</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToCellWeightsTensor(tensorInfo16);</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToOutputWeightsTensor(tensorInfo16);</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellToInputWeightsTensor(tensorInfo4);</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> forgetGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellBiasTensor(tensorInfo4);</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> outputGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> </div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToInputWeightsTensor, &inputToInputWeights[0][0]);</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToInputWeightsTensor, &recurrentToInputWeights[0][0]);</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellToInputWeightsTensor, &cellToInputWeights[0]);</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputGateBiasTensor, &inputGateBias[0]);</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&forgetGateBiasTensor, &forgetGateBias[0]);</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellBiasTensor, &cellBias[0]);</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&outputGateBiasTensor, &outputGateBias[0]);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span> </div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a08a1932be591c315a512a877d38b22df">m_InputToInputWeights</a> = &inputToInputWeightsTensor;</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a3ea82566d98c5a657c76c3d851c47848">m_InputToForgetWeights</a> = &inputToForgetWeightsTensor;</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a28ad98d17603fd8b12e046f8ece58970">m_InputToCellWeights</a> = &inputToCellWeightsTensor;</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a83dc9086b2e4a4e4cadb66bd874df798">m_InputToOutputWeights</a> = &inputToOutputWeightsTensor;</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a98d377149071d8842d610cc0734d1cfe">m_RecurrentToInputWeights</a> = &recurrentToInputWeightsTensor;</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a45d73e66cbb2b65049e4016c20657ccf">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeightsTensor;</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#aea142bd50ffb93631c2e08324ec92a1e">m_RecurrentToCellWeights</a> = &recurrentToCellWeightsTensor;</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#adebc1771e5a1f4b113a7aa594ea74d2c">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeightsTensor;</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#acb3aade8fae984f7293e222dcbe66030">m_InputGateBias</a> = &inputGateBiasTensor;</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#aba3ffe91d818266b8785ce971548eb59">m_ForgetGateBias</a> = &forgetGateBiasTensor;</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a75980b5795efd899a0c678a06a900c6d">m_CellBias</a> = &cellBiasTensor;</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a332551528a4b3534c2d6c89ce816fcd9">m_OutputGateBias</a> = &outputGateBiasTensor;</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span> </div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="comment">// Flags to set test configuration</span></div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> = 4;</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span> </div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#ab6bd7aaf685d4e956d780f8655a6f174">CreateLstm</a>(data, info);</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  inputHandle->Allocate();</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  outputStateInHandle->Allocate();</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  cellStateInHandle->Allocate();</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span> </div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  scratchHandle->Allocate();</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  outputStateOutHandle->Allocate();</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  cellStateOutHandle->Allocate();</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  outputHandle->Allocate();</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> </div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &inputTensor[0][0]);</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(outputStateInHandle.get(), &outputStateInTensor[0][0]);</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(cellStateInHandle.get(), &cellStateInTensor[0][0]);</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span> </div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  workload->Execute();</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span> </div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.output[0][0], outputHandle.get());</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span> </div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span> }</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span> </div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a></div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span> LstmLayerNoCifgWithPeepholeWithProjectionTestImpl(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keyword">const</span> boost::multi_array<T, 2>& input,</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <span class="keyword">const</span> boost::multi_array<T, 2>& outputExpected,</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <span class="keywordtype">float</span> qScale = 0.0f,</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  int32_t qOffset = 0,</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> constantDataType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>)</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span> {</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 2;</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 16;</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 5;</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="keywordtype">unsigned</span> numUnits = 20;</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span> </div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({batchSize , inputSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateInTensorInfo({batchSize , numUnits}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateInTensorInfo({batchSize , outputSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span> </div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="comment">// Scratch buffer size without CIFG [batchSize, numUnits * 4]</span></div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> scratchBufferTensorInfo({batchSize, numUnits * 4}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateOutTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateOutTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span> </div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> ret(outputTensorInfo);</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span> </div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  std::vector<float> inputVector;</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  inputVector.assign(input.data(), input.data() + (batchSize * inputSize));</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="keyword">auto</span> inputTensor = MakeTensor<float,2>(inputTensorInfo, inputVector);</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span> </div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="keyword">auto</span> cellStateInTensor = MakeTensor<float,2>(cellStateInTensorInfo, cellStateInVector);</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span> </div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="keyword">auto</span> outputStateInTensor = MakeTensor<float,2>(outputStateInTensorInfo, outputStateInVector);</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span> </div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  std::vector<float> scratchBufferVector(batchSize * numUnits * 4, 0.f);</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <span class="keyword">auto</span> scratchBufferTensor = MakeTensor<float,2>(scratchBufferTensorInfo, scratchBufferVector);</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span> </div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  std::vector<float> outputStateOutVector(batchSize * outputSize, 0.f);</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="keyword">auto</span> outputStateOutTensor = MakeTensor<float,2>(outputStateOutTensorInfo, outputStateOutVector);</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span> </div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  std::vector<float> cellStateOutVector(batchSize * numUnits, 0.f);</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="keyword">auto</span> cellStateOutTensor = MakeTensor<float,2>(cellStateOutTensorInfo, cellStateOutVector);</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> </div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  std::vector<float> outputVector;</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize));</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  ret.outputExpected = MakeTensor<float, 2>(outputTensorInfo, outputVector);</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> </div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(cellStateInTensorInfo);</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputStateInTensorInfo);</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span> </div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  std::unique_ptr<armnn::ITensorHandle> scratchHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(scratchBufferTensorInfo);</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputStateOutTensorInfo);</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(cellStateOutTensorInfo);</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span> </div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml">armnn::LstmQueueDescriptor</a> data;</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span> </div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span> </div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchHandle.get());</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span> </div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo16({outputSize}, constantDataType, qScale, qOffset);</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20({numUnits}, constantDataType, qScale, qOffset);</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20x5({numUnits, inputSize}, constantDataType, qScale, qOffset);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20x16({numUnits, outputSize}, constantDataType, qScale, qOffset);</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo16x20({outputSize, numUnits}, constantDataType, qScale, qOffset);</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> </div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keyword">auto</span> inputToInputWeights =</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  MakeTensor<float, 2>(tensorInfo20x5, {0.021393683f,0.06124551f, 0.046905167f,-0.014657677f,-0.03149463f,</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  0.09171803f, 0.14647801f,0.10797193f, -0.0057968358f,0.0019193048f,</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  -0.2726754f, 0.10154029f, -0.018539885f, 0.080349885f, -0.10262385f,</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  -0.022599787f,-0.09121155f, -0.008675967f, -0.045206103f,-0.0821282f,</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  -0.008045952f,0.015478081f, 0.055217247f, 0.038719587f, 0.044153627f,</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  -0.06453243f,0.05031825f, -0.046935108f, -0.008164439f, 0.014574226f,</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  -0.1671009f, -0.15519552f, -0.16819797f,-0.13971269f,-0.11953059f,</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  0.25005487f, -0.22790983f, 0.009855087f, -0.028140958f, -0.11200698f,</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  0.11295408f, -0.0035217577f, 0.054485075f, 0.05184695f, 0.064711206f,</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  0.10989193f, 0.11674786f, 0.03490607f, 0.07727357f, 0.11390585f,</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  -0.1863375f, -0.1034451f, -0.13945189f, -0.049401227f, -0.18767063f,</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  0.042483903f, 0.14233552f, 0.13832581f, 0.18350165f, 0.14545603f,</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  -0.028545704f,0.024939531f,0.050929718f,0.0076203286f,-0.0029723682f,</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  -0.042484224f, -0.11827596f, -0.09171104f, -0.10808628f,-0.16327988f,</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  -0.2273378f, -0.0993647f, -0.017155107f,0.0023917493f,0.049272764f,</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  0.0038534778f, 0.054764505f, 0.089753784f, 0.06947234f, 0.08014476f,</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  -0.04544234f, -0.0497073f,-0.07135631f, -0.048929106f,-0.004042012f,</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  -0.009284026f, 0.018042054f, 0.0036860977f,-0.07427302f, -0.11434604f,</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  -0.018995456f, 0.031487543f, 0.012834908f,0.019977754f,0.044256654f,</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  -0.39292613f, -0.18519334f, -0.11651281f,-0.06809892f, 0.011373677f</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  });</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span> </div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="keyword">auto</span> inputToForgetWeights =</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  MakeTensor<float, 2>(tensorInfo20x5, {-0.0018401089f, -0.004852237f,0.03698424f, 0.014181704f,0.028273236f,</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  -0.016726194f, -0.05249759f,-0.10204261f, 0.00861066f,-0.040979505f,</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  -0.009899187f,0.01923892f,-0.028177269f, -0.08535103f,-0.14585495f,</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  0.10662567f,-0.01909731f,-0.017883534f,-0.0047269356f,-0.045103323f,</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  0.0030784295f,0.076784775f,0.07463696f, 0.094531395f,0.0814421f,</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  -0.12257899f, -0.033945758f,-0.031303465f, 0.045630626f,0.06843887f,</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  -0.13492945f, -0.012480007f,-0.0811829f, -0.07224499f,-0.09628791f,</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  0.045100946f,0.0012300825f, 0.013964662f, 0.099372394f,0.02543059f,</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  0.06958324f, 0.034257296f, 0.0482646f, 0.06267997f,0.052625068f,</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  0.12784666f, 0.07077897f, 0.025725935f, 0.04165009f,0.07241905f,</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  0.018668644f, -0.037377294f,-0.06277783f,-0.08833636f,-0.040120605f,</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  -0.011405586f,-0.007808335f,-0.010301386f,-0.005102167f,0.027717464f,</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  0.05483423f, 0.11449111f, 0.11289652f,0.10939839f, 0.13396506f,</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  -0.08402166f,-0.01901462f, -0.044678304f,-0.07720565f,0.014350063f,</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  -0.11757958f, -0.0652038f, -0.08185733f,-0.076754324f,-0.092614375f,</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  0.10405491f, 0.052960336f, 0.035755895f,0.035839386f,-0.012540553f,</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  0.036881298f, 0.02913376f, 0.03420159f,0.05448447f,-0.054523353f,</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  0.02582715f, 0.02327355f, -0.011857179f,-0.0011980024f,-0.034641717f,</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  -0.026125094f,-0.17582615f,-0.15923657f,-0.27486774f,-0.0006143371f,</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  0.0001771948f, -8.470171e-05f, 0.02651807f,0.045790765f,0.06956496f</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  });</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span> </div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <span class="keyword">auto</span> inputToCellWeights =</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  MakeTensor<float, 2>(tensorInfo20x5, {-0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f,</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f,</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f,</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f,</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f,</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f,</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  -0.13002433f, -0.036816437f, -0.02130134f, -0.016518239f,</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  0.0047691227f, -0.0025825808f, 0.066017866f, 0.029991534f,</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  -0.10652836f, -0.1037554f, -0.13056071f, -0.03266643f,</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  -0.033702414f, -0.006473424f, -0.04611692f, 0.014419339f,</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  -0.025174323f, 0.0396852f, 0.081777506f, 0.06157468f,</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  0.10210095f, -0.009658194f, 0.046511717f, 0.03603906f,</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  0.0069369148f, 0.015960095f, -0.06507666f, 0.09551598f,</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  0.053568836f, 0.06408714f, 0.12835667f, -0.008714329f,</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  -0.20211966f, -0.12093674f, 0.029450472f, 0.2849013f,</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  -0.029227901f, 0.1164364f, -0.08560263f, 0.09941786f,</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  -0.036999565f, -0.028842626f, -0.0033637602f, -0.017012902f,</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  -0.09720865f, -0.11193351f, -0.029155117f, -0.017936034f,</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  -0.009768936f, -0.04223324f, -0.036159635f, 0.06505112f,</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  -0.021742892f, -0.023377212f, -0.07221364f, -0.06430552f,</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  0.05453865f, 0.091149814f, 0.06387331f, 0.007518393f,</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  0.055960953f, 0.069779344f, 0.046411168f, 0.10509911f,</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  0.07463894f, 0.0075130584f, 0.012850982f, 0.04555431f,</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  0.056955688f, 0.06555285f, 0.050801456f, -0.009862683f,</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  0.00826772f, -0.026555609f, -0.0073611983f, -0.0014897042f</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  });</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span> </div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <span class="keyword">auto</span> inputToOutputWeights =</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  MakeTensor<float, 2>(tensorInfo20x5, {-0.0998932f, -0.07201956f, -0.052803773f,-0.15629593f,-0.15001918f,</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  -0.07650751f,0.02359855f, -0.075155355f, -0.08037709f, -0.15093534f,</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  0.029517552f, -0.04751393f, 0.010350531f,-0.02664851f, -0.016839722f,</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  -0.023121163f, 0.0077019283f, 0.012851257f, -0.05040649f,-0.0129761f,</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  -0.021737747f,-0.038305793f,-0.06870586f, -0.01481247f,-0.001285394f,</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  0.10124236f, 0.083122835f, 0.053313006f,-0.062235646f,-0.075637154f,</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  -0.027833903f, 0.029774971f, 0.1130802f, 0.09218906f, 0.09506135f,</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  -0.086665764f,-0.037162706f,-0.038880914f,-0.035832845f,-0.014481564f,</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  -0.09825003f,-0.12048569f,-0.097665586f,-0.05287633f, -0.0964047f,</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  -0.11366429f, 0.035777505f, 0.13568819f, 0.052451383f,0.050649304f,</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  0.05798951f, -0.021852335f,-0.099848844f,0.014740475f,-0.078897946f,</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  0.04974699f, 0.014160473f, 0.06973932f, 0.04964942f, 0.033364646f,</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  0.08190124f, 0.025535367f, 0.050893165f, 0.048514254f,0.06945813f,</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  -0.078907564f,-0.06707616f, -0.11844508f, -0.09986688f,-0.07509403f,</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  0.06263226f, 0.14925587f, 0.20188436f, 0.12098451f,0.14639415f,</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  0.0015017595f, -0.014267382f, -0.03417257f,0.012711468f,0.0028300495f,</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  -0.024758482f, -0.05098548f,-0.0821182f, 0.014225672f, 0.021544158f,</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  0.08949725f, 0.07505268f, -0.0020780868f, 0.04908258f,0.06476295f,</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  -0.022907063f,0.027562456f,0.040185735f, 0.019567577f,-0.015598739f,</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  -0.049097303f, -0.017121866f, -0.083368234f,-0.02332002f,-0.0840956f</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  });</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span> </div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <span class="keyword">auto</span> inputGateBias =</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  MakeTensor<float, 1>(tensorInfo20, {0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f, 0.053110216f,</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  -0.06928846f, -0.13942584f, -0.11816189f, 0.19483899f, 0.03652339f,</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  -0.10250295f, 0.036714908f, -0.18426876f, 0.036065217f, 0.21810818f,</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  0.02383196f, -0.043370757f, 0.08690144f, -0.04444982f, 0.00030581196f</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  });</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span> </div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  <span class="keyword">auto</span> forgetGateBias =</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  MakeTensor<float, 1>(tensorInfo20, {0.035185695f, -0.042891346f, -0.03032477f, 0.23027696f,</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  0.11098921f, 0.15378423f, 0.09263801f, 0.09790885f,</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  0.09508917f, 0.061199076f, 0.07665568f, -0.015443159f,</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  -0.03499149f, 0.046190713f, 0.08895977f, 0.10899629f,</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  0.40694186f, 0.06030037f, 0.012413437f, -0.06108739f</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  });</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span> </div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <span class="keyword">auto</span> cellBias =</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  MakeTensor<float, 1>(tensorInfo20, {-0.024379363f, 0.0055531194f, 0.23377132f, 0.033463873f,</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  -0.1483596f, -0.10639995f, -0.091433935f, 0.058573797f,</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  -0.06809782f, -0.07889636f, -0.043246906f, -0.09829136f,</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  -0.4279842f, 0.034901652f, 0.18797937f, 0.0075234566f,</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  0.016178843f, 0.1749513f, 0.13975595f, 0.92058027f</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  });</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span> </div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  <span class="keyword">auto</span> outputGateBias =</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  MakeTensor<float, 1>(tensorInfo20, {0.046159424f, -0.0012809046f, 0.03563469f, 0.12648113f, 0.027195795f,</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  0.35373217f, -0.018957434f, 0.008907322f, -0.0762701f, 0.12018895f,</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  0.04216877f, 0.0022856654f, 0.040952638f, 0.3147856f, 0.08225149f,</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  -0.057416286f, -0.14995944f, -0.008040261f, 0.13208859f, 