1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
|
<!-- 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/UnidirectionalSequenceLstmTestImpl.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">21.08</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('_unidirectional_sequence_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">UnidirectionalSequenceLstmTestImpl.cpp</div> </div>
</div><!--header-->
<div class="contents">
<a href="_unidirectional_sequence_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 © 2021 Arm Ltd and Contributors. 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="_unidirectional_sequence_lstm_test_impl_8hpp.xhtml">UnidirectionalSequenceLstmTestImpl.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="_numeric_cast_8hpp.xhtml">armnn/utility/NumericCast.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> <span class="preprocessor">#include <<a class="code" href="_tensor_handle_8hpp.xhtml">backendsCommon/TensorHandle.hpp</a>></span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> </div><div class="line"><a name="l00012"></a><span class="lineno"> 12</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="l00013"></a><span class="lineno"> 13</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="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <<a class="code" href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a>></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="keyword">namespace </span>{</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> </div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a> UnidirectionalSequenceLstmLayerFloat32TestImpl(</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</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="l00023"></a><span class="lineno"> 23</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="keyword">const</span> std::vector<T>& input,</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keyword">const</span> std::vector<T>& outputExpected,</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& inputShape,</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& outputExpectedShape,</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keywordtype">float</span> qScale = 0.0f,</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  int32_t qOffset = 0,</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</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="l00031"></a><span class="lineno"> 31</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputShape[0]);</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> timeSize = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputShape[1]);</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputShape[2]);</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputExpectedShape[2]);</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keywordtype">unsigned</span> numUnits = outputSize;</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({batchSize, timeSize, inputSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</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="l00040"></a><span class="lineno"> 40</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="l00041"></a><span class="lineno"> 41</span> </div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({batchSize, timeSize, outputSize}, ArmnnType, qScale, qOffset);</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>  std::vector<T> inputVector;</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  inputVector.assign(input.data(), input.data() + (batchSize * timeSize * inputSize));</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  std::vector<T> cellStateInVector(batchSize * numUnits, T());</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  std::vector<T> outputStateInVector(batchSize * outputSize, T());</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  std::vector<T> actualOutput(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  std::vector<T> outputVector;</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * timeSize * outputSize));</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> </div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(cellStateInTensorInfo);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputStateInTensorInfo);</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputTensorInfo);</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="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml">armnn::UnidirectionalSequenceLstmQueueDescriptor</a> data;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</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="l00065"></a><span class="lineno"> 65</span> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> </div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> </div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo4({numUnits}, constantDataType, qScale, qOffset);</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo12({numUnits, 3}, constantDataType, qScale, qOffset);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</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="l00075"></a><span class="lineno"> 75</span> </div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f,</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  -0.117484632f, 0.3298470976f, -0.1179017122f,</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  0.214305695f, 0.42135173085f, 0.003878414626f,</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  -0.348303917f, -0.1881275477f, 0.0343011027f };</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f,</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  -0.3810434485f, 0.268383264f, -0.009807467424f,</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  -0.3522925403f, -0.24275735512f, -0.28344226125f,</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  0.13512269116f, -0.4932442977f, -0.10039821991f };</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f,</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  0.386399507f, -0.259465157985f, -0.16545993089f,</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  -0.4230232555f, 0.341664791103f, -0.18127849691f,</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  -0.2277662414f, -0.55275535589f, 0.34184026718f };</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>  std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f,</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  0.53969591851f, 0.23393625035f, -0.27140527306f,</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  0.50009280443f, 0.07511717046f, 0.3998299249f,</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  -0.51717478049f, 0.1889653282f, -0.367323637f };</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> </div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f,</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f,</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f,</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f };</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> </div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f,</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f,</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f,</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f };</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f,</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f,</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f,</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f };</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> </div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f,</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f,</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f,</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f };</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> </div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  std::vector<float> inputGateBias = { 0., 0., 0., 0. };</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  std::vector<float> forgetGateBias = { 1., 1., 1., 1. };</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> </div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  std::vector<float> cellBias = { 0., 0., 0., 0. };</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>  std::vector<float> outputGateBias = { 0., 0., 0., 0. };</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>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToInputWeightsTensor(tensorInfo12);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToForgetWeightsTensor(tensorInfo12);</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToCellWeightsTensor(tensorInfo12);</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToOutputWeightsTensor(tensorInfo12);</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToInputWeightsTensor(tensorInfo16);</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToForgetWeightsTensor(tensorInfo16);</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToCellWeightsTensor(tensorInfo16);</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToOutputWeightsTensor(tensorInfo16);</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> forgetGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> cellBiasTensor(tensorInfo4);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> outputGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> </div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToInputWeightsTensor, inputToInputWeights.data());</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToForgetWeightsTensor, inputToForgetWeights.data());</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToCellWeightsTensor, inputToCellWeights.data());</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToOutputWeightsTensor, inputToOutputWeights.data());</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputGateBiasTensor, inputGateBias.data());</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&forgetGateBiasTensor, forgetGateBias.data());</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellBiasTensor, cellBias.data());</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&outputGateBiasTensor, outputGateBias.data());</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> </div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#ab160eba2493d5fe52185c0986dcb190c">m_InputToInputWeights</a> = &inputToInputWeightsTensor;</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#aab77f54a037658ca9b2bf9cc8a1fadf1">m_InputToForgetWeights</a> = &inputToForgetWeightsTensor;</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a841439e3b8dc157a7368b19c9ecb7d03">m_InputToCellWeights</a> = &inputToCellWeightsTensor;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a17ba1c8bcc71a55a95b2a3913f8cb203">m_InputToOutputWeights</a> = &inputToOutputWeightsTensor;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a299587d4f3fca029492700f3e2585bd8">m_RecurrentToInputWeights</a> = &recurrentToInputWeightsTensor;</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#adf8571dd1867ee91082bd005f94f2610">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeightsTensor;</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#ac18c8b8b2039267d8282e91b4162d8aa">m_RecurrentToCellWeights</a> = &recurrentToCellWeightsTensor;</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a4c27716f61bb68e8ea0bd4e8389ba01a">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeightsTensor;</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a44eb7524badcca9b2073359e3814c98b">m_InputGateBias</a> = &inputGateBiasTensor;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a29fa293fffbf9c6f00cd75db1dc0a52a">m_ForgetGateBias</a> = &forgetGateBiasTensor;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a6e9593869b82984de198fed27f72cdcf">m_CellBias</a> = &cellBiasTensor;</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a5ff4158b1b363b898d0da04c42d37ce0">m_OutputGateBias</a> = &outputGateBiasTensor;</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>  <span class="comment">// Flags to set test configuration</span></div><div class="line"><a name="l00164"></a><span class="lineno"> 164</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#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> = 10;</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</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#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a> = 0;</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</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="l00167"></a><span class="lineno"> 167</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="l00168"></a><span class="lineno"> 168</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="l00169"></a><span class="lineno"> 169</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="l00170"></a><span class="lineno"> 