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<div class="title">LstmSerializationTests.cpp</div>  </div>
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<a href="_lstm_serialization_tests_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment">// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;</div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="preprocessor">#include &quot;../Serializer.hpp&quot;</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_serializer_test_utils_8hpp.xhtml">SerializerTestUtils.hpp</a>&quot;</span></div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;</div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_descriptors_8hpp.xhtml">armnn/Descriptors.hpp</a>&gt;</span></div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_i_network_8hpp.xhtml">armnn/INetwork.hpp</a>&gt;</span></div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_i_runtime_8hpp.xhtml">armnn/IRuntime.hpp</a>&gt;</span></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_i_deserializer_8hpp.xhtml">armnnDeserializer/IDeserializer.hpp</a>&gt;</span></div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_ignore_unused_8hpp.xhtml">armnn/utility/IgnoreUnused.hpp</a>&gt;</span></div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_lstm_params_8hpp.xhtml">armnn/LstmParams.hpp</a>&gt;</span></div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_quantized_lstm_params_8hpp.xhtml">armnn/QuantizedLstmParams.hpp</a>&gt;</span></div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;</div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="preprocessor">#include &lt;boost/test/unit_test.hpp&gt;</span></div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;</div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="preprocessor">#include &lt;fmt/format.h&gt;</span></div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;</div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;</div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<a class="code" href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a>(SerializerTests)</div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;</div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Descriptor&gt;</div><div class="line"><a name="l00025"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#a0a3344924b451a5e8bdfeaa02d9e7688">   25</a></span>&#160;<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> <a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a0a3344924b451a5e8bdfeaa02d9e7688">ConstantVector2LstmInputParams</a>(<span class="keyword">const</span> std::vector&lt;armnn::ConstTensor&gt;&amp; constants,</div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;                                                      Descriptor&amp; descriptor)</div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;{</div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;    <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> lstmInputParams;</div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;    <span class="keywordtype">size_t</span> i = 0;</div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;</div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;    <span class="comment">// Inserting basic paramters</span></div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;    lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a>     = &amp;constants[i++];</div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;    lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a>       = &amp;constants[i++];</div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;    lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a>     = &amp;constants[i++];</div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;    lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &amp;constants[i++];</div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;    lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a>   = &amp;constants[i++];</div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;    lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &amp;constants[i++];</div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;    lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a>           = &amp;constants[i++];</div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;    lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a>                 = &amp;constants[i++];</div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;    lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a>           = &amp;constants[i++];</div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;    <span class="keywordflow">if</span> (!descriptor.m_CifgEnabled)</div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;    {</div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;        lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a>     = &amp;constants[i++];</div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;        lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a> = &amp;constants[i++];</div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;        lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a>           = &amp;constants[i++];</div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;    }</div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;</div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;    <span class="keywordflow">if</span> (descriptor.m_PeepholeEnabled)</div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;    {</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;        <span class="keywordflow">if</span> (!descriptor.m_CifgEnabled)</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;        {</div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;            lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">m_CellToInputWeights</a> = &amp;constants[i++];</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;        }</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;        lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a> = &amp;constants[i++];</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;        lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a> = &amp;constants[i++];</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;    }</div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;    <span class="keywordflow">if</span> (descriptor.m_ProjectionEnabled)</div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;    {</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;        lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a> = &amp;constants[i++];</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;        lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a>    = &amp;constants[i++];</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;    }</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;    <span class="keywordflow">if</span> (descriptor.m_LayerNormEnabled)</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;    {</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;        <span class="keywordflow">if</span> (!descriptor.m_CifgEnabled)</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;        {</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;            lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a0cd848f65ec31778d708852f0042fe37">m_InputLayerNormWeights</a> = &amp;constants[i++];</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;        }</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;        lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">m_ForgetLayerNormWeights</a> = &amp;constants[i++];</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;        lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">m_CellLayerNormWeights</a>   = &amp;constants[i++];</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;        lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">m_OutputLayerNormWeights</a> = &amp;constants[i++];</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;    }</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;    <span class="keywordflow">return</span> lstmInputParams;</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;}</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;<span class="comment">// Works for Lstm and QLstm (QuantizedLstm uses different parameters)</span></div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Descriptor&gt;</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;<span class="keyword">class </span>VerifyLstmLayer : <span class="keyword">public</span> <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor</a>&lt;Descriptor&gt;</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;{</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;    VerifyLstmLayer(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;                    <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;                    <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos,</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;                    <span class="keyword">const</span> Descriptor&amp; descriptor,</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;                    <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a>&amp; inputParams)</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;        : <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor&lt;Descriptor&gt;</a>(layerName, inputInfos, outputInfos, descriptor)</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;        , m_InputParams(inputParams) {}</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;                         <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a>&amp; descriptor,</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;                         <span class="keyword">const</span> std::vector&lt;armnn::ConstTensor&gt;&amp; constants,</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;                         <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;                         <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span> = 0)<span class="keyword"> override</span></div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;<span class="keyword">    </span>{</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(constants, <span class="keywordtype">id</span>);</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;        <span class="keywordflow">switch</span> (layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">GetType</a>())</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;        {</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;            <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;            <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;            <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a06b091bc9aea697ba473c71f0bb55925">armnn::LayerType::Lstm</a>:</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;            {</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;                this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a56e5da77beb8c601e09bf78371b95828">VerifyNameAndConnections</a>(layer, name);</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;                <span class="keyword">const</span> Descriptor&amp; internalDescriptor = <span class="keyword">static_cast&lt;</span><span class="keyword">const </span>Descriptor&amp;<span class="keyword">&gt;</span>(descriptor);</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;                this-&gt;<a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a05ef6b0820f03499921f103759525a80">VerifyDescriptor</a>(internalDescriptor);</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;                <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> lstmParams = <a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a0a3344924b451a5e8bdfeaa02d9e7688">ConstantVector2LstmInputParams</a>(constants, internalDescriptor);</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;                VerifyInputParameters(lstmParams);</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;                <span class="keywordflow">break</span>;</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;            }</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;            <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a91880b71ea6d007439b7bc7c320b5c25">armnn::LayerType::QLstm</a>:</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;            {</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;                this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a56e5da77beb8c601e09bf78371b95828">VerifyNameAndConnections</a>(layer, name);</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;                <span class="keyword">const</span> Descriptor&amp; internalDescriptor = <span class="keyword">static_cast&lt;</span><span class="keyword">const </span>Descriptor&amp;<span class="keyword">&gt;</span>(descriptor);</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;                this-&gt;<a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a05ef6b0820f03499921f103759525a80">VerifyDescriptor</a>(internalDescriptor);</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;                <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> lstmParams = <a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a0a3344924b451a5e8bdfeaa02d9e7688">ConstantVector2LstmInputParams</a>(constants, internalDescriptor);</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;                VerifyInputParameters(lstmParams);</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;                <span class="keywordflow">break</span>;</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;            }</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;            <span class="keywordflow">default</span>:</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;            {</div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;                <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(<span class="stringliteral">&quot;Unexpected layer type in Lstm test model&quot;</span>);</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;            }</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;        }</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;    }</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;<span class="keyword">protected</span>:</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    <span class="keywordtype">void</span> VerifyInputParameters(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a>&amp; params)</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    {</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;            <span class="stringliteral">&quot;m_InputToInputWeights&quot;</span>, m_InputParams.m_InputToInputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a>);</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;            <span class="stringliteral">&quot;m_InputToForgetWeights&quot;</span>, m_InputParams.m_InputToForgetWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a>);</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;            <span class="stringliteral">&quot;m_InputToCellWeights&quot;</span>, m_InputParams.m_InputToCellWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a>);</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;            <span class="stringliteral">&quot;m_InputToOutputWeights&quot;</span>, m_InputParams.m_InputToOutputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a>);</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;            <span class="stringliteral">&quot;m_RecurrentToInputWeights&quot;</span>, m_InputParams.m_RecurrentToInputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a>);</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;            <span class="stringliteral">&quot;m_RecurrentToForgetWeights&quot;</span>, m_InputParams.m_RecurrentToForgetWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a>);</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;            <span class="stringliteral">&quot;m_RecurrentToCellWeights&quot;</span>, m_InputParams.m_RecurrentToCellWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a>);</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;            <span class="stringliteral">&quot;m_RecurrentToOutputWeights&quot;</span>, m_InputParams.m_RecurrentToOutputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a>);</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;            <span class="stringliteral">&quot;m_CellToInputWeights&quot;</span>, m_InputParams.m_CellToInputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">m_CellToInputWeights</a>);</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;            <span class="stringliteral">&quot;m_CellToForgetWeights&quot;</span>, m_InputParams.m_CellToForgetWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a>);</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;            <span class="stringliteral">&quot;m_CellToOutputWeights&quot;</span>, m_InputParams.m_CellToOutputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a>);</div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;            <span class="stringliteral">&quot;m_InputGateBias&quot;</span>, m_InputParams.m_InputGateBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a>);</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;            <span class="stringliteral">&quot;m_ForgetGateBias&quot;</span>, m_InputParams.m_ForgetGateBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a>);</div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;            <span class="stringliteral">&quot;m_CellBias&quot;</span>, m_InputParams.m_CellBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a>);</div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;            <span class="stringliteral">&quot;m_OutputGateBias&quot;</span>, m_InputParams.m_OutputGateBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a>);</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;            <span class="stringliteral">&quot;m_ProjectionWeights&quot;</span>, m_InputParams.m_ProjectionWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a>);</div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;            <span class="stringliteral">&quot;m_ProjectionBias&quot;</span>, m_InputParams.m_ProjectionBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a>);</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;            <span class="stringliteral">&quot;m_InputLayerNormWeights&quot;</span>, m_InputParams.m_InputLayerNormWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a0cd848f65ec31778d708852f0042fe37">m_InputLayerNormWeights</a>);</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;            <span class="stringliteral">&quot;m_ForgetLayerNormWeights&quot;</span>, m_InputParams.m_ForgetLayerNormWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">m_ForgetLayerNormWeights</a>);</div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;            <span class="stringliteral">&quot;m_CellLayerNormWeights&quot;</span>, m_InputParams.m_CellLayerNormWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">m_CellLayerNormWeights</a>);</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;        this-&gt;<a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;            <span class="stringliteral">&quot;m_OutputLayerNormWeights&quot;</span>, m_InputParams.m_OutputLayerNormWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">m_OutputLayerNormWeights</a>);</div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;    }</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;    <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> m_InputParams;</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;};</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;</div><div class="line"><a name="l00178"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">  178</a></span>&#160;<a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeLstmCifgPeepholeNoProjection)</div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;{</div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;    <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a> descriptor;</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> = 4;</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a> = 0.0f;</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> = 0.0f;</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = <span class="keyword">true</span>; <span class="comment">// if this is true then we DON&#39;T need to set the OptCifgParams</span></div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    descriptor.