0.029760877f</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  });</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span> </div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <span class="keyword">auto</span> recurrentToInputWeights =</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  MakeTensor<float, 2>(tensorInfo20x16, {-0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f,</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f,</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f,</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f,</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f,</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  0.08981f, -0.045407712f, 0.08682226f, -0.06867011f,</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f,</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  0.14283475f, -0.07390571f, -0.06402044f, 0.062524505f,</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  -0.093129106f, 0.04860203f, -0.08364217f, -0.08119002f,</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  0.009352075f, 0.22920375f, 0.0016303885f, 0.11583097f,</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  -0.13732095f, 0.012405723f, -0.07551853f, 0.06343048f,</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  0.12162708f, -0.031923793f, -0.014335606f, 0.01790974f,</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  -0.10650317f, -0.0724401f, 0.08554849f, -0.05727212f,</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  0.06556731f, -0.042729504f, -0.043227166f, 0.011683251f,</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  -0.013082158f, -0.029302018f, -0.010899579f, -0.062036745f,</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  -0.022509435f, -0.00964907f, -0.01567329f, 0.04260106f,</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  -0.07787477f, -0.11576462f, 0.017356863f, 0.048673786f,</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  -0.017577527f, -0.05527947f, -0.082487635f, -0.040137455f,</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  -0.10820036f, -0.04666372f, 0.022746278f, -0.07851417f,</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  0.01068115f, 0.032956902f, 0.022433773f, 0.0026891115f,</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  0.08944216f, -0.0685835f, 0.010513544f, 0.07228705f,</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  0.02032331f, -0.059686817f, -0.0005566496f, -0.086984694f,</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  0.040414046f, -0.1380399f, 0.094208956f, -0.05722982f,</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  0.012092817f, -0.04989123f, -0.086576f, -0.003399834f,</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  -0.04696032f, -0.045747425f, 0.10091314f, 0.048676282f,</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  -0.029037097f, 0.031399418f, -0.0040285117f, 0.047237843f,</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  0.09504992f, 0.041799378f, -0.049185462f, -0.031518843f,</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  -0.10516937f, 0.026374253f, 0.10058866f, -0.0033195973f,</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  -0.041975245f, 0.0073591834f, 0.0033782164f, -0.004325073f,</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  -0.10167381f, 0.042500053f, -0.01447153f, 0.06464186f,</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  -0.017142897f, 0.03312627f, 0.009205989f, 0.024138335f,</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  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class="line"><a name="l00587"></a><span class="lineno"> 587</span>  -0.028379958f, -0.01556313f, 0.06554592f, -0.045599163f,</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  -0.01672207f, -0.020169014f, -0.011877351f, -0.20212261f,</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  0.010889619f, 0.0047078193f, 0.038385306f, 0.08540671f,</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  -0.017140968f, -0.0035865551f, 0.016678626f, 0.005633034f,</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  0.015963363f, 0.00871737f, 0.060130805f, 0.028611384f,</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  0.10109069f, -0.015060172f, -0.07894427f, 0.06401885f,</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  0.011584063f, -0.024466386f, 0.0047652307f, -0.09041358f,</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  0.030737216f, -0.0046374933f, 0.14215417f, -0.11823516f,</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  0.019899689f, 0.006106124f, -0.027092824f, 0.0786356f,</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  0.05052217f, -0.058925f, -0.011402121f, -0.024987547f,</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  -0.0013661642f, -0.06832946f, -0.015667673f, -0.1083353f,</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  -0.00096863037f, -0.06988685f, -0.053350925f, -0.027275559f,</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  -0.033664223f, -0.07978348f, -0.025200296f, -0.017207067f,</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  -0.058403496f, -0.055697463f, 0.005798788f, 0.12965427f,</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  -0.062582195f, 0.0013350133f, -0.10482091f, 0.0379771f,</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  0.072521195f, -0.0029455067f, -0.13797039f, -0.03628521f,</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  0.013806405f, -0.017858358f, -0.01008298f, -0.07700066f,</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  -0.017081132f, 0.019358726f, 0.0027079724f, 0.004635139f,</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  0.062634714f, -0.02338735f, -0.039547626f, -0.02050681f,</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  0.03385117f, -0.083611414f, 0.002862572f, -0.09421313f,</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  0.058618143f, -0.08598433f, 0.00972939f, 0.023867095f,</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  -0.053934585f, -0.023203006f, 0.07452513f, -0.048767887f,</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  -0.07314807f, -0.056307215f, -0.10433547f, -0.06440842f,</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  0.04328182f, 0.04389765f, -0.020006588f, -0.09076438f,</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  -0.11652589f, -0.021705797f, 0.03345259f, -0.010329105f,</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  -0.025767034f, 0.013057034f, -0.07316461f, -0.10145612f,</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  0.06358255f, 0.18531723f, 0.07759293f, 0.12006465f,</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  0.1305557f, 0.058638252f, -0.03393652f, 0.09622831f,</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  -0.16253184f, -2.4580743e-06f, 0.079869635f, -0.070196845f,</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  -0.005644518f, 0.06857898f, -0.12598175f, -0.035084512f,</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  0.03156317f, -0.12794146f, -0.031963028f, 0.04692781f,</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  0.030070418f, 0.0071660685f, -0.095516115f, -0.004643372f,</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  0.040170413f, -0.062104587f, -0.0037324072f, 0.0554317f,</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  0.08184801f, -0.019164372f, 0.06791302f, 0.034257166f,</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  -0.10307039f, 0.021943003f, 0.046745934f, 0.0790918f,</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  -0.0265588f, -0.007824208f, 0.042546265f, -0.00977924f,</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  -0.0002440307f, -0.017384544f, -0.017990116f, 0.12252321f,</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  -0.014512694f, -0.08251313f, 0.08861942f, 0.13589665f,</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  0.026351685f, 0.012641483f, 0.07466548f, 0.044301085f,</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  -0.045414884f, -0.051112458f, 0.03444247f, -0.08502782f,</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  -0.04106223f, -0.028126027f, 0.028473156f, 0.10467447f</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  });</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span> </div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  <span class="keyword">auto</span> recurrentToForgetWeights =</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  MakeTensor<float, 2>(tensorInfo20x16, {-0.057784554f, -0.026057621f, 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name="l00639"></a><span class="lineno"> 639</span>  -0.04421272f, -0.044019096f, 0.085488975f, 0.04058006f,</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  -0.06890133f, -0.030951202f, -0.024628663f, -0.07672815f,</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  0.034293607f, 0.08556707f, -0.05293577f, -0.033561368f,</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  -0.04899627f, 0.0241671f, 0.015736353f, -0.095442444f,</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  -0.029564252f, 0.016493602f, -0.035026584f, 0.022337519f,</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  -0.026871363f, 0.004780428f, 0.0077918363f, -0.03601621f,</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  0.016435321f, -0.03263031f, -0.09543275f, -0.047392778f,</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  0.013454138f, 0.028934088f, 0.01685226f, -0.086110644f,</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  -0.046250615f, -0.01847454f, 0.047608484f, 0.07339695f,</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  0.034546845f, -0.04881143f, 0.009128804f, -0.08802852f,</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  0.03761666f, 0.008096139f, -0.014454086f, 0.014361001f,</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  -0.023502491f, -0.0011840804f, -0.07607001f, 0.001856849f,</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  -0.06509276f, -0.006021153f, -0.08570962f, -0.1451793f,</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  0.060212336f, 0.055259194f, 0.06974018f, 0.049454916f,</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  -0.027794661f, -0.08077226f, -0.016179763f, 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class="lineno"> 661</span>  -0.011724666f, -0.0033288454f, -0.033542685f, -0.04764985f,</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  -0.13873616f, 0.040668588f, 0.034832682f, -0.015319203f,</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  -0.018715994f, 0.046002675f, 0.0599172f, -0.043107376f,</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  0.0294216f, -0.002314414f, -0.022424703f, 0.0030315618f,</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  0.0014641669f, 0.0029166266f, -0.11878115f, 0.013738511f,</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  0.12375372f, -0.0006038222f, 0.029104086f, 0.087442465f,</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  0.052958444f, 0.07558703f, 0.04817258f, 0.044462286f,</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  -0.015213451f, -0.08783778f, -0.0561384f, -0.003008196f,</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  0.047060397f, -0.002058388f, 0.03429439f, -0.018839769f,</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  0.024734668f, 0.024614193f, -0.042046934f, 0.09597743f,</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  -0.0043254104f, 0.04320769f, 0.0064070094f, -0.0019131786f,</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  -0.02558259f, -0.022822596f, -0.023273505f, -0.02464396f,</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  -0.10991725f, -0.006240552f, 0.0074488563f, 0.024044557f,</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  0.04383914f, -0.046476185f, 0.028658995f, 0.060410924f,</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  0.050786525f, 0.009452605f, -0.0073054377f, -0.024810238f,</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  0.0052906186f, 0.0066939713f, -0.0020913032f, 0.014515517f,</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  0.015898481f, 0.021362653f, -0.030262267f, 0.016587038f,</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  -0.011442813f, 0.041154444f, -0.007631438f, -0.03423484f,</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  -0.010977775f, 0.036152758f, 0.0066366293f, 0.11915515f,</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  0.02318443f, -0.041350313f, 0.021485701f, -0.10906167f,</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  -0.028218046f, -0.00954771f, 0.020531068f, -0.11995105f,</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  -0.03672871f, 0.024019798f, 0.014255957f, -0.05221243f,</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  -0.00661567f, -0.04630967f, 0.033188973f, 0.10107534f,</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  -0.014027541f, 0.030796422f, -0.10270911f, -0.035999842f,</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  0.15443139f, 0.07684145f, 0.036571592f, -0.035900835f,</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  -0.0034699554f, 0.06209149f, 0.015920248f, -0.031122351f,</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  -0.03858649f, 0.01849943f, 0.13872518f, 0.01503974f,</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  0.069941424f, -0.06948533f, -0.0088794185f, 0.061282158f,</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  -0.047401894f, 0.03100163f, -0.041533746f, -0.10430945f,</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  0.044574402f, -0.01425562f, -0.024290353f, 0.034563623f,</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  0.05866852f, 0.023947537f, -0.09445152f, 0.035450947f,</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  0.02247216f, -0.0042998926f, 0.061146557f, -0.10250651f,</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  0.020881841f, -0.06747029f, 0.10062043f, -0.0023941975f,</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  0.03532124f, -0.016341697f, 0.09685456f, -0.016764693f,</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  0.051808182f, 0.05875331f, -0.04536488f, 0.001626336f,</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  -0.028892258f, -0.01048663f, -0.009793449f, -0.017093895f,</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  0.010987891f, 0.02357273f, -0.00010856845f, 0.0099760275f,</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  -0.001845119f, -0.03551521f, 0.0018358806f, 0.05763657f,</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  -0.01769146f, 0.040995963f, 0.02235177f, -0.060430344f,</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  0.11475477f, -0.023854522f, 0.10071741f, 0.0686208f,</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  -0.014250481f, 0.034261297f, 0.047418304f, 0.08562733f,</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  -0.030519066f, 0.0060542435f, 0.014653856f, -0.038836084f,</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  0.04096551f, 0.032249358f, -0.08355519f, -0.026823482f,</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  0.056386515f, -0.010401743f, -0.028396193f, 0.08507674f,</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  0.014410365f, 0.020995233f, 0.17040324f, 0.11511526f,</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  0.02459721f, 0.0066619175f, 0.025853224f, -0.023133837f,</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  -0.081302024f, 0.017264642f, -0.009585969f, 0.09491168f,</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  -0.051313367f, 0.054532815f, -0.014298593f, 0.10657464f,</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  0.007076659f, 0.10964551f, 0.0409152f, 0.008275321f,</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  -0.07283536f, 0.07937492f, 0.04192024f, -0.1075027f</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  });</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span> </div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  <span class="keyword">auto</span> recurrentToCellWeights =</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  MakeTensor<float, 2>(tensorInfo20x16, {-0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f,</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f,</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f,</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  -0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f,</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  0.053676967f, 0.044480428f, -0.07663568f, 0.0071805613f,</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  0.08089997f, 0.05143358f, 0.038261272f, 0.03339287f,</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  -0.027673481f, 0.044746667f, 0.028349208f, 0.020090483f,</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  -0.019443132f, -0.030755889f, -0.0040000007f, 0.04465846f,</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  -0.021585021f, 0.0031670958f, 0.0053199246f, -0.056117613f,</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  -0.10893326f, 0.076739706f, -0.08509834f, -0.027997585f,</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  0.037871376f, 0.01449768f, -0.09002357f, -0.06111149f,</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  -0.046195522f, 0.0422062f, -0.005683705f, -0.1253618f,</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  -0.012925729f, -0.04890792f, 0.06985068f, 0.037654128f,</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  0.03398274f, -0.004781977f, 0.007032333f, -0.031787455f,</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  0.010868644f, -0.031489216f, 0.09525667f, 0.013939797f,</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  0.0058680447f, 0.0167067f, 0.02668468f, -0.04797466f,</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  -0.048885044f, -0.12722108f, 0.035304096f, 0.06554885f,</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  0.00972396f, -0.039238118f, -0.05159735f, -0.11329045f,</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  0.1613692f, -0.03750952f, 0.06529313f, -0.071974665f,</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  -0.11769596f, 0.015524369f, -0.0013754242f, -0.12446318f,</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  0.02786344f, -0.014179351f, 0.005264273f, 0.14376344f,</div><div class="line"><a name="l00735"></a><span 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name="l00875"></a><span class="lineno"> 875</span>  });</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span> </div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  <span class="keyword">auto</span> cellToInputWeights =</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  MakeTensor<float, 1>(tensorInfo20, {0.040369894f, 0.030746894f, 0.24704495f, 0.018586371f, -0.037586458f,</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  -0.15312155f, -0.11812848f, -0.11465643f, 0.20259799f, 0.11418174f,</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  -0.10116027f, -0.011334949f, 0.12411352f, -0.076769054f,-0.052169047f,</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  0.21198851f, -0.38871562f, -0.09061183f, -0.09683246f, -0.21929175f</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  });</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span> </div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span> </div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>  <span class="keyword">auto</span> cellToForgetWeights =</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span>  MakeTensor<float, 1>(tensorInfo20, {-0.01998659f,-0.15568835f,-0.24248174f, -0.012770197f, 0.041331276f,</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  -0.072311886f, -0.052123554f,-0.0066330447f,-0.043891653f,0.036225766f,</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  -0.047248036f, 0.021479502f,0.033189066f, 0.11952997f, -0.020432774f,</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  0.64658105f, -0.06650122f, -0.03467612f, 0.095340036f, 0.23647355f</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>  });</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span> </div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  <span class="keyword">auto</span> cellToOutputWeights =</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  MakeTensor<float, 1>(tensorInfo20, {0.08286371f, -0.08261836f, -0.51210177f, 0.002913762f, 0.17764764f,</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  -0.5495371f, -0.08460716f, -0.24552552f, 0.030037103f, 0.04123544f,</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  -0.11940523f, 0.007358328f, 0.1890978f, 0.4833202f, -0.34441817f,</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  0.36312827f, -0.26375428f, 0.1457655f, -0.19724406f, 0.15548733f</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  });</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span> </div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  <span class="keyword">auto</span> projectionWeights =</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  MakeTensor<float, 2>(tensorInfo16x20,</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  {-0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f,</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f,</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f,</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>  -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f,</div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f,</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f,</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  0.08682067f, 0.17240396f, 0.014975425f, 0.056431185f, 0.031037588f,</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  0.16702051f, 0.0077946745f, 0.15140012f, 0.29405436f, 0.120285f,</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  -0.188994f, -0.027265169f, 0.043389652f, -0.022061434f, 0.014777949f,</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  -0.20203483f, 0.094781205f, 0.19100232f, 0.13987629f, -0.036132768f,</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  -0.06426278f, -0.05108664f, 0.13221376f, 0.009441198f, -0.16715929f,</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  0.15859416f, -0.040437475f, 0.050779544f, -0.022187516f, 0.012166504f,</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>  0.027685808f, -0.07675938f, -0.0055694645f, -0.09444123f, 0.0046453946f,</div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  0.050794356f, 0.10770313f, -0.20790008f, -0.07149004f, -0.11425117f,</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span>  0.008225835f, -0.035802525f, 0.14374903f, 0.15262283f, 0.048710253f,</div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span>  0.1847461f, -0.007487823f, 0.11000021f, -0.09542012f, 0.22619456f,</div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  -0.029149994f, 0.08527916f, 0.009043713f, 0.0042746216f, 0.016261552f,</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  0.022461696f, 0.12689082f, -0.043589946f, -0.12035478f, -0.08361797f,</div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  -0.050666027f, -0.1248618f, -0.1275799f, -0.071875185f, 0.07377272f,</div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  0.09944291f, -0.18897448f, -0.1593054f, -0.06526116f, -0.040107165f,</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  -0.004618631f, -0.067624845f, -0.007576253f, 0.10727444f, 0.041546922f,</div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  -0.20424393f, 0.06907816f, 0.050412357f, 0.00724631f, 0.039827548f,</div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  0.12449835f, 0.10747581f, 0.13708383f, 0.09134148f, -0.12617786f,</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  -0.06428341f, 0.09956831f, 0.1208086f, -0.14676677f, -0.0727722f,</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  0.1126304f, 0.010139365f, 0.015571211f, -0.038128063f, 0.022913318f,</div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  -0.042050496f, 0.16842307f, -0.060597885f, 0.10531834f, -0.06411776f,</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  -0.07451711f, -0.03410368f, -0.13393489f, 0.06534304f, 0.003620307f,</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  0.04490757f, 0.05970546f, 0.05197996f, 0.02839995f, 0.10434969f,</div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  -0.013699693f, -0.028353551f, -0.07260381f, 0.047201227f, -0.024575593f,</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>  -0.036445823f, 0.07155557f, 0.009672501f, -0.02328883f, 0.009533515f,</div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  -0.03606021f, -0.07421458f, -0.028082801f, -0.2678904f, -0.13221288f,</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  