170</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#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> </div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a812e39048892d764ccf0c751c84c000f">CreateUnidirectionalSequenceLstm</a>(data, info);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  inputHandle->Allocate();</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  outputStateInHandle->Allocate();</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  cellStateInHandle->Allocate();</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> </div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  outputHandle->Allocate();</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>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), inputVector.data());</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(outputStateInHandle.get(), outputStateInVector.data());</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(cellStateInHandle.get(), cellStateInVector.data());</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> </div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  workload->Execute();</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> </div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(actualOutput.data(), outputHandle.get());</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> </div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keywordflow">return</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a>(actualOutput,</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  outputVector,</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  outputHandle->GetShape(),</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  outputTensorInfo.GetShape());</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> </div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl(</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</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="l00198"></a><span class="lineno"> 198</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keyword">const</span> std::vector<T>& input,</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keyword">const</span> std::vector<T>& outputExpected,</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& inputShape,</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& outputExpectedShape,</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keywordtype">float</span> qScale = 0.0f,</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  int32_t qOffset = 0,</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</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="l00206"></a><span class="lineno"> 206</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputShape[1]);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> timeSize = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputShape[0]);</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputShape[2]);</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputExpectedShape[2]);</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keywordtype">unsigned</span> numUnits = outputSize;</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span> </div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({timeSize, batchSize, inputSize}, ArmnnType, 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> cellStateInTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</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="l00216"></a><span class="lineno"> 216</span> </div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({timeSize, batchSize, outputSize}, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  std::vector<T> inputVector;</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  inputVector.assign(input.data(), input.data() + (batchSize * timeSize * inputSize));</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> </div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  std::vector<T> cellStateInVector(batchSize * numUnits, T());</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  std::vector<T> outputStateInVector(batchSize * outputSize, T());</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> </div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  std::vector<T> actualOutput(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span> </div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  std::vector<T> outputVector;</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * timeSize * outputSize));</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> </div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(cellStateInTensorInfo);</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputStateInTensorInfo);</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> </div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> </div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml">armnn::UnidirectionalSequenceLstmQueueDescriptor</a> data;</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> </div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span> </div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> </div><div class="line"><a name="l00247"></a><span class="lineno"> 247</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="l00248"></a><span class="lineno"> 248</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo12({numUnits, 3}, constantDataType, qScale, qOffset);</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</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="l00250"></a><span class="lineno"> 250</span> </div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  std::vector<float> inputToInputWeights = { 0.27277296781539917f, 0.3813590407371521f, -0.394489049911499f,</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  0.2782636880874634f, -0.3793870210647583f, -0.018918335437774658f,</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  0.2724653482437134f, -0.19314253330230713f, -0.2947450876235962f,</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  -0.30253493785858154f, 0.4241350293159485f, -0.22560018301010132f };</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> </div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  std::vector<float> inputToForgetWeights = { -0.2667974531650543f, -0.05505800247192383f, -0.20932340621948242f,</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  -0.14345619082450867f, 0.09666192531585693f, -0.2604355812072754f,</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  -0.2681812047958374f, -0.3314584493637085f, 0.4485899806022644f,</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  -0.23467743396759033f, 0.5072842240333557f, -0.4192768931388855f };</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> </div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  std::vector<float> inputToCellWeights = { -0.15782442688941956f, -0.027530014514923096f, 0.4789854884147644f,</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  0.23227906227111816f, 0.28259342908859253f, -0.030095696449279785f,</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  0.10071521997451782f, -0.08535495400428772f, 0.18563997745513916f,</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  -0.3049069046974182f, -0.478048175573349f, 0.025234103202819824f };</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>  std::vector<float> inputToOutputWeights = { -0.04584759473800659f, -0.2716066539287567f, 0.012970447540283203f,</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  -0.4729190170764923f, -0.37422770261764526f, 0.49352723360061646f,</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  0.3163864016532898f, -0.436781644821167f, -0.33074596524238586f,</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  -0.32885751128196716f, -0.40959352254867554f, -0.2124689817428589f };</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> </div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  std::vector<float> recurrentToInputWeights = { 0.23788475990f, -0.24948765337f, 0.50044941902f, 0.14431896805f,</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  -0.115940228137f, -0.717082679f, -0.17208620906f, 0.17850610617f,</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  -0.16702319684f, -0.11384502053f, -0.309785276245f, -0.3316611672f,</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  0.52380162477f, -0.06839632987f, -0.391478359627f, -0.10756178963f };</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span> </div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  std::vector<float> recurrentToForgetWeights = { 0.11383482068f, 0.1676601767f, -0.08550968004f, 0.03399394089f,</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  0.08042152225f, -0.2133381964f, 0.05182432704f, 0.38161808255f,</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  -0.5018365979f, -0.08043262364f, 0.07894329014f, -0.07547105155f,</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  0.12047368288f, 0.2986997961f, 0.0485043078f, -0.13372567296f };</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span> </div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  std::vector<float> recurrentToCellWeights = { 0.0433832928545f, 0.07587072294f, -0.120520234107f, 0.604576051f,</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  -0.434353142986f, 0.009314475068f, 0.005085289478f, 0.08488202038f,</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  -0.00025437487886f, 0.15245915082f, -0.1936587542f, 0.004754020f,</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  -0.1582719236f, 0.3307867646f, 0.0236605107784f, 0.307716339826f };</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> </div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  std::vector<float> recurrentToOutputWeights = { -0.079031050201f, 0.041414566286f, -0.583727357285f, 0.1025384515f,</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  -0.172372072937f, 0.09214124082f, 0.178184121827f, -0.2439443916f,</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  0.104485116899f, 0.2600405514f, 0.064414866268f, 0.24141204357f,</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  0.281875759363f, -0.14234502664f, 0.15126448862f, -0.24421440064f };</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span> </div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  std::vector<float> inputGateBias = { 0., 0., 0., 0. };</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span> </div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  std::vector<float> forgetGateBias = { 1., 1., 1., 1. };</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span> </div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  std::vector<float> cellBias = { 0., 0., 0., 0. };</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span> </div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  std::vector<float> outputGateBias = { 0., 0., 0., 0. };</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span> </div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToInputWeightsTensor(tensorInfo12);</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToForgetWeightsTensor(tensorInfo12);</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToCellWeightsTensor(tensorInfo12);</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToOutputWeightsTensor(tensorInfo12);</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToInputWeightsTensor(tensorInfo16);</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToForgetWeightsTensor(tensorInfo16);</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToCellWeightsTensor(tensorInfo16);</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToOutputWeightsTensor(tensorInfo16);</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> forgetGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> cellBiasTensor(tensorInfo4);</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> outputGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span> </div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToInputWeightsTensor, inputToInputWeights.data());</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToForgetWeightsTensor, inputToForgetWeights.data());</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToCellWeightsTensor, inputToCellWeights.data());</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToOutputWeightsTensor, inputToOutputWeights.data());</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputGateBiasTensor, inputGateBias.data());</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&forgetGateBiasTensor, forgetGateBias.data());</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellBiasTensor, cellBias.data());</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&outputGateBiasTensor, outputGateBias.data());</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span> </div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#ab160eba2493d5fe52185c0986dcb190c">m_InputToInputWeights</a> = &inputToInputWeightsTensor;</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#aab77f54a037658ca9b2bf9cc8a1fadf1">m_InputToForgetWeights</a> = &inputToForgetWeightsTensor;</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a841439e3b8dc157a7368b19c9ecb7d03">m_InputToCellWeights</a> = &inputToCellWeightsTensor;</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a17ba1c8bcc71a55a95b2a3913f8cb203">m_InputToOutputWeights</a> = &inputToOutputWeightsTensor;</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a299587d4f3fca029492700f3e2585bd8">m_RecurrentToInputWeights</a> = &recurrentToInputWeightsTensor;</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#adf8571dd1867ee91082bd005f94f2610">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeightsTensor;</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#ac18c8b8b2039267d8282e91b4162d8aa">m_RecurrentToCellWeights</a> = &recurrentToCellWeightsTensor;</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a4c27716f61bb68e8ea0bd4e8389ba01a">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeightsTensor;</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a44eb7524badcca9b2073359e3814c98b">m_InputGateBias</a> = &inputGateBiasTensor;</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a29fa293fffbf9c6f00cd75db1dc0a52a">m_ForgetGateBias</a> = &forgetGateBiasTensor;</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a6e9593869b82984de198fed27f72cdcf">m_CellBias</a> = &cellBiasTensor;</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a5ff4158b1b363b898d0da04c42d37ce0">m_OutputGateBias</a> = &outputGateBiasTensor;</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="comment">// Flags