<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="l00186"></a><span class="lineno">  186</span>&#160;    descriptor.<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="l00187"></a><span class="lineno">  187</span>&#160;</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;    <span class="keyword">const</span> uint32_t batchSize = 1;</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;    <span class="keyword">const</span> uint32_t inputSize = 2;</div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;    <span class="keyword">const</span> uint32_t numUnits = 4;</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;    <span class="keyword">const</span> uint32_t outputSize = numUnits;</div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputWeightsInfo1({numUnits, inputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;    std::vector&lt;float&gt; inputToForgetWeightsData = GenerateRandomData&lt;float&gt;(inputWeightsInfo1.GetNumElements());</div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(inputWeightsInfo1, inputToForgetWeightsData);</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;    std::vector&lt;float&gt; inputToCellWeightsData = GenerateRandomData&lt;float&gt;(inputWeightsInfo1.GetNumElements());</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(inputWeightsInfo1, inputToCellWeightsData);</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;    std::vector&lt;float&gt; inputToOutputWeightsData = GenerateRandomData&lt;float&gt;(inputWeightsInfo1.GetNumElements());</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToOutputWeights(inputWeightsInfo1, inputToOutputWeightsData);</div><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;</div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputWeightsInfo2({numUnits, outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;    std::vector&lt;float&gt; recurrentToForgetWeightsData = GenerateRandomData&lt;float&gt;(inputWeightsInfo2.GetNumElements());</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(inputWeightsInfo2, recurrentToForgetWeightsData);</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    std::vector&lt;float&gt; recurrentToCellWeightsData = GenerateRandomData&lt;float&gt;(inputWeightsInfo2.GetNumElements());</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(inputWeightsInfo2, recurrentToCellWeightsData);</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;</div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;    std::vector&lt;float&gt; recurrentToOutputWeightsData = GenerateRandomData&lt;float&gt;(inputWeightsInfo2.GetNumElements());</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(inputWeightsInfo2, recurrentToOutputWeightsData);</div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputWeightsInfo3({numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;    std::vector&lt;float&gt; cellToForgetWeightsData = GenerateRandomData&lt;float&gt;(inputWeightsInfo3.GetNumElements());</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToForgetWeights(inputWeightsInfo3, cellToForgetWeightsData);</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;    std::vector&lt;float&gt; cellToOutputWeightsData = GenerateRandomData&lt;float&gt;(inputWeightsInfo3.GetNumElements());</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToOutputWeights(inputWeightsInfo3, cellToOutputWeightsData);</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;    std::vector&lt;float&gt; forgetGateBiasData(numUnits, 1.0f);</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(inputWeightsInfo3, forgetGateBiasData);</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;    std::vector&lt;float&gt; cellBiasData(numUnits, 0.0f);</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(inputWeightsInfo3, cellBiasData);</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;    std::vector&lt;float&gt; outputGateBiasData(numUnits, 0.0f);</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(inputWeightsInfo3, outputGateBiasData);</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;    <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> params;</div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &amp;inputToForgetWeights;</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = &amp;inputToCellWeights;</div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &amp;inputToOutputWeights;</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &amp;recurrentToForgetWeights;</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = &amp;recurrentToCellWeights;</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &amp;recurrentToOutputWeights;</div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &amp;forgetGateBias;</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &amp;cellBias;</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &amp;outputGateBias;</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a> = &amp;cellToForgetWeights;</div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a> = &amp;cellToOutputWeights;</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer   = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateIn = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateIn = network-&gt;AddInputLayer(2);</div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;lstm&quot;</span>);</div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> lstmLayer = network-&gt;AddLstmLayer(descriptor, params, layerName.c_str());</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> scratchBuffer  = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateOut  = network-&gt;AddOutputLayer(1);</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateOut  = network-&gt;AddOutputLayer(2);</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer  = network-&gt;AddOutputLayer(3);</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;    <span class="comment">// connect up</span></div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160; 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   <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfoScratchBuff({ batchSize, numUnits * 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;</div><div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;</div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;    outputStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;    outputStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;</div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;    cellStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;    cellStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;</div><div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(scratchBuffer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(lstmTensorInfoScratchBuff);</div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;</div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputStateOut-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;</div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;</div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(3).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(3).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;    BOOST_CHECK(deserializedNetwork);</div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;</div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;    VerifyLstmLayer&lt;armnn::LstmDescriptor&gt; checker(</div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;        layerName,</div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;        {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},</div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;        {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;        descriptor,</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;        params);</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;    deserializedNetwork-&gt;ExecuteStrategy(checker);</div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;}</div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;</div><div class="line"><a name="l00292"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#a884784964d5b3e12dd1a0b76e63a85f9">  292</a></span>&#160;<a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeLstmNoCifgWithPeepholeAndProjection)</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;{</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;    <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a> descriptor;</div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> = 4;</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a> = 0.0f;</div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> = 0.0f;</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = <span class="keyword">false</span>; <span class="comment">// if this is true then we DON&#39;T need to set the OptCifgParams</span></div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    descriptor.<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="l00300"></a><span class="lineno">  300</span>&#160;    descriptor.<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="l00301"></a><span class="lineno">  301</span>&#160;</div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;    <span class="keyword">const</span> uint32_t batchSize = 2;</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;    <span class="keyword">const</span> uint32_t inputSize = 5;</div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;    <span class="keyword">const</span> uint32_t numUnits = 20;</div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;    <span class="keyword">const</span> uint32_t outputSize = 16;</div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;</div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20x5({numUnits, inputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;    std::vector&lt;float&gt; inputToInputWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);</div><div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;</div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;    std::vector&lt;float&gt; inputToForgetWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;    std::vector&lt;float&gt; inputToCellWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);</div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160; 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   <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputGateBias(tensorInfo20, inputGateBiasData);</div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;</div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;    std::vector&lt;float&gt; forgetGateBiasData = GenerateRandomData&lt;float&gt;(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(tensorInfo20, forgetGateBiasData);</div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;</div><div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;    std::vector&lt;float&gt; cellBiasData = GenerateRandomData&lt;float&gt;(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(tensorInfo20, cellBiasData);</div><div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;</div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160; 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   <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);</div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;</div><div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;    std::vector&lt;float&gt; recurrentToForgetWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);</div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;</div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;    std::vector&lt;float&gt; recurrentToCellWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160; 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   BOOST_CHECK(deserializedNetwork);</div><div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;</div><div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;    VerifyLstmLayer&lt;armnn::LstmDescriptor&gt; checker(</div><div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;        layerName,</div><div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;        {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},</div><div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;        {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;        descriptor,</div><div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;        params);</div><div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;    deserializedNetwork-&gt;ExecuteStrategy(checker);</div><div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;}</div><div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;</div><div class="line"><a name="l00438"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#ab6ffd7bf1455358bc87321974530cc58">  438</a></span>&#160;<a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeLstmNoCifgWithPeepholeWithProjectionWithLayerNorm)</div><div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;{</div><div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160; 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<span class="comment">// if this is true then we DON&#39;T need to set the OptCifgParams</span></div><div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;    descriptor.<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="l00446"></a><span class="lineno">  446</span>&#160;    descriptor.<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="l00447"></a><span class="lineno">  447</span>&#160;    descriptor.<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="l00448"></a><span class="lineno">  448</span>&#160;</div><div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160; 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   std::vector&lt;float&gt; inputToInputWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);</div><div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;</div><div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;    std::vector&lt;float&gt; inputToForgetWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);</div><div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;</div><div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160; 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   <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20({numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;    std::vector&lt;float&gt; inputGateBiasData = GenerateRandomData&lt;float&gt;(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputGateBias(tensorInfo20, inputGateBiasData);</div><div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;</div><div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;    std::vector&lt;float&gt; forgetGateBiasData = GenerateRandomData&lt;float&gt;(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(tensorInfo20, forgetGateBiasData);</div><div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;</div><div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;    std::vector&lt;float&gt; cellBiasData = GenerateRandomData&lt;float&gt;(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(tensorInfo20, cellBiasData);</div><div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;</div><div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;    std::vector&lt;float&gt; outputGateBiasData = GenerateRandomData&lt;float&gt;(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(tensorInfo20, outputGateBiasData);</div><div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;</div><div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160; 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   <span class="comment">// additional params because: descriptor.m_CifgEnabled = false</span></div><div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a> = &amp;inputToInputWeights;</div><div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a> = &amp;recurrentToInputWeights;</div><div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">m_CellToInputWeights</a> = &amp;cellToInputWeights;</div><div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a> = &amp;inputGateBias;</div><div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;</div><div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;    <span class="comment">// additional params because: descriptor.m_ProjectionEnabled = true</span></div><div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a> = &amp;projectionWeights;</div><div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a> = &amp;projectionBias;</div><div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;</div><div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;    <span class="comment">// additional params because: descriptor.m_PeepholeEnabled = true</span></div><div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a> = &amp;cellToForgetWeights;</div><div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a> = &amp;cellToOutputWeights;</div><div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;</div><div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160;    <span class="comment">// additional params because: despriptor.m_LayerNormEnabled = true</span></div><div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a0cd848f65ec31778d708852f0042fe37">m_InputLayerNormWeights</a> = &amp;inputLayerNormWeights;</div><div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">m_ForgetLayerNormWeights</a> = &amp;forgetLayerNormWeights;</div><div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">m_CellLayerNormWeights</a> = &amp;cellLayerNormWeights;</div><div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">m_OutputLayerNormWeights</a> = &amp;outLayerNormWeights;</div><div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;</div><div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer   = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l00555"></a><span class="lineno">  555</span>&#160; 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   <span class="comment">// connect up</span></div><div class="line"><a name="l00565"></a><span class="lineno">  565</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ batchSize, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00566"></a><span class="lineno">  566</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateTensorInfo({ batchSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateTensorInfo({ batchSize, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00568"></a><span class="lineno">  568</span>&#160; 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   outputStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00574"></a><span class="lineno">  574</span>&#160;    outputStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l00575"></a><span class="lineno">  575</span>&#160;</div><div class="line"><a name="l00576"></a><span class="lineno">  576</span>&#160;    cellStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l00577"></a><span class="lineno">  577</span>&#160;    cellStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l00578"></a><span class="lineno">  578</span>&#160;</div><div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(scratchBuffer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00580"></a><span class="lineno">  580</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(lstmTensorInfoScratchBuff);</div><div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160;</div><div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputStateOut-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00583"></a><span class="lineno">  583</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160;</div><div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160; 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           {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160;            descriptor,</div><div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160;            params);</div><div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160;    deserializedNetwork-&gt;ExecuteStrategy(checker);</div><div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160;}</div><div class="line"><a name="l00602"></a><span class="lineno">  602</span>&#160;</div><div class="line"><a name="l00603"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#ab1042b9567ba6e028411498f0387fbbd">  603</a></span>&#160;<a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">BOOST_AUTO_TEST_CASE</a>(EnsureLstmLayersBackwardCompatibility)</div><div class="line"><a name="l00604"></a><span class="lineno">  604</span>&#160;{</div><div class="line"><a name="l00605"></a><span class="lineno">  605</span>&#160;    <span class="comment">// The hex data below is a flat buffer containing a lstm layer with no Cifg, with peephole and projection</span></div><div class="line"><a name="l00606"></a><span class="lineno">  606</span>&#160;    <span class="comment">// enabled. That data was obtained before additional layer normalization parameters where added to the</span></div><div class="line"><a name="l00607"></a><span class="lineno">  607</span>&#160;    <span class="comment">// lstm serializer. That way it can be tested if a lstm model with the old parameter configuration can</span></div><div class="line"><a name="l00608"></a><span class="lineno">  608</span>&#160;    <span class="comment">// still be loaded</span></div><div class="line"><a name="l00609"></a><span class="lineno">  609</span>&#160;    <span class="keyword">const</span> std::vector&lt;uint8_t&gt; lstmNoCifgWithPeepholeAndProjectionModel =</div><div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160;    {</div><div class="line"><a name="l00611"></a><span class="lineno">  611</span>&#160;        0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l00612"></a><span class="lineno">  612</span>&#160;        0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x2C, 0x00, 0x00, 0x00, 0x38, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00613"></a><span class="lineno">  613</span>&#160;        0xDC, 0x29, 0x00, 0x00, 0x38, 0x29, 0x00, 0x00, 0xB4, 0x28, 0x00, 0x00, 0x94, 0x01, 0x00, 0x00, 0x3C, 0x01,</div><div class="line"><a name="l00614"></a><span class="lineno">  614</span>&#160;        0x00, 0x00, 0xE0, 0x00, 0x00, 0x00, 0x84, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00615"></a><span class="lineno">  615</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l00616"></a><span class="lineno">  616</span>&#160;        0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x70, 0xD6, 0xFF, 0xFF,</div><div class="line"><a name="l00617"></a><span class="lineno">  617</span>&#160;        0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x06, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x88, 0xD7,</div><div class="line"><a name="l00618"></a><span class="lineno">  618</span>&#160;        0xFF, 0xFF, 0x08, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xF6, 0xD6, 0xFF, 0xFF, 0x07, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00619"></a><span class="lineno">  619</span>&#160;        0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00620"></a><span class="lineno">  620</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00621"></a><span class="lineno">  621</span>&#160;        0xE8, 0xD7, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xC8, 0xD6, 0xFF, 0xFF, 0x00, 0x00,</div><div class="line"><a name="l00622"></a><span class="lineno">  622</span>&#160;        0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x5E, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xE0, 0xD7, 0xFF, 0xFF,</div><div class="line"><a name="l00623"></a><span class="lineno">  623</span>&#160;        0x08, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x4E, 0xD7, 0xFF, 0xFF, 0x06, 0x00, 0x00, 0x00, 0x10, 0x00,</div><div class="line"><a name="l00624"></a><span class="lineno">  624</span>&#160;        0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00625"></a><span class="lineno">  625</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0xD8,</div><div class="line"><a name="l00626"></a><span class="lineno">  626</span>&#160;        0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x20, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B,</div><div class="line"><a name="l00627"></a><span class="lineno">  627</span>&#160;        0x04, 0x00, 0x00, 0x00, 0xB6, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x38, 0xD8, 0xFF, 0xFF, 0x08, 0x00,</div><div class="line"><a name="l00628"></a><span class="lineno">  628</span>&#160;        0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0xA6, 0xD7, 0xFF, 0xFF, 0x05, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00629"></a><span class="lineno">  629</span>&#160;        0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00630"></a><span class="lineno">  630</span>&#160;        0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xD8, 0xFF, 0xFF,</div><div class="line"><a name="l00631"></a><span class="lineno">  631</span>&#160;        0x03, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x78, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00,</div><div class="line"><a name="l00632"></a><span class="lineno">  632</span>&#160;        0x00, 0x00, 0x0E, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x16, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00633"></a><span class="lineno">  633</span>&#160;        0xFA, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00,</div><div class="line"><a name="l00634"></a><span class="lineno">  634</span>&#160;        0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00635"></a><span class="lineno">  635</span>&#160;        0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xD8, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00636"></a><span class="lineno">  636</span>&#160;        0x00, 0x00, 0x6C, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x23, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l00637"></a><span class="lineno">  637</span>&#160;        0x12, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0xE0, 0x25, 0x00, 0x00, 0xD0, 0x25,</div><div class="line"><a name="l00638"></a><span class="lineno">  638</span>&#160;        0x00, 0x00, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0x00, 0x48, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00,</div><div class="line"><a name="l00639"></a><span class="lineno">  639</span>&#160;        0x10, 0x00, 0x14, 0x00, 0x18, 0x00, 0x1C, 0x00, 0x20, 0x00, 0x24, 0x00, 0x28, 0x00, 0x2C, 0x00, 0x30, 0x00,</div><div class="line"><a name="l00640"></a><span class="lineno">  640</span>&#160;        0x34, 0x00, 0x38, 0x00, 0x3C, 0x00, 0x40, 0x00, 0x44, 0x00, 0x26, 0x00, 0x00, 0x00, 0xC4, 0x23, 0x00, 0x00,</div><div class="line"><a name="l00641"></a><span class="lineno">  641</span>&#160;        0xF8, 0x21, 0x00, 0x00, 0x2C, 0x20, 0x00, 0x00, 0xF0, 0x1A, 0x00, 0x00, 0xB4, 0x15, 0x00, 0x00, 0x78, 0x10,</div><div class="line"><a name="l00642"></a><span class="lineno">  642</span>&#160;        0x00, 0x00, 0xF0, 0x0F, 0x00, 0x00, 0x68, 0x0F, 0x00, 0x00, 0xE0, 0x0E, 0x00, 0x00, 0x14, 0x0D, 0x00, 0x00,</div><div class="line"><a name="l00643"></a><span class="lineno">  643</span>&#160;        0xD8, 0x07, 0x00, 0x00, 0x50, 0x07, 0x00, 0x00, 0xC8, 0x06, 0x00, 0x00, 0x8C, 0x01, 0x00, 0x00, 0x14, 0x01,</div><div class="line"><a name="l00644"></a><span class="lineno">  644</span>&#160;        0x00, 0x00, 0x8C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xEE, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03,</div><div class="line"><a name="l00645"></a><span class="lineno">  645</span>&#160;        0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xFE, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x14, 0x00,</div><div class="line"><a name="l00646"></a><span class="lineno">  646</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00647"></a><span class="lineno">  647</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00648"></a><span class="lineno">  648</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00649"></a><span class="lineno">  649</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00650"></a><span class="lineno">  650</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5A, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01,</div><div class="line"><a name="l00651"></a><span class="lineno">  651</span>&#160;        0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x72, 0xD8,</div><div class="line"><a name="l00652"></a><span class="lineno">  652</span>&#160;        0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x82, 0xD9, 0xFF, 0xFF,</div><div class="line"><a name="l00653"></a><span class="lineno">  653</span>&#160;        0x04, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00654"></a><span class="lineno">  654</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00655"></a><span class="lineno">  655</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00656"></a><span class="lineno">  656</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00657"></a><span class="lineno">  657</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDE, 0xD8,</div><div class="line"><a name="l00658"></a><span class="lineno">  658</span>&#160;        0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00659"></a><span class="lineno">  659</span>&#160;        0x14, 0x00, 0x00, 0x00, 0xF6, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x54, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l00660"></a><span class="lineno">  660</span>&#160;        0x00, 0x00, 0x06, 0xDA, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00661"></a><span class="lineno">  661</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00662"></a><span class="lineno">  662</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00663"></a><span class="lineno">  663</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00664"></a><span class="lineno">  664</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xD9, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00665"></a><span class="lineno">  665</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x6A, 0xD9, 0xFF, 0xFF, 0x00, 0x00,</div><div class="line"><a name="l00666"></a><span class="lineno">  666</span>&#160;        0x00, 0x03, 0x14, 0x05, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x7A, 0xDA, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00667"></a><span class="lineno">  667</span>&#160;        0x40, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00668"></a><span class="lineno">  668</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00669"></a><span class="lineno">  669</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00670"></a><span class="lineno">  670</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00671"></a><span class="lineno">  671</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00672"></a><span class="lineno">  672</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00673"></a><span class="lineno">  673</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00674"></a><span class="lineno">  674</span>&#160; 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   <span class="comment">// additional params because: descriptor.m_ProjectionEnabled = true</span></div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a>        = &amp;projectionWeights;</div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a>           = &amp;projectionBias;</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span>&#160;</div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>&#160;    <span class="comment">// additional params because: descriptor.m_PeepholeEnabled = true</span></div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a>      = &amp;cellToForgetWeights;</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a>      = &amp;cellToOutputWeights;</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>&#160;</div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;lstm&quot;</span>);</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ batchSize, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateTensorInfo({ batchSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateTensorInfo({ batchSize, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>&#160;</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>&#160;    VerifyLstmLayer&lt;armnn::LstmDescriptor&gt; checker(</div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>&#160;            layerName,</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>&#160;            {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},</div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>&#160;            {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>&#160;            descriptor,</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>&#160;            params);</div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>&#160;    deserializedNetwork-&gt;ExecuteStrategy(checker);</div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>&#160;}</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>&#160;</div><div class="line"><a name="l01335"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#a5b74bdb50eae6e911a1eb4c3a311f536"> 1335</a></span>&#160;<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a> <a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a5b74bdb50eae6e911a1eb4c3a311f536">ConstantsVector2QuantizedLstmInputParams</a>(</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>&#160;        <span class="keyword">const</span> std::vector&lt;armnn::ConstTensor&gt;&amp; constants)</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>&#160;{</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>&#160;    <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a> params;</div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>&#160;</div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>&#160;    <span class="comment">// index for constants vector</span></div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>&#160;    <span class="keywordtype">size_t</span> i = 0;</div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span>&#160;</div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span>&#160;    <span class="comment">// Get input parameters</span></div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a>  = &amp;constants[i++];</div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &amp;constants[i++];</div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a>   = &amp;constants[i++];</div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &amp;constants[i++];</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>&#160;</div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a>  = &amp;constants[i++];</div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &amp;constants[i++];</div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a>   = &amp;constants[i++];</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &amp;constants[i++];</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span>&#160;</div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a>  = &amp;constants[i++];</div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &amp;constants[i++];</div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a>       = &amp;constants[i++];</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &amp;constants[i++];</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>&#160;</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>&#160;    <span class="keywordflow">return</span> params;</div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>&#160;}</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>&#160;</div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>&#160;<span class="keyword">class </span>VerifyQuantizedLstmLayer : <span class="keyword">public</span> <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a></div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>&#160;{</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>&#160;</div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>&#160;    VerifyQuantizedLstmLayer(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>&#160;                             <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span>&#160;                             <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos,</div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span>&#160;                             <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a>&amp; inputParams)</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>&#160;        : <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a>(layerName, inputInfos, outputInfos), m_InputParams(inputParams) {}</div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span>&#160;</div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span>&#160;    <span class="keywordtype">void</span> ExecuteStrategy(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span>&#160;                         <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a>&amp; descriptor,</div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span>&#160;                         <span class="keyword">const</span> std::vector&lt;armnn::ConstTensor&gt;&amp; constants,</div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>&#160;                         <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>&#160;                         <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span> = 0)<span class="keyword"> override</span></div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span>&#160;<span class="keyword">    </span>{</div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(descriptor, constants, <span class="keywordtype">id</span>);</div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>&#160;        <span class="keywordflow">switch</span> (layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">GetType</a>())</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>&#160;        {</div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span>&#160;            <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>&#160;            <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span>&#160; 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               <span class="keywordflow">break</span>;</div><div class="line"><a name="l01389"></a><span class="lineno"> 1389</span>&#160;            }</div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</span>&#160;            <span class="keywordflow">default</span>:</div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>&#160;            {</div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>&#160;                <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(fmt::format(<span class="stringliteral">&quot;Unexpected layer type in QuantizedLstm test model:&quot;</span>,</div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>&#160;                                                           layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>()));</div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>&#160;            }</div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>&#160;        }</div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>&#160;    }</div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>&#160;</div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span>&#160;<span class="keyword">protected</span>:</div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span>&#160;    <span class="keywordtype">void</span> VerifyInputParameters(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a>&amp; params)</div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>&#160;    {</div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_InputToInputWeights&quot;</span>,</div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>&#160;                           m_InputParams.m_InputToInputWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a>);</div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_InputToForgetWeights&quot;</span>,</div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>&#160;                           m_InputParams.m_InputToForgetWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a>);</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_InputToCellWeights&quot;</span>,</div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>&#160;                           m_InputParams.m_InputToCellWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a>);</div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_InputToOutputWeights&quot;</span>,</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>&#160;                           m_InputParams.m_InputToOutputWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a>);</div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_RecurrentToInputWeights&quot;</span>,</div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span>&#160;                           m_InputParams.m_RecurrentToInputWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a>);</div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_RecurrentToForgetWeights&quot;</span>,</div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>&#160;                           