0.18419984f, -0.13012612f, -0.014588381f, -0.035059117f, -0.04824723f,</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  0.07830115f, -0.056184657f, 0.03277091f, 0.025466874f, 0.14494097f,</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  -0.12522776f, -0.098633975f, -0.10766018f, -0.08317623f, 0.08594209f,</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  0.07749552f, 0.039474737f, 0.1776665f, -0.07409566f, -0.0477268f,</div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  0.29323658f, 0.10801441f, 0.1154011f, 0.013952499f, 0.10739139f,</div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  0.10708251f, -0.051456142f, 0.0074137426f, -0.10430189f, 0.10034707f,</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  0.045594677f, 0.0635285f, -0.0715442f, -0.089667566f, -0.10811871f,</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  0.00026344223f, 0.08298446f, -0.009525053f, 0.006585689f, -0.24567553f,</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  -0.09450807f, 0.09648481f, 0.026996298f, -0.06419476f, -0.04752702f,</div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  -0.11063944f, -0.23441927f, -0.17608605f, -0.052156363f, 0.067035615f,</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  0.19271925f, -0.0032889997f, -0.043264326f, 0.09663576f, -0.057112187f,</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  -0.10100678f, 0.0628376f, 0.04447668f, 0.017961001f, -0.10094388f,</div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  -0.10190601f, 0.18335468f, 0.10494553f, -0.052095775f, -0.0026118709f,</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  0.10539724f, -0.04383912f, -0.042349473f, 0.08438151f, -0.1947263f,</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  0.02251204f, 0.11216432f, -0.10307853f, 0.17351969f, -0.039091777f,</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  0.08066188f, -0.00561982f, 0.12633002f, 0.11335965f, -0.0088127935f,</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  -0.019777594f, 0.06864014f, -0.059751723f, 0.016233567f, -0.06894641f,</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  -0.28651384f, -0.004228674f, 0.019708522f, -0.16305895f, -0.07468996f,</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  -0.0855457f, 0.099339016f, -0.07580735f, -0.13775392f, 0.08434318f,</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  0.08330512f, -0.12131499f, 0.031935584f, 0.09180414f, -0.08876437f,</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  -0.08049874f, 0.008753825f, 0.03498998f, 0.030215185f, 0.03907079f,</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  0.089751154f, 0.029194152f, -0.03337423f, -0.019092513f, 0.04331237f,</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  0.04299654f, -0.036394123f, -0.12915532f, 0.09793732f, 0.07512415f,</div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  -0.11319543f, -0.032502122f, 0.15661901f, 0.07671967f, -0.005491124f,</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  -0.19379048f, -0.218606f, 0.21448623f, 0.017840758f, 0.1416943f,</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  -0.07051762f, 0.19488361f, 0.02664691f, -0.18104725f, -0.09334311f,</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  0.15026465f, -0.15493552f, -0.057762887f, -0.11604192f, -0.262013f,</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  -0.01391798f, 0.012185008f, 0.11156489f, -0.07483202f, 0.06693364f,</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  -0.26151478f, 0.046425626f, 0.036540434f, -0.16435726f, 0.17338543f,</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  -0.21401681f, -0.11385144f, -0.08283257f, -0.069031075f, 0.030635102f,</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  0.010969227f, 0.11109743f, 0.010919218f, 0.027526086f, 0.13519906f,</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  0.01891392f, -0.046839405f, -0.040167913f, 0.017953383f, -0.09700955f,</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  0.0061885654f, -0.07000971f, 0.026893595f, -0.038844477f, 0.14543656f</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  });</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span> </div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  std::vector<float> projectionBiasVector(outputSize, 0.f);</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  <span class="keyword">auto</span> projectionBias = MakeTensor<float,1>(tensorInfo16, projectionBiasVector);</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span> </div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToInputWeightsTensor(tensorInfo20x5);</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToForgetWeightsTensor(tensorInfo20x5);</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToCellWeightsTensor(tensorInfo20x5);</div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToOutputWeightsTensor(tensorInfo20x5);</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToForgetWeightsTensor(tensorInfo20x16);</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToInputWeightsTensor(tensorInfo20x16);</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToCellWeightsTensor(tensorInfo20x16);</div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToOutputWeightsTensor(tensorInfo20x16);</div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellToInputWeightsTensor(tensorInfo20);</div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputGateBiasTensor(tensorInfo20);</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> forgetGateBiasTensor(tensorInfo20);</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellBiasTensor(tensorInfo20);</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> outputGateBiasTensor(tensorInfo20);</div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellToForgetWeightsTensor(tensorInfo20);</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellToOutputWeightsTensor(tensorInfo20);</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> projectionWeightsTensor(tensorInfo16x20);</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> projectionBiasTensor(tensorInfo16);</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span> </div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToInputWeightsTensor, &inputToInputWeights[0][0]);</div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);</div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);</div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToInputWeightsTensor, &recurrentToInputWeights[0][0]);</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);</div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);</div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);</div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellToInputWeightsTensor, &cellToInputWeights[0]);</div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputGateBiasTensor, &inputGateBias[0]);</div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&forgetGateBiasTensor, &forgetGateBias[0]);</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellBiasTensor, &cellBias[0]);</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&outputGateBiasTensor, &outputGateBias[0]);</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellToForgetWeightsTensor, &cellToForgetWeights[0]);</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellToOutputWeightsTensor, &cellToOutputWeights[0]);</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&projectionWeightsTensor, &projectionWeights[0][0]);</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&projectionBiasTensor, &projectionBias[0]);</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span> </div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a08a1932be591c315a512a877d38b22df">m_InputToInputWeights</a> = &inputToInputWeightsTensor;</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a3ea82566d98c5a657c76c3d851c47848">m_InputToForgetWeights</a> = &inputToForgetWeightsTensor;</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a28ad98d17603fd8b12e046f8ece58970">m_InputToCellWeights</a> = &inputToCellWeightsTensor;</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a83dc9086b2e4a4e4cadb66bd874df798">m_InputToOutputWeights</a> = &inputToOutputWeightsTensor;</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a98d377149071d8842d610cc0734d1cfe">m_RecurrentToInputWeights</a> = &recurrentToInputWeightsTensor;</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a45d73e66cbb2b65049e4016c20657ccf">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeightsTensor;</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#aea142bd50ffb93631c2e08324ec92a1e">m_RecurrentToCellWeights</a> = &recurrentToCellWeightsTensor;</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#adebc1771e5a1f4b113a7aa594ea74d2c">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeightsTensor;</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a5c1c0a7ead7273788976c9e97cffaab7">m_CellToInputWeights</a> = &cellToInputWeightsTensor;</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#acb3aade8fae984f7293e222dcbe66030">m_InputGateBias</a> = &inputGateBiasTensor;</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#aba3ffe91d818266b8785ce971548eb59">m_ForgetGateBias</a> = &forgetGateBiasTensor;</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a75980b5795efd899a0c678a06a900c6d">m_CellBias</a> = &cellBiasTensor;</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a332551528a4b3534c2d6c89ce816fcd9">m_OutputGateBias</a> = &outputGateBiasTensor;</div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#acefa49d7faf26933e27e473e7bdb4175">m_CellToForgetWeights</a> = &cellToForgetWeightsTensor;</div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a6f74071b0e07bbe2cb20a8f78826e084">m_CellToOutputWeights</a> = &cellToOutputWeightsTensor;</div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#af3c52626a6f05597d82ed095d0765962">m_ProjectionWeights</a> = &projectionWeightsTensor;</div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a2ba352eb1fdf6dc5ecf7f2e6b6b48f94">m_ProjectionBias</a> = &projectionBiasTensor;</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span> </div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  <span class="comment">// Flags to set test configuration</span></div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> = 4;</div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span> </div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span> </div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#ab6bd7aaf685d4e956d780f8655a6f174">CreateLstm</a>(data, info);</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>  inputHandle->Allocate();</div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>  outputStateInHandle->Allocate();</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>  cellStateInHandle->Allocate();</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span> </div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  scratchHandle->Allocate();</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>  outputStateOutHandle->Allocate();</div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>  cellStateOutHandle->Allocate();</div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>  outputHandle->Allocate();</div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span> </div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &inputTensor[0][0]);</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(outputStateInHandle.get(), &outputStateInTensor[0][0]);</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(cellStateInHandle.get(), &cellStateInTensor[0][0]);</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span> </div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>  workload->Execute();</div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span> </div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.output[0][0], outputHandle.get());</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span> </div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span> </div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span> }</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span> </div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  <span class="keyword">const</span> boost::multi_array<T, 2>& input,</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  <span class="keyword">const</span> boost::multi_array<T, 2>& outputExpected,</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>  <span class="keywordtype">float</span> qScale = 0.0f,</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  int32_t qOffset = 0,</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> constantDataType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>)</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span> {</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  <span class="keywordtype">bool</span> cifgEnabled = <span class="keyword">true</span>;</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  <span class="keywordtype">bool</span> peepholeEnabled = <span class="keyword">true</span>;</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>  <span class="keywordtype">bool</span> projectionEnabled = <span class="keyword">false</span>;</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>  <span class="comment">// These are not the input and the output of Lstm yet</span></div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(input.shape()[0]);</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(input.shape()[1]);</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span> </div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputExpected.shape()[1]);</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span> </div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> cellSize = outputSize;</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span> </div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>  <span class="comment">// Decide the shape of all input tensors</span></div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({batchSize , inputSize}, ArmnnType, qScale, qOffset); <span class="comment">// change to ArmnnType</span></div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateInTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateInTensorInfo({batchSize, cellSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span> </div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> scratchBufferSize = cifgEnabled ? cellSize * 3 : cellSize * 4;</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> scratchBufferTensorInfo({batchSize, scratchBufferSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateOutTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateOutTensorInfo({batchSize, cellSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span> </div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  <span class="comment">// List of inputs</span></div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  std::vector<float> inputData;</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  inputData.assign(input.data(), input.data() + batchSize*inputSize);</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  <span class="keyword">auto</span> inputTensor = MakeTensor<float,2>(inputTensorInfo, inputData);</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span> </div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>  <span class="keyword">auto</span> outputStateInTensor = MakeTensor<float, 2>(outputStateInTensorInfo, outputStateInVector);</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span> </div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>  std::vector<float> cellStateInVector(batchSize * cellSize, 0.f);</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  <span class="keyword">auto</span> cellStateInTensor = MakeTensor<float, 2>(cellStateInTensorInfo, cellStateInVector);</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span> </div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span> </div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>  <span class="comment">// Prepare all the weights in the descriptor for LSTM</span></div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>  <a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml">armnn::LstmQueueDescriptor</a> data;</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfoInput({cellSize, inputSize}, constantDataType, qScale, qOffset);</div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfoOutput({cellSize, outputSize}, constantDataType, qScale, qOffset);</div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfoNumUnits({cellSize}, constantDataType, qScale, qOffset);</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span> </div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  <span class="keyword">auto</span> inputToCellWeights = MakeTensor<float, 2>(tensorInfoInput,</div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>  {-0.49770179f, -0.27711356f, -0.09624726f, 0.05100781f,</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>  0.04717243f, 0.48944736f, -0.38535351f,</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>  -0.17212132f});</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>  <span class="keyword">auto</span> inputToForgetWeights = MakeTensor<float, 2>(tensorInfoInput,</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>  {-0.55291498f, -0.42866567f, 0.13056988f,</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  -0.3633365f, -0.22755712f, 0.28253698f, 0.24407166f,</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>  0.33826375f});</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>  <span class="keyword">auto</span> inputToOutputWeights = MakeTensor<float, 2>(tensorInfoInput,</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>  {0.10725588f, -0.02335852f, -0.55932593f,</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>  -0.09426838f, -0.44257352f, 0.54939759f,</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>  0.01533556f, 0.42751634f});</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  <span class="keyword">auto</span> cellBias = MakeTensor<float, 1>(tensorInfoNumUnits, {0.f, 0.f, 0.f, 0.f});</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>  <span class="keyword">auto</span> forgetGateBias = MakeTensor<float, 1>(tensorInfoNumUnits, {1.f, 1.f, 1.f, 1.f});</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>  <span class="keyword">auto</span> outputGateBias = MakeTensor<float, 1>(tensorInfoNumUnits, {0.f, 0.f, 0.f, 0.f});</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span> </div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>  <span class="keyword">auto</span> recurrentToCellWeights = MakeTensor<float, 2>(tensorInfoOutput,</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>  {0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, 0.42957711f,</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>  0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f, 0.20675004f,</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>  0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f, 0.44901288f,</div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>  0.21193194f});</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>  <span class="keyword">auto</span> recurrentToForgetWeights = MakeTensor<float, 2>(tensorInfoOutput,</div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>  {-0.13832897f, -0.0515101f, -0.2359007f, -0.16661474f, -0.14340827f,</div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  0.36986142f, 0.23414481f, 0.55899f, 0.10798943f, -0.41174671f, 0.17751795f,</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  -0.34484994f, -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f});</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span> </div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>  <span class="keyword">auto</span> recurrentToOutputWeights = MakeTensor<float, 2>(tensorInfoOutput,</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  {0.41613156f, 0.42610586f, -0.16495961f, -0.5663873f, 0.30579174f, -0.05115908f,</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  -0.33941799f, 0.23364776f, 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f,</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f});</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span> </div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  <span class="keyword">auto</span> cellToForgetWeights = MakeTensor<float, 1>(tensorInfoNumUnits,</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f});</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  <span class="keyword">auto</span> cellToOutputWeights = MakeTensor<float, 1>(tensorInfoNumUnits,</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>  {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f});</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span> </div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToCellWeightsTensor(tensorInfoInput);</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToForgetWeightsTensor(tensorInfoInput);</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToOutputWeightsTensor(tensorInfoInput);</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span> </div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellBiasTensor(tensorInfoNumUnits);</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> forgetGateBiasTensor(tensorInfoNumUnits);</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> outputGateBiasTensor(tensorInfoNumUnits);</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span> </div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToCellWeightsTensor(tensorInfoOutput);</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToForgetWeightsTensor(tensorInfoOutput);</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToOutputWeightsTensor(tensorInfoOutput);</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span> </div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span> </div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellToForgetWeightsTensor(tensorInfoNumUnits);</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellToOutputWeightsTensor(tensorInfoNumUnits);</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span> </div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span> </div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellBiasTensor, &cellBias[0]);</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&forgetGateBiasTensor, &forgetGateBias[0]);</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&outputGateBiasTensor, &outputGateBias[0]);</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span> </div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span> </div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellToForgetWeightsTensor, &cellToForgetWeights[0]);</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellToOutputWeightsTensor, &cellToOutputWeights[0]);</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span> </div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span> </div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  data.m_InputToCellWeights = &inputToCellWeightsTensor;</div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  data.m_InputToForgetWeights = &inputToForgetWeightsTensor;</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  data.m_InputToOutputWeights = &inputToOutputWeightsTensor;</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span> </div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>  data.m_CellBias = &cellBiasTensor;</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  data.m_ForgetGateBias = &forgetGateBiasTensor;</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  data.m_OutputGateBias = &outputGateBiasTensor;</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span> </div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>  data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;</div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span> </div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  data.m_CellToForgetWeights = &cellToForgetWeightsTensor;</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  data.m_CellToOutputWeights = &cellToOutputWeightsTensor;</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span> </div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>  <span class="comment">// other parameters for the descriptor</span></div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  data.m_Parameters.m_CifgEnabled = cifgEnabled;</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>  data.m_Parameters.m_ProjectionEnabled = projectionEnabled;</div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  data.m_Parameters.m_PeepholeEnabled = peepholeEnabled;</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span> </div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  