to set test configuration</span></div><div class="line"><a name="l00339"></a><span class="lineno"> 339</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#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> = 10;</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</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#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a> = 0;</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</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="l00342"></a><span class="lineno"> 342</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="l00343"></a><span class="lineno"> 343</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="l00344"></a><span class="lineno"> 344</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="l00345"></a><span class="lineno"> 345</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#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span> </div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a812e39048892d764ccf0c751c84c000f">CreateUnidirectionalSequenceLstm</a>(data, info);</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  inputHandle->Allocate();</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  outputStateInHandle->Allocate();</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  cellStateInHandle->Allocate();</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span> </div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  outputHandle->Allocate();</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="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), inputVector.data());</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(outputStateInHandle.get(), outputStateInVector.data());</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(cellStateInHandle.get(), cellStateInVector.data());</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>  workload->Execute();</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span> </div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(actualOutput.data(), outputHandle.get());</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span> </div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="keywordflow">return</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a>(actualOutput,</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  outputVector,</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  outputHandle->GetShape(),</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  outputTensorInfo.GetShape());</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span> }</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span> </div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span> } <span class="comment">// anonymous namespace</span></div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span> </div><div class="line"><a name="l00370"></a><span class="lineno"><a class="line" href="_unidirectional_sequence_lstm_test_impl_8hpp.xhtml#ab5aedf0fbe3810773302b480ff1ef0fe"> 370</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 3></a> <a class="code" href="_unidirectional_sequence_lstm_test_impl_8cpp.xhtml#ab5aedf0fbe3810773302b480ff1ef0fe">UnidirectionalSequenceLstmLayerFloat32Test</a>(</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</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="l00373"></a><span class="lineno"> 373</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory) {</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({3, 2, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  std::vector<float> input = { 1., 2., 3., 4., 5., 4.,</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  3., 2., 1., 2., 3., 4.,</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  5., 4., 3., 2., 1., 2. };</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span> </div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({3, 2, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  std::vector<float> expectedOutput = { -0.07149004f, -0.1621171f, -0.17516759f, -0.0232934225f,</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  -0.16810727f, -0.41412935f, -0.5498753f, -0.00803578f,</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  -0.06687349f, 0.204077631f, -0.4276504f, -0.03123213f,</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  -0.12000261f, -0.0941918f, -0.45639035f, -0.02870186f,</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  -0.03429216f, 0.20824050f, -0.6569892f, -0.004152651f,</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  -0.10493034f, 0.14210969f, -0.58347696f, -0.03297536f };</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <span class="keywordflow">return</span> UnidirectionalSequenceLstmLayerFloat32TestImpl<armnn::DataType::Float32>(</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  workloadFactory, memoryManager, tensorHandleFactory,</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape());</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span> }</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> </div><div class="line"><a name="l00391"></a><span class="lineno"><a class="line" href="_unidirectional_sequence_lstm_test_impl_8hpp.xhtml#a3e62d48c04adef20dc1e813544e5792f"> 391</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 3></a> <a class="code" href="_unidirectional_sequence_lstm_test_impl_8cpp.xhtml#a3e62d48c04adef20dc1e813544e5792f">UnidirectionalSequenceLstmLayerFloat32TimeMajorTest</a>(</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</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="l00394"></a><span class="lineno"> 394</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory) {</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({2, 3, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  std::vector<float> input = { 1., 2., 3., 4., 5., 4.,</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  3., 2., 1., 2., 3., 4.,</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  5., 4., 3., 2., 1., 2. };</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span> </div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({2, 3, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  std::vector<float> expectedOutput = { 0.135657698f, 0.124672532f, 0.0212090332f, -0.0530203655f,</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  0.106138252f, 0.0404792242f, 0.0151643595f, -0.00675163185f,</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  -0.0128514022f, 0.0644884035f, 0.0709072053f, -0.0454045124f,</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  0.16288602f, 0.16649379f, 0.02770456f, -0.03698075f,</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  0.11171641f, 0.043119f , 0.0762981f , -0.01228541f,</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  0.10439701f, 0.21439962f, 0.11919238f, -0.08390583f };</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keywordflow">return</span> UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl<armnn::DataType::Float32>(</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  workloadFactory, memoryManager, tensorHandleFactory,</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape());</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span> }</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span> </div><div class="line"><a name="l00412"></a><span class="lineno"><a class="line" href="_unidirectional_sequence_lstm_test_impl_8hpp.xhtml#ab8fb90e22b99c4d41ad3566fd5968829"> 412</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 3></a> <a class="code" href="_unidirectional_sequence_lstm_test_impl_8cpp.xhtml#ab8fb90e22b99c4d41ad3566fd5968829">UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionTest</a>(</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</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="l00415"></a><span class="lineno"> 415</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span> {</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 2;</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> timeSize = 3;</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 5;</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 4;</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="keywordtype">unsigned</span> numUnits = 6;</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span> </div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({batchSize, timeSize, inputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateInTensorInfo({batchSize , numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateInTensorInfo({batchSize , outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({batchSize, timeSize, outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span> </div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="keyword">const</span> std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  3., 2., 1., 2., 3., 4.,</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  5., 4., 3., 2., 1., 2.,</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  1., 2., 3., 4., 5., 4.};</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span> </div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span> </div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  std::vector<float> actualOutput(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span> </div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  <span class="keyword">const</span> std::vector<float> expectedOutput = { -0.0135612f, -0.0263441f, 0.0314008f, -0.00883455f, 0.00763052f,</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  -0.00126877f, -0.0292959f, 0.0449957f, -0.00976195f, -0.00492338f,</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  -0.0175702f, -0.0431753f, 0.0597117f, -0.0169154f, 0.0142087f,</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  0.00472515f, -0.0196355f, 0.0342524f, -0.00407936f, -0.0253189f,</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  -0.00512944f, -0.0293754f, 0.0512771f, -0.0151874f, -0.0246433f,</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  -0.00744986f, -0.0345103f, 0.0450666f, -0.00944991f, 0.0127171f };</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span> </div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(cellStateInTensorInfo);</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputStateInTensorInfo);</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span> </div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml">armnn::UnidirectionalSequenceLstmQueueDescriptor</a> data;</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span> </div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span> </div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo5({outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo6({numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo6x4({numUnits, inputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo6x5({numUnits, outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo5x6({outputSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span> </div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  std::vector<float> inputToInputWeights = { 0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f,</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f,</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f,</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f,</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f,</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f };</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span> </div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  