m_InputParams.m_RecurrentToForgetWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a>);</div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_RecurrentToCellWeights&quot;</span>,</div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span>&#160;                           m_InputParams.m_RecurrentToCellWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a>);</div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_RecurrentToOutputWeights&quot;</span>,</div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span>&#160;                           m_InputParams.m_RecurrentToOutputWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a>);</div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_InputGateBias&quot;</span>,</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>&#160;                           m_InputParams.m_InputGateBias, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a>);</div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_ForgetGateBias&quot;</span>,</div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>&#160;                           m_InputParams.m_ForgetGateBias, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a>);</div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_CellBias&quot;</span>,</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>&#160;                           m_InputParams.m_CellBias, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a>);</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_OutputGateBias&quot;</span>,</div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>&#160;                           m_InputParams.m_OutputGateBias, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a>);</div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>&#160;    }</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>&#160;</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>&#160;    <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a> m_InputParams;</div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>&#160;};</div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span>&#160;</div><div class="line"><a name="l01431"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#a1cb7aec5bf87ff679cfd0ee9aa7d41c2"> 1431</a></span>&#160;<a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeQuantizedLstm)</div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span>&#160;{</div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span>&#160;    <span class="keyword">const</span> uint32_t batchSize = 1;</div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>&#160;    <span class="keyword">const</span> uint32_t inputSize = 2;</div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span>&#160; 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   int32_t cellStateOffset = 0;</div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>&#160;</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>&#160;    <span class="keywordtype">float</span> weightsScale = 0.00408021f;</div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>&#160;    int32_t weightsOffset = 100;</div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</span>&#160;</div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span>&#160;    <span class="keywordtype">float</span> biasScale = 3.1876640625e-05f;</div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span>&#160;    int32_t biasOffset = 0;</div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span>&#160;</div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>&#160;    <span class="comment">// The shape of weight data is {outputSize, inputSize} = {4, 2}</span></div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>&#160; 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   <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToInputWeights(inputToInputWeightsInfo, inputToInputWeightsData);</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>&#160;</div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputToForgetWeightsShape = {4, 2};</div><div class="line"><a name="l01461"></a><span class="lineno"> 1461</span>&#160;    std::vector&lt;uint8_t&gt; inputToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};</div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputToForgetWeightsInfo(inputToForgetWeightsShape,</div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span>&#160;                                               <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</span>&#160;                                               weightsScale,</div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span>&#160;                                               weightsOffset);</div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(inputToForgetWeightsInfo, inputToForgetWeightsData);</div><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span>&#160;</div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputToCellWeightsShape = {4, 2};</div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span>&#160;    std::vector&lt;uint8_t&gt; inputToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};</div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputToCellWeightsInfo(inputToCellWeightsShape,</div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span>&#160;                                             <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</span>&#160;                                             weightsScale,</div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span>&#160;                                             weightsOffset);</div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(inputToCellWeightsInfo, inputToCellWeightsData);</div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span>&#160;</div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputToOutputWeightsShape = {4, 2};</div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span>&#160; 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                                                 weightsScale,</div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>&#160;                                                  weightsOffset);</div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToInputWeights(recurrentToInputWeightsInfo, recurrentToInputWeightsData);</div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span>&#160;</div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> recurrentToForgetWeightsShape = {4, 4};</div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span>&#160;    std::vector&lt;uint8_t&gt; recurrentToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};</div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span>&#160; 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                                                weightsOffset);</div><div class="line"><a name="l01507"></a><span class="lineno"> 1507</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(recurrentToCellWeightsInfo, recurrentToCellWeightsData);</div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span>&#160;</div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> recurrentToOutputWeightsShape = {4, 4};</div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>&#160;    std::vector&lt;uint8_t&gt; recurrentToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};</div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> recurrentToOutputWeightsInfo(recurrentToOutputWeightsShape,</div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>&#160;                                                   <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span>&#160;                                                   weightsScale,</div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span>&#160;                                                   weightsOffset);</div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(recurrentToOutputWeightsInfo, recurrentToOutputWeightsData);</div><div class="line"><a name="l01516"></a><span class="lineno"> 1516</span>&#160;</div><div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>&#160;    <span class="comment">// The shape of bias data is {outputSize} = {4}</span></div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputGateBiasShape = {4};</div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span>&#160;    std::vector&lt;int32_t&gt; inputGateBiasData = {1, 2, 3, 4};</div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputGateBiasInfo(inputGateBiasShape,</div><div class="line"><a name="l01521"></a><span class="lineno"> 1521</span>&#160;                                        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span>&#160;                                        biasScale,</div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>&#160;                                        biasOffset);</div><div class="line"><a name="l01524"></a><span class="lineno"> 1524</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputGateBias(inputGateBiasInfo, inputGateBiasData);</div><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span>&#160;</div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> forgetGateBiasShape = {4};</div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span>&#160;    std::vector&lt;int32_t&gt; forgetGateBiasData = {1, 2, 3, 4};</div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> forgetGateBiasInfo(forgetGateBiasShape,</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>&#160;                                         <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span>&#160;                                         biasScale,</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span>&#160;                                         biasOffset);</div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(forgetGateBiasInfo, forgetGateBiasData);</div><div class="line"><a name="l01533"></a><span class="lineno"> 1533</span>&#160;</div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> cellBiasShape = {4};</div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span>&#160;    std::vector&lt;int32_t&gt; cellBiasData = {1, 2, 3, 4};</div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellBiasInfo(cellBiasShape,</div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span>&#160;                                   <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span>&#160;                                   biasScale,</div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>&#160;                                   biasOffset);</div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(cellBiasInfo, cellBiasData);</div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span>&#160;</div><div class="line"><a name="l01542"></a><span class="lineno"> 1542</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> outputGateBiasShape = {4};</div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span>&#160;    std::vector&lt;int32_t&gt; outputGateBiasData = {1, 2, 3, 4};</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputGateBiasInfo(outputGateBiasShape,</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span>&#160;                                         <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>&#160;                                         biasScale,</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>&#160;                                         biasOffset);</div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(outputGateBiasInfo, outputGateBiasData);</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span>&#160;</div><div class="line"><a name="l01550"></a><span class="lineno"> 1550</span>&#160;    <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a> params;</div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a> = &amp;inputToInputWeights;</div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &amp;inputToForgetWeights;</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = &amp;inputToCellWeights;</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &amp;inputToOutputWeights;</div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a> = &amp;recurrentToInputWeights;</div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &amp;recurrentToForgetWeights;</div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = &amp;recurrentToCellWeights;</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &amp;recurrentToOutputWeights;</div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a> = &amp;inputGateBias;</div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &amp;forgetGateBias;</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &amp;cellBias;</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &amp;outputGateBias;</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span>&#160;</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateIn = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateIn = network-&gt;AddInputLayer(2);</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;QuantizedLstm&quot;</span>);</div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> quantizedLstmLayer = network-&gt;AddQuantizedLstmLayer(params, layerName.c_str());</div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateOut = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(1);</div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span>&#160;</div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span>&#160;    <span class="comment">// Connect up</span></div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ batchSize, inputSize },</div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span>&#160;                                      <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span>&#160;                                      inputOutputScale,</div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span>&#160;                                      inputOutputOffset);</div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateTensorInfo({ batchSize, numUnits },</div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span>&#160;                                          <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span>&#160;                                          cellStateScale,</div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span>&#160;                                          cellStateOffset);</div><div class="line"><a name="l01582"></a><span class="lineno"> 1582</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateTensorInfo({ batchSize, outputSize },</div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span>&#160;                                            <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01584"></a><span class="lineno"> 1584</span>&#160;                                            inputOutputScale,</div><div class="line"><a name="l01585"></a><span class="lineno"> 1585</span>&#160;                                            inputOutputOffset);</div><div class="line"><a name="l01586"></a><span class="lineno"> 1586</span>&#160;</div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(quantizedLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l01589"></a><span class="lineno"> 1589</span>&#160;</div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span>&#160;    cellStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(quantizedLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>&#160;    cellStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span>&#160;</div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>&#160;    outputStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(quantizedLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>&#160;    outputStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span>&#160;</div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span>&#160;    quantizedLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>&#160;    quantizedLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l01598"></a><span class="lineno"> 1598</span>&#160;</div><div class="line"><a name="l01599"></a><span class="lineno"> 1599</span>&#160;    quantizedLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>&#160;    quantizedLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l01601"></a><span class="lineno"> 1601</span>&#160;</div><div class="line"><a name="l01602"></a><span class="lineno"> 1602</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01603"></a><span class="lineno"> 1603</span>&#160;    BOOST_CHECK(deserializedNetwork);</div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</span>&#160;</div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</span>&#160;    VerifyQuantizedLstmLayer checker(layerName,</div><div class="line"><a name="l01606"></a><span class="lineno"> 1606</span>&#160;                                     {inputTensorInfo, cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span>&#160;                                     {cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span>&#160;                                     params);</div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span>&#160;</div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span>&#160;    deserializedNetwork-&gt;ExecuteStrategy(checker);</div><div class="line"><a name="l01611"></a><span class="lineno"> 1611</span>&#160;}</div><div class="line"><a name="l01612"></a><span class="lineno"> 1612</span>&#160;</div><div class="line"><a name="l01613"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#abc688c3d986dd56e23ecb486712596d0"> 1613</a></span>&#160;<a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeQLstmBasic)</div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span>&#160;{</div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span>&#160;    <a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml">armnn::QLstmDescriptor</a> descriptor;</div><div class="line"><a name="l01616"></a><span class="lineno"> 1616</span>&#160;</div><div class="line"><a name="l01617"></a><span class="lineno"> 1617</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a>       = <span class="keyword">true</span>;</div><div class="line"><a name="l01618"></a><span class="lineno"> 1618</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a>   = <span class="keyword">false</span>;</div><div class="line"><a name="l01620"></a><span class="lineno"> 1620</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">m_LayerNormEnabled</a>  = <span class="keyword">false</span>;</div><div class="line"><a name="l01621"></a><span class="lineno"> 1621</span>&#160;</div><div class="line"><a name="l01622"></a><span class="lineno"> 1622</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#ac81fb0e66dc623dc37c77f219f53a6d3">m_CellClip</a>       = 0.0f;</div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa6a518b65088f34803b3214334bdff61">m_ProjectionClip</a> = 0.0f;</div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span>&#160;</div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a09e1f097944f61cc901240f9300364cf">m_InputIntermediateScale</a>  = 0.00001f;</div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#afec7f36158448f723b426a9527acb189">m_ForgetIntermediateScale</a> = 0.00001f;</div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a0477ee1b44ace6090119178eea78cb0b">m_CellIntermediateScale</a>   = 0.00001f;</div><div class="line"><a name="l01628"></a><span class="lineno"> 1628</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa43409f9b457352c95c89f20ce5d844d">m_OutputIntermediateScale</a> = 0.00001f;</div><div class="line"><a name="l01629"></a><span class="lineno"> 1629</span>&#160;</div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#af8f724af7210b52529216feefa993c98">m_HiddenStateScale</a>     = 0.07f;</div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4556cbd764d4848d8ad0637a9eed580d">m_HiddenStateZeroPoint</a> = 0;</div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span>&#160;</div><div class="line"><a name="l01633"></a><span class="lineno"> 1633</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numBatches = 2;</div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize  = 5;</div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 4;</div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numUnits   = 4;</div><div class="line"><a name="l01637"></a><span class="lineno"> 1637</span>&#160;</div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span>&#160; 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   int32_t cellStateOffset = 0;</div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>&#160;</div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>&#160;    <span class="keywordtype">float</span> weightsScale    = 0.007f;</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span>&#160;    int32_t weightsOffset = 