data.m_Parameters.m_ActivationFunc = 4;</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>  data.m_Parameters.m_ClippingThresProj = 0.0;</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  data.m_Parameters.m_ClippingThresCell = 0.0;</div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span> </div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span> </div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  <span class="comment">// List of outputs</span></div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  std::vector<float> scratchBufferVector(batchSize * scratchBufferSize, 0.f);</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  <span class="keyword">auto</span> scratchBufferTensor = MakeTensor<float,2>(scratchBufferTensorInfo, scratchBufferVector);</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> ret0(scratchBufferTensorInfo);</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span> </div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  <span class="comment">// Output state for a certain time step</span></div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  std::vector<float> outputStateOutVector(batchSize * outputSize, 0.f);</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  <span class="keyword">auto</span> outputStateOutTensor = MakeTensor<float,2>(outputStateOutTensorInfo, outputStateOutVector);</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> ret1(outputStateOutTensorInfo);</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span> </div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>  <span class="comment">// Cell state for a certain time step</span></div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>  std::vector<float> cellStateOutVector(batchSize * cellSize, 0.f);</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>  <span class="keyword">auto</span> cellStateOutTensor = MakeTensor<float,2>(cellStateOutTensorInfo, cellStateOutVector);</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> ret2(cellStateOutTensorInfo);</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span> </div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  <span class="comment">// Output for a certain time step</span></div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>  std::vector<float> outputVector(batchSize * outputSize, 0.f);</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>  <span class="keyword">auto</span> outputTensor = MakeTensor<float, 2>(outputTensorInfo, outputVector);</div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>  std::vector<float> outputData;</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  outputData.assign(outputExpected.data(), outputExpected.data() + batchSize*outputSize);</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> ret3(outputTensorInfo);</div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>  ret3.outputExpected = MakeTensor<float, 2>(outputTensorInfo, outputData);</div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span> </div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>  <span class="comment">// Prepare the inputs and outputs for the workload</span></div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle =</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputStateInTensorInfo);</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(cellStateInTensorInfo);</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span> </div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>  std::unique_ptr<armnn::ITensorHandle> scratchBufferHandle =</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(scratchBufferTensorInfo);</div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>  std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputStateOutTensorInfo);</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>  std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(cellStateOutTensorInfo);</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle =</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span> </div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>  AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span> </div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchBufferHandle.get());</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span> </div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#ab6bd7aaf685d4e956d780f8655a6f174">CreateLstm</a>(data, info);</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span> </div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span> </div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  inputHandle->Allocate();</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>  outputStateInHandle->Allocate();</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>  cellStateInHandle->Allocate();</div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span> </div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  scratchBufferHandle->Allocate();</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>  outputStateOutHandle->Allocate();</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>  cellStateOutHandle->Allocate();</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>  outputHandle->Allocate();</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span> </div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span> </div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &inputTensor[0][0]);</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(outputStateInHandle.get(), &outputStateInTensor[0][0]);</div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(cellStateInHandle.get(), &cellStateInTensor[0][0]);</div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span> </div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(scratchBufferHandle.get(), &scratchBufferTensor[0][0]);</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(outputStateOutHandle.get(), &outputStateOutTensor[0][0]);</div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(cellStateOutHandle.get(), &cellStateOutTensor[0][0]);</div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span> </div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>  workload->Execute();</div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span> </div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret0.output[0][0], scratchBufferHandle.get());</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret1.output[0][0], outputStateOutHandle.get());</div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret2.output[0][0], cellStateOutHandle.get());</div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret3.output[0][0], outputHandle.get());</div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span> </div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>  <span class="keywordflow">return</span> ret3;</div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span> }</div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span> </div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a></div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span> LstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTestImpl(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>  <span class="keyword">const</span> boost::multi_array<T, 2>& input,</div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span>  <span class="keyword">const</span> boost::multi_array<T, 2>& outputExpected,</div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>  <span class="keywordtype">float</span> qScale = 0.0f,</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>  int32_t qOffset = 0,</div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> constantDataType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>)</div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span> {</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 2;</div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 3;</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 5;</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>  <span class="keywordtype">unsigned</span> numUnits = 4;</div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span> </div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({batchSize , inputSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateInTensorInfo({batchSize , numUnits}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateInTensorInfo({batchSize , outputSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span> </div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>  <span class="comment">// Scratch buffer size without CIFG [batchSize, numUnits * 4]</span></div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> scratchBufferTensorInfo({batchSize, numUnits * 4}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateOutTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateOutTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span> </div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> ret(outputTensorInfo);</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span> </div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>  std::vector<float> inputVector;</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>  inputVector.assign(input.data(), input.data() + (batchSize * inputSize));</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>  <span class="keyword">auto</span> inputTensor = MakeTensor<float,2>(inputTensorInfo, inputVector);</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span> </div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>  std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>  <span class="keyword">auto</span> cellStateInTensor = MakeTensor<float,2>(cellStateInTensorInfo, cellStateInVector);</div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span> </div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>  std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);</div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>  <span class="keyword">auto</span> outputStateInTensor = MakeTensor<float,2>(outputStateInTensorInfo, outputStateInVector);</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span> </div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>  std::vector<float> scratchBufferVector(batchSize * numUnits * 4, 0.f);</div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>  <span class="keyword">auto</span> scratchBufferTensor = MakeTensor<float,2>(scratchBufferTensorInfo, scratchBufferVector);</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span> </div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>  std::vector<float> outputStateOutVector(batchSize * outputSize, 0.f);</div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>  <span class="keyword">auto</span> outputStateOutTensor = MakeTensor<float,2>(outputStateOutTensorInfo, outputStateOutVector);</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span> </div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  std::vector<float> cellStateOutVector(batchSize * numUnits, 0.f);</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>  <span class="keyword">auto</span> cellStateOutTensor = MakeTensor<float,2>(cellStateOutTensorInfo, cellStateOutVector);</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span> </div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>  std::vector<float> outputVector;</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>  outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize));</div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  ret.outputExpected = MakeTensor<float, 2>(outputTensorInfo, outputVector);</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span> </div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>  std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(cellStateInTensorInfo);</div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>  std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =</div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputStateInTensorInfo);</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span> </div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>  std::unique_ptr<armnn::ITensorHandle> scratchHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(scratchBufferTensorInfo);</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>  std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputStateOutTensorInfo);</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>  std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =</div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(cellStateOutTensorInfo);</div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span> </div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span>  <a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml">armnn::LstmQueueDescriptor</a> data;</div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span> </div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span>  AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());</div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>  AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span> </div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>  AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchHandle.get());</div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>  AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());</div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>  AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span> </div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo3({outputSize}, constantDataType, qScale, qOffset);</div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo4({numUnits}, constantDataType, qScale, qOffset);</div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo4x5({numUnits, inputSize}, constantDataType, qScale, qOffset);</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo4x3({numUnits, outputSize}, constantDataType, qScale, qOffset);</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo3x4({outputSize, numUnits}, constantDataType, qScale, qOffset);</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span> </div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>  <span class="keyword">auto</span> inputToInputWeights =</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>  MakeTensor<float, 2>(tensorInfo4x5, { 0.5f, 0.6f, 0.7f, -0.8f, -0.9f,</div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>  0.1f, 0.2f, 0.3f, -0.4f, 0.5f,</div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>  -0.8f, 0.7f, -0.6f, 0.5f, -0.4f,</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>  -0.5f, -0.4f, -0.3f, -0.2f, -0.1f}); <span class="comment">//{numUnits, inputSize}</span></div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span> </div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>  <span class="keyword">auto</span> inputToForgetWeights =</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>  MakeTensor<float, 2>(tensorInfo4x5, {-0.6f, -0.1f, 0.3f, 0.2f, 0.9f,</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span>  -0.5f, -0.2f, -0.4f, 0.3f, -0.8f,</div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span>  -0.4f, 0.3f, -0.5f, -0.4f, -0.6f,</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>  0.3f, -0.4f, -0.6f, -0.5f, -0.5f}); <span class="comment">//{numUnits, inputSize}</span></div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span> </div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span>  <span class="keyword">auto</span> inputToCellWeights =</div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span>  MakeTensor<float, 2>(tensorInfo4x5, {-0.4f, -0.3f, -0.2f, -0.1f, -0.5f,</div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span>  0.5f, -0.2f, -0.3f, -0.2f, -0.6f,</div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>  0.6f, -0.1f, -0.4f, -0.3f, -0.7f,</div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>  0.7f, -0.9f, -0.5f, 0.8f, 0.6f}); <span class="comment">//{numUnits, inputSize}</span></div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span> </div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>  <span class="keyword">auto</span> inputToOutputWeights =</div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>  MakeTensor<float, 2>(tensorInfo4x5, {-0.8f, -0.4f, -0.2f, -0.9f, -0.1f,</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>  -0.7f, 0.3f, -0.3f, -0.8f, -0.2f,</div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span>  0.6f, -0.2f, 0.4f, -0.7f, -0.3f,</div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>  -0.5f, 0.1f, 0.5f, -0.6f, -0.4f}); <span class="comment">//{numUnits, inputSize}</span></div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span> </div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span>  <span class="keyword">auto</span> inputGateBias =</div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>  MakeTensor<float, 1>(tensorInfo4, {0.03f, 0.15f, 0.22f, 0.38f}); <span class="comment">//{numUnits}</span></div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span> </div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>  <span class="keyword">auto</span> forgetGateBias =</div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>  MakeTensor<float, 1>(tensorInfo4, {0.1f, -0.3f, -0.2f, 0.1f}); <span class="comment">//{numUnits}</span></div><div class="line"><a name="l01389"></a><span class="lineno"> 1389</span> </div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</span>  <span class="keyword">auto</span> cellBias =</div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>  MakeTensor<float, 1>(tensorInfo4, {-0.05f, 0.72f, 0.25f, 0.08f}); <span class="comment">//{numUnits}</span></div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span> </div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>  <span class="keyword">auto</span> outputGateBias =</div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>  MakeTensor<float, 1>(tensorInfo4, {0.05f, -0.01f, 0.2f, 0.1f}); <span class="comment">//{numUnits}</span></div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span> </div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>  <span class="keyword">auto</span> recurrentToInputWeights =</div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>  MakeTensor<float, 2>(tensorInfo4x3, {-0.2f, -0.3f, 0.4f,</div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span>  0.1f, -0.5f, 0.9f,</div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span>  -0.2f, -0.3f, -0.7f,</div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>  0.05f, -0.2f, -0.6f}); <span class="comment">//{numUnits, outputSize}</span></div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span> </div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>  <span class="keyword">auto</span> recurrentToCellWeights =</div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>  MakeTensor<float, 2>(tensorInfo4x3, {-0.3f, 0.2f, 0.1f,</div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>  -0.3f, 0.8f, -0.08f,</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>  -0.2f, 0.3f, 0.8f,</div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>  -0.6f, -0.1f, 0.2f}); <span class="comment">//{numUnits, outputSize}</span></div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span> </div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>  <span class="keyword">auto</span> recurrentToForgetWeights =</div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span>  MakeTensor<float, 2>(tensorInfo4x3, {-0.5f, -0.3f, -0.5f,</div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span>  -0.2f, 0.6f, 0.4f,</div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>  0.9f, 0.3f, -0.1f,</div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>  0.2f, 0.5f, 0.2f}); <span class="comment">//{numUnits, outputSize}</span></div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span> </div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span>  <span class="keyword">auto</span> recurrentToOutputWeights =</div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span>  MakeTensor<float, 2>(tensorInfo4x3, { 0.3f, -0.1f, 0.1f,</div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span>  -0.2f, -0.5f, -0.7f,</div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>  -0.2f, -0.6f, -0.1f,</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>  -0.4f, -0.7f, -0.2f}); <span class="comment">//{numUnits, outputSize}</span></div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span> </div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>  <span class="keyword">auto</span> cellToInputWeights =</div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>  MakeTensor<float, 1>(tensorInfo4, {0.05f, 0.1f, 0.25f, 0.15f}); <span class="comment">//{numUnits}</span></div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span> </div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>  <span class="keyword">auto</span> cellToForgetWeights =</div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>  MakeTensor<float, 1>(tensorInfo4, {-0.02f, -0.15f, -0.25f, -0.03f}); <span class="comment">//{numUnits}</span></div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span> </div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>  <span class="keyword">auto</span> cellToOutputWeights =</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span>  MakeTensor<float, 1>(tensorInfo4, {0.1f, -0.1f, -0.5f, 0.05f}); <span class="comment">//{numUnits}</span></div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span> </div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>  <span class="keyword">auto</span> projectionWeights =</div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span>  MakeTensor<float, 2>(tensorInfo3x4,</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>  {-0.1f, 0.2f, 0.01f, -0.2f,</div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span>  0.1f, 0.5f, 0.3f, 0.08f,</div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span>  0.07f, 0.2f, -0.4f, 0.2f}); <span class="comment">//{outputSize, numUnits}</span></div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span> </div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span>  std::vector<float> projectionBiasVector(outputSize, 0.f);</div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span>  <span class="keyword">auto</span> projectionBias = MakeTensor<float,1>(tensorInfo3, projectionBiasVector); <span class="comment">//{outputSize}</span></div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span> </div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span>  <span class="keyword">auto</span> inputLayerNormWeights =</div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span>  MakeTensor<float, 1>(tensorInfo4, {0.1f, 0.2f, 0.3f, 0.5f}); <span class="comment">//{numUnits}</span></div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span> </div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span>  <span class="keyword">auto</span> forgetLayerNormWeights =</div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>  MakeTensor<float, 1>(tensorInfo4, {0.2f, 0.2f, 0.4f, 0.3f}); <span class="comment">//{numUnits}</span></div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span> </div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>  <span class="keyword">auto</span> cellLayerNormWeights =</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>  MakeTensor<float, 1>(tensorInfo4, {0.7f, 0.2f, 0.3f, 0.8f}); <span class="comment">//{numUnits}</span></div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span> </div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</span>  <span class="keyword">auto</span> outputLayerNormWeights =</div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span>  MakeTensor<float, 1>(tensorInfo4, {0.6f, 0.2f, 0.2f, 0.5f}); <span class="comment">//{numUnits}</span></div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span> </div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span> </div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToInputWeightsTensor(tensorInfo4x5);</div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToForgetWeightsTensor(tensorInfo4x5);</div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToCellWeightsTensor(tensorInfo4x5);</div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToOutputWeightsTensor(tensorInfo4x5);</div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToForgetWeightsTensor(tensorInfo4x3);</div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToInputWeightsTensor(tensorInfo4x3);</div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToCellWeightsTensor(tensorInfo4x3);</div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToOutputWeightsTensor(tensorInfo4x3);</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellToInputWeightsTensor(tensorInfo4);</div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l01461"></a><span