std::vector<float> inputToForgetWeights = { -0.0018401089f, -0.004852237f, 0.03698424f, 0.014181704f,</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  0.028273236f, -0.016726194f, -0.05249759f, -0.10204261f,</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  0.00861066f, -0.040979505f, -0.009899187f, 0.01923892f,</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  -0.028177269f, -0.08535103f, -0.14585495f, 0.10662567f,</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  -0.01909731f, -0.017883534f, -0.0047269356f, -0.045103323f,</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  0.0030784295f, 0.076784775f, 0.07463696f, 0.094531395f};</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span> </div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  std::vector<float> inputToCellWeights = { -0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f,</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f,</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f,</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f,</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f,</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f };</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span> </div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  std::vector<float> inputToOutputWeights = { -0.0998932f, -0.07201956f, -0.052803773f, -0.15629593f,</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  -0.15001918f, -0.07650751f, 0.02359855f, -0.075155355f,</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  -0.08037709f, -0.15093534f, 0.029517552f, -0.04751393f,</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  0.010350531f, -0.02664851f, -0.016839722f, -0.023121163f,</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  0.0077019283f, 0.012851257f, -0.05040649f, -0.0129761f,</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  -0.021737747f, -0.038305793f, -0.06870586f, -0.01481247f };</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span> </div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  std::vector<float> inputGateBias = { 0.02234832f, 0.14757581f, 0.18176508f,</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  0.10380666f, 0.053110216f, -0.06928846f };</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span> </div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  std::vector<float> forgetGateBias = { 0.035185695f, -0.042891346f, -0.03032477f,</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  0.23027696f, 0.11098921f, 0.08989442f };</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span> </div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  std::vector<float> cellBias = { -0.024379363f, 0.0055531194f, 0.23377132f,</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  0.033463873f, -0.1483596f, 0.029460307f };</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span> </div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  std::vector<float> outputGateBias = { 0.046159424f, -0.0012809046f, 0.03563469f,</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  0.12648113f, 0.027195795f, 0.35373217f };</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span> </div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  std::vector<float> recurrentToInputWeights = { -0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f,</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f,</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f,</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f,</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f,</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  0.08981f, -0.045407712f, 0.08682226f, -0.06867011f,</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f,</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  0.14283475f, -0.07390571f };</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>  std::vector<float> recurrentToCellWeights = { -0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f,</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f,</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f,</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  -0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f,</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  0.053676967f, 0.044480428f, -0.07663568f, 0.0071805613f,</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  0.08089997f, 0.05143358f, 0.038261272f, 0.03339287f,</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  -0.027673481f, 0.044746667f, 0.028349208f, 0.020090483f,</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  -0.019443132f, -0.030755889f };</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span> </div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  std::vector<float> recurrentToForgetWeights = { -0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f,</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f,</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  -0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f,</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f,</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  0.032950465f, -0.10985798f, -0.023809856f, 0.0021431844f,</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  -0.02196096f, -0.00326074f, 0.00058621005f, -0.074678116f,</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  -0.06193199f, 0.055729095f, 0.03736828f, 0.020123724f,</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  0.061878487f, -0.04729229f };</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span> </div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  std::vector<float> recurrentToOutputWeights = { 0.025825322f, -0.05813119f, 0.09495884f,</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  -0.045984812f,-0.01255415f, -0.0026479573f,</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  -0.08196161f, -0.054914974f, -0.0046604523f,</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  -0.029587349f, -0.044576716f, -0.07480124f,</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  -0.082868785f, 0.023254942f, 0.027502948f,</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  -0.0039728214f, -0.08683098f, -0.08116779f,</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  -0.014675607f, -0.037924774f, -0.023314456f,</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  -0.007401714f, -0.09255757f, 0.029460307f,</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  -0.08829125f, -0.005139627f, -0.08989442f,</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  -0.0555066f, 0.13596267f, 0.025062224f };</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span> </div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  std::vector<float> cellToInputWeights = { 0.040369894f, 0.030746894f, 0.24704495f,</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  0.018586371f, -0.037586458f, -0.15312155f };</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span> </div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  std::vector<float> cellToForgetWeights = { -0.01998659f, -0.15568835f, -0.24248174f,</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  -0.012770197f, 0.041331276f, -0.072311886f };</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span> </div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  std::vector<float> cellToOutputWeights = { 0.08286371f, -0.08261836f, -0.51210177f,</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  0.002913762f, 0.17764764f, -0.5495371f };</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span> </div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  std::vector<float> projectionWeights = { -0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f,</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f,</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f,</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f,</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f,</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f };</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span> </div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  std::vector<float> projectionBiasVector(outputSize, 0.f); <span class="comment">//{outputSize}</span></div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span> </div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToInputWeightsTensor(tensorInfo6x4);</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToForgetWeightsTensor(tensorInfo6x4);</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToCellWeightsTensor(tensorInfo6x4);</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToOutputWeightsTensor(tensorInfo6x4);</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToForgetWeightsTensor(tensorInfo6x5);</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToInputWeightsTensor(tensorInfo6x5);</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToCellWeightsTensor(tensorInfo6x5);</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToOutputWeightsTensor(tensorInfo6x5);</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> cellToInputWeightsTensor(tensorInfo6);</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputGateBiasTensor(tensorInfo6);</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> forgetGateBiasTensor(tensorInfo6);</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> cellBiasTensor(tensorInfo6);</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> outputGateBiasTensor(tensorInfo6);</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> cellToForgetWeightsTensor(tensorInfo6);</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> cellToOutputWeightsTensor(tensorInfo6);</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> projectionWeightsTensor(tensorInfo5x6);</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> projectionBiasTensor(tensorInfo5);</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span> </div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToInputWeightsTensor, inputToInputWeights.data());</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToForgetWeightsTensor, inputToForgetWeights.data());</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToCellWeightsTensor, inputToCellWeights.data());</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToOutputWeightsTensor, inputToOutputWeights.data());</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellToInputWeightsTensor, cellToInputWeights.data());</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputGateBiasTensor, inputGateBias.data());</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&forgetGateBiasTensor, forgetGateBias.data());</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellBiasTensor, cellBias.data());</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&outputGateBiasTensor, outputGateBias.data());</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellToForgetWeightsTensor, cellToForgetWeights.data());</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellToOutputWeightsTensor, cellToOutputWeights.data());</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&projectionWeightsTensor, projectionWeights.data());</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&projectionBiasTensor, projectionBiasVector.data());</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span> </div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#ab160eba2493d5fe52185c0986dcb190c">m_InputToInputWeights</a> = &inputToInputWeightsTensor;</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#aab77f54a037658ca9b2bf9cc8a1fadf1">m_InputToForgetWeights</a> = &inputToForgetWeightsTensor;</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a841439e3b8dc157a7368b19c9ecb7d03">m_InputToCellWeights</a> = &inputToCellWeightsTensor;</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a17ba1c8bcc71a55a95b2a3913f8cb203">m_InputToOutputWeights</a> = &inputToOutputWeightsTensor;</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a299587d4f3fca029492700f3e2585bd8">m_RecurrentToInputWeights</a> = &recurrentToInputWeightsTensor;</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#adf8571dd1867ee91082bd005f94f2610">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeightsTensor;</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#ac18c8b8b2039267d8282e91b4162d8aa">m_RecurrentToCellWeights</a> = &recurrentToCellWeightsTensor;</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a4c27716f61bb68e8ea0bd4e8389ba01a">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeightsTensor;</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a951b7c90b862138071a298065f16be61">m_CellToInputWeights</a> = &cellToInputWeightsTensor;</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a44eb7524badcca9b2073359e3814c98b">m_InputGateBias</a> = &inputGateBiasTensor;</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a29fa293fffbf9c6f00cd75db1dc0a52a">m_ForgetGateBias</a> = &forgetGateBiasTensor;</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a6e9593869b82984de198fed27f72cdcf">m_CellBias</a> = &cellBiasTensor;</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a5ff4158b1b363b898d0da04c42d37ce0">m_OutputGateBias</a> = &outputGateBiasTensor;</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a20c10fcb26657477377d07b7b1e13120">m_CellToForgetWeights</a> = &cellToForgetWeightsTensor;</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#aa09f7bdb9fd0d06b6386e412a4e72dd6">m_CellToOutputWeights</a> = &cellToOutputWeightsTensor;</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a3ead2ef8da00b2709d561d85996fc513">m_ProjectionWeights</a> = &projectionWeightsTensor;</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#ac668b31de6fb0f19d4c793d5ed3c3316">m_ProjectionBias</a> = &projectionBiasTensor;</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span> </div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <span class="comment">// Flags to set test configuration</span></div><div class="line"><a name="l00618"></a><span class="lineno"> 618</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="l00619"></a><span class="lineno"> 619</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="l00620"></a><span class="lineno"> 620</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="l00621"></a><span class="lineno"> 621</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="l00622"></a><span class="lineno"> 622</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">false</span>;</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</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#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</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#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> = 10.0f;</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span> </div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span> </div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a812e39048892d764ccf0c751c84c000f">CreateUnidirectionalSequenceLstm</a>(data, info);</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  inputHandle->Allocate();</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  outputStateInHandle->Allocate();</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  cellStateInHandle->Allocate();</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  outputHandle->Allocate();</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span> </div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), inputVector.data());</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(outputStateInHandle.get(), outputStateInVector.data());</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(cellStateInHandle.get(), cellStateInVector.data());</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span> </div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  workload->Execute();</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span> </div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(actualOutput.data(), outputHandle.get());</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span> </div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  <span class="keywordflow">return</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 3></a>(actualOutput,</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  expectedOutput,</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  outputHandle->GetShape(),</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  outputTensorInfo.GetShape());</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span> }</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span> </div><div class="line"><a name="l00647"></a><span class="lineno"><a class="line" href="_unidirectional_sequence_lstm_test_impl_8hpp.xhtml#adf8faacc3aacfa0b1342d78cfe1b61fe"> 647</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 3></a> <a class="code" href="_unidirectional_sequence_lstm_test_impl_8cpp.xhtml#adf8faacc3aacfa0b1342d78cfe1b61fe">UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTest</a>(</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</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="l00650"></a><span class="lineno"> 650</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span> {</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 3;</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> timeSize = 2;</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 4;</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 3;</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <span class="keywordtype">unsigned</span> numUnits = 5;</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span> </div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({batchSize, timeSize, inputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateInTensorInfo({batchSize , numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateInTensorInfo({batchSize , outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({batchSize, timeSize, outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span> </div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  <span class="keyword">const</span> std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  3., 2., 1., 2., 3., 4.,</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  5., 4., 3., 2., 1., 2. };</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span> </div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span> </div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  std::vector<float> actualOutput(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span> </div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <span class="keyword">const</span> std::vector<float> expectedOutput = { 0.0642256f, 0.0343966f, 0.184122f, 0.114717f,</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  0.11458f, 0.0407109f, 0.300327f, 0.174301f,</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  0.0864761f, 0.0362912f, 0.178635f, 0.115689f,</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  0.108008f, 0.0386623f, 0.273471f, 0.167115f,</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  0.0859545f, 0.0331481f, 0.186051f, 0.11888f,</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  0.106649f, 0.0276847f, 0.229863f, 0.166958f };</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span> </div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(cellStateInTensorInfo);</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputStateInTensorInfo);</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span> </div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span> </div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  <a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml">armnn::UnidirectionalSequenceLstmQueueDescriptor</a> data;</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span> </div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span> </div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span> </div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo4({outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo5({numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo5x3({numUnits, inputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo5x4({numUnits, outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo4x5({outputSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span> </div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f,</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  -0.117484632f, 0.3298470976f, -0.1179017122f,</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  0.214305695f, 0.42135173085f, 0.003878414626f,</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  -0.348303917f, -0.1881275477f, 0.0343011027f,</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  -0.38837709614f, -0.05636804124f, 0.4259087456f};</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span> </div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f,</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  -0.3810434485f, 0.268383264f, -0.009807467424f,</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  -0.3522925403f, -0.24275735512f, -0.28344226125f,</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  0.13512269116f, -0.4932442977f, -0.10039821991f,</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  0.2726137042f, 0.09216640889f, -0.06551410215f};</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span> </div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f,</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  0.386399507f, -0.259465157985f, -0.16545993089f,</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  -0.4230232555f, 0.341664791103f, -0.18127849691f,</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  -0.2277662414f, -0.55275535589f, 0.34184026718f,</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  0.3954237699f, -0.19407111404f, 0.30412107706f};</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span> </div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f,</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  0.53969591851f, 0.23393625035f, -0.27140527306f,</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  0.50009280443f, 0.07511717046f, 0.3998299249f,</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  -0.51717478049f, 0.1889653282f, -0.367323637f,</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  -0.12584099173f, -0.12319286912f, 0.2407919466f};</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span> </div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  std::vector<float> inputGateBias{ 0.03f, 0.15f, 0.22f, 0.38f, 0.05f };</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  std::vector<float> forgetGateBias{ 0.1f, -0.3f, -0.2f, 0.1f, 0.4f };</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  std::vector<float> cellBias{ -0.05f, 0.72f, 0.25f, 0.08f, 0.1f };</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  std::vector<float> outputGateBias{ 0.05f, -0.01f, 0.2f, 0.1f, -0.2f };</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span> </div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f,</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f,</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f,</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f,</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f };</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span> </div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f,</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f,</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f,</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f,</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f };</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span> </div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f,</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f,</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f,</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f,</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f };</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span> </div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f,</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f,</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f,</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f,</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f };</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span> </div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  std::vector<float> cellToInputWeights { 0.05f, 0.1f, 0.25f, 0.15f, -0.02f };</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  std::vector<float> cellToForgetWeights { -0.02f, -0.15f, -0.25f, -0.03f, 0.15f };</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  std::vector<float> cellToOutputWeights { 0.1f, -0.1f, -0.5f, 0.05f, 0.01f };</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span> </div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  std::vector<float> projectionWeights{ -0.1f, 0.2f, 0.01f, -0.2f,</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  0.1f, 0.5f, 0.3f, 0.08f,</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  0.07f, 0.2f, -0.4f, 0.2f,</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  0.5f, -0.4f, 0.3f, -0.2f,</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  0.3f, 0.08f, -0.07f, 0.2f};</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span> </div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  std::vector<float> projectionBiasVector(outputSize, 0.f); <span class="comment">//{outputSize}</span></div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span> </div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  std::vector<float> inputLayerNormWeights{ 0.1f, 0.2f, 0.3f, 0.5f, 0.8f };</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  std::vector<float> forgetLayerNormWeights{ 0.1f, 0.2f, 0.3f, 0.5f, 0.2f };</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  std::vector<float> cellLayerNormWeights{ 0.7f, 0.2f, 0.3f, 