0;</div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>&#160;</div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>&#160;    <span class="keywordtype">float</span> biasScale    = 3.5002e-05f / 1024;</div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>&#160;    int32_t biasOffset = 0;</div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span>&#160;</div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>&#160;    <span class="comment">// Weights and bias tensor and quantization info</span></div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputWeightsInfo({numUnits, inputSize},</div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span>&#160;                                       <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>,</div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span>&#160;                                       weightsScale,</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span>&#160;                                       weightsOffset);</div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span>&#160;</div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> recurrentWeightsInfo({numUnits, outputSize},</div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span>&#160;                                           <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>,</div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span>&#160;                                           weightsScale,</div><div class="line"><a name="l01663"></a><span class="lineno"> 1663</span>&#160;                                           weightsOffset);</div><div class="line"><a name="l01664"></a><span class="lineno"> 1664</span>&#160;</div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasInfo({numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>, biasScale, biasOffset);</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span>&#160;</div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span>&#160; 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   <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToOutputWeights(inputWeightsInfo, inputToOutputWeightsData);</div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span>&#160;</div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span>&#160;    std::vector&lt;int8_t&gt; recurrentToForgetWeightsData =</div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span>&#160;            GenerateRandomData&lt;int8_t&gt;(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>&#160;    std::vector&lt;int8_t&gt; recurrentToCellWeightsData   =</div><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span>&#160;            GenerateRandomData&lt;int8_t&gt;(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span>&#160;    std::vector&lt;int8_t&gt; recurrentToOutputWeightsData =</div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span>&#160;            GenerateRandomData&lt;int8_t&gt;(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</span>&#160;</div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);</div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>&#160;</div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>&#160; 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   <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> input         = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateIn = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateIn   = network-&gt;AddInputLayer(2);</div><div class="line"><a name="l01715"></a><span class="lineno"> 1715</span>&#160;</div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> qLstmLayer = network-&gt;AddQLstmLayer(descriptor, params, layerName.c_str());</div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span>&#160;</div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateOut = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateOut   = network-&gt;AddOutputLayer(1);</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer    = network-&gt;AddOutputLayer(2);</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>&#160;</div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>&#160;    <span class="comment">// Input/Output tensor info</span></div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({numBatches , inputSize},</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span>&#160;                                <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>,</div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span>&#160;                                inputScale,</div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>&#160;                                inputOffset);</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span>&#160;</div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateInfo({numBatches , numUnits},</div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>&#160;                                    <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>&#160;                                    cellStateScale,</div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span>&#160;                                    cellStateOffset);</div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span>&#160;</div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateInfo({numBatches , outputSize},</div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span>&#160;                                      <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>,</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span>&#160;                                      outputScale,</div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span>&#160;                                      outputOffset);</div><div class="line"><a name="l01737"></a><span class="lineno"> 1737</span>&#160;</div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span>&#160;    <span class="comment">// Connect input/output slots</span></div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span>&#160;</div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span>&#160;    outputStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>&#160;    outputStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateInfo);</div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span>&#160;</div><div class="line"><a name="l01745"></a><span class="lineno"> 1745</span>&#160;    cellStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l01746"></a><span class="lineno"> 1746</span>&#160;    cellStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateInfo);</div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span>&#160;</div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span>&#160;    qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputStateOut-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span>&#160;    qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateInfo);</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span>&#160;</div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span>&#160;    qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span>&#160;    qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateInfo);</div><div class="line"><a name="l01753"></a><span class="lineno"> 1753</span>&#160;</div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span>&#160;    qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span>&#160;    qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateInfo);</div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span>&#160;</div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span>&#160;    BOOST_CHECK(deserializedNetwork);</div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>&#160;</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span>&#160;    VerifyLstmLayer&lt;armnn::QLstmDescriptor&gt; checker(</div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>&#160;            layerName,</div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span>&#160;            {inputInfo, cellStateInfo, outputStateInfo},</div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>&#160;            {outputStateInfo, cellStateInfo, outputStateInfo},</div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>&#160;            descriptor,</div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span>&#160;            params);</div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span>&#160;</div><div class="line"><a name="l01767"></a><span class="lineno"> 1767</span>&#160;    deserializedNetwork-&gt;ExecuteStrategy(checker);</div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span>&#160;}</div><div class="line"><a name="l01769"></a><span class="lineno"> 1769</span>&#160;</div><div class="line"><a name="l01770"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#ac6d8113d32eed6458ee490bd3de911d5"> 1770</a></span>&#160;<a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeQLstmCifgLayerNorm)</div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span>&#160;{</div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span>&#160;    <a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml">armnn::QLstmDescriptor</a> descriptor;</div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span>&#160;</div><div class="line"><a name="l01774"></a><span class="lineno"> 1774</span>&#160;    <span class="comment">// CIFG params are used when CIFG is disabled</span></div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a>       = <span class="keyword">true</span>;</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a>   = <span class="keyword">false</span>;</div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">m_LayerNormEnabled</a>  = <span class="keyword">true</span>;</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>&#160;</div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#ac81fb0e66dc623dc37c77f219f53a6d3">m_CellClip</a>       = 0.0f;</div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa6a518b65088f34803b3214334bdff61">m_ProjectionClip</a> = 0.0f;</div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>&#160;</div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a09e1f097944f61cc901240f9300364cf">m_InputIntermediateScale</a>  = 0.00001f;</div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#afec7f36158448f723b426a9527acb189">m_ForgetIntermediateScale</a> = 0.00001f;</div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a0477ee1b44ace6090119178eea78cb0b">m_CellIntermediateScale</a>   = 0.00001f;</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa43409f9b457352c95c89f20ce5d844d">m_OutputIntermediateScale</a> = 0.00001f;</div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span>&#160;</div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#af8f724af7210b52529216feefa993c98">m_HiddenStateScale</a>     = 0.07f;</div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4556cbd764d4848d8ad0637a9eed580d">m_HiddenStateZeroPoint</a> = 0;</div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span>&#160;</div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numBatches = 2;</div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize  = 5;</div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 4;</div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numUnits   = 4;</div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span>&#160;</div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span>&#160; 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   int32_t biasOffset = 0;</div><div class="line"><a name="l01814"></a><span class="lineno"> 1814</span>&#160;</div><div class="line"><a name="l01815"></a><span class="lineno"> 1815</span>&#160;    <span class="comment">// Weights and bias tensor and quantization info</span></div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputWeightsInfo({numUnits, inputSize},</div><div class="line"><a name="l01817"></a><span class="lineno"> 1817</span>&#160;                                       <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>,</div><div class="line"><a name="l01818"></a><span class="lineno"> 1818</span>&#160;                                       weightsScale,</div><div class="line"><a name="l01819"></a><span class="lineno"> 1819</span>&#160;                                       weightsOffset);</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span>&#160;</div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> recurrentWeightsInfo({numUnits, outputSize},</div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span>&#160;                                           <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>,</div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span>&#160;                                           weightsScale,</div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span>&#160;                                           weightsOffset);</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>&#160;</div><div class="line"><a name="l01826"></a><span class="lineno"> 1826</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasInfo({numUnits},</div><div class="line"><a name="l01827"></a><span class="lineno"> 1827</span>&#160;                               <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l01828"></a><span class="lineno"> 1828</span>&#160;                               biasScale,</div><div class="line"><a name="l01829"></a><span class="lineno"> 1829</span>&#160;                               biasOffset);</div><div class="line"><a name="l01830"></a><span class="lineno"> 1830</span>&#160;</div><div class="line"><a name="l01831"></a><span class="lineno"> 1831</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> layerNormWeightsInfo({numUnits},</div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</span>&#160;                                           <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</span>&#160;                                           layerNormScale,</div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>&#160;                                           layerNormOffset);</div><div class="line"><a name="l01835"></a><span class="lineno"> 1835</span>&#160;</div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>&#160;    <span class="comment">// Mandatory params</span></div><div class="line"><a name="l01837"></a><span class="lineno"> 1837</span>&#160;    std::vector&lt;int8_t&gt; inputToForgetWeightsData = GenerateRandomData&lt;int8_t&gt;(inputWeightsInfo.GetNumElements());</div><div class="line"><a name="l01838"></a><span class="lineno"> 1838</span>&#160;    std::vector&lt;int8_t&gt; inputToCellWeightsData   = GenerateRandomData&lt;int8_t&gt;(inputWeightsInfo.GetNumElements());</div><div class="line"><a name="l01839"></a><span class="lineno"> 1839</span>&#160;    std::vector&lt;int8_t&gt; inputToOutputWeightsData = GenerateRandomData&lt;int8_t&gt;(inputWeightsInfo.GetNumElements());</div><div class="line"><a name="l01840"></a><span class="lineno"> 1840</span>&#160;</div><div class="line"><a name="l01841"></a><span class="lineno"> 1841</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(inputWeightsInfo, inputToForgetWeightsData);</div><div class="line"><a name="l01842"></a><span class="lineno"> 1842</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(inputWeightsInfo, inputToCellWeightsData);</div><div class="line"><a name="l01843"></a><span class="lineno"> 1843</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToOutputWeights(inputWeightsInfo, inputToOutputWeightsData);</div><div class="line"><a name="l01844"></a><span class="lineno"> 1844</span>&#160;</div><div class="line"><a name="l01845"></a><span class="lineno"> 1845</span>&#160;    std::vector&lt;int8_t&gt; recurrentToForgetWeightsData =</div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span>&#160;            GenerateRandomData&lt;int8_t&gt;(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span>&#160;    std::vector&lt;int8_t&gt; recurrentToCellWeightsData   =</div><div class="line"><a name="l01848"></a><span class="lineno"> 1848</span>&#160;            GenerateRandomData&lt;int8_t&gt;(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l01849"></a><span class="lineno"> 1849</span>&#160;    std::vector&lt;int8_t&gt; recurrentToOutputWeightsData =</div><div class="line"><a name="l01850"></a><span class="lineno"> 1850</span>&#160;            GenerateRandomData&lt;int8_t&gt;(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l01851"></a><span class="lineno"> 1851</span>&#160;</div><div class="line"><a name="l01852"></a><span class="lineno"> 1852</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);</div><div class="line"><a name="l01853"></a><span class="lineno"> 1853</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);</div><div class="line"><a name="l01854"></a><span class="lineno"> 1854</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);</div><div class="line"><a name="l01855"></a><span class="lineno"> 1855</span>&#160;</div><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span>&#160;    std::vector&lt;int32_t&gt; forgetGateBiasData(numUnits, 1);</div><div class="line"><a name="l01857"></a><span class="lineno"> 1857</span>&#160;    std::vector&lt;int32_t&gt; cellBiasData(numUnits, 0);</div><div class="line"><a name="l01858"></a><span class="lineno"> 1858</span>&#160;    std::vector&lt;int32_t&gt; outputGateBiasData(numUnits, 0);</div><div class="line"><a name="l01859"></a><span class="lineno"> 1859</span>&#160;</div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(biasInfo, forgetGateBiasData);</div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(biasInfo, cellBiasData);</div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(biasInfo, outputGateBiasData);</div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span>&#160;</div><div class="line"><a name="l01864"></a><span class="lineno"> 1864</span>&#160;    <span class="comment">// Layer Norm</span></div><div class="line"><a name="l01865"></a><span class="lineno"> 1865</span>&#160;    std::vector&lt;int16_t&gt; forgetLayerNormWeightsData =</div><div class="line"><a name="l01866"></a><span class="lineno"> 1866</span>&#160;            GenerateRandomData&lt;int16_t&gt;(layerNormWeightsInfo.GetNumElements());</div><div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>&#160;    std::vector&lt;int16_t&gt; cellLayerNormWeightsData =</div><div class="line"><a name="l01868"></a><span class="lineno"> 1868</span>&#160;            GenerateRandomData&lt;int16_t&gt;(layerNormWeightsInfo.GetNumElements());</div><div class="line"><a name="l01869"></a><span class="lineno"> 1869</span>&#160;    std::vector&lt;int16_t&gt; outputLayerNormWeightsData =</div><div class="line"><a name="l01870"></a><span class="lineno"> 1870</span>&#160;            GenerateRandomData&lt;int16_t&gt;(layerNormWeightsInfo.GetNumElements());</div><div class="line"><a name="l01871"></a><span class="lineno"> 1871</span>&#160;</div><div class="line"><a name="l01872"></a><span class="lineno"> 1872</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetLayerNormWeights(layerNormWeightsInfo, forgetLayerNormWeightsData);</div><div class="line"><a name="l01873"></a><span class="lineno"> 1873</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellLayerNormWeights(layerNormWeightsInfo, cellLayerNormWeightsData);</div><div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputLayerNormWeights(layerNormWeightsInfo, outputLayerNormWeightsData);</div><div class="line"><a name="l01875"></a><span class="lineno"> 1875</span>&#160;</div><div class="line"><a name="l01876"></a><span class="lineno"> 1876</span>&#160;    <span class="comment">// Set up params</span></div><div class="line"><a name="l01877"></a><span class="lineno"> 1877</span>&#160;    <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> params;</div><div class="line"><a name="l01878"></a><span class="lineno"> 1878</span>&#160;</div><div class="line"><a name="l01879"></a><span class="lineno"> 1879</span>&#160;    <span class="comment">// Mandatory params</span></div><div class="line"><a name="l01880"></a><span class="lineno"> 1880</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &amp;inputToForgetWeights;</div><div class="line"><a name="l01881"></a><span class="lineno"> 1881</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a>   = &amp;inputToCellWeights;</div><div class="line"><a name="l01882"></a><span class="lineno"> 1882</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &amp;inputToOutputWeights;</div><div class="line"><a name="l01883"></a><span class="lineno"> 1883</span>&#160;</div><div class="line"><a name="l01884"></a><span class="lineno"> 1884</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &amp;recurrentToForgetWeights;</div><div class="line"><a name="l01885"></a><span