class="lineno"> 1461</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> forgetGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellBiasTensor(tensorInfo4);</div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> outputGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellToForgetWeightsTensor(tensorInfo4);</div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellToOutputWeightsTensor(tensorInfo4);</div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> projectionWeightsTensor(tensorInfo3x4);</div><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> projectionBiasTensor(tensorInfo3);</div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</span> </div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputLayerNormWeightsTensor(tensorInfo4);</div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> forgetLayerNormWeightsTensor(tensorInfo4);</div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellLayerNormWeightsTensor(tensorInfo4);</div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> outputLayerNormWeightsTensor(tensorInfo4);</div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span> </div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToInputWeightsTensor, &inputToInputWeights[0][0]);</div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);</div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);</div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);</div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToInputWeightsTensor, &recurrentToInputWeights[0][0]);</div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);</div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);</div><div class="line"><a name="l01481"></a><span class="lineno"> 1481</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);</div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellToInputWeightsTensor, &cellToInputWeights[0]);</div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputGateBiasTensor, &inputGateBias[0]);</div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&forgetGateBiasTensor, &forgetGateBias[0]);</div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellBiasTensor, &cellBias[0]);</div><div class="line"><a name="l01486"></a><span class="lineno"> 1486</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&outputGateBiasTensor, &outputGateBias[0]);</div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellToForgetWeightsTensor, &cellToForgetWeights[0]);</div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellToOutputWeightsTensor, &cellToOutputWeights[0]);</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&projectionWeightsTensor, &projectionWeights[0][0]);</div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&projectionBiasTensor, &projectionBias[0]);</div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span> </div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputLayerNormWeightsTensor, &inputLayerNormWeights[0]);</div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&forgetLayerNormWeightsTensor, &forgetLayerNormWeights[0]);</div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellLayerNormWeightsTensor, &cellLayerNormWeights[0]);</div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&outputLayerNormWeightsTensor, &outputLayerNormWeights[0]);</div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span> </div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a08a1932be591c315a512a877d38b22df">m_InputToInputWeights</a> = &inputToInputWeightsTensor;</div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a3ea82566d98c5a657c76c3d851c47848">m_InputToForgetWeights</a> = &inputToForgetWeightsTensor;</div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a28ad98d17603fd8b12e046f8ece58970">m_InputToCellWeights</a> = &inputToCellWeightsTensor;</div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a83dc9086b2e4a4e4cadb66bd874df798">m_InputToOutputWeights</a> = &inputToOutputWeightsTensor;</div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a98d377149071d8842d610cc0734d1cfe">m_RecurrentToInputWeights</a> = &recurrentToInputWeightsTensor;</div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a45d73e66cbb2b65049e4016c20657ccf">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeightsTensor;</div><div class="line"><a name="l01503"></a><span class="lineno"> 1503</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#aea142bd50ffb93631c2e08324ec92a1e">m_RecurrentToCellWeights</a> = &recurrentToCellWeightsTensor;</div><div class="line"><a name="l01504"></a><span class="lineno"> 1504</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#adebc1771e5a1f4b113a7aa594ea74d2c">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeightsTensor;</div><div class="line"><a name="l01505"></a><span class="lineno"> 1505</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a5c1c0a7ead7273788976c9e97cffaab7">m_CellToInputWeights</a> = &cellToInputWeightsTensor;</div><div class="line"><a name="l01506"></a><span class="lineno"> 1506</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#acb3aade8fae984f7293e222dcbe66030">m_InputGateBias</a> = &inputGateBiasTensor;</div><div class="line"><a name="l01507"></a><span class="lineno"> 1507</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#aba3ffe91d818266b8785ce971548eb59">m_ForgetGateBias</a> = &forgetGateBiasTensor;</div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a75980b5795efd899a0c678a06a900c6d">m_CellBias</a> = &cellBiasTensor;</div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a332551528a4b3534c2d6c89ce816fcd9">m_OutputGateBias</a> = &outputGateBiasTensor;</div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#acefa49d7faf26933e27e473e7bdb4175">m_CellToForgetWeights</a> = &cellToForgetWeightsTensor;</div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a6f74071b0e07bbe2cb20a8f78826e084">m_CellToOutputWeights</a> = &cellToOutputWeightsTensor;</div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#af3c52626a6f05597d82ed095d0765962">m_ProjectionWeights</a> = &projectionWeightsTensor;</div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a2ba352eb1fdf6dc5ecf7f2e6b6b48f94">m_ProjectionBias</a> = &projectionBiasTensor;</div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span> </div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a9cc28aa4fff6ba9a8abdb340c1abdd57">m_InputLayerNormWeights</a> = &inputLayerNormWeightsTensor;</div><div class="line"><a name="l01516"></a><span class="lineno"> 1516</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a453a4af385d0c060c9aac990fceaa1ef">m_ForgetLayerNormWeights</a> = &forgetLayerNormWeightsTensor;</div><div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a518f0195d0278a892b49649b8860d17f">m_CellLayerNormWeights</a> = &cellLayerNormWeightsTensor;</div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span>  data.<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#aa3f07e27230d6d99adc2c82ba681df2b">m_OutputLayerNormWeights</a> = &outputLayerNormWeightsTensor;</div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span> </div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</span>  <span class="comment">// Flags to set test configuration</span></div><div class="line"><a name="l01521"></a><span class="lineno"> 1521</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> = 4;</div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01524"></a><span class="lineno"> 1524</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">m_LayerNormEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span> </div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span> </div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#ab6bd7aaf685d4e956d780f8655a6f174">CreateLstm</a>(data, info);</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>  inputHandle->Allocate();</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span>  outputStateInHandle->Allocate();</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span>  cellStateInHandle->Allocate();</div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span> </div><div class="line"><a name="l01533"></a><span class="lineno"> 1533</span>  scratchHandle->Allocate();</div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span>  outputStateOutHandle->Allocate();</div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span>  cellStateOutHandle->Allocate();</div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>  outputHandle->Allocate();</div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span> </div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &inputTensor[0][0]);</div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(outputStateInHandle.get(), &outputStateInTensor[0][0]);</div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(cellStateInHandle.get(), &cellStateInTensor[0][0]);</div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span> </div><div class="line"><a name="l01542"></a><span class="lineno"> 1542</span>  workload->Execute();</div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span> </div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.output[0][0], outputHandle.get());</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span> </div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span> }</div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span> </div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 2></a> QuantizedLstmTestImpl(</div><div class="line"><a name="l01550"></a><span class="lineno"> 1550</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>  <span class="keyword">const</span> boost::multi_array<uint8_t, 2>& input,</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>  <span class="keyword">const</span> boost::multi_array<uint8_t, 2>& outputExpected)</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span> {</div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>  <span class="keyword">auto</span> numBatches = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(input.shape()[0]);</div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>  <span class="keyword">auto</span> inputSize = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(input.shape()[1]);</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span>  <span class="keyword">auto</span> outputSize = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputExpected.shape()[1]);</div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span> </div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>  <span class="comment">// Scale/Offset for input/output, cellState In/Out, weights, bias</span></div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>  <span class="keywordtype">float</span> inputOutputScale = 0.0078125f;</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>  int32_t inputOutputOffset = 128;</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span> </div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>  <span class="keywordtype">float</span> cellStateScale = 0.00048828125f;</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>  int32_t cellStateOffset = 0;</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span> </div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>  <span class="keywordtype">float</span> weightsScale = 0.00408021f;</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span>  int32_t weightsOffset = 100;</div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span> </div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>  <span class="keywordtype">float</span> biasScale = 3.1876640625e-05f;</div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span>  int32_t biasOffset = 0;</div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span> </div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span>  <span class="comment">// Input/Output tensor info</span></div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({numBatches , inputSize},</div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span>  inputOutputScale,</div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span>  inputOutputOffset);</div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span> </div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateInfo({numBatches , outputSize},</div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span>  cellStateScale,</div><div class="line"><a name="l01582"></a><span class="lineno"> 1582</span>  cellStateOffset);</div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span> </div><div class="line"><a name="l01584"></a><span class="lineno"> 1584</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateInfo({numBatches , outputSize},</div><div class="line"><a name="l01585"></a><span class="lineno"> 1585</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01586"></a><span class="lineno"> 1586</span>  inputOutputScale,</div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>  inputOutputOffset);</div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span> </div><div class="line"><a name="l01589"></a><span class="lineno"> 1589</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 2></a> ret(outputStateInfo);</div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span> </div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>  <span class="comment">// Input0</span></div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span>  std::vector<uint8_t> inputVector;</div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>  inputVector.assign(input.data(), input.data() + (numBatches * inputSize));</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>  <span class="keyword">auto</span> inputTensor = MakeTensor<uint8_t, 2>(inputInfo, inputVector);</div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span> </div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span>  <span class="comment">// Input1</span></div><div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>  std::vector<int16_t> cellStateInVector = {876, 1034, 955, -909, 761, 1029, 796, -1036}; <span class="comment">// 13</span></div><div class="line"><a name="l01598"></a><span class="lineno"> 1598</span>  <span class="keyword">auto</span> cellStateInTensor = MakeTensor<int16_t, 2>(cellStateInfo, cellStateInVector);</div><div class="line"><a name="l01599"></a><span class="lineno"> 1599</span> </div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>  <span class="comment">// Input2</span></div><div class="line"><a name="l01601"></a><span class="lineno"> 1601</span>  std::vector<uint8_t> outputStateInVector = {136, 150, 140, 115, 135, 152, 138, 112}; <span class="comment">// 14</span></div><div class="line"><a name="l01602"></a><span class="lineno"> 1602</span>  <span class="keyword">auto</span> outputStateInTensor = MakeTensor<uint8_t, 2>(outputStateInfo, outputStateInVector);</div><div class="line"><a name="l01603"></a><span class="lineno"> 1603</span> </div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</span>  <span class="comment">// Output0</span></div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</span>  std::vector<int16_t> cellStateOutVector = {1485, 1177, 1373, -1023, 1019, 1355, 1097, -1235}; <span class="comment">// 0</span></div><div class="line"><a name="l01606"></a><span class="lineno"> 1606</span>  <span class="keyword">auto</span> cellStateOutTensor = MakeTensor<int16_t, 2>(cellStateInfo, cellStateOutVector);</div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span> </div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span>  <span class="comment">// Output1</span></div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span>  std::vector<uint8_t> outputVector; <span class="comment">// 1</span></div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span>  outputVector.assign(outputExpected.data(), outputExpected.data() + (numBatches * outputSize));</div><div class="line"><a name="l01611"></a><span class="lineno"> 1611</span>  ret.outputExpected = MakeTensor<uint8_t, 2>(outputStateInfo, outputVector);</div><div class="line"><a name="l01612"></a><span class="lineno"> 1612</span> </div><div class="line"><a name="l01613"></a><span class="lineno"> 1613</span>  <span class="comment">// Create tensor handles</span></div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputInfo);</div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span>  std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =</div><div class="line"><a name="l01616"></a><span class="lineno"> 1616</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(cellStateInfo);</div><div class="line"><a name="l01617"></a><span class="lineno"> 1617</span>  std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =</div><div class="line"><a name="l01618"></a><span class="lineno"> 1618</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputStateInfo);</div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span> </div><div class="line"><a name="l01620"></a><span class="lineno"> 1620</span>  std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =</div><div class="line"><a name="l01621"></a><span class="lineno"> 1621</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(cellStateInfo);</div><div class="line"><a name="l01622"></a><span class="lineno"> 1622</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputStateInfo);</div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span> </div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span>  <a class="code" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml">armnn::QuantizedLstmQueueDescriptor</a> data;</div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span> </div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span>  <span class="comment">// Add inputs and outputs to workload</span></div><div class="line"><a name="l01628"></a><span class="lineno"> 1628</span>  AddInputToWorkload(data, info, inputInfo, inputHandle.get());</div><div class="line"><a name="l01629"></a><span class="lineno"> 1629</span>  AddInputToWorkload(data, info, cellStateInfo, cellStateInHandle.get());</div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span>  AddInputToWorkload(data, info, outputStateInfo, outputStateInHandle.get());</div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span> </div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span>  AddOutputToWorkload(data, info, cellStateInfo, cellStateOutHandle.get());</div><div class="line"><a name="l01633"></a><span class="lineno"> 1633</span>  AddOutputToWorkload(data, info, outputStateInfo, outputHandle.get());</div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span> </div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span>  <span class="comment">// Weights and bias tensor and quantization info</span></div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputWeightsInfo({outputSize, inputSize},</div><div class="line"><a name="l01637"></a><span class="lineno"> 1637</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span>  weightsScale,</div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span>  weightsOffset);</div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span> </div><div class="line"><a name="l01641"></a><span class="lineno"> 1641</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> recurrentWeightsInfo({outputSize, outputSize},</div><div class="line"><a name="l01642"></a><span class="lineno"> 1642</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span>  weightsScale,</div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span>  weightsOffset);</div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span> </div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasInfo({outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>, biasScale, biasOffset);</div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span> </div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>  <span class="comment">// Weights and bias tensor data</span></div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span>  <span class="keyword">auto</span> inputToInputWeights = MakeTensor<uint8_t, 2>(inputWeightsInfo, {146, 250, 235, 171, 10, 218, 171, 108});</div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>  <span class="keyword">auto</span> inputToForgetWeights = MakeTensor<uint8_t, 2>(inputWeightsInfo, {24, 50, 132, 179, 158, 110, 3, 169});</div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>  <span class="keyword">auto</span> inputToCellWeights = MakeTensor<uint8_t, 2>(inputWeightsInfo, {133, 34, 29, 49, 206, 109, 54, 183});</div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>  <span class="keyword">auto</span> inputToOutputWeights = MakeTensor<uint8_t, 2>(inputWeightsInfo, {195, 187, 11, 99, 109, 10, 218, 48});</div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span> </div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>  <span class="keyword">auto</span> recurrentToInputWeights = MakeTensor<uint8_t, 2>(recurrentWeightsInfo,</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span>  {254, 206, 77, 168, 71, 20, 215, 6, 223, 7, 118, 225, 59, 130, 174, 26});</div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span>  <span class="keyword">auto</span> recurrentToForgetWeights = MakeTensor<uint8_t, 2>(recurrentWeightsInfo,</div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span>  {137, 240, 103, 52, 68, 51, 237, 112, 0, 220, 89, 23, 69, 4, 207, 253});</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span>  <span class="keyword">auto</span> recurrentToCellWeights = MakeTensor<uint8_t, 2>(recurrentWeightsInfo,</div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span>  {172, 60, 205, 65, 14, 0, 140, 168, 240, 223, 133, 56, 142, 64, 246, 216});</div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span>  <span class="keyword">auto</span> recurrentToOutputWeights = MakeTensor<uint8_t, 2>(recurrentWeightsInfo,</div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span>  {106, 214, 67, 23, 59, 158, 45, 3, 119, 132, 49, 205, 129, 218, 11, 98});</div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span> </div><div class="line"><a name="l01663"></a><span class="lineno"> 1663</span>  <span class="keyword">auto</span> inputGateBias = MakeTensor<int32_t, 1>(biasInfo, {-7876, 13488, -726, 32839});</div><div class="line"><a name="l01664"></a><span class="lineno"> 1664</span>  <span class="keyword">auto</span> forgetGateBias = MakeTensor<int32_t, 1>(biasInfo, {9206, -46884, -11693, -38724});</div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>  <span class="keyword">auto</span> cellBias = MakeTensor<int32_t, 1>(biasInfo, {39481, 48624, 48976, -21419});</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span>  <span class="keyword">auto</span> outputGateBias = MakeTensor<int32_t, 1>(biasInfo, {-58999, -17050, -41852, -40538});</div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span> </div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span>  <span class="comment">// ScopedCpuTensorHandles</span></div><div class="line"><a name="l01669"></a><span class="lineno"> 1669</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToInputWeightsTensor(inputWeightsInfo);</div><div class="line"><a name="l01670"></a><span class="lineno"> 1670</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToForgetWeightsTensor(inputWeightsInfo);</div><div class="line"><a name="l01671"></a><span class="lineno"> 1671</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToCellWeightsTensor(inputWeightsInfo);</div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToOutputWeightsTensor(inputWeightsInfo);</div><div class="line"><a name="l01673"></a><span class="lineno"> 1673</span> </div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToInputWeightsTensor(recurrentWeightsInfo);</div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToForgetWeightsTensor(recurrentWeightsInfo);</div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToCellWeightsTensor(recurrentWeightsInfo);</div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToOutputWeightsTensor(recurrentWeightsInfo);</div><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span> </div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputGateBiasTensor(biasInfo);</div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> forgetGateBiasTensor(biasInfo);</div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellBiasTensor(biasInfo);</div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> outputGateBiasTensor(biasInfo);</div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span> </div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>  <span class="comment">// Allocate and copy data</span></div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToInputWeightsTensor, &inputToInputWeights[0][0]);</div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);</div><div class="line"><a name="l01688"></a><span class="lineno"> 1688</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);</div><div class="line"><a name="l01689"></a><span class="lineno"> 1689</span> </div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToInputWeightsTensor, &recurrentToInputWeights[0][0]);</div><div class="line"><a name="l01691"></a><span class="lineno"> 1691</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);</div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);</div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span> </div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputGateBiasTensor, &inputGateBias[0]);</div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&forgetGateBiasTensor, &forgetGateBias[0]);</div><div class="line"><a name="l01697"></a><span class="lineno"> 1697</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellBiasTensor, &cellBias[0]);</div><div class="line"><a name="l01698"></a><span class="lineno"> 1698</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&outputGateBiasTensor, &outputGateBias[0]);</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span> </div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span>  <span class="comment">// Setup queue descriptor</span></div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span>  data.