0.8f, 0.5f };</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  std::vector<float> outputLayerNormWeights{ 0.6f, 0.2f, 0.2f, 0.5f, 0.1f };</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span> </div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToInputWeightsTensor(tensorInfo5x3);</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToForgetWeightsTensor(tensorInfo5x3);</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToCellWeightsTensor(tensorInfo5x3);</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToOutputWeightsTensor(tensorInfo5x3);</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToForgetWeightsTensor(tensorInfo5x4);</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToInputWeightsTensor(tensorInfo5x4);</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToCellWeightsTensor(tensorInfo5x4);</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToOutputWeightsTensor(tensorInfo5x4);</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> cellToInputWeightsTensor(tensorInfo5);</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputGateBiasTensor(tensorInfo5);</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> forgetGateBiasTensor(tensorInfo5);</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> cellBiasTensor(tensorInfo5);</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> outputGateBiasTensor(tensorInfo5);</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> cellToForgetWeightsTensor(tensorInfo5);</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> cellToOutputWeightsTensor(tensorInfo5);</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> projectionWeightsTensor(tensorInfo4x5);</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> projectionBiasTensor(tensorInfo4);</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span> </div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputLayerNormWeightsTensor(tensorInfo5);</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> forgetLayerNormWeightsTensor(tensorInfo5);</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> cellLayerNormWeightsTensor(tensorInfo5);</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> outputLayerNormWeightsTensor(tensorInfo5);</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span> </div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToInputWeightsTensor, inputToInputWeights.data());</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToForgetWeightsTensor, inputToForgetWeights.data());</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToCellWeightsTensor, inputToCellWeights.data());</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToOutputWeightsTensor, inputToOutputWeights.data());</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());</div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellToInputWeightsTensor, cellToInputWeights.data());</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputGateBiasTensor, inputGateBias.data());</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&forgetGateBiasTensor, forgetGateBias.data());</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellBiasTensor, cellBias.data());</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&outputGateBiasTensor, outputGateBias.data());</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellToForgetWeightsTensor, cellToForgetWeights.data());</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellToOutputWeightsTensor, cellToOutputWeights.data());</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&projectionWeightsTensor, projectionWeights.data());</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&projectionBiasTensor, projectionBiasVector.data());</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span> </div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputLayerNormWeightsTensor, inputLayerNormWeights.data());</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&forgetLayerNormWeightsTensor, forgetLayerNormWeights.data());</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellLayerNormWeightsTensor, cellLayerNormWeights.data());</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&outputLayerNormWeightsTensor, outputLayerNormWeights.data());</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span> </div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#ab160eba2493d5fe52185c0986dcb190c">m_InputToInputWeights</a> = &inputToInputWeightsTensor;</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#aab77f54a037658ca9b2bf9cc8a1fadf1">m_InputToForgetWeights</a> = &inputToForgetWeightsTensor;</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a841439e3b8dc157a7368b19c9ecb7d03">m_InputToCellWeights</a> = &inputToCellWeightsTensor;</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a17ba1c8bcc71a55a95b2a3913f8cb203">m_InputToOutputWeights</a> = &inputToOutputWeightsTensor;</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a299587d4f3fca029492700f3e2585bd8">m_RecurrentToInputWeights</a> = &recurrentToInputWeightsTensor;</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#adf8571dd1867ee91082bd005f94f2610">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeightsTensor;</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#ac18c8b8b2039267d8282e91b4162d8aa">m_RecurrentToCellWeights</a> = &recurrentToCellWeightsTensor;</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a4c27716f61bb68e8ea0bd4e8389ba01a">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeightsTensor;</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a951b7c90b862138071a298065f16be61">m_CellToInputWeights</a> = &cellToInputWeightsTensor;</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a44eb7524badcca9b2073359e3814c98b">m_InputGateBias</a> = &inputGateBiasTensor;</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a29fa293fffbf9c6f00cd75db1dc0a52a">m_ForgetGateBias</a> = &forgetGateBiasTensor;</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a6e9593869b82984de198fed27f72cdcf">m_CellBias</a> = &cellBiasTensor;</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a5ff4158b1b363b898d0da04c42d37ce0">m_OutputGateBias</a> = &outputGateBiasTensor;</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a20c10fcb26657477377d07b7b1e13120">m_CellToForgetWeights</a> = &cellToForgetWeightsTensor;</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#aa09f7bdb9fd0d06b6386e412a4e72dd6">m_CellToOutputWeights</a> = &cellToOutputWeightsTensor;</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a3ead2ef8da00b2709d561d85996fc513">m_ProjectionWeights</a> = &projectionWeightsTensor;</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#ac668b31de6fb0f19d4c793d5ed3c3316">m_ProjectionBias</a> = &projectionBiasTensor;</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span> </div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a1dbad32cad5c0437e1272f59fedf52ea">m_InputLayerNormWeights</a> = &inputLayerNormWeightsTensor;</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#ad9442e26aa79f896da5f404ab825a9c8">m_ForgetLayerNormWeights</a> = &forgetLayerNormWeightsTensor;</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#aeef6f1ac3efad8ec8b0a7118652b64c9">m_CellLayerNormWeights</a> = &cellLayerNormWeightsTensor;</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a0e0f66bd03c88f3d2dc666f581d3cf12">m_OutputLayerNormWeights</a> = &outputLayerNormWeightsTensor;</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span> </div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  <span class="comment">// Flags to set test configuration</span></div><div class="line"><a name="l00843"></a><span class="lineno"> 843</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="l00844"></a><span class="lineno"> 844</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="l00845"></a><span class="lineno"> 845</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="l00846"></a><span class="lineno"> 846</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="l00847"></a><span class="lineno"> 847</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="l00848"></a><span class="lineno"> 848</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#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</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#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> = 10.0f;</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span> </div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a812e39048892d764ccf0c751c84c000f">CreateUnidirectionalSequenceLstm</a>(data, info);</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  inputHandle->Allocate();</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  outputStateInHandle->Allocate();</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  cellStateInHandle->Allocate();</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  outputHandle->Allocate();</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span> </div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), inputVector.data());</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(outputStateInHandle.get(), outputStateInVector.data());</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(cellStateInHandle.get(), cellStateInVector.data());</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span> </div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  workload->Execute();</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span> </div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(actualOutput.data(), outputHandle.get());</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span> </div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  <span class="keywordflow">return</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 3></a>(actualOutput,</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  expectedOutput,</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  outputHandle->GetShape(),</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  outputTensorInfo.GetShape());</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span> }</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span> </div><div class="line"><a name="l00871"></a><span class="lineno"><a class="line" href="_unidirectional_sequence_lstm_test_impl_8hpp.xhtml#a5c36dcbd8e95871d57e8a50222a02494"> 871</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 3></a> <a class="code" href="_unidirectional_sequence_lstm_test_impl_8cpp.xhtml#a5c36dcbd8e95871d57e8a50222a02494">UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest</a>(</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</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="l00874"></a><span class="lineno"> 874</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span> {</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 3;</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> timeSize = 2;</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 3;</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 4;</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  <span class="keywordtype">unsigned</span> numUnits = outputSize;</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({batchSize, timeSize, inputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateInTensorInfo({batchSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateInTensorInfo({batchSize, outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span> </div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({batchSize, timeSize, outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span> </div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>  3., 2., 1., 2., 3., 4.,</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>  5., 4., 3., 2., 1., 2. };</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span> </div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span> </div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  std::vector<float> actualOutput(outputTensorInfo.GetNumElements());</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>  std::vector<float> outputVector = { -0.0129257f, -0.070531f, -0.153508f, -0.0392391f,</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  -0.0300169f, -0.195717f, -0.528679f, -0.0818106f,</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  -0.0332748f, 0.155429f, -0.353966f, -0.0801505f,</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  -0.032312f, -0.0407911f, -0.435053f, -0.0932317f,</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  -0.0108233f, 0.165584f, -0.640424f, -0.0447535f,</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  -0.031675f, 0.125987f, -0.526695f, -0.110093f };</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span> </div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(cellStateInTensorInfo);</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputStateInTensorInfo);</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span> </div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span> </div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>  <a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml">armnn::UnidirectionalSequenceLstmQueueDescriptor</a> data;</div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span> </div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());</div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span> </div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span> </div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo4({numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo12({numUnits, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo16({numUnits, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span> </div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f,</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  -0.3810434485f, 0.268383264f, -0.009807467424f,</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  -0.3522925403f, -0.24275735512f, -0.28344226125f,</div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  0.13512269116f, -0.4932442977f, -0.10039821991f };</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span> </div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f,</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  0.386399507f, -0.259465157985f, -0.16545993089f,</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  -0.4230232555f, 0.341664791103f, -0.18127849691f,</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  -0.2277662414f, -0.55275535589f, 0.34184026718f };</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span> </div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f,</div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  0.53969591851f, 0.23393625035f, -0.27140527306f,</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  0.50009280443f, 0.07511717046f, 0.3998299249f,</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  -0.51717478049f, 0.1889653282f, -0.367323637f };</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span> </div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f,</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f,</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f,</div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f };</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span> </div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f,</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f,</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f,</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f };</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span> </div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f,</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f,</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f,</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f };</div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span> </div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  std::vector<float> cellToForgetWeights{ 0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f };</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span> </div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  std::vector<float> cellToOutputWeights{ -0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f };</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span> </div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  std::vector<float> forgetGateBias = { 1., 1., 1., 1. };</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span> </div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  std::vector<float> cellBias = { 0., 0., 0., 0. };</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span> </div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  std::vector<float> outputGateBias = { 0., 0., 0., 0. };</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>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToForgetWeightsTensor(tensorInfo12);</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToCellWeightsTensor(tensorInfo12);</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> inputToOutputWeightsTensor(tensorInfo12);</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToForgetWeightsTensor(tensorInfo16);</div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToCellWeightsTensor(tensorInfo16);</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> recurrentToOutputWeightsTensor(tensorInfo16);</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> cellToForgetWeightsTensor(tensorInfo4);</div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> cellToOutputWeightsTensor(tensorInfo4);</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> forgetGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> cellBiasTensor(tensorInfo4);</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a> outputGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span> </div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToForgetWeightsTensor, inputToForgetWeights.data());</div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToCellWeightsTensor, inputToCellWeights.data());</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&inputToOutputWeightsTensor, inputToOutputWeights.data());</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());</div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellToForgetWeightsTensor, cellToForgetWeights.data());</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellToOutputWeightsTensor, cellToOutputWeights.data());</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&forgetGateBiasTensor, forgetGateBias.data());</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&cellBiasTensor, cellBias.data());</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>(&outputGateBiasTensor, outputGateBias.data());</div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span> </div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#aab77f54a037658ca9b2bf9cc8a1fadf1">m_InputToForgetWeights</a> = &inputToForgetWeightsTensor;</div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a841439e3b8dc157a7368b19c9ecb7d03">m_InputToCellWeights</a> = &inputToCellWeightsTensor;</div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a17ba1c8bcc71a55a95b2a3913f8cb203">m_InputToOutputWeights</a> = &inputToOutputWeightsTensor;</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#adf8571dd1867ee91082bd005f94f2610">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeightsTensor;</div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#ac18c8b8b2039267d8282e91b4162d8aa">m_RecurrentToCellWeights</a> = &recurrentToCellWeightsTensor;</div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a4c27716f61bb68e8ea0bd4e8389ba01a">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeightsTensor;</div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a20c10fcb26657477377d07b7b1e13120">m_CellToForgetWeights</a> = &cellToForgetWeightsTensor;</div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#aa09f7bdb9fd0d06b6386e412a4e72dd6">m_CellToOutputWeights</a> = &cellToOutputWeightsTensor;</div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a29fa293fffbf9c6f00cd75db1dc0a52a">m_ForgetGateBias</a> = &forgetGateBiasTensor;</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a6e9593869b82984de198fed27f72cdcf">m_CellBias</a> = &cellBiasTensor;</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  data.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a5ff4158b1b363b898d0da04c42d37ce0">m_OutputGateBias</a> = &outputGateBiasTensor;</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span> </div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  <span class="comment">// Flags to set test configuration</span></div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</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#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> = 10;</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</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#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a> = 0;</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</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="l01006"></a><span class="lineno"> 1006</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">true</span>;</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</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="l01008"></a><span class="lineno"> 1008</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="l01009"></a><span class="lineno"> 1009</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#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span> </div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a812e39048892d764ccf0c751c84c000f">CreateUnidirectionalSequenceLstm</a>(data, info);</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  inputHandle->Allocate();</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  outputStateInHandle->Allocate();</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  cellStateInHandle->Allocate();</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span> </div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>  outputHandle->Allocate();</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span> </div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), inputVector.data());</div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(outputStateInHandle.get(), outputStateInVector.data());</div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(cellStateInHandle.get(), cellStateInVector.data());</div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span> </div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  workload->Execute();</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>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(actualOutput.data(), outputHandle.get());</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span> </div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  <span class="keywordflow">return</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 3></a>(actualOutput,</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  outputVector,</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  outputHandle->GetShape(),</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  outputTensorInfo.GetShape());</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span> }</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#l00959">Descriptors.hpp:959</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_unidirectional_sequence_lstm_queue_descriptor_xhtml_a44eb7524badcca9b2073359e3814c98b"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a44eb7524badcca9b2073359e3814c98b">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_InputGateBias</a></div><div class="ttdeci">const ConstTensorHandle * m_InputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00736">WorkloadData.hpp:736</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a86e88bef0df4df96df752b4b8955a3af"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">armnn::LstmDescriptor::m_ClippingThresProj</a></div><div class="ttdeci">float m_ClippingThresProj</div><div class="ttdoc">Clipping threshold value for the projection. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00953">Descriptors.hpp:953</a></div></div>
<div class="ttc" id="_tensor_handle_8hpp_xhtml"><div class="ttname"><a href="_tensor_handle_8hpp.xhtml">TensorHandle.hpp</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#l00152">Tensor.hpp:152</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#l00022">WorkloadFactory.hpp:22</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="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_ac18c8b8b2039267d8282e91b4162d8aa"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#ac18c8b8b2039267d8282e91b4162d8aa">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToCellWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_RecurrentToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00731">WorkloadData.hpp:731</a></div></div>
<div class="ttc" id="_resolve_type_8hpp_xhtml"><div class="ttname"><a href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_ab160eba2493d5fe52185c0986dcb190c"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#ab160eba2493d5fe52185c0986dcb190c">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_InputToInputWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_InputToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00725">WorkloadData.hpp:725</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a3dcd10ca3ea2e132558b1e2814668c15"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a3dcd10ca3ea2e132558b1e2814668c15">armnn::LstmDescriptor::m_TimeMajor</a></div><div class="ttdeci">bool m_TimeMajor</div><div class="ttdoc">Enable/disable time major. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00963">Descriptors.hpp:963</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="_unidirectional_sequence_lstm_test_impl_8cpp_xhtml_ab5aedf0fbe3810773302b480ff1ef0fe"><div class="ttname"><a href="_unidirectional_sequence_lstm_test_impl_8cpp.xhtml#ab5aedf0fbe3810773302b480ff1ef0fe">UnidirectionalSequenceLstmLayerFloat32Test</a></div><div class="ttdeci">LayerTestResult< float, 3 > UnidirectionalSequenceLstmLayerFloat32Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_unidirectional_sequence_lstm_test_impl_8cpp_source.xhtml#l00370">UnidirectionalSequenceLstmTestImpl.cpp:370</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</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#l00057">WorkloadData.hpp:57</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_a5ff4158b1b363b898d0da04c42d37ce0"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a5ff4158b1b363b898d0da04c42d37ce0">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_OutputGateBias</a></div><div class="ttdeci">const ConstTensorHandle * m_OutputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00739">WorkloadData.hpp:739</a></div></div>