class="lineno"> 1885</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a>   = &amp;recurrentToCellWeights;</div><div class="line"><a name="l01886"></a><span class="lineno"> 1886</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &amp;recurrentToOutputWeights;</div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</span>&#160;</div><div class="line"><a name="l01888"></a><span class="lineno"> 1888</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &amp;forgetGateBias;</div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a>       = &amp;cellBias;</div><div class="line"><a name="l01890"></a><span class="lineno"> 1890</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &amp;outputGateBias;</div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span>&#160;</div><div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>&#160;    <span class="comment">// Layer Norm</span></div><div class="line"><a name="l01893"></a><span class="lineno"> 1893</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">m_ForgetLayerNormWeights</a> = &amp;forgetLayerNormWeights;</div><div class="line"><a name="l01894"></a><span class="lineno"> 1894</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">m_CellLayerNormWeights</a>   = &amp;cellLayerNormWeights;</div><div class="line"><a name="l01895"></a><span class="lineno"> 1895</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">m_OutputLayerNormWeights</a> = &amp;outputLayerNormWeights;</div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span>&#160;</div><div class="line"><a name="l01897"></a><span class="lineno"> 1897</span>&#160;    <span class="comment">// Create network</span></div><div class="line"><a name="l01898"></a><span class="lineno"> 1898</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01899"></a><span class="lineno"> 1899</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;qLstm&quot;</span>);</div><div class="line"><a name="l01900"></a><span class="lineno"> 1900</span>&#160;</div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> input         = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateIn = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l01903"></a><span class="lineno"> 1903</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateIn   = network-&gt;AddInputLayer(2);</div><div class="line"><a name="l01904"></a><span class="lineno"> 1904</span>&#160;</div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> qLstmLayer = network-&gt;AddQLstmLayer(descriptor, params, layerName.c_str());</div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span>&#160;</div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateOut  = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateOut  = network-&gt;AddOutputLayer(1);</div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer  = network-&gt;AddOutputLayer(2);</div><div class="line"><a name="l01910"></a><span class="lineno"> 1910</span>&#160;</div><div class="line"><a name="l01911"></a><span class="lineno"> 1911</span>&#160;    <span class="comment">// Input/Output tensor info</span></div><div class="line"><a name="l01912"></a><span class="lineno"> 1912</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({numBatches , inputSize},</div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>&#160;                                <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>,</div><div class="line"><a name="l01914"></a><span class="lineno"> 1914</span>&#160;                                inputScale,</div><div class="line"><a name="l01915"></a><span class="lineno"> 1915</span>&#160;                                inputOffset);</div><div class="line"><a name="l01916"></a><span class="lineno"> 1916</span>&#160;</div><div class="line"><a name="l01917"></a><span class="lineno"> 1917</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateInfo({numBatches , numUnits},</div><div class="line"><a name="l01918"></a><span class="lineno"> 1918</span>&#160;                                    <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l01919"></a><span class="lineno"> 1919</span>&#160;                                    cellStateScale,</div><div class="line"><a name="l01920"></a><span class="lineno"> 1920</span>&#160;                                    cellStateOffset);</div><div class="line"><a name="l01921"></a><span class="lineno"> 1921</span>&#160;</div><div class="line"><a name="l01922"></a><span class="lineno"> 1922</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateInfo({numBatches , outputSize},</div><div class="line"><a name="l01923"></a><span class="lineno"> 1923</span>&#160;                                      <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>,</div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span>&#160;                                      outputScale,</div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span>&#160;                                      outputOffset);</div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span>&#160;</div><div class="line"><a name="l01927"></a><span class="lineno"> 1927</span>&#160;    <span class="comment">// Connect input/output slots</span></div><div class="line"><a name="l01928"></a><span class="lineno"> 1928</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01930"></a><span class="lineno"> 1930</span>&#160;</div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>&#160;    outputStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>&#160;    outputStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateInfo);</div><div class="line"><a name="l01933"></a><span class="lineno"> 1933</span>&#160;</div><div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>&#160;    cellStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span>&#160;    cellStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateInfo);</div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span>&#160;</div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>&#160;    qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputStateOut-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span>&#160;    qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateInfo);</div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>&#160;</div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span>&#160;    qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</span>&#160;    qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateInfo);</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span>&#160;</div><div class="line"><a name="l01943"></a><span class="lineno"> 1943</span>&#160;    qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>&#160;    qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateInfo);</div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span>&#160;</div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>&#160;    BOOST_CHECK(deserializedNetwork);</div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span>&#160;</div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span>&#160;    VerifyLstmLayer&lt;armnn::QLstmDescriptor&gt; checker(layerName,</div><div class="line"><a name="l01950"></a><span class="lineno"> 1950</span>&#160;                                                    {inputInfo, cellStateInfo, outputStateInfo},</div><div class="line"><a name="l01951"></a><span class="lineno"> 1951</span>&#160;                                                    {outputStateInfo, cellStateInfo, outputStateInfo},</div><div class="line"><a name="l01952"></a><span class="lineno"> 1952</span>&#160;                                                    descriptor,</div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span>&#160;                                                    params);</div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>&#160;</div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>&#160;    deserializedNetwork-&gt;ExecuteStrategy(checker);</div><div class="line"><a name="l01956"></a><span class="lineno"> 1956</span>&#160;}</div><div class="line"><a name="l01957"></a><span class="lineno"> 1957</span>&#160;</div><div class="line"><a name="l01958"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#ae4e2b73628cdb905a7b9f6aacae368a0"> 1958</a></span>&#160;<a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeQLstmAdvanced)</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span>&#160;{</div><div class="line"><a name="l01960"></a><span class="lineno"> 1960</span>&#160;    <a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml">armnn::QLstmDescriptor</a> descriptor;</div><div class="line"><a name="l01961"></a><span class="lineno"> 1961</span>&#160;</div><div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a>       = <span class="keyword">false</span>;</div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01964"></a><span class="lineno"> 1964</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a>   = <span class="keyword">true</span>;</div><div class="line"><a name="l01965"></a><span class="lineno"> 1965</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">m_LayerNormEnabled</a>  = <span class="keyword">true</span>;</div><div class="line"><a name="l01966"></a><span class="lineno"> 1966</span>&#160;</div><div class="line"><a name="l01967"></a><span class="lineno"> 1967</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#ac81fb0e66dc623dc37c77f219f53a6d3">m_CellClip</a>       = 0.1f;</div><div class="line"><a name="l01968"></a><span class="lineno"> 1968</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa6a518b65088f34803b3214334bdff61">m_ProjectionClip</a> = 0.1f;</div><div class="line"><a name="l01969"></a><span class="lineno"> 1969</span>&#160;</div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a09e1f097944f61cc901240f9300364cf">m_InputIntermediateScale</a>  = 0.00001f;</div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#afec7f36158448f723b426a9527acb189">m_ForgetIntermediateScale</a> = 0.00001f;</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a0477ee1b44ace6090119178eea78cb0b">m_CellIntermediateScale</a>   = 0.00001f;</div><div class="line"><a name="l01973"></a><span class="lineno"> 1973</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa43409f9b457352c95c89f20ce5d844d">m_OutputIntermediateScale</a> = 0.00001f;</div><div class="line"><a name="l01974"></a><span class="lineno"> 1974</span>&#160;</div><div class="line"><a name="l01975"></a><span class="lineno"> 1975</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#af8f724af7210b52529216feefa993c98">m_HiddenStateScale</a>     = 0.07f;</div><div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4556cbd764d4848d8ad0637a9eed580d">m_HiddenStateZeroPoint</a> = 0;</div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>&#160;</div><div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numBatches = 2;</div><div class="line"><a name="l01979"></a><span class="lineno"> 1979</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize  = 5;</div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 4;</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numUnits   = 4;</div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>&#160;</div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>&#160;    <span class="comment">// Scale/Offset quantization info</span></div><div class="line"><a name="l01984"></a><span class="lineno"> 1984</span>&#160;    <span class="keywordtype">float</span> inputScale    = 0.0078f;</div><div class="line"><a name="l01985"></a><span class="lineno"> 1985</span>&#160;    int32_t inputOffset = 0;</div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</span>&#160;</div><div class="line"><a name="l01987"></a><span class="lineno"> 1987</span>&#160;    <span class="keywordtype">float</span> outputScale    = 0.0078f;</div><div class="line"><a name="l01988"></a><span class="lineno"> 1988</span>&#160;    int32_t outputOffset = 0;</div><div class="line"><a name="l01989"></a><span class="lineno"> 1989</span>&#160;</div><div class="line"><a name="l01990"></a><span class="lineno"> 1990</span>&#160;    <span class="keywordtype">float</span> cellStateScale    = 3.5002e-05f;</div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>&#160;    int32_t cellStateOffset = 0;</div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>&#160;</div><div class="line"><a name="l01993"></a><span class="lineno"> 1993</span>&#160;    <span class="keywordtype">float</span> weightsScale    = 0.007f;</div><div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>&#160;    int32_t weightsOffset = 0;</div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>&#160;</div><div class="line"><a name="l01996"></a><span class="lineno"> 1996</span>&#160;    <span class="keywordtype">float</span> layerNormScale    = 3.5002e-05f;</div><div class="line"><a name="l01997"></a><span class="lineno"> 1997</span>&#160;    int32_t layerNormOffset = 0;</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>&#160;</div><div class="line"><a name="l01999"></a><span class="lineno"> 1999</span>&#160;    <span class="keywordtype">float</span> biasScale    = layerNormScale / 1024;</div><div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>&#160;    int32_t biasOffset = 0;</div><div class="line"><a name="l02001"></a><span class="lineno"> 2001</span>&#160;</div><div class="line"><a name="l02002"></a><span class="lineno"> 2002</span>&#160;    <span class="comment">// Weights and bias tensor and quantization info</span></div><div class="line"><a name="l02003"></a><span class="lineno"> 2003</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputWeightsInfo({numUnits, inputSize},</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>&#160;                                       <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>,</div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span>&#160;                                       weightsScale,</div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>&#160;                                       weightsOffset);</div><div class="line"><a name="l02007"></a><span class="lineno"> 2007</span>&#160;</div><div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> recurrentWeightsInfo({numUnits, outputSize},</div><div class="line"><a name="l02009"></a><span class="lineno"> 2009</span>&#160;                                           <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>,</div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>&#160;                                           weightsScale,</div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>&#160;                                           weightsOffset);</div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span>&#160;</div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasInfo({numUnits},</div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>&#160;                               <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span>&#160;                               biasScale,</div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>&#160;                               biasOffset);</div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span>&#160;</div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> peepholeWeightsInfo({numUnits},</div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>&#160;                                          <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l02020"></a><span class="lineno"> 2020</span>&#160;                                          weightsScale,</div><div class="line"><a name="l02021"></a><span class="lineno"> 2021</span>&#160;                                          weightsOffset);</div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span>&#160;</div><div class="line"><a name="l02023"></a><span class="lineno"> 2023</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> layerNormWeightsInfo({numUnits},</div><div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>&#160;                                           <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span>&#160;                                           layerNormScale,</div><div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>&#160;                                           layerNormOffset);</div><div class="line"><a name="l02027"></a><span class="lineno"> 2027</span>&#160;</div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> projectionWeightsInfo({outputSize, numUnits},</div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>&#160;                                             <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>,</div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>&#160;                                             weightsScale,</div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span>&#160;                                             weightsOffset);</div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span>&#160;</div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span>&#160;    <span class="comment">// Mandatory params</span></div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span>&#160;    std::vector&lt;int8_t&gt; inputToForgetWeightsData = GenerateRandomData&lt;int8_t&gt;(inputWeightsInfo.GetNumElements());</div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>&#160;    std::vector&lt;int8_t&gt; inputToCellWeightsData   = GenerateRandomData&lt;int8_t&gt;(inputWeightsInfo.GetNumElements());</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span>&#160;    std::vector&lt;int8_t&gt; inputToOutputWeightsData = GenerateRandomData&lt;int8_t&gt;(inputWeightsInfo.GetNumElements());</div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>&#160;</div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(inputWeightsInfo, inputToForgetWeightsData);</div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(inputWeightsInfo, inputToCellWeightsData);</div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToOutputWeights(inputWeightsInfo, inputToOutputWeightsData);</div><div class="line"><a name="l02041"></a><span class="lineno"> 2041</span>&#160;</div><div class="line"><a name="l02042"></a><span class="lineno"> 2042</span>&#160;    std::vector&lt;int8_t&gt; recurrentToForgetWeightsData =</div><div class="line"><a name="l02043"></a><span class="lineno"> 2043</span>&#160;            GenerateRandomData&lt;int8_t&gt;(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l02044"></a><span class="lineno"> 2044</span>&#160;    std::vector&lt;int8_t&gt; recurrentToCellWeightsData   =</div><div class="line"><a name="l02045"></a><span class="lineno"> 2045</span>&#160;            GenerateRandomData&lt;int8_t&gt;(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span>&#160;    std::vector&lt;int8_t&gt; recurrentToOutputWeightsData =</div><div class="line"><a name="l02047"></a><span class="lineno"> 2047</span>&#160;            GenerateRandomData&lt;int8_t&gt;(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span>&#160;</div><div class="line"><a name="l02049"></a><span class="lineno"> 2049</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);</div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);</div><div class="line"><a name="l02051"></a><span class="lineno"> 2051</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);</div><div class="line"><a name="l02052"></a><span class="lineno"> 2052</span>&#160;</div><div class="line"><a name="l02053"></a><span class="lineno"> 2053</span>&#160;    std::vector&lt;int32_t&gt; forgetGateBiasData(numUnits, 1);</div><div class="line"><a name="l02054"></a><span class="lineno"> 2054</span>&#160;    std::vector&lt;int32_t&gt; cellBiasData(numUnits, 0);</div><div class="line"><a name="l02055"></a><span class="lineno"> 2055</span>&#160;    std::vector&lt;int32_t&gt; outputGateBiasData(numUnits, 0);</div><div class="line"><a name="l02056"></a><span class="lineno"> 2056</span>&#160;</div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(biasInfo, forgetGateBiasData);</div><div class="line"><a name="l02058"></a><span class="lineno"> 2058</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(biasInfo, cellBiasData);</div><div class="line"><a name="l02059"></a><span class="lineno"> 2059</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(biasInfo, outputGateBiasData);</div><div class="line"><a name="l02060"></a><span class="lineno"> 2060</span>&#160;</div><div class="line"><a name="l02061"></a><span class="lineno"> 2061</span>&#160;    <span class="comment">// CIFG</span></div><div class="line"><a name="l02062"></a><span class="lineno"> 2062</span>&#160;    std::vector&lt;int8_t&gt; inputToInputWeightsData = GenerateRandomData&lt;int8_t&gt;(inputWeightsInfo.GetNumElements());</div><div class="line"><a name="l02063"></a><span