<a class="code" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a08a1932be591c315a512a877d38b22df">m_InputToInputWeights</a> = &inputToInputWeightsTensor;</div><div class="line"><a name="l01702"></a><span class="lineno"> 1702</span>  data.<a class="code" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a3ea82566d98c5a657c76c3d851c47848">m_InputToForgetWeights</a> = &inputToForgetWeightsTensor;</div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span>  data.<a class="code" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a28ad98d17603fd8b12e046f8ece58970">m_InputToCellWeights</a> = &inputToCellWeightsTensor;</div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span>  data.<a class="code" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a83dc9086b2e4a4e4cadb66bd874df798">m_InputToOutputWeights</a> = &inputToOutputWeightsTensor;</div><div class="line"><a name="l01705"></a><span class="lineno"> 1705</span> </div><div class="line"><a name="l01706"></a><span class="lineno"> 1706</span>  data.<a class="code" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a98d377149071d8842d610cc0734d1cfe">m_RecurrentToInputWeights</a> = &recurrentToInputWeightsTensor;</div><div class="line"><a name="l01707"></a><span class="lineno"> 1707</span>  data.<a class="code" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a45d73e66cbb2b65049e4016c20657ccf">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeightsTensor;</div><div class="line"><a name="l01708"></a><span class="lineno"> 1708</span>  data.<a class="code" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#aea142bd50ffb93631c2e08324ec92a1e">m_RecurrentToCellWeights</a> = &recurrentToCellWeightsTensor;</div><div class="line"><a name="l01709"></a><span class="lineno"> 1709</span>  data.<a class="code" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#adebc1771e5a1f4b113a7aa594ea74d2c">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeightsTensor;</div><div class="line"><a name="l01710"></a><span class="lineno"> 1710</span> </div><div class="line"><a name="l01711"></a><span class="lineno"> 1711</span>  data.<a class="code" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#acb3aade8fae984f7293e222dcbe66030">m_InputGateBias</a> = &inputGateBiasTensor;</div><div class="line"><a name="l01712"></a><span class="lineno"> 1712</span>  data.<a class="code" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#aba3ffe91d818266b8785ce971548eb59">m_ForgetGateBias</a> = &forgetGateBiasTensor;</div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>  data.<a class="code" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a75980b5795efd899a0c678a06a900c6d">m_CellBias</a> = &cellBiasTensor;</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span>  data.<a class="code" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a332551528a4b3534c2d6c89ce816fcd9">m_OutputGateBias</a> = &outputGateBiasTensor;</div><div class="line"><a name="l01715"></a><span class="lineno"> 1715</span> </div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span>  <span class="comment">// Create workload and allocate tensor handles</span></div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#ab5ceda49651dcd53fb7eb05658b5a0cb">CreateQuantizedLstm</a>(data, info);</div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span>  inputHandle->Allocate();</div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>  outputStateInHandle->Allocate();</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>  cellStateInHandle->Allocate();</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span> </div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>  cellStateOutHandle->Allocate();</div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>  outputHandle->Allocate();</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span> </div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &inputTensor[0][0]);</div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(outputStateInHandle.get(), &outputStateInTensor[0][0]);</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(cellStateInHandle.get(), &cellStateInTensor[0][0]);</div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span> </div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>  workload->Execute();</div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span> </div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.output[0][0], outputHandle.get());</div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span> </div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span> }</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span> </div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span> } <span class="comment">// anonymous namespace</span></div><div class="line"><a name="l01737"></a><span class="lineno"> 1737</span> </div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span> <span class="preprocessor">#if defined(ARMNNREF_ENABLED)</span></div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span> </div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span> <span class="comment">// The LSTM test units are run only for the reference backend at the moment</span></div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span> </div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span> <span class="keywordtype">void</span> LstmUtilsZeroVectorTest()</div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span> {</div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01745"></a><span class="lineno"> 1745</span>  boost::multi_array<float, 1> input = MakeTensor<float, 1>(inputDesc, std::vector<float>(</div><div class="line"><a name="l01746"></a><span class="lineno"> 1746</span>  {2., 3., 3., 4.}));</div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span> </div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span>  boost::multi_array<float, 1> expectedOutput = MakeTensor<float, 1>(inputDesc, std::vector<float>(</div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span>  {0., 0., 0., 0.}));</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span> </div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span>  <span class="keywordflow">return</span> LstmUtilsZeroVectorTestImpl<armnn::DataType::Float32>(input, 4, expectedOutput);</div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span> }</div><div class="line"><a name="l01753"></a><span class="lineno"> 1753</span> </div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span> <span class="keywordtype">void</span> LstmUtilsMeanStddevNormalizationNoneZeroInputTest()</div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span> {</div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span>  uint32_t batchSize = 2;</div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span>  uint32_t vecSize = 4;</div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({batchSize, vecSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>  boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span>  { 0.1f, 0.2f, 0.3f, 0.4f, <span class="comment">//batch 0</span></div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>  0.9f, 1.0f, 1.1f, 1.2f })); <span class="comment">//batch 1</span></div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span> </div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>  boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(inputDesc, std::vector<float>(</div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>  { -1.34164071f, -0.447213531f, 0.44721365f, 1.34164071f, <span class="comment">//batch 0</span></div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span>  -1.34163153f, -0.447210163f, 0.447211236f, 1.3416326f })); <span class="comment">//batch 1</span></div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span> </div><div class="line"><a name="l01767"></a><span class="lineno"> 1767</span>  <span class="keywordflow">return</span> LstmUtilsMeanStddevNormalizationTestImpl<armnn::DataType::Float32>(input,</div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span>  vecSize, batchSize, expectedOutput);</div><div class="line"><a name="l01769"></a><span class="lineno"> 1769</span> }</div><div class="line"><a name="l01770"></a><span class="lineno"> 1770</span> </div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span> <span class="keywordtype">void</span> LstmUtilsMeanStddevNormalizationAllZeroInputTest()</div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span> {</div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span>  uint32_t batchSize = 2;</div><div class="line"><a name="l01774"></a><span class="lineno"> 1774</span>  uint32_t vecSize = 4;</div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({batchSize, vecSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>  boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(</div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span>  { 0.0f, 0.0f, 0.0f, 0.0f, <span class="comment">//batch 0</span></div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>  0.0f, 0.0f, 0.0f, 0.0f })); <span class="comment">//batch 1</span></div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span> </div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span>  boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(inputDesc, std::vector<float>(</div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>  { 0.0f, 0.0f, 0.0f, 0.0f, <span class="comment">//batch 0</span></div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>  0.0f, 0.0f, 0.0f, 0.0f })); <span class="comment">//batch 1</span></div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span> </div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span>  <span class="keywordflow">return</span> LstmUtilsMeanStddevNormalizationTestImpl<armnn::DataType::Float32>(input,</div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>  vecSize, batchSize, expectedOutput);</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span> }</div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span> </div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span> <span class="keywordtype">void</span> LstmUtilsMeanStddevNormalizationMixedZeroInputTest()</div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span> {</div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span>  uint32_t batchSize = 2;</div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span>  uint32_t vecSize = 4;</div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({batchSize, vecSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>  boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(</div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span>  { 0.0f, 0.0f, 0.0f, 0.0f, <span class="comment">//batch 0</span></div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span>  0.1f, 0.2f, 0.3f, 0.4f })); <span class="comment">//batch 1</span></div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span> </div><div class="line"><a name="l01797"></a><span class="lineno"> 1797</span>  boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(inputDesc, std::vector<float>(</div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span>  { 0.0f, 0.0f, 0.0f, 0.0f, <span class="comment">//batch 0</span></div><div class="line"><a name="l01799"></a><span class="lineno"> 1799</span>  -1.34164071f, -0.447213531f, 0.44721365f, 1.34164071f })); <span class="comment">//batch 1</span></div><div class="line"><a name="l01800"></a><span class="lineno"> 1800</span> </div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span>  <span class="keywordflow">return</span> LstmUtilsMeanStddevNormalizationTestImpl<armnn::DataType::Float32>(input,</div><div class="line"><a name="l01802"></a><span class="lineno"> 1802</span>  vecSize, batchSize, expectedOutput);</div><div class="line"><a name="l01803"></a><span class="lineno"> 1803</span> }</div><div class="line"><a name="l01804"></a><span class="lineno"> 1804</span> </div><div class="line"><a name="l01805"></a><span class="lineno"> 1805</span> <span class="keywordtype">void</span> LstmUtilsVectorBatchVectorCwiseProductTest()</div><div class="line"><a name="l01806"></a><span class="lineno"> 1806</span> {</div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span>  uint32_t batchSize = 4;</div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span>  uint32_t vecSize = 29;</div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> vecDesc({vecSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span>  boost::multi_array<float, 1> vector = MakeTensor<float, 1>(vecDesc, std::vector<float>(</div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span>  { 1.1f, 2.2f, 3.3f, 4.4f, 5.5f, 6.6f, 7.7f, 8.8f, 9.9f, 10.1f,</div><div class="line"><a name="l01812"></a><span class="lineno"> 1812</span>  11.11f, 12.12f, 13.13f, 14.14f, 15.15f, 16.16f, 17.17f, 18.18f, 19.19f, 20.2f,</div><div class="line"><a name="l01813"></a><span class="lineno"> 1813</span>  21.21f, 22.22f, 23.23f, 24.24f, 25.25f, 26.26f, 27.27f, 28.28f, 0.0f}));</div><div class="line"><a name="l01814"></a><span class="lineno"> 1814</span> </div><div class="line"><a name="l01815"></a><span class="lineno"> 1815</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> batchVecDesc({batchSize, vecSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span>  boost::multi_array<float, 2> batchVector = MakeTensor<float, 2>(batchVecDesc, std::vector<float>(</div><div class="line"><a name="l01817"></a><span class="lineno"> 1817</span>  { <span class="comment">/* batch 0 */</span></div><div class="line"><a name="l01818"></a><span class="lineno"> 1818</span>  1.1f, 2.2f, 3.3f, 4.4f, 5.5f, 6.6f, 7.7f, 8.8f, 9.9f, 10.1f,</div><div class="line"><a name="l01819"></a><span class="lineno"> 1819</span>  11.11f, 12.12f, 13.13f, 14.14f, 15.15f, 16.16f, 17.17f, 18.18f, 19.19f, 20.2f,</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span>  21.21f, 22.22f, 23.23f, 24.24f, 25.25f, 26.26f, 27.27f, 28.28f, 0.0f,</div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>  <span class="comment">/* batch 1 */</span></div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span>  -1.1f, -2.2f, -3.3f, -4.4f, -5.5f, -6.6f, -7.7f, -8.8f, -9.9f, -10.1f,</div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span>  -11.11f, -12.12f, -13.13f, -14.14f, -15.15f, -16.16f, -17.17f, -18.18f, -19.19f, -20.2f,</div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span>  -21.21f, -22.22f, -23.23f, -24.24f, -25.25f, -26.26f, -27.27f, -28.28f, 0.0f,</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>  <span class="comment">/* batch 2 */</span></div><div class="line"><a name="l01826"></a><span class="lineno"> 1826</span>  1.1f, -2.2f, 3.3f, -4.4f, 5.5f, -6.6f, 7.7f, -8.8f, 9.9f, -10.1f,</div><div class="line"><a name="l01827"></a><span class="lineno"> 1827</span>  11.11f, -12.12f, 13.13f, -14.14f, 15.15f, -16.16f, 17.17f, -18.18f, 19.19f, -20.2f,</div><div class="line"><a name="l01828"></a><span class="lineno"> 1828</span>  21.21f, -22.22f, 23.23f, -24.24f, 25.25f, -26.26f, 27.27f, -28.28f, 0.0f,</div><div class="line"><a name="l01829"></a><span class="lineno"> 1829</span>  <span class="comment">/* batch 3 */</span></div><div class="line"><a name="l01830"></a><span class="lineno"> 1830</span>  -1.1f, 2.2f, -3.3f, 4.4f, -5.5f, 6.6f, -7.7f, 8.8f, -9.9f, 10.1f,</div><div class="line"><a name="l01831"></a><span class="lineno"> 1831</span>  -11.11f, 12.12f, -13.13f, 14.14f, -15.15f, 16.16f, -17.17f, 18.18f, -19.19f, 20.2f,</div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</span>  -21.21f, 22.22f, -23.23f, 24.24f, -25.25f, 26.26f, -27.27f, 28.28f, 0.0f}));</div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</span> </div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>  <span class="comment">// Expect output = input * output + output.</span></div><div class="line"><a name="l01835"></a><span class="lineno"> 1835</span>  boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(batchVecDesc, std::vector<float>(</div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>  { <span class="comment">/* batch 0 */</span></div><div class="line"><a name="l01837"></a><span class="lineno"> 1837</span>  1.210000f, 4.840000f, 10.889999f, 19.360001f, 30.250000f, 43.559998f,</div><div class="line"><a name="l01838"></a><span class="lineno"> 1838</span>  59.289997f, 77.440002f, 98.009995f, 102.010010f, 123.432091f, 146.894394f,</div><div class="line"><a name="l01839"></a><span class="lineno"> 1839</span>  172.396896f, 199.939606f, 229.522491f, 261.145599f, 294.808899f, 330.512421f,</div><div class="line"><a name="l01840"></a><span class="lineno"> 1840</span>  368.256134f, 408.040039f, 449.864075f, 493.728363f, 539.632874f, 587.577576f,</div><div class="line"><a name="l01841"></a><span class="lineno"> 1841</span>  637.562500f, 689.587585f, 743.652954f, 799.758423f, 0.000000f,</div><div class="line"><a name="l01842"></a><span class="lineno"> 1842</span>  <span class="comment">/* batch 1 */</span></div><div class="line"><a name="l01843"></a><span class="lineno"> 1843</span>  -1.210000f, -4.840000f, -10.889999f, -19.360001f, -30.250000f, -43.559998f,</div><div class="line"><a name="l01844"></a><span class="lineno"> 1844</span>  -59.289997f, -77.440002f, -98.009995f, -102.010010f, -123.432091f, -146.894394f,</div><div class="line"><a name="l01845"></a><span class="lineno"> 1845</span>  -172.396896f, -199.939606f, -229.522491f, -261.145599f, -294.808899f, -330.512421f,</div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span>  -368.256134f, -408.040039f, -449.864075f, -493.728363f, -539.632874f, -587.577576f,</div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span>  -637.562500f, -689.587585f, -743.652954f, -799.758423f, 0.000000f,</div><div class="line"><a name="l01848"></a><span class="lineno"> 1848</span>  <span class="comment">/* batch 2 */</span></div><div class="line"><a name="l01849"></a><span class="lineno"> 1849</span>  1.210000f, -4.840000f, 10.889999f, -19.360001f, 30.250000f, -43.559998f,</div><div class="line"><a name="l01850"></a><span class="lineno"> 1850</span>  59.289997f, -77.440002f, 98.009995f, -102.010010f, 123.432091f, -146.894394f,</div><div class="line"><a name="l01851"></a><span class="lineno"> 1851</span>  172.396896f, -199.939606f, 229.522491f, -261.145599f, 294.808899f, -330.512421f,</div><div class="line"><a name="l01852"></a><span class="lineno"> 1852</span>  368.256134f, -408.040039f, 449.864075f, -493.728363f, 539.632874f, -587.577576f,</div><div class="line"><a name="l01853"></a><span class="lineno"> 1853</span>  637.562500f, -689.587585f, 743.652954f, -799.758423f, 0.000000f,</div><div class="line"><a name="l01854"></a><span class="lineno"> 1854</span>  <span class="comment">/* batch 3 */</span></div><div class="line"><a name="l01855"></a><span class="lineno"> 1855</span>  -1.210000f, 4.840000f, -10.889999f, 19.360001f, -30.250000f, 43.559998f,</div><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span>  -59.289997f, 77.440002f, -98.009995f, 102.010010f, -123.432091f, 146.894394f,</div><div class="line"><a name="l01857"></a><span class="lineno"> 1857</span>  -172.396896f, 199.939606f, -229.522491f, 261.145599f, -294.808899f, 330.512421f,</div><div class="line"><a name="l01858"></a><span class="lineno"> 1858</span>  -368.256134f, 408.040039f, -449.864075f, 493.728363f, -539.632874f, 587.577576f,</div><div class="line"><a name="l01859"></a><span class="lineno"> 1859</span>  -637.562500f, 689.587585f, -743.652954f, 799.758423f, 0.000000f}));</div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span> </div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span>  <span class="keywordflow">return</span> LstmUtilsVectorBatchVectorCwiseProductTestImpl<armnn::DataType::Float32>(vector, batchVector,</div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span>  vecSize, batchSize, expectedOutput);</div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span> }</div><div class="line"><a name="l01864"></a><span class="lineno"> 1864</span> </div><div class="line"><a name="l01865"></a><span class="lineno"> 1865</span> <span class="keywordtype">void</span> LstmUtilsVectorBatchVectorAddTest()</div><div class="line"><a name="l01866"></a><span class="lineno"> 1866</span> {</div><div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>  uint32_t batchSize = 2;</div><div class="line"><a name="l01868"></a><span class="lineno"> 1868</span>  uint32_t vecSize = 3;</div><div class="line"><a name="l01869"></a><span class="lineno"> 1869</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> vecDesc({vecSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01870"></a><span class="lineno"> 1870</span>  boost::multi_array<float, 1> vector = MakeTensor<float, 1>(vecDesc, std::vector<float>(</div><div class="line"><a name="l01871"></a><span class="lineno"> 1871</span>  { 0.0f, -0.5f, 1.0f}));</div><div class="line"><a name="l01872"></a><span class="lineno"> 1872</span> </div><div class="line"><a name="l01873"></a><span class="lineno"> 1873</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> batchVecDesc({batchSize, vecSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>  boost::multi_array<float, 2> batchVector = MakeTensor<float, 2>(batchVecDesc, std::vector<float>(</div><div class="line"><a name="l01875"></a><span class="lineno"> 1875</span>  { 1.0f, 2.0f, 3.0f, <span class="comment">//batch 0</span></div><div class="line"><a name="l01876"></a><span class="lineno"> 1876</span>  4.0f, 5.0f, 6.0f})); <span class="comment">//batch 1</span></div><div class="line"><a name="l01877"></a><span class="lineno"> 1877</span> </div><div class="line"><a name="l01878"></a><span class="lineno"> 1878</span>  boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(batchVecDesc, std::vector<float>(</div><div class="line"><a name="l01879"></a><span class="lineno"> 1879</span>  { 1.0f, 1.5f, 4.0f,</div><div class="line"><a name="l01880"></a><span class="lineno"> 1880</span>  4.0f, 4.5f, 7.0f}));</div><div class="line"><a name="l01881"></a><span class="lineno"> 1881</span> </div><div class="line"><a name="l01882"></a><span class="lineno"> 1882</span>  <span class="keywordflow">return</span> LstmUtilsVectorBatchVectorAddTestImpl<armnn::DataType::Float32>(vector, batchVector,</div><div class="line"><a name="l01883"></a><span class="lineno"> 1883</span>  vecSize, batchSize, expectedOutput);</div><div class="line"><a name="l01884"></a><span class="lineno"> 1884</span> }</div><div class="line"><a name="l01885"></a><span class="lineno"> 1885</span> </div><div class="line"><a name="l01886"></a><span class="lineno"> 1886</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</span> </div><div class="line"><a name="l01888"></a><span class="lineno"><a class="line" href="_lstm_test_impl_8hpp.xhtml#ae5aae49a1e7c7ff2cb80cba1ae421ea6"> 1888</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 2></a> <a class="code" href="_lstm_test_impl_8cpp.xhtml#ae5aae49a1e7c7ff2cb80cba1ae421ea6">LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest</a>(</div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01890"></a><span class="lineno"> 1890</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span> {</div><div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({ 2, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01893"></a><span class="lineno"> 1893</span>  boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(</div><div class="line"><a name="l01894"></a><span class="lineno"> 1894</span>  { 2., 3., 3., 4. }));</div><div class="line"><a name="l01895"></a><span class="lineno"> 1895</span> </div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({ 2, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01897"></a><span class="lineno"> 1897</span>  boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>(</div><div class="line"><a name="l01898"></a><span class="lineno"> 1898</span>  {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f,</div><div class="line"><a name="l01899"></a><span class="lineno"> 1899</span>  -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f}));</div><div class="line"><a name="l01900"></a><span class="lineno"> 1900</span>  <span class="keywordflow">return</span> LstmLayerWithCifgWithPeepholeNoProjectionTestImpl<armnn::DataType::Float32>(</div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span>  workloadFactory, memoryManager, input, expectedOutput);</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span> }</div><div class="line"><a name="l01903"></a><span class="lineno"> 1903</span> </div><div class="line"><a name="l01904"></a><span class="lineno"><a class="line" href="_lstm_test_impl_8hpp.xhtml#ac53b419b4aa942d611bcc973afdd7716"> 1904</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 2></a> <a class="code" href="_lstm_test_impl_8cpp.xhtml#ac53b419b4aa942d611bcc973afdd7716">LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest</a>(</div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span> {</div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({ 2, 5 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>  boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(</div><div class="line"><a name="l01910"></a><span class="lineno"> 1910</span>  {0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f,</div><div class="line"><a name="l01911"></a><span class="lineno"> 1911</span>  0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f}));</div><div class="line"><a name="l01912"></a><span class="lineno"> 1912</span> </div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({ 2, 16 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01914"></a><span class="lineno"> 1914</span>  boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>(</div><div class="line"><a name="l01915"></a><span class="lineno"> 1915</span>  {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f,</div><div class="line"><a name="l01916"></a><span class="lineno"> 1916</span>  -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f,</div><div class="line"><a name="l01917"></a><span class="lineno"> 1917</span>  -0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f,</div><div class="line"><a name="l01918"></a><span class="lineno"> 1918</span>  0.0134203f, -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f,</div><div class="line"><a name="l01919"></a><span class="lineno"> 1919</span>  -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f,</div><div class="line"><a name="l01920"></a><span class="lineno"> 1920</span>  0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f,</div><div class="line"><a name="l01921"></a><span class="lineno"> 1921</span>  0.02168f}));</div><div class="line"><a name="l01922"></a><span class="lineno"> 1922</span>  <span class="keywordflow">return</span> LstmLayerNoCifgWithPeepholeWithProjectionTestImpl<armnn::DataType::Float32>(</div><div class="line"><a name="l01923"></a><span class="lineno"> 1923</span>  workloadFactory, memoryManager, input, expectedOutput);</div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span> }</div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span> </div><div class="line"><a name="l01926"></a><span class="lineno"><a class="line" href="_lstm_test_impl_8hpp.xhtml#aec44c163ed62de4b5c9bd573cb7f7b3e"> 1926</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 2></a> <a class="code" href="_lstm_test_impl_8cpp.xhtml#aec44c163ed62de4b5c9bd573cb7f7b3e">LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest</a>(</div><div class="line"><a name="l01927"></a><span class="lineno"> 1927</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01928"></a><span class="lineno"> 1928</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span> {</div><div class="line"><a name="l01930"></a><span class="lineno"> 1930</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>  boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(</div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>  {2., 3., 3., 4.}));</div><div class="line"><a name="l01933"></a><span class="lineno"> 1933</span> </div><div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({2, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span>  boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>(</div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span>  {{-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f,</div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>  -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f}}));</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span> </div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>  <span class="keywordflow">return</span> LstmNoCifgNoPeepholeNoProjectionTestImpl<armnn::DataType::Float32>(</div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span>  workloadFactory, memoryManager, input, expectedOutput);</div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</span> }</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span> </div><div class="line"><a name="l01943"></a><span class="lineno"><a class="line" href="_lstm_test_impl_8hpp.xhtml#a9901861134e17a59931e45a4137ddb39"> 1943</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 2></a> <a class="code" href="_lstm_test_impl_8cpp.xhtml#a9901861134e17a59931e45a4137ddb39">LstmLayerFloat32NoCifgWithPeepholeWithProjectionWithLayerNormTest</a>(</div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span> {</div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({ 2, 5 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span>  boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(</div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span>  {0.7f, 0.8f, 0.1f, 0.2f, 0.3f, <span class="comment">//batch 0</span></div><div class="line"><a name="l01950"></a><span class="lineno"> 1950</span>  0.3f, 0.2f, 0.9f, 0.8f, 0.1f})); <span class="comment">//batch 1</span></div><div class="line"><a name="l01951"></a><span class="lineno"> 1951</span> </div><div class="line"><a name="l01952"></a><span class="lineno"> 1952</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({ 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span>  boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>(</div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>  { 0.0244077f, 0.128027f, -0.00170918f, <span class="comment">//batch 0</span></div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>  -0.00692428f, 0.0848741f, 0.063445f})); <span class="comment">//batch 1</span></div><div class="line"><a name="l01956"></a><span class="lineno"> 1956</span>  <span class="keywordflow">return</span> LstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTestImpl<armnn::DataType::Float32>(</div><div class="line"><a name="l01957"></a><span class="lineno"> 1957</span>  workloadFactory, memoryManager, input, expectedOutput);</div><div class="line"><a name="l01958"></a><span class="lineno"> 1958</span> }</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span> </div><div class="line"><a name="l01960"></a><span class="lineno"><a class="line" href="_lstm_test_impl_8hpp.xhtml#a52b75282b3f67fd2e1063eb93ad28441"> 1960</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 2></a> <a class="code" href="_lstm_test_impl_8cpp.xhtml#a52b75282b3f67fd2e1063eb93ad28441">LstmLayerInt16NoCifgNoPeepholeNoProjectionTest</a>(</div><div class="line"><a name="l01961"></a><span class="lineno"> 1961</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span> {</div><div class="line"><a name="l01964"></a><span class="lineno"> 1964</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> qScale = 1.0f;</div><div class="line"><a name="l01965"></a><span class="lineno"> 1965</span>  <span class="keyword">const</span> int32_t qOffset = 0;</div><div class="line"><a name="l01966"></a><span class="lineno"> 1966</span> </div><div class="line"><a name="l01967"></a><span class="lineno"> 1967</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> datatype = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>;</div><div class="line"><a name="l01968"></a><span class="lineno"> 1968</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> constantDatatype = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>;</div><div class="line"><a name="l01969"></a><span class="lineno"> 1969</span> </div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({2, 2}, datatype);</div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>  boost::multi_array<int16_t , 2> input = MakeTensor<int16_t , 2>(</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>  inputDesc,</div><div class="line"><a name="l01973"></a><span class="lineno"> 1973</span>  armnnUtils::QuantizedVector<int16_t>({ 2.f, 3.f, 3.f, 4.f }, qScale, qOffset));</div><div class="line"><a name="l01974"></a><span class="lineno"> 1974</span> </div><div class="line"><a name="l01975"></a><span class="lineno"> 1975</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({2, 4}, datatype);</div><div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>  boost::multi_array<int16_t, 2> expectedOutput = MakeTensor<int16_t, 2>(</div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>  outputDesc,</div><div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>  armnnUtils::QuantizedVector<int16_t>(</div><div class="line"><a name="l01979"></a><span class="lineno"> 1979</span>  {</div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>  -0.02973187f, 0.12294730f, 0.20885126f, -0.15358765f,</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>  -0.01854220f, 0.11281417f, 0.24466537f, -0.18262920f</div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>  },</div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>  qScale, qOffset));</div><div class="line"><a name="l01984"></a><span class="lineno"> 1984</span> </div><div class="line"><a name="l01985"></a><span class="lineno"> 1985</span>  <span class="keywordflow">return</span> LstmNoCifgNoPeepholeNoProjectionTestImpl<datatype>(</div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</span>  workloadFactory, memoryManager, input, expectedOutput, qScale, qOffset, constantDatatype);</div><div class="line"><a name="l01987"></a><span class="lineno"> 1987</span> </div><div class="line"><a name="l01988"></a><span class="lineno"> 1988</span> }</div><div class="line"><a name="l01989"></a><span class="lineno"> 1989</span> </div><div class="line"><a name="l01990"></a><span class="lineno"><a class="line" href="_lstm_test_impl_8hpp.xhtml#a99aca3dec6d8a2eedfc40570fc34ecfb"> 1990</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 2></a> <a class="code" href="_lstm_test_impl_8cpp.xhtml#a99aca3dec6d8a2eedfc40570fc34ecfb">LstmLayerInt16WithCifgWithPeepholeNoProjectionTest</a>(</div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01993"></a><span class="lineno"> 1993</span> {</div><div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> qScale = 1.0f;</div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>  <span class="keyword">const</span> int32_t qOffset = 0;</div><div class="line"><a name="l01996"></a><span class="lineno"> 1996</span> </div><div class="line"><a name="l01997"></a><span class="lineno"> 1997</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> datatype = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>;</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> constantDatatype = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>;</div><div class="line"><a name="l01999"></a><span class="lineno"> 1999</span> </div><div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({ 2, 2 }, datatype);</div><div class="line"><a name="l02001"></a><span class="lineno"> 2001</span>  boost::multi_array<int16_t, 2> input =</div><div class="line"><a name="l02002"></a><span class="lineno"> 2002</span>  MakeTensor<int16_t, 2>(</div><div class="line"><a name="l02003"></a><span class="lineno"> 2003</span>  inputDesc,</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>  armnnUtils::QuantizedVector<int16_t>({ 2.f, 3.f, 3.f, 4.f }, qScale, qOffset));</div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span> </div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({ 2, 4 }, datatype);</div><div class="line"><a name="l02007"></a><span class="lineno"> 2007</span>  boost::multi_array<int16_t, 2> expectedOutput =</div><div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>  MakeTensor<int16_t, 2>(</div><div class="line"><a name="l02009"></a><span class="lineno"> 2009</span>  outputDesc,</div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>  armnnUtils::QuantizedVector<int16_t>(</div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>  {</div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span>  -0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f,</div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>  -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f</div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>  },</div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span>  qScale, qOffset));</div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span> </div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span>  <span class="keywordflow">return</span> LstmLayerWithCifgWithPeepholeNoProjectionTestImpl<datatype>(</div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>  workloadFactory, memoryManager, input, expectedOutput, qScale, qOffset, constantDatatype);</div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span> }</div><div class="line"><a name="l02020"></a><span class="lineno"> 2020</span> </div><div class="line"><a name="l02021"></a><span class="lineno"><a class="line" href="_lstm_test_impl_8hpp.xhtml#acbc18f78670dccdd94e0cbb04627b9aa"> 2021</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 2></a> <a class="code" href="_lstm_test_impl_8cpp.xhtml#acbc18f78670dccdd94e0cbb04627b9aa">LstmLayerInt16NoCifgWithPeepholeWithProjectionTest</a>(</div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02023"></a><span class="lineno"> 2023</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l02024"></a><span class="lineno"> 2024</span> {</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> qScale = 2.0f;</div><div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>  <span class="keyword">const</span> int32_t qOffset = 0;</div><div class="line"><a name="l02027"></a><span class="lineno"> 2027</span> </div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> datatype = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>;</div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> constantDatatype = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>;</div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span> </div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({ 2, 5 }, datatype);</div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span>  boost::multi_array<int16_t, 2> input =</div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span>  MakeTensor<int16_t, 2>(</div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span>  inputDesc,</div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>  armnnUtils::QuantizedVector<int16_t>(</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span>  {</div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>  0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f,</div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span>  0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f</div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span>  },</div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>  qScale, qOffset));</div><div class="line"><a name="l02041"></a><span class="lineno"> 2041</span> </div><div class="line"><a name="l02042"></a><span class="lineno"> 2042</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({ 2, 16 }, datatype);</div><div class="line"><a name="l02043"></a><span class="lineno"> 2043</span>  boost::multi_array<int16_t, 2> expectedOutput =</div><div class="line"><a name="l02044"></a><span class="lineno"> 2044</span>  MakeTensor<int16_t, 2>(</div><div class="line"><a name="l02045"></a><span class="lineno"> 2045</span>  outputDesc,</div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span>  armnnUtils::QuantizedVector<int16_t>(</div><div class="line"><a name="l02047"></a><span class="lineno"> 2047</span>  {</div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span>  -0.00396806f, 0.02935200f, -0.00279226f, 0.01599770f,</div><div class="line"><a name="l02049"></a><span class="lineno"> 2049</span>  -0.00835576f, -0.02117790f, 0.02835120f, -0.01145970f,</div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span>  0.00907307f, -0.02440040f, -0.01521910f, -0.02590630f,</div><div class="line"><a name="l02051"></a><span class="lineno"> 2051</span>  0.00914318f, 0.00415118f, 0.01714700f, 0.01342030f,</div><div class="line"><a name="l02052"></a><span class="lineno"> 2052</span>  -0.01386900f, 0.02872680f, -0.00334693f, 0.00733398f,</div><div class="line"><a name="l02053"></a><span class="lineno"> 2053</span>  -0.02879260f, -0.01869260f, 0.01936620f, -0.01154370f,</div><div class="line"><a name="l02054"></a><span class="lineno"> 2054</span>  0.00422612f, -0.03452320f, 0.00223253f, -0.00957321f,</div><div class="line"><a name="l02055"></a><span class="lineno"> 2055</span>  0.02106240f, 0.01333100f, 0.01509540f, 0.02168000f</div><div class="line"><a name="l02056"></a><span class="lineno"> 2056</span>  },</div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</span>  qScale, qOffset));</div><div class="line"><a name="l02058"></a><span class="lineno"> 2058</span> </div><div class="line"><a name="l02059"></a><span class="lineno"> 2059</span>  <span class="keywordflow">return</span> LstmLayerNoCifgWithPeepholeWithProjectionTestImpl<datatype>(</div><div class="line"><a name="l02060"></a><span class="lineno"> 2060</span>  workloadFactory, memoryManager, input, expectedOutput, qScale, qOffset, constantDatatype);</div><div class="line"><a name="l02061"></a><span class="lineno"> 2061</span> }</div><div class="line"><a name="l02062"></a><span class="lineno"> 2062</span> </div><div class="line"><a name="l02063"></a><span class="lineno"><a class="line" href="_lstm_test_impl_8hpp.xhtml#a9c20ecb007b2beacc8c4fa05778eb450"> 2063</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 2></a> <a class="code" href="_lstm_test_impl_8cpp.xhtml#a9c20ecb007b2beacc8c4fa05778eb450">LstmLayerInt16NoCifgNoPeepholeNoProjectionInt16ConstantTest</a>(</div><div class="line"><a name="l02064"></a><span class="lineno"> 2064</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02065"></a><span class="lineno"> 2065</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l02066"></a><span class="lineno"> 2066</span> {</div><div class="line"><a name="l02067"></a><span class="lineno"> 2067</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> qScale = 1.0f;</div><div class="line"><a name="l02068"></a><span class="lineno"> 2068</span>  <span class="keyword">const</span> int32_t qOffset = 0;</div><div class="line"><a name="l02069"></a><span class="lineno"> 2069</span> </div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> datatype = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>; <span class="comment">// datatype & constants set to QSymm16</span></div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span> </div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({2, 2}, datatype);</div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span>  boost::multi_array<int16_t , 2> input =</div><div class="line"><a name="l02074"></a><span class="lineno"> 2074</span>  MakeTensor<int16_t , 2>(inputDesc,</div><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span>  armnnUtils::QuantizedVector<int16_t>({ 2.f, 3.f, 3.f, 4.f }, qScale, qOffset));</div><div class="line"><a name="l02076"></a><span class="lineno"> 2076</span> </div><div class="line"><a name="l02077"></a><span class="lineno"> 2077</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({2, 4}, datatype);</div><div class="line"><a name="l02078"></a><span class="lineno"> 2078</span>  boost::multi_array<int16_t, 2> expectedOutput =</div><div class="line"><a name="l02079"></a><span class="lineno"> 2079</span>  MakeTensor<int16_t, 2>(</div><div class="line"><a name="l02080"></a><span class="lineno"> 2080</span>  outputDesc,</div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>  armnnUtils::QuantizedVector<int16_t>(</div><div class="line"><a name="l02082"></a><span class="lineno"> 2082</span>  {</div><div class="line"><a name="l02083"></a><span class="lineno"> 2083</span>  -0.02973187f, 0.12294730f, 0.20885126f, -0.15358765f,</div><div class="line"><a name="l02084"></a><span class="lineno"> 2084</span>  -0.01854220f, 0.11281417f, 0.24466537f, -0.18262920f</div><div class="line"><a name="l02085"></a><span class="lineno"> 2085</span>  },</div><div class="line"><a name="l02086"></a><span class="lineno"> 2086</span>  qScale, qOffset));</div><div class="line"><a name="l02087"></a><span class="lineno"> 2087</span> </div><div class="line"><a name="l02088"></a><span class="lineno"> 2088</span>  <span class="keywordflow">return</span> LstmNoCifgNoPeepholeNoProjectionTestImpl<datatype>(</div><div class="line"><a name="l02089"></a><span class="lineno"> 2089</span>  workloadFactory, memoryManager, input, expectedOutput, qScale, qOffset, datatype);</div><div class="line"><a name="l02090"></a><span class="lineno"> 2090</span> }</div><div class="line"><a name="l02091"></a><span class="lineno"> 2091</span> </div><div class="line"><a name="l02092"></a><span class="lineno"> 2092</span> <span class="comment">//</span></div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</span> <span class="comment">// QuantizedLstm</span></div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span> <span class="comment">//</span></div><div class="line"><a name="l02095"></a><span class="lineno"> 2095</span> </div><div class="line"><a name="l02096"></a><span class="lineno"><a class="line" href="_lstm_test_impl_8hpp.xhtml#a03b7b4da7af98cb4fa9bbecb785ce5dd"> 2096</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 2></a> <a class="code" href="_lstm_test_impl_8cpp.xhtml#a03b7b4da7af98cb4fa9bbecb785ce5dd">QuantizedLstmTest</a>(</div><div class="line"><a name="l02097"></a><span class="lineno"> 2097</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02098"></a><span class="lineno"> 2098</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l02099"></a><span class="lineno"> 2099</span> {</div><div class="line"><a name="l02100"></a><span class="lineno"> 2100</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>);</div><div class="line"><a name="l02101"></a><span class="lineno"> 2101</span>  boost::multi_array<uint8_t, 2> input = MakeTensor<uint8_t, 2>(inputDesc, std::vector<uint8_t>(</div><div class="line"><a name="l02102"></a><span class="lineno"> 2102</span>  {166, 179, 50, 150}));</div><div class="line"><a name="l02103"></a><span class="lineno"> 2103</span> </div><div class="line"><a name="l02104"></a><span class="lineno"> 2104</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({2, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>);</div><div class="line"><a name="l02105"></a><span class="lineno"> 2105</span>  boost::multi_array<uint8_t, 2> expectedOutput = MakeTensor<uint8_t, 2>(outputDesc, std::vector<uint8_t>(</div><div class="line"><a name="l02106"></a><span class="lineno"> 2106</span>  {140, 151, 146, 112, 136, 156, 142, 112 }));</div><div class="line"><a name="l02107"></a><span class="lineno"> 2107</span> </div><div class="line"><a name="l02108"></a><span class="lineno"> 2108</span>  <span class="keywordflow">return</span> QuantizedLstmTestImpl(workloadFactory, memoryManager, input, expectedOutput);</div><div class="line"><a name="l02109"></a><span class="lineno"> 2109</span> }</div><div class="ttc" id="_lstm_utils_8cpp_xhtml_a0ed27dd6d6125a06bf654080f4184360"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a></div><div class="ttdeci">void MeanStddevNormalization(armnn::Decoder< float > &input_vector, armnn::Encoder< float > &output_vector, uint32_t v_size, uint32_t n_batch, float normalization_epsilon)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00040">LstmUtils.cpp:40</a></div></div> +<div class="ttc" id="_lstm_utils_8cpp_xhtml_a389c4bbafd0fff7060cbb183f20a2134"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a></div><div class="ttdeci">void VectorBatchVectorAdd(armnn::Decoder< float > &vector, uint32_t vSize, armnn::Decoder< float > &batchVector, uint32_t nBatch, armnn::Encoder< float > &outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00016">LstmUtils.cpp:16</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a6c9de81fc65b3c4924cab11907075a17"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">armnn::LstmDescriptor::m_ProjectionEnabled</a></div><div class="ttdeci">bool m_ProjectionEnabled</div><div class="ttdoc">Enable/disable the projection layer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00871">Descriptors.hpp:871</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_queue_descriptor_xhtml_a45d73e66cbb2b65049e4016c20657ccf"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a45d73e66cbb2b65049e4016c20657ccf">armnn::QuantizedLstmQueueDescriptor::m_RecurrentToForgetWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_RecurrentToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00537">WorkloadData.hpp:537</a></div></div> +<div class="ttc" id="_tensor_copy_utils_8hpp_xhtml"><div class="ttname"><a href="_tensor_copy_utils_8hpp.xhtml">TensorCopyUtils.