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| author | Matthew Sloyan <matthew.sloyan@arm.com> | 2021-08-24 16:27:15 +0100 |
|---|---|---|
| committer | Matthew Sloyan <matthew.sloyan@arm.com> | 2021-08-24 16:27:40 +0100 |
| commit | f86be93b7492b381370cae7bf71eca8572a0cbae (patch) | |
| tree | 2a16d9b1892db2305851b2d91850f1c1635390b0 /21.08/_unidirectional_sequence_lstm_test_impl_8cpp_source.xhtml | |
| parent | ff4682943c0a64acb22643aac7793ad2ec2a1194 (diff) | |
| download | armnn-f86be93b7492b381370cae7bf71eca8572a0cbae.tar.gz | |
IVGCVSW-5924 Update 21.08 Doxygen Documents
* Also updated latest symlink.
Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: If9b4e0e52464abdf797b9eb858ae19bcc64c2aea
Diffstat (limited to '21.08/_unidirectional_sequence_lstm_test_impl_8cpp_source.xhtml')
| -rw-r--r-- | 21.08/_unidirectional_sequence_lstm_test_impl_8cpp_source.xhtml | 173 |
1 files changed, 173 insertions, 0 deletions
diff --git a/21.08/_unidirectional_sequence_lstm_test_impl_8cpp_source.xhtml b/21.08/_unidirectional_sequence_lstm_test_impl_8cpp_source.xhtml new file mode 100644 index 0000000000..c680c32550 --- /dev/null +++ b/21.08/_unidirectional_sequence_lstm_test_impl_8cpp_source.xhtml @@ -0,0 +1,173 @@ +<!-- 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" 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