class="lineno"> 2063</span>&#160;    std::vector&lt;int8_t&gt; recurrentToInputWeightsData =</div><div class="line"><a name="l02064"></a><span class="lineno"> 2064</span>&#160;            GenerateRandomData&lt;int8_t&gt;(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l02065"></a><span class="lineno"> 2065</span>&#160;    std::vector&lt;int32_t&gt; inputGateBiasData(numUnits, 1);</div><div class="line"><a name="l02066"></a><span class="lineno"> 2066</span>&#160;</div><div class="line"><a name="l02067"></a><span class="lineno"> 2067</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToInputWeights(inputWeightsInfo, inputToInputWeightsData);</div><div class="line"><a name="l02068"></a><span class="lineno"> 2068</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToInputWeights(recurrentWeightsInfo, recurrentToInputWeightsData);</div><div class="line"><a name="l02069"></a><span class="lineno"> 2069</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputGateBias(biasInfo, inputGateBiasData);</div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>&#160;</div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span>&#160;    <span class="comment">// Peephole</span></div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>&#160;    std::vector&lt;int16_t&gt; cellToInputWeightsData  = GenerateRandomData&lt;int16_t&gt;(peepholeWeightsInfo.GetNumElements());</div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span>&#160;    std::vector&lt;int16_t&gt; cellToForgetWeightsData = GenerateRandomData&lt;int16_t&gt;(peepholeWeightsInfo.GetNumElements());</div><div class="line"><a name="l02074"></a><span class="lineno"> 2074</span>&#160;    std::vector&lt;int16_t&gt; cellToOutputWeightsData = GenerateRandomData&lt;int16_t&gt;(peepholeWeightsInfo.GetNumElements());</div><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span>&#160;</div><div class="line"><a name="l02076"></a><span class="lineno"> 2076</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToInputWeights(peepholeWeightsInfo, cellToInputWeightsData);</div><div class="line"><a name="l02077"></a><span class="lineno"> 2077</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToForgetWeights(peepholeWeightsInfo, cellToForgetWeightsData);</div><div class="line"><a name="l02078"></a><span class="lineno"> 2078</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToOutputWeights(peepholeWeightsInfo, cellToOutputWeightsData);</div><div class="line"><a name="l02079"></a><span class="lineno"> 2079</span>&#160;</div><div class="line"><a name="l02080"></a><span class="lineno"> 2080</span>&#160;    <span class="comment">// Projection</span></div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>&#160;    std::vector&lt;int8_t&gt; projectionWeightsData = GenerateRandomData&lt;int8_t&gt;(projectionWeightsInfo.GetNumElements());</div><div class="line"><a name="l02082"></a><span class="lineno"> 2082</span>&#160;    std::vector&lt;int32_t&gt; projectionBiasData(outputSize, 1);</div><div class="line"><a name="l02083"></a><span class="lineno"> 2083</span>&#160;</div><div class="line"><a name="l02084"></a><span class="lineno"> 2084</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> projectionWeights(projectionWeightsInfo, projectionWeightsData);</div><div class="line"><a name="l02085"></a><span class="lineno"> 2085</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> projectionBias(biasInfo, projectionBiasData);</div><div class="line"><a name="l02086"></a><span class="lineno"> 2086</span>&#160;</div><div class="line"><a name="l02087"></a><span class="lineno"> 2087</span>&#160;    <span class="comment">// Layer Norm</span></div><div class="line"><a name="l02088"></a><span class="lineno"> 2088</span>&#160;    std::vector&lt;int16_t&gt; inputLayerNormWeightsData =</div><div class="line"><a name="l02089"></a><span class="lineno"> 2089</span>&#160;            GenerateRandomData&lt;int16_t&gt;(layerNormWeightsInfo.GetNumElements());</div><div class="line"><a name="l02090"></a><span class="lineno"> 2090</span>&#160;    std::vector&lt;int16_t&gt; forgetLayerNormWeightsData =</div><div class="line"><a name="l02091"></a><span class="lineno"> 2091</span>&#160;            GenerateRandomData&lt;int16_t&gt;(layerNormWeightsInfo.GetNumElements());</div><div class="line"><a name="l02092"></a><span class="lineno"> 2092</span>&#160;    std::vector&lt;int16_t&gt; cellLayerNormWeightsData =</div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</span>&#160;            GenerateRandomData&lt;int16_t&gt;(layerNormWeightsInfo.GetNumElements());</div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span>&#160;    std::vector&lt;int16_t&gt; outputLayerNormWeightsData =</div><div class="line"><a name="l02095"></a><span class="lineno"> 2095</span>&#160;            GenerateRandomData&lt;int16_t&gt;(layerNormWeightsInfo.GetNumElements());</div><div class="line"><a name="l02096"></a><span class="lineno"> 2096</span>&#160;</div><div class="line"><a name="l02097"></a><span class="lineno"> 2097</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputLayerNormWeights(layerNormWeightsInfo, inputLayerNormWeightsData);</div><div class="line"><a name="l02098"></a><span class="lineno"> 2098</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetLayerNormWeights(layerNormWeightsInfo, forgetLayerNormWeightsData);</div><div class="line"><a name="l02099"></a><span class="lineno"> 2099</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellLayerNormWeights(layerNormWeightsInfo, cellLayerNormWeightsData);</div><div class="line"><a name="l02100"></a><span class="lineno"> 2100</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputLayerNormWeights(layerNormWeightsInfo, outputLayerNormWeightsData);</div><div class="line"><a name="l02101"></a><span class="lineno"> 2101</span>&#160;</div><div class="line"><a name="l02102"></a><span class="lineno"> 2102</span>&#160; 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   params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &amp;inputToOutputWeights;</div><div class="line"><a name="l02109"></a><span class="lineno"> 2109</span>&#160;</div><div class="line"><a name="l02110"></a><span class="lineno"> 2110</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &amp;recurrentToForgetWeights;</div><div class="line"><a name="l02111"></a><span class="lineno"> 2111</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a>   = &amp;recurrentToCellWeights;</div><div class="line"><a name="l02112"></a><span class="lineno"> 2112</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &amp;recurrentToOutputWeights;</div><div class="line"><a name="l02113"></a><span class="lineno"> 2113</span>&#160;</div><div class="line"><a name="l02114"></a><span class="lineno"> 2114</span>&#160; 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   params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a> = &amp;cellToOutputWeights;</div><div class="line"><a name="l02127"></a><span class="lineno"> 2127</span>&#160;</div><div class="line"><a name="l02128"></a><span class="lineno"> 2128</span>&#160;    <span class="comment">// Projection</span></div><div class="line"><a name="l02129"></a><span class="lineno"> 2129</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a> = &amp;projectionWeights;</div><div class="line"><a name="l02130"></a><span class="lineno"> 2130</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a>    = &amp;projectionBias;</div><div class="line"><a name="l02131"></a><span class="lineno"> 2131</span>&#160;</div><div class="line"><a name="l02132"></a><span class="lineno"> 2132</span>&#160; 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   <span class="comment">// Create network</span></div><div class="line"><a name="l02139"></a><span class="lineno"> 2139</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02140"></a><span class="lineno"> 2140</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;qLstm&quot;</span>);</div><div class="line"><a name="l02141"></a><span class="lineno"> 2141</span>&#160;</div><div class="line"><a name="l02142"></a><span class="lineno"> 2142</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> input         = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l02143"></a><span class="lineno"> 2143</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateIn = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l02144"></a><span class="lineno"> 2144</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateIn   = network-&gt;AddInputLayer(2);</div><div class="line"><a name="l02145"></a><span class="lineno"> 2145</span>&#160;</div><div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> qLstmLayer = network-&gt;AddQLstmLayer(descriptor, params, layerName.c_str());</div><div class="line"><a name="l02147"></a><span class="lineno"> 2147</span>&#160;</div><div class="line"><a name="l02148"></a><span class="lineno"> 2148</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateOut = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l02149"></a><span class="lineno"> 2149</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateOut   = network-&gt;AddOutputLayer(1);</div><div class="line"><a name="l02150"></a><span class="lineno"> 2150</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer    = network-&gt;AddOutputLayer(2);</div><div class="line"><a name="l02151"></a><span class="lineno"> 2151</span>&#160;</div><div class="line"><a name="l02152"></a><span class="lineno"> 2152</span>&#160;    <span class="comment">// Input/Output tensor info</span></div><div class="line"><a name="l02153"></a><span class="lineno"> 2153</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({numBatches , inputSize},</div><div class="line"><a name="l02154"></a><span class="lineno"> 2154</span>&#160;                                <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>,</div><div class="line"><a name="l02155"></a><span class="lineno"> 2155</span>&#160;                                inputScale,</div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span>&#160;                                inputOffset);</div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span>&#160;</div><div class="line"><a name="l02158"></a><span class="lineno"> 2158</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateInfo({numBatches , numUnits},</div><div class="line"><a name="l02159"></a><span class="lineno"> 2159</span>&#160;                                    <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</span>&#160;                                    cellStateScale,</div><div class="line"><a name="l02161"></a><span class="lineno"> 2161</span>&#160;                                    cellStateOffset);</div><div class="line"><a name="l02162"></a><span class="lineno"> 2162</span>&#160;</div><div class="line"><a name="l02163"></a><span class="lineno"> 2163</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateInfo({numBatches , outputSize},</div><div class="line"><a name="l02164"></a><span class="lineno"> 2164</span>&#160;                                      <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>,</div><div class="line"><a name="l02165"></a><span class="lineno"> 2165</span>&#160;                                      outputScale,</div><div class="line"><a name="l02166"></a><span class="lineno"> 2166</span>&#160;                                      outputOffset);</div><div class="line"><a name="l02167"></a><span class="lineno"> 2167</span>&#160;</div><div class="line"><a name="l02168"></a><span class="lineno"> 2168</span>&#160;    <span class="comment">// Connect input/output slots</span></div><div class="line"><a name="l02169"></a><span class="lineno"> 2169</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02170"></a><span class="lineno"> 2170</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l02171"></a><span class="lineno"> 2171</span>&#160;</div><div class="line"><a name="l02172"></a><span class="lineno"> 2172</span>&#160;    outputStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02173"></a><span class="lineno"> 2173</span>&#160;    outputStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateInfo);</div><div class="line"><a name="l02174"></a><span class="lineno"> 2174</span>&#160;</div><div class="line"><a name="l02175"></a><span class="lineno"> 2175</span>&#160;    cellStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l02176"></a><span class="lineno"> 2176</span>&#160;    cellStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateInfo);</div><div class="line"><a name="l02177"></a><span class="lineno"> 2177</span>&#160;</div><div class="line"><a name="l02178"></a><span class="lineno"> 2178</span>&#160;    qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputStateOut-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02179"></a><span class="lineno"> 2179</span>&#160;    qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateInfo);</div><div class="line"><a name="l02180"></a><span class="lineno"> 2180</span>&#160;</div><div class="line"><a name="l02181"></a><span class="lineno"> 2181</span>&#160;    qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02182"></a><span class="lineno"> 2182</span>&#160;    qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateInfo);</div><div class="line"><a name="l02183"></a><span class="lineno"> 2183</span>&#160;</div><div class="line"><a name="l02184"></a><span class="lineno"> 2184</span>&#160;    qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02185"></a><span class="lineno"> 2185</span>&#160;    qLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateInfo);</div><div class="line"><a name="l02186"></a><span class="lineno"> 2186</span>&#160;</div><div class="line"><a name="l02187"></a><span class="lineno"> 2187</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02188"></a><span class="lineno"> 2188</span>&#160;    BOOST_CHECK(deserializedNetwork);</div><div class="line"><a name="l02189"></a><span class="lineno"> 2189</span>&#160;</div><div class="line"><a name="l02190"></a><span class="lineno"> 2190</span>&#160;    VerifyLstmLayer&lt;armnn::QLstmDescriptor&gt; checker(layerName,</div><div class="line"><a name="l02191"></a><span class="lineno"> 2191</span>&#160;                                                    {inputInfo, cellStateInfo, outputStateInfo},</div><div class="line"><a name="l02192"></a><span class="lineno"> 2192</span>&#160;                                                    {outputStateInfo, cellStateInfo, outputStateInfo},</div><div class="line"><a name="l02193"></a><span class="lineno"> 2193</span>&#160;                                                    descriptor,</div><div class="line"><a name="l02194"></a><span class="lineno"> 2194</span>&#160;                                                    params);</div><div class="line"><a name="l02195"></a><span class="lineno"> 2195</span>&#160;</div><div class="line"><a name="l02196"></a><span class="lineno"> 2196</span>&#160;    deserializedNetwork-&gt;ExecuteStrategy(checker);</div><div class="line"><a name="l02197"></a><span class="lineno"> 2197</span>&#160;}</div><div class="line"><a name="l02198"></a><span class="lineno"> 2198</span>&#160;</div><div class="line"><a name="l02199"></a><span class="lineno"> 2199</span>&#160;<a class="code" href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a>()</div><div class="ttc" id="_output_shape_of_squeeze_8cpp_xhtml_ae3a6cb217a792718f2bd0e8f45e3ca9e"><div class="ttname"><a href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE(TensorflowLiteParser)</div></div>
<div class="ttc" id="_ignore_unused_8hpp_xhtml"><div class="ttname"><a href="_ignore_unused_8hpp.xhtml">IgnoreUnused.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a6c9de81fc65b3c4924cab11907075a17"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">armnn::LstmDescriptor::m_ProjectionEnabled</a></div><div class="ttdeci">bool m_ProjectionEnabled</div><div class="ttdoc">Enable/disable the projection layer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00945">Descriptors.hpp:945</a></div></div>
<div class="ttc" id="_lstm_serialization_tests_8cpp_xhtml_a0a3344924b451a5e8bdfeaa02d9e7688"><div class="ttname"><a href="_lstm_serialization_tests_8cpp.xhtml#a0a3344924b451a5e8bdfeaa02d9e7688">ConstantVector2LstmInputParams</a></div><div class="ttdeci">armnn::LstmInputParams ConstantVector2LstmInputParams(const std::vector&lt; armnn::ConstTensor &gt; &amp;constants, Descriptor &amp;descriptor)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_serialization_tests_8cpp_source.xhtml#l00025">LstmSerializationTests.cpp:25</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ab03e6e1514f74427916c892f473fe04c"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">armnn::LstmInputParams::m_ProjectionWeights</a></div><div class="ttdeci">const ConstTensor * m_ProjectionWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00055">LstmParams.hpp:55</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a49e11acda22742cbaf6f1b259ead0d84"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">armnn::QuantizedLstmInputParams::m_InputToCellWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00035">QuantizedLstmParams.hpp:35</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00062">INetwork.hpp:62</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a4a9d678146f572808a361dbdc5489f38"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">armnn::LstmInputParams::m_CellBias</a></div><div class="ttdeci">const ConstTensor * m_CellBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00053">LstmParams.hpp:53</a></div></div>
<div class="ttc" id="class_layer_verifier_base_with_descriptor_xhtml"><div class="ttname"><a href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8hpp_source.xhtml#l00051">SerializerTestUtils.hpp:51</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a4a9d678146f572808a361dbdc5489f38"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">armnn::QuantizedLstmInputParams::m_CellBias</a></div><div class="ttdeci">const ConstTensor * m_CellBias</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00045">QuantizedLstmParams.hpp:45</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a56b81ca8ba4b4937e0787e4951f043fc"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">armnn::QuantizedLstmInputParams::m_RecurrentToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00041">QuantizedLstmParams.hpp:41</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#l00939">Descriptors.hpp:939</a></div></div>
<div class="ttc" id="_lstm_serialization_tests_8cpp_xhtml_a5b74bdb50eae6e911a1eb4c3a311f536"><div class="ttname"><a href="_lstm_serialization_tests_8cpp.xhtml#a5b74bdb50eae6e911a1eb4c3a311f536">ConstantsVector2QuantizedLstmInputParams</a></div><div class="ttdeci">armnn::QuantizedLstmInputParams ConstantsVector2QuantizedLstmInputParams(const std::vector&lt; armnn::ConstTensor &gt; &amp;constants)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_serialization_tests_8cpp_source.xhtml#l01335">LstmSerializationTests.cpp:1335</a></div></div>
<div class="ttc" id="_serializer_test_utils_8cpp_xhtml_a59d03e40f8f051241e46091cca50d31f"><div class="ttname"><a href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a></div><div class="ttdeci">armnn::INetworkPtr DeserializeNetwork(const std::string &amp;serializerString)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8cpp_source.xhtml#l00146">SerializerTestUtils.cpp:146</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_afe204ca375b74e9a72640c9227918d0a"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">armnn::LstmInputParams::m_CellToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_CellToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00050">LstmParams.hpp:50</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_ae83131e16df1cace69395a5f99bc5ecb"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">armnn::QuantizedLstmInputParams::m_RecurrentToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00039">QuantizedLstmParams.hpp:39</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="structarmnn_1_1_q_lstm_descriptor_xhtml_a2837b4396f20c956952d1a7286cab5f8"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">armnn::QLstmDescriptor::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#l01193">Descriptors.hpp:1193</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_ace7a1f1f1041b412b7d8ef82b95ff352"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">armnn::QuantizedLstmInputParams::m_ForgetGateBias</a></div><div class="ttdeci">const ConstTensor * m_ForgetGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00044">QuantizedLstmParams.hpp:44</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_af8f724af7210b52529216feefa993c98"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#af8f724af7210b52529216feefa993c98">armnn::QLstmDescriptor::m_HiddenStateScale</a></div><div class="ttdeci">float m_HiddenStateScale</div><div class="ttdoc">Hidden State quantization scale. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01209">Descriptors.hpp:1209</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a484bafa2f8453a7c5a4a00b92a61b006"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">armnn::LstmInputParams::m_CellToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_CellToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00048">LstmParams.hpp:48</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a></div></div>