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_queue_descriptor_xhtml_acb3aade8fae984f7293e222dcbe66030"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#acb3aade8fae984f7293e222dcbe66030">armnn::QuantizedLstmQueueDescriptor::m_InputGateBias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_InputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00541">WorkloadData.hpp:541</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_a45d73e66cbb2b65049e4016c20657ccf"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a45d73e66cbb2b65049e4016c20657ccf">armnn::LstmQueueDescriptor::m_RecurrentToForgetWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_RecurrentToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00390">WorkloadData.hpp:390</a></div></div> +<div class="ttc" id="_lstm_test_impl_8cpp_xhtml_acbc18f78670dccdd94e0cbb04627b9aa"><div class="ttname"><a href="_lstm_test_impl_8cpp.xhtml#acbc18f78670dccdd94e0cbb04627b9aa">LstmLayerInt16NoCifgWithPeepholeWithProjectionTest</a></div><div class="ttdeci">LayerTestResult< int16_t, 2 > LstmLayerInt16NoCifgWithPeepholeWithProjectionTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_test_impl_8cpp_source.xhtml#l02021">LstmTestImpl.cpp:2021</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml">armnn::QuantizedLstmQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00512">WorkloadData.hpp:512</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_a6f74071b0e07bbe2cb20a8f78826e084"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a6f74071b0e07bbe2cb20a8f78826e084">armnn::LstmQueueDescriptor::m_CellToOutputWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_CellToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00395">WorkloadData.hpp:395</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_a28ad98d17603fd8b12e046f8ece58970"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a28ad98d17603fd8b12e046f8ece58970">armnn::LstmQueueDescriptor::m_InputToCellWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_InputToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00387">WorkloadData.hpp:387</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_a83dc9086b2e4a4e4cadb66bd874df798"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a83dc9086b2e4a4e4cadb66bd874df798">armnn::LstmQueueDescriptor::m_InputToOutputWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_InputToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00388">WorkloadData.hpp:388</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div> +<div class="ttc" id="_quantize_helper_8hpp_xhtml"><div class="ttname"><a href="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8hpp_source.xhtml#l00021">WorkloadFactory.hpp:21</a></div></div> +<div class="ttc" id="_workload_test_utils_8hpp_xhtml"><div class="ttname"><a href="_workload_test_utils_8hpp.xhtml">WorkloadTestUtils.hpp</a></div></div> +<div class="ttc" id="_lstm_test_impl_8cpp_xhtml_ac53b419b4aa942d611bcc973afdd7716"><div class="ttname"><a href="_lstm_test_impl_8cpp.xhtml#ac53b419b4aa942d611bcc973afdd7716">LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest</a></div><div class="ttdeci">LayerTestResult< float, 2 > LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_test_impl_8cpp_source.xhtml#l01904">LstmTestImpl.cpp:1904</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_queue_descriptor_xhtml_a28ad98d17603fd8b12e046f8ece58970"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a28ad98d17603fd8b12e046f8ece58970">armnn::QuantizedLstmQueueDescriptor::m_InputToCellWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_InputToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00533">WorkloadData.hpp:533</a></div></div> +<div class="ttc" id="_tensor_helpers_8hpp_xhtml_a0b8fbb443d2cf34a41f6aaae934e3dcb"><div class="ttname"><a href="_tensor_helpers_8hpp.xhtml#a0b8fbb443d2cf34a41f6aaae934e3dcb">CompareTensors</a></div><div class="ttdeci">boost::test_tools::predicate_result CompareTensors(const boost::multi_array< T, n > &a, const boost::multi_array< T, n > &b, bool compareBoolean=false)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_helpers_8hpp_source.xhtml#l00075">TensorHelpers.hpp:75</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_a2ba352eb1fdf6dc5ecf7f2e6b6b48f94"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a2ba352eb1fdf6dc5ecf7f2e6b6b48f94">armnn::LstmQueueDescriptor::m_ProjectionBias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_ProjectionBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00401">WorkloadData.hpp:401</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_ab6bd7aaf685d4e956d780f8655a6f174"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#ab6bd7aaf685d4e956d780f8655a6f174">armnn::IWorkloadFactory::CreateLstm</a></div><div class="ttdeci">virtual std::unique_ptr< IWorkload > CreateLstm(const LstmQueueDescriptor &descriptor, const WorkloadInfo &info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01262">WorkloadFactory.cpp:1262</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_a332551528a4b3534c2d6c89ce816fcd9"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a332551528a4b3534c2d6c89ce816fcd9">armnn::LstmQueueDescriptor::m_OutputGateBias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_OutputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00399">WorkloadData.hpp:399</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_ab5ceda49651dcd53fb7eb05658b5a0cb"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#ab5ceda49651dcd53fb7eb05658b5a0cb">armnn::IWorkloadFactory::CreateQuantizedLstm</a></div><div class="ttdeci">virtual std::unique_ptr< IWorkload > CreateQuantizedLstm(const QuantizedLstmQueueDescriptor &descriptor, const WorkloadInfo &info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01364">WorkloadFactory.cpp:1364</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_a5c1c0a7ead7273788976c9e97cffaab7"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a5c1c0a7ead7273788976c9e97cffaab7">armnn::LstmQueueDescriptor::m_CellToInputWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_CellToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00393">WorkloadData.hpp:393</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_a518f0195d0278a892b49649b8860d17f"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a518f0195d0278a892b49649b8860d17f">armnn::LstmQueueDescriptor::m_CellLayerNormWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_CellLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00404">WorkloadData.hpp:404</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div></div> +<div class="ttc" id="_lstm_utils_8cpp_xhtml_a4c20bc573b70e89327b334f924da97b5"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a></div><div class="ttdeci">void ZeroVector(armnn::Encoder< float > &vector, uint32_t vSize)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00076">LstmUtils.cpp:76</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &&...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_acefa49d7faf26933e27e473e7bdb4175"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#acefa49d7faf26933e27e473e7bdb4175">armnn::LstmQueueDescriptor::m_CellToForgetWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_CellToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00394">WorkloadData.hpp:394</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_a75980b5795efd899a0c678a06a900c6d"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a75980b5795efd899a0c678a06a900c6d">armnn::LstmQueueDescriptor::m_CellBias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_CellBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00398">WorkloadData.hpp:398</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_a98d377149071d8842d610cc0734d1cfe"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a98d377149071d8842d610cc0734d1cfe">armnn::LstmQueueDescriptor::m_RecurrentToInputWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_RecurrentToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00389">WorkloadData.hpp:389</a></div></div> +<div class="ttc" id="structarmnn_1_1_queue_descriptor_with_parameters_xhtml_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00049">WorkloadData.hpp:49</a></div></div> +<div class="ttc" id="_lstm_utils_8cpp_xhtml_a1d7ad9698b02282a57fdb17b3af745f9"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a></div><div class="ttdeci">void VectorBatchVectorCwiseProduct(armnn::Decoder< float > &vector, uint32_t vSize, armnn::Decoder< float > &batchVector, uint32_t nBatch, armnn::Encoder< float > &outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00152">LstmUtils.cpp:152</a></div></div> +<div class="ttc" id="_encoders_8hpp_xhtml"><div class="ttname"><a href="_encoders_8hpp.xhtml">Encoders.hpp</a></div></div> +<div class="ttc" id="_tensor_helpers_8hpp_xhtml"><div class="ttname"><a href="_tensor_helpers_8hpp.xhtml">TensorHelpers.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_a9cc28aa4fff6ba9a8abdb340c1abdd57"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a9cc28aa4fff6ba9a8abdb340c1abdd57">armnn::LstmQueueDescriptor::m_InputLayerNormWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_InputLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00402">WorkloadData.hpp:402</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_aa3f07e27230d6d99adc2c82ba681df2b"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#aa3f07e27230d6d99adc2c82ba681df2b">armnn::LstmQueueDescriptor::m_OutputLayerNormWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_OutputLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00405">WorkloadData.hpp:405</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00032">Types.hpp:32</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml">armnn::LstmQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00358">WorkloadData.hpp:358</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_aba3ffe91d818266b8785ce971548eb59"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#aba3ffe91d818266b8785ce971548eb59">armnn::LstmQueueDescriptor::m_ForgetGateBias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_ForgetGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00397">WorkloadData.hpp:397</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_backend_internal_xhtml_a693b40e6b94e958836aeb0410ca186bd"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a></div><div class="ttdeci">std::shared_ptr< IMemoryManager > IMemoryManagerSharedPtr</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_backend_internal_8hpp_source.xhtml#l00090">IBackendInternal.hpp:90</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_queue_descriptor_xhtml_aba3ffe91d818266b8785ce971548eb59"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#aba3ffe91d818266b8785ce971548eb59">armnn::QuantizedLstmQueueDescriptor::m_ForgetGateBias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_ForgetGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00542">WorkloadData.hpp:542</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_a08a1932be591c315a512a877d38b22df"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a08a1932be591c315a512a877d38b22df">armnn::LstmQueueDescriptor::m_InputToInputWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_InputToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00385">WorkloadData.hpp:385</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a2837b4396f20c956952d1a7286cab5f8"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">armnn::LstmDescriptor::m_PeepholeEnabled</a></div><div class="ttdeci">bool m_PeepholeEnabled</div><div class="ttdoc">Enable/disable peephole. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00869">Descriptors.hpp:869</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_queue_descriptor_xhtml_a98d377149071d8842d610cc0734d1cfe"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a98d377149071d8842d610cc0734d1cfe">armnn::QuantizedLstmQueueDescriptor::m_RecurrentToInputWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_RecurrentToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00536">WorkloadData.hpp:536</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a37fa39012e90d568df7f774cd6d1e956"><div class="ttname"><a href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t< std::is_unsigned< Source >::value &&std::is_unsigned< Dest >::value, Dest > numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00033">NumericCast.hpp:33</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_queue_descriptor_xhtml_aea142bd50ffb93631c2e08324ec92a1e"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#aea142bd50ffb93631c2e08324ec92a1e">armnn::QuantizedLstmQueueDescriptor::m_RecurrentToCellWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_RecurrentToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00538">WorkloadData.hpp:538</a></div></div> +<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_afaaca8c3f3a467d124bba44067d2afa8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a></div><div class="ttdeci">void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00019">TensorCopyUtils.cpp:19</a></div></div> +<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_a99b626c58a926dc7d6df78d22ec186c8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00014">TensorCopyUtils.cpp:14</a></div></div> +<div class="ttc" id="_lstm_test_impl_8cpp_xhtml_a99aca3dec6d8a2eedfc40570fc34ecfb"><div class="ttname"><a href="_lstm_test_impl_8cpp.xhtml#a99aca3dec6d8a2eedfc40570fc34ecfb">LstmLayerInt16WithCifgWithPeepholeNoProjectionTest</a></div><div class="ttdeci">LayerTestResult< int16_t, 2 > LstmLayerInt16WithCifgWithPeepholeNoProjectionTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_test_impl_8cpp_source.xhtml#l01990">LstmTestImpl.cpp:1990</a></div></div> +<div class="ttc" id="_decoders_8hpp_xhtml"><div class="ttname"><a href="_decoders_8hpp.xhtml">Decoders.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_queue_descriptor_xhtml_adebc1771e5a1f4b113a7aa594ea74d2c"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#adebc1771e5a1f4b113a7aa594ea74d2c">armnn::QuantizedLstmQueueDescriptor::m_RecurrentToOutputWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_RecurrentToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00539">WorkloadData.hpp:539</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_ae1b07ed928036004bd257169e5aeeef4"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">armnn::LstmDescriptor::m_ActivationFunc</a></div><div class="ttdeci">uint32_t m_ActivationFunc</div><div class="ttdoc">The activation function to use. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00861">Descriptors.hpp:861</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a15c140be4ddceffee16436f009d3ed94"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">armnn::IWorkloadFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr< ITensorHandle > CreateTensorHandle(const TensorInfo &tensorInfo, const bool IsMemoryManaged=true) const =0</div></div> +<div class="ttc" id="_cpu_tensor_handle_8hpp_xhtml"><div class="ttname"><a href="_cpu_tensor_handle_8hpp.xhtml">CpuTensorHandle.hpp</a></div></div> +<div class="ttc" id="_lstm_test_impl_8cpp_xhtml_ae5aae49a1e7c7ff2cb80cba1ae421ea6"><div class="ttname"><a href="_lstm_test_impl_8cpp.xhtml#ae5aae49a1e7c7ff2cb80cba1ae421ea6">LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest</a></div><div class="ttdeci">LayerTestResult< float, 2 > LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_test_impl_8cpp_source.xhtml#l01888">LstmTestImpl.cpp:1888</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_ad474e5c51a0b194ef32e812b86c0cbdb"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">armnn::LstmDescriptor::m_CifgEnabled</a></div><div class="ttdeci">bool m_CifgEnabled</div><div class="ttdoc">Enable/disable cifg (coupled input & forget gate). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00867">Descriptors.hpp:867</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_adebc1771e5a1f4b113a7aa594ea74d2c"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#adebc1771e5a1f4b113a7aa594ea74d2c">armnn::LstmQueueDescriptor::m_RecurrentToOutputWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_RecurrentToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00392">WorkloadData.hpp:392</a></div></div> +<div class="ttc" id="_lstm_test_impl_8hpp_xhtml"><div class="ttname"><a href="_lstm_test_impl_8hpp.xhtml">LstmTestImpl.hpp</a></div></div> +<div class="ttc" id="_lstm_test_impl_8cpp_xhtml_a52b75282b3f67fd2e1063eb93ad28441"><div class="ttname"><a href="_lstm_test_impl_8cpp.xhtml#a52b75282b3f67fd2e1063eb93ad28441">LstmLayerInt16NoCifgNoPeepholeNoProjectionTest</a></div><div class="ttdeci">LayerTestResult< int16_t, 2 > LstmLayerInt16NoCifgNoPeepholeNoProjectionTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_test_impl_8cpp_source.xhtml#l01960">LstmTestImpl.cpp:1960</a></div></div> +<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div> +<div class="ttc" id="_lstm_test_impl_8cpp_xhtml_a9c20ecb007b2beacc8c4fa05778eb450"><div class="ttname"><a href="_lstm_test_impl_8cpp.xhtml#a9c20ecb007b2beacc8c4fa05778eb450">LstmLayerInt16NoCifgNoPeepholeNoProjectionInt16ConstantTest</a></div><div class="ttdeci">LayerTestResult< int16_t, 2 > LstmLayerInt16NoCifgNoPeepholeNoProjectionInt16ConstantTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_test_impl_8cpp_source.xhtml#l02063">LstmTestImpl.cpp:2063</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_a453a4af385d0c060c9aac990fceaa1ef"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a453a4af385d0c060c9aac990fceaa1ef">armnn::LstmQueueDescriptor::m_ForgetLayerNormWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_ForgetLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00403">WorkloadData.hpp:403</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_queue_descriptor_xhtml_a75980b5795efd899a0c678a06a900c6d"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a75980b5795efd899a0c678a06a900c6d">armnn::QuantizedLstmQueueDescriptor::m_CellBias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_CellBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00543">WorkloadData.hpp:543</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div> +<div class="ttc" id="_lstm_test_impl_8cpp_xhtml_a03b7b4da7af98cb4fa9bbecb785ce5dd"><div class="ttname"><a href="_lstm_test_impl_8cpp.xhtml#a03b7b4da7af98cb4fa9bbecb785ce5dd">QuantizedLstmTest</a></div><div class="ttdeci">LayerTestResult< uint8_t, 2 > QuantizedLstmTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_test_impl_8cpp_source.xhtml#l02096">LstmTestImpl.cpp:2096</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_queue_descriptor_xhtml_a332551528a4b3534c2d6c89ce816fcd9"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a332551528a4b3534c2d6c89ce816fcd9">armnn::QuantizedLstmQueueDescriptor::m_OutputGateBias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_OutputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00544">WorkloadData.hpp:544</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_acb3aade8fae984f7293e222dcbe66030"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#acb3aade8fae984f7293e222dcbe66030">armnn::LstmQueueDescriptor::m_InputGateBias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_InputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00396">WorkloadData.hpp:396</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a4a8ec49f130084445d44297549254780"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">armnn::LstmDescriptor::m_LayerNormEnabled</a></div><div class="ttdeci">bool m_LayerNormEnabled</div><div class="ttdoc">Enable/disable layer normalization. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00873">Descriptors.hpp:873</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div> +<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_aea142bd50ffb93631c2e08324ec92a1e"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#aea142bd50ffb93631c2e08324ec92a1e">armnn::LstmQueueDescriptor::m_RecurrentToCellWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_RecurrentToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00391">WorkloadData.hpp:391</a></div></div> +<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_queue_descriptor_xhtml_a3ea82566d98c5a657c76c3d851c47848"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a3ea82566d98c5a657c76c3d851c47848">armnn::QuantizedLstmQueueDescriptor::m_InputToForgetWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_InputToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00532">WorkloadData.hpp:532</a></div></div> +<div class="ttc" id="_lstm_test_impl_8cpp_xhtml_aec44c163ed62de4b5c9bd573cb7f7b3e"><div class="ttname"><a href="_lstm_test_impl_8cpp.xhtml#aec44c163ed62de4b5c9bd573cb7f7b3e">LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest</a></div><div class="ttdeci">LayerTestResult< float, 2 > LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_test_impl_8cpp_source.xhtml#l01926">LstmTestImpl.cpp:1926</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_queue_descriptor_xhtml_a83dc9086b2e4a4e4cadb66bd874df798"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a83dc9086b2e4a4e4cadb66bd874df798">armnn::QuantizedLstmQueueDescriptor::m_InputToOutputWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_InputToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00534">WorkloadData.hpp:534</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_queue_descriptor_xhtml_a08a1932be591c315a512a877d38b22df"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a08a1932be591c315a512a877d38b22df">armnn::QuantizedLstmQueueDescriptor::m_InputToInputWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_InputToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00531">WorkloadData.hpp:531</a></div></div> +<div class="ttc" id="_lstm_utils_8hpp_xhtml"><div class="ttname"><a href="_lstm_utils_8hpp.xhtml">LstmUtils.hpp</a></div></div> +<div class="ttc" id="_lstm_test_impl_8cpp_xhtml_a9901861134e17a59931e45a4137ddb39"><div class="ttname"><a href="_lstm_test_impl_8cpp.xhtml#a9901861134e17a59931e45a4137ddb39">LstmLayerFloat32NoCifgWithPeepholeWithProjectionWithLayerNormTest</a></div><div class="ttdeci">LayerTestResult< float, 2 > LstmLayerFloat32NoCifgWithPeepholeWithProjectionWithLayerNormTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_test_impl_8cpp_source.xhtml#l01943">LstmTestImpl.cpp:1943</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_af3c52626a6f05597d82ed095d0765962"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#af3c52626a6f05597d82ed095d0765962">armnn::LstmQueueDescriptor::m_ProjectionWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_ProjectionWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00400">WorkloadData.hpp:400</a></div></div> +<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_ae15f1a3c55d2db87683577de9fa4437c"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a></div><div class="ttdeci">void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00009">TensorCopyUtils.cpp:9</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_a3ea82566d98c5a657c76c3d851c47848"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a3ea82566d98c5a657c76c3d851c47848">armnn::LstmQueueDescriptor::m_InputToForgetWeights</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_InputToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00386">WorkloadData.hpp:386</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_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" href="dir_0f3cdec46afbc61a1ded8e1687c9c9a0.xhtml">backends</a></li><li class="navelem"><a class="el" href="dir_797a213d7d01b98ef12d53b0820ea64e.xhtml">backendsCommon</a></li><li class="navelem"><a class="el" href="dir_28bfe507f7e135bdae07c2a6b7f66696.xhtml">test</a></li><li class="navelem"><a class="el" href="dir_99a30439342d160875b21dac3498ad7f.xhtml">layerTests</a></li><li class="navelem"><a class="el" href="_lstm_test_impl_8cpp.xhtml">LstmTestImpl.cpp</a></li> + <li class="footer">Generated on Fri Mar 13 2020 16:09:11 for ArmNN by + <a href="http://www.doxygen.org/index.html"> + 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