<div class="ttc" id="_numeric_cast_8hpp_xhtml"><div class="ttname"><a href="_numeric_cast_8hpp.xhtml">NumericCast.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_ac668b31de6fb0f19d4c793d5ed3c3316"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#ac668b31de6fb0f19d4c793d5ed3c3316">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_ProjectionBias</a></div><div class="ttdeci">const ConstTensorHandle * m_ProjectionBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00741">WorkloadData.hpp:741</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a812e39048892d764ccf0c751c84c000f"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a812e39048892d764ccf0c751c84c000f">armnn::IWorkloadFactory::CreateUnidirectionalSequenceLstm</a></div><div class="ttdeci">virtual std::unique_ptr< IWorkload > CreateUnidirectionalSequenceLstm(const UnidirectionalSequenceLstmQueueDescriptor &descriptor, const WorkloadInfo &info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01889">WorkloadFactory.cpp:1889</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_a17ba1c8bcc71a55a95b2a3913f8cb203"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a17ba1c8bcc71a55a95b2a3913f8cb203">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_InputToOutputWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_InputToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00728">WorkloadData.hpp:728</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_a841439e3b8dc157a7368b19c9ecb7d03"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a841439e3b8dc157a7368b19c9ecb7d03">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_InputToCellWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_InputToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00727">WorkloadData.hpp:727</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_aab77f54a037658ca9b2bf9cc8a1fadf1"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#aab77f54a037658ca9b2bf9cc8a1fadf1">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_InputToForgetWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_InputToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00726">WorkloadData.hpp:726</a></div></div>
<div class="ttc" id="_unidirectional_sequence_lstm_test_impl_8cpp_xhtml_ab8fb90e22b99c4d41ad3566fd5968829"><div class="ttname"><a href="_unidirectional_sequence_lstm_test_impl_8cpp.xhtml#ab8fb90e22b99c4d41ad3566fd5968829">UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionTest</a></div><div class="ttdeci">LayerTestResult< float, 3 > UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_unidirectional_sequence_lstm_test_impl_8cpp_source.xhtml#l00412">UnidirectionalSequenceLstmTestImpl.cpp:412</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_aeef6f1ac3efad8ec8b0a7118652b64c9"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#aeef6f1ac3efad8ec8b0a7118652b64c9">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_CellLayerNormWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_CellLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00744">WorkloadData.hpp:744</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#l00035">Types.hpp:35</a></div></div>
<div class="ttc" id="_unidirectional_sequence_lstm_test_impl_8cpp_xhtml_adf8faacc3aacfa0b1342d78cfe1b61fe"><div class="ttname"><a href="_unidirectional_sequence_lstm_test_impl_8cpp.xhtml#adf8faacc3aacfa0b1342d78cfe1b61fe">UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTest</a></div><div class="ttdeci">LayerTestResult< float, 3 > UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_unidirectional_sequence_lstm_test_impl_8cpp_source.xhtml#l00647">UnidirectionalSequenceLstmTestImpl.cpp:647</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_a951b7c90b862138071a298065f16be61"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a951b7c90b862138071a298065f16be61">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_CellToInputWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_CellToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00733">WorkloadData.hpp:733</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_a3ead2ef8da00b2709d561d85996fc513"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a3ead2ef8da00b2709d561d85996fc513">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_ProjectionWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_ProjectionWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00740">WorkloadData.hpp:740</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#l00099">IBackendInternal.hpp:99</a></div></div>
<div class="ttc" id="_unidirectional_sequence_lstm_test_impl_8cpp_xhtml_a5c36dcbd8e95871d57e8a50222a02494"><div class="ttname"><a href="_unidirectional_sequence_lstm_test_impl_8cpp.xhtml#a5c36dcbd8e95871d57e8a50222a02494">UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest</a></div><div class="ttdeci">LayerTestResult< float, 3 > UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_unidirectional_sequence_lstm_test_impl_8cpp_source.xhtml#l00871">UnidirectionalSequenceLstmTestImpl.cpp:871</a></div></div>
<div class="ttc" id="_unidirectional_sequence_lstm_test_impl_8hpp_xhtml"><div class="ttname"><a href="_unidirectional_sequence_lstm_test_impl_8hpp.xhtml">UnidirectionalSequenceLstmTestImpl.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_scoped_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_handle_8hpp_source.xhtml#l00106">TensorHandle.hpp:106</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#l00957">Descriptors.hpp:957</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="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#l00949">Descriptors.hpp:949</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a531a3907ec13d3772370da88030191a5"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">armnn::LstmDescriptor::m_ClippingThresCell</a></div><div class="ttdeci">float m_ClippingThresCell</div><div class="ttdoc">Clipping threshold value for the cell state. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00951">Descriptors.hpp:951</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_ad9442e26aa79f896da5f404ab825a9c8"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#ad9442e26aa79f896da5f404ab825a9c8">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_ForgetLayerNormWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_ForgetLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00743">WorkloadData.hpp:743</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_a0e0f66bd03c88f3d2dc666f581d3cf12"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a0e0f66bd03c88f3d2dc666f581d3cf12">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_OutputLayerNormWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_OutputLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00745">WorkloadData.hpp:745</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#l00955">Descriptors.hpp:955</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_a29fa293fffbf9c6f00cd75db1dc0a52a"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a29fa293fffbf9c6f00cd75db1dc0a52a">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_ForgetGateBias</a></div><div class="ttdeci">const ConstTensorHandle * m_ForgetGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00737">WorkloadData.hpp:737</a></div></div>
<div class="ttc" id="_unidirectional_sequence_lstm_test_impl_8cpp_xhtml_a3e62d48c04adef20dc1e813544e5792f"><div class="ttname"><a href="_unidirectional_sequence_lstm_test_impl_8cpp.xhtml#a3e62d48c04adef20dc1e813544e5792f">UnidirectionalSequenceLstmLayerFloat32TimeMajorTest</a></div><div class="ttdeci">LayerTestResult< float, 3 > UnidirectionalSequenceLstmLayerFloat32TimeMajorTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_unidirectional_sequence_lstm_test_impl_8cpp_source.xhtml#l00391">UnidirectionalSequenceLstmTestImpl.cpp:391</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="classarmnn_1_1_i_tensor_handle_factory_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_tensor_handle_factory_8hpp_source.xhtml#l00042">ITensorHandleFactory.hpp:42</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_a6e9593869b82984de198fed27f72cdcf"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a6e9593869b82984de198fed27f72cdcf">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_CellBias</a></div><div class="ttdeci">const ConstTensorHandle * m_CellBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00738">WorkloadData.hpp:738</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#l00961">Descriptors.hpp:961</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a375ca3cff9f1b005d1412dc5f3cf5b6e"><div class="ttname"><a href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">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#l00035">NumericCast.hpp:35</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_unidirectional_sequence_lstm_queue_descriptor_xhtml_a299587d4f3fca029492700f3e2585bd8"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a299587d4f3fca029492700f3e2585bd8">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToInputWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_RecurrentToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00729">WorkloadData.hpp:729</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 TensorInfos of 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="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#l00015">LayerTestResult.hpp:15</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_a20c10fcb26657477377d07b7b1e13120"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a20c10fcb26657477377d07b7b1e13120">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_CellToForgetWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_CellToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00734">WorkloadData.hpp:734</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml">armnn::UnidirectionalSequenceLstmQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00698">WorkloadData.hpp:698</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_a4c27716f61bb68e8ea0bd4e8389ba01a"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a4c27716f61bb68e8ea0bd4e8389ba01a">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToOutputWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_RecurrentToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00732">WorkloadData.hpp:732</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_factory_xhtml_a375f11dd42ff042435e8771cf287b20c"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">armnn::ITensorHandleFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr< ITensorHandle > CreateTensorHandle(const TensorInfo &tensorInfo) const =0</div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_adf8571dd1867ee91082bd005f94f2610"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#adf8571dd1867ee91082bd005f94f2610">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToForgetWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_RecurrentToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00730">WorkloadData.hpp:730</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_aa09f7bdb9fd0d06b6386e412a4e72dd6"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#aa09f7bdb9fd0d06b6386e412a4e72dd6">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_CellToOutputWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_CellToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00735">WorkloadData.hpp:735</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml_a1dbad32cad5c0437e1272f59fedf52ea"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml#a1dbad32cad5c0437e1272f59fedf52ea">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_InputLayerNormWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_InputLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00742">WorkloadData.hpp:742</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><!-- 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="_unidirectional_sequence_lstm_test_impl_8cpp.xhtml">UnidirectionalSequenceLstmTestImpl.cpp</a></li>
<li class="footer">Generated on Tue Aug 24 2021 16:18:43 for ArmNN by
<a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.13 </li>
</ul>
</div>
</body>
</html>
|