<div class="ttc" id="_quantized_lstm_params_8hpp_xhtml"><div class="ttname"><a href="_quantized_lstm_params_8hpp.xhtml">QuantizedLstmParams.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_aa43409f9b457352c95c89f20ce5d844d"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa43409f9b457352c95c89f20ce5d844d">armnn::QLstmDescriptor::m_OutputIntermediateScale</a></div><div class="ttdeci">float m_OutputIntermediateScale</div><div class="ttdoc">Output intermediate quantization scale. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01205">Descriptors.hpp:1205</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a9e081a9b94defb30d1558dc912507e0e"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">armnn::LstmInputParams::m_InputGateBias</a></div><div class="ttdeci">const ConstTensor * m_InputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00051">LstmParams.hpp:51</a></div></div>
<div class="ttc" id="_i_runtime_8hpp_xhtml"><div class="ttname"><a href="_i_runtime_8hpp.xhtml">IRuntime.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a9e081a9b94defb30d1558dc912507e0e"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">armnn::QuantizedLstmInputParams::m_InputGateBias</a></div><div class="ttdeci">const ConstTensor * m_InputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00043">QuantizedLstmParams.hpp:43</a></div></div>
<div class="ttc" id="class_layer_verifier_base_with_descriptor_xhtml_a05ef6b0820f03499921f103759525a80"><div class="ttname"><a href="class_layer_verifier_base_with_descriptor.xhtml#a05ef6b0820f03499921f103759525a80">LayerVerifierBaseWithDescriptor::VerifyDescriptor</a></div><div class="ttdeci">void VerifyDescriptor(const Descriptor &amp;descriptor)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8hpp_source.xhtml#l00083">SerializerTestUtils.hpp:83</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a1759754ccb88ecc9af44f3aae6e244ee"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">armnn::LstmInputParams::m_RecurrentToCellWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00046">LstmParams.hpp:46</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div></div>
<div class="ttc" id="class_layer_verifier_base_with_descriptor_xhtml_a49f7f1098adb86fd2197d9aee3924de2"><div class="ttname"><a href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">LayerVerifierBaseWithDescriptor::ExecuteStrategy</a></div><div class="ttdeci">void ExecuteStrategy(const armnn::IConnectableLayer *layer, const armnn::BaseDescriptor &amp;descriptor, const std::vector&lt; armnn::ConstTensor &gt; &amp;constants, const char *name, const armnn::LayerBindingId id=0) override</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8hpp_source.xhtml#l00061">SerializerTestUtils.hpp:61</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_af0f796fba1a2be9c56b4c9ee534577ee"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">armnn::LstmInputParams::m_ForgetLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_ForgetLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00058">LstmParams.hpp:58</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a35b112e30c3eb153806a2a8c16d178e3"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">armnn::LstmInputParams::m_CellToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_CellToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00049">LstmParams.hpp:49</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 &amp;&amp;...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a></div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00013">QuantizedLstmParams.hpp:13</a></div></div>
<div class="ttc" id="_lstm_params_8hpp_xhtml"><div class="ttname"><a href="_lstm_params_8hpp.xhtml">LstmParams.hpp</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="_lstm_serialization_tests_8cpp_xhtml_a6aa1ada80d8a67ff3212b2dcab708960"><div class="ttname"><a href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">BOOST_AUTO_TEST_CASE</a></div><div class="ttdeci">BOOST_AUTO_TEST_CASE(SerializeDeserializeLstmCifgPeepholeNoProjection)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_serialization_tests_8cpp_source.xhtml#l00178">LstmSerializationTests.cpp:178</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ab8cf8f9fb6792e654c2d8d8382f6f01b"><div class="ttname"><a href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a></div><div class="ttdeci">int LayerBindingId</div><div class="ttdoc">Type of identifiers for bindable layers (inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00210">Types.hpp:210</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a8c0f6d48705f40c5590dde09be262222"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">armnn::LstmInputParams::m_OutputGateBias</a></div><div class="ttdeci">const ConstTensor * m_OutputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00054">LstmParams.hpp:54</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a5ee4a6c9a2481245487b1b1a70d20fd0"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">armnn::IOutputSlot::SetTensorInfo</a></div><div class="ttdeci">virtual void SetTensorInfo(const TensorInfo &amp;tensorInfo)=0</div></div>
<div class="ttc" id="structarmnn_1_1_base_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a></div><div class="ttdoc">Base class for all descriptors. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00022">Descriptors.hpp:22</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_affcee5f4ab5994a21bee3b78b4e43de3"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">armnn::QuantizedLstmInputParams::m_InputToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00033">QuantizedLstmParams.hpp:33</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a0cd848f65ec31778d708852f0042fe37"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a0cd848f65ec31778d708852f0042fe37">armnn::LstmInputParams::m_InputLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_InputLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00057">LstmParams.hpp:57</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a4a8ec49f130084445d44297549254780"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">armnn::QLstmDescriptor::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#l01197">Descriptors.hpp:1197</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a56b81ca8ba4b4937e0787e4951f043fc"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">armnn::LstmInputParams::m_RecurrentToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00047">LstmParams.hpp:47</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a></div><div class="ttdoc">An LstmDescriptor for the LstmLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00911">Descriptors.hpp:911</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a44b0e6b16708df7f0d2bbab141688aaa"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">armnn::LstmInputParams::m_ProjectionBias</a></div><div class="ttdeci">const ConstTensor * m_ProjectionBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00056">LstmParams.hpp:56</a></div></div>
<div class="ttc" id="class_layer_verifier_base_xhtml_a56e5da77beb8c601e09bf78371b95828"><div class="ttname"><a href="class_layer_verifier_base.xhtml#a56e5da77beb8c601e09bf78371b95828">LayerVerifierBase::VerifyNameAndConnections</a></div><div class="ttdeci">void VerifyNameAndConnections(const armnn::IConnectableLayer *layer, const char *name)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8cpp_source.xhtml#l00038">SerializerTestUtils.cpp:38</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a31da1ead6794dd64571afdd0b6efc771"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">armnn::QuantizedLstmInputParams::m_InputToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00034">QuantizedLstmParams.hpp:34</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_aa6a518b65088f34803b3214334bdff61"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa6a518b65088f34803b3214334bdff61">armnn::QLstmDescriptor::m_ProjectionClip</a></div><div class="ttdeci">float m_ProjectionClip</div><div class="ttdoc">Clipping threshold value for the projection. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01189">Descriptors.hpp:1189</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div>
<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00314">Tensor.hpp:314</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a09e1f097944f61cc901240f9300364cf"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#a09e1f097944f61cc901240f9300364cf">armnn::QLstmDescriptor::m_InputIntermediateScale</a></div><div class="ttdeci">float m_InputIntermediateScale</div><div class="ttdoc">Input intermediate quantization scale. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01199">Descriptors.hpp:1199</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a></div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00013">LstmParams.hpp:13</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a8c0f6d48705f40c5590dde09be262222"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">armnn::QuantizedLstmInputParams::m_OutputGateBias</a></div><div class="ttdeci">const ConstTensor * m_OutputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00046">QuantizedLstmParams.hpp:46</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#l00943">Descriptors.hpp:943</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml">armnn::QLstmDescriptor</a></div><div class="ttdoc">A QLstmDescriptor for the QLstmLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01153">Descriptors.hpp:1153</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#l00935">Descriptors.hpp:935</a></div></div>
<div class="ttc" id="class_layer_verifier_base_xhtml_a9c63da545906a03b453fff6b556ed6ad"><div class="ttname"><a href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">LayerVerifierBase::VerifyConstTensors</a></div><div class="ttdeci">void VerifyConstTensors(const std::string &amp;tensorName, const armnn::ConstTensor *expectedPtr, const armnn::ConstTensor *actualPtr)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8cpp_source.xhtml#l00071">SerializerTestUtils.cpp:71</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_adceb04ae84c524e4d01881e3754a4d59"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">armnn::IConnectableLayer::GetType</a></div><div class="ttdeci">virtual LayerType GetType() const =0</div><div class="ttdoc">Returns the armnn::LayerType of this layer. </div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a435d3651482bbfcc11263b4e4e0c900f"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">armnn::QuantizedLstmInputParams::m_RecurrentToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00038">QuantizedLstmParams.hpp:38</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#l00937">Descriptors.hpp:937</a></div></div>
<div class="ttc" id="_i_network_8hpp_xhtml"><div class="ttname"><a href="_i_network_8hpp.xhtml">INetwork.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_afec7f36158448f723b426a9527acb189"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#afec7f36158448f723b426a9527acb189">armnn::QLstmDescriptor::m_ForgetIntermediateScale</a></div><div class="ttdeci">float m_ForgetIntermediateScale</div><div class="ttdoc">Forget intermediate quantization scale. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01201">Descriptors.hpp:1201</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ad0b8c32bb5381f4cc999093ba3b98b43"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">armnn::LstmInputParams::m_CellLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_CellLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00059">LstmParams.hpp:59</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ace7a1f1f1041b412b7d8ef82b95ff352"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">armnn::LstmInputParams::m_ForgetGateBias</a></div><div class="ttdeci">const ConstTensor * m_ForgetGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00052">LstmParams.hpp:52</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a49e11acda22742cbaf6f1b259ead0d84"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">armnn::LstmInputParams::m_InputToCellWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00042">LstmParams.hpp:42</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a6e30c7b3451da3ea9cf4259fb602e6e6"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">armnn::LstmInputParams::m_InputToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00043">LstmParams.hpp:43</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_ac81fb0e66dc623dc37c77f219f53a6d3"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#ac81fb0e66dc623dc37c77f219f53a6d3">armnn::QLstmDescriptor::m_CellClip</a></div><div class="ttdeci">float m_CellClip</div><div class="ttdoc">Clipping threshold value for the cell state. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01187">Descriptors.hpp:1187</a></div></div>
<div class="ttc" id="_profiler_tests_8cpp_xhtml_af7f71af5c6c124222dd1c42c5df892f4"><div class="ttname"><a href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE_END()</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 &amp; forget gate). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00941">Descriptors.hpp:941</a></div></div>
<div class="ttc" id="_serializer_test_utils_8hpp_xhtml"><div class="ttname"><a href="_serializer_test_utils_8hpp.xhtml">SerializerTestUtils.hpp</a></div></div>
<div class="ttc" id="_i_deserializer_8hpp_xhtml"><div class="ttname"><a href="_i_deserializer_8hpp.xhtml">IDeserializer.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ae83131e16df1cace69395a5f99bc5ecb"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">armnn::LstmInputParams::m_RecurrentToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00045">LstmParams.hpp:45</a></div></div>
<div class="ttc" id="classarmnn_1_1_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_exception.xhtml">armnn::Exception</a></div><div class="ttdoc">Base class for all ArmNN exceptions so that users can filter to just those. </div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00046">Exceptions.hpp:46</a></div></div>
<div class="ttc" id="_descriptors_8hpp_xhtml"><div class="ttname"><a href="_descriptors_8hpp.xhtml">Descriptors.hpp</a></div></div>
<div class="ttc" id="class_layer_verifier_base_xhtml"><div class="ttname"><a href="class_layer_verifier_base.xhtml">LayerVerifierBase</a></div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8hpp_source.xhtml#l00024">SerializerTestUtils.hpp:24</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a6c9de81fc65b3c4924cab11907075a17"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">armnn::QLstmDescriptor::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#l01195">Descriptors.hpp:1195</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a890a37ff3bfe123414ba7e6f052b49f3"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a890a37ff3bfe123414ba7e6f052b49f3">armnn::LayerType::QuantizedLstm</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a1759754ccb88ecc9af44f3aae6e244ee"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">armnn::QuantizedLstmInputParams::m_RecurrentToCellWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00040">QuantizedLstmParams.hpp:40</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot &amp; GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index. </div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a435d3651482bbfcc11263b4e4e0c900f"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">armnn::LstmInputParams::m_RecurrentToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00044">LstmParams.hpp:44</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a6e30c7b3451da3ea9cf4259fb602e6e6"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">armnn::QuantizedLstmInputParams::m_InputToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00036">QuantizedLstmParams.hpp:36</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a06b091bc9aea697ba473c71f0bb55925"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a06b091bc9aea697ba473c71f0bb55925">armnn::LayerType::Lstm</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#l00947">Descriptors.hpp:947</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="classarmnn_1_1_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot &amp; GetOutputSlot(unsigned int index) const =0</div><div class="ttdoc">Get the const output slot handle by slot index. </div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_afcc1c3a20bd2860e0ddd21674389246f"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">armnn::IConnectableLayer::GetName</a></div><div class="ttdeci">virtual const char * GetName() const =0</div><div class="ttdoc">Returns the name of the layer. </div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr&lt; INetwork, void(*)(INetwork *network)&gt; INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00173">INetwork.hpp:173</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_ac1835f8756a9f03c02fcf9664e3a0fce"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">armnn::IOutputSlot::Connect</a></div><div class="ttdeci">virtual int Connect(IInputSlot &amp;destination)=0</div></div>
<div class="ttc" id="_serializer_test_utils_8cpp_xhtml_a228162aa622e2e39abb4f498c761ab5e"><div class="ttname"><a href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a></div><div class="ttdeci">std::string SerializeNetwork(const armnn::INetwork &amp;network)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8cpp_source.xhtml#l00152">SerializerTestUtils.cpp:152</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a9b18daea2e9f42386055326fd016519a"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">armnn::LstmInputParams::m_OutputLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_OutputLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00060">LstmParams.hpp:60</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a0477ee1b44ace6090119178eea78cb0b"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#a0477ee1b44ace6090119178eea78cb0b">armnn::QLstmDescriptor::m_CellIntermediateScale</a></div><div class="ttdeci">float m_CellIntermediateScale</div><div class="ttdoc">Cell intermediate quantization scale. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01203">Descriptors.hpp:1203</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a464f0ff87b1aabf71febaa71321dd40b"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create(NetworkOptions networkOptions={})</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00510">Network.cpp:510</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_ad474e5c51a0b194ef32e812b86c0cbdb"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">armnn::QLstmDescriptor::m_CifgEnabled</a></div><div class="ttdeci">bool m_CifgEnabled</div><div class="ttdoc">Enable/disable CIFG (coupled input &amp; forget gate). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01191">Descriptors.hpp:1191</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a31da1ead6794dd64571afdd0b6efc771"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">armnn::LstmInputParams::m_InputToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00041">LstmParams.hpp:41</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a4556cbd764d4848d8ad0637a9eed580d"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4556cbd764d4848d8ad0637a9eed580d">armnn::QLstmDescriptor::m_HiddenStateZeroPoint</a></div><div class="ttdeci">int32_t m_HiddenStateZeroPoint</div><div class="ttdoc">Hidden State zero point. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01207">Descriptors.hpp:1207</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a91880b71ea6d007439b7bc7c320b5c25"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a91880b71ea6d007439b7bc7c320b5c25">armnn::LayerType::QLstm</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_affcee5f4ab5994a21bee3b78b4e43de3"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">armnn::LstmInputParams::m_InputToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00040">LstmParams.hpp:40</a></div></div>
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