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117 <div class="title">FullyConnectedLayer.cpp</div> </div>
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120 <a href="validation_2reference_2_fully_connected_layer_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2017 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "<a class="code" href="_fully_connected_layer_8h.xhtml">FullyConnectedLayer.h</a>"</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_types_8h.xhtml">arm_compute/core/Types.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="tests_2validation_2_fixed_point_8h.xhtml">tests/validation/FixedPoint.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_utils_quantized_asymm_8h.xhtml">tests/validation/reference/UtilsQuantizedAsymm.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="_asymm_helpers_8h.xhtml">arm_compute/core/utils/quantization/AsymmHelpers.h</a>"</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include <numeric></span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> {</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="keyword">namespace </span>test</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> {</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="keyword">namespace </span>validation</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> {</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="keyword">namespace </span>reference</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> {</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> {</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="comment">// Vector matrix multiply for floating point</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> template < typename T, typename TB, typename std::enable_if < is_floating_point<T>::value &&is_floating_point<TB>::value, <span class="keywordtype">int</span> >::type = 0 ></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="keywordtype">void</span> vector_matrix_multiply(<span class="keyword">const</span> SimpleTensor<T> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, <span class="keyword">const</span> SimpleTensor<T> &weights, <span class="keyword">const</span> SimpleTensor<TB> &bias, SimpleTensor<T> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>, <span class="keywordtype">int</span> offset_src, <span class="keywordtype">int</span> offset_dst, <span class="keywordtype">int</span> cols_weights,</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keywordtype">int</span> rows_weights, uint8_t fixed_point_position)</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> {</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(fixed_point_position);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> </div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keyword">const</span> T *src_ptr = src.data() + offset_src;</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keyword">const</span> T *weights_ptr = weights.data();</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keyword">const</span> TB *bias_ptr = bias.data();</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  T *dst_ptr = dst.data() + offset_dst;</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> </div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> y = 0; y < rows_weights; ++y)</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  {</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  dst_ptr[y] = std::inner_product(src_ptr, src_ptr + cols_weights, weights_ptr, static_cast<T>(0)) + bias_ptr[y];</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  weights_ptr += cols_weights;</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  }</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> }</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> </div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> <span class="comment">// Vector matrix multiply for fixed point type</span></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> template < typename T, typename TB, typename std::enable_if < std::is_integral<T>::value &&std::is_integral<TB>::value, <span class="keywordtype">int</span> >::type = 0 ></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> <span class="keywordtype">void</span> vector_matrix_multiply(<span class="keyword">const</span> SimpleTensor<T> &src, <span class="keyword">const</span> SimpleTensor<T> &weights, <span class="keyword">const</span> SimpleTensor<TB> &bias, SimpleTensor<T> &dst, <span class="keywordtype">int</span> offset_src, <span class="keywordtype">int</span> offset_dst, <span class="keywordtype">int</span> cols_weights,</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="keywordtype">int</span> rows_weights, uint8_t fixed_point_position)</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> {</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keyword">const</span> T *src_ptr = src.data() + offset_src;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keyword">const</span> T *weights_ptr = weights.data();</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keyword">const</span> TB *bias_ptr = bias.data();</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  T *dst_ptr = dst.data() + offset_dst;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keyword">using namespace </span>fixed_point_arithmetic;</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keyword">using</span> promoted_type = fixed_point_arithmetic::traits::promote_t<T>;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> y = 0; y < rows_weights; ++y)</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  {</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="comment">// Reset accumulator</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  fixed_point<promoted_type> acc(0, fixed_point_position);</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> x = 0; x < cols_weights; ++x)</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  {</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keyword">const</span> fixed_point<promoted_type> i_value(src_ptr[x], fixed_point_position, <span class="keyword">true</span>);</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keyword">const</span> fixed_point<promoted_type> w_value(weights_ptr[x], fixed_point_position, <span class="keyword">true</span>);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  acc = acc + i_value * w_value;</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  }</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> </div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="comment">// Get the bias</span></div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keyword">const</span> fixed_point<T> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>(bias_ptr[y], fixed_point_position, <span class="keyword">true</span>);</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="comment">// Convert back and accumulate the bias</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  fixed_point<T> res(acc);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  res = res + <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>;</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> </div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="comment">// Store the result</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  dst_ptr[y] = res.raw();</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> </div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  weights_ptr += cols_weights;</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  }</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> }</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> </div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> <span class="comment">// Vector matrix multiply for quantized type</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> <span class="keyword">template</span> <></div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> <span class="keywordtype">void</span> vector_matrix_multiply(<span class="keyword">const</span> SimpleTensor<uint8_t> &src, <span class="keyword">const</span> SimpleTensor<uint8_t> &weights, <span class="keyword">const</span> SimpleTensor<int32_t> &bias, SimpleTensor<uint8_t> &dst, <span class="keywordtype">int</span> offset_src, <span class="keywordtype">int</span> offset_dst,</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keywordtype">int</span> cols_weights, <span class="keywordtype">int</span> rows_weights, uint8_t fixed_point_position)</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> {</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(fixed_point_position);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> </div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keyword">const</span> uint8_t *src_ptr = src.data() + offset_src;</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keyword">const</span> uint8_t *weights_ptr = weights.data();</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keyword">const</span> int32_t *bias_ptr = bias.data();</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  uint8_t *dst_ptr = dst.data() + offset_dst;</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> </div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_offset = -src.quantization_info().offset;</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> input_scale = src.quantization_info().scale;</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> weights_offset = -weights.quantization_info().offset;</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> weights_scale = weights.quantization_info().scale;</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> output_offset = dst.quantization_info().offset;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> output_scale = dst.quantization_info().scale;</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> </div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keywordtype">int</span> output_multiplier = 0;</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keywordtype">int</span> output_shift = 0;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> multiplier = input_scale * weights_scale / output_scale;</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <a class="code" href="namespacearm__compute_1_1quantization.xhtml#aa7bd9c3a3bcfe392c90d78e29429db26">arm_compute::quantization::calculate_quantized_multiplier_less_than_one</a>(multiplier, &output_multiplier, &output_shift);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> </div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> y = 0; y < rows_weights; ++y)</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  {</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="comment">// Reset accumulator</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  int32_t acc = 0;</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> x = 0; x < cols_weights; ++x)</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  {</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  acc += (src_ptr[x] + input_offset) * (weights_ptr[x] + weights_offset);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  }</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> </div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="comment">// Accumulate the bias</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  acc += bias_ptr[y];</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> </div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  acc = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5bab95cbeb5c6bf05049df7afd32d823">asymm_rounding_divide_by_pow2</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aea27abcd3d58d627282320dfdd213596">asymm_int_mult</a>(acc, output_multiplier), output_shift);</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  acc += output_offset;</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  acc = utility::clamp<int32_t>(acc, 0, 255);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> </div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="comment">// Store the result</span></div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  dst_ptr[y] = <span class="keyword">static_cast<</span>uint8_t<span class="keyword">></span>(acc);</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> </div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  weights_ptr += cols_weights;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  }</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> }</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> } <span class="comment">// namespace</span></div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> </div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> TB></div><div class="line"><a name="l00152"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aec801fd424adad36e632d433eb113c01"> 152</a></span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<T></a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aec801fd424adad36e632d433eb113c01">fully_connected_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<T></a> &src, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<T></a> &weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<TB></a> &bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &dst_shape)</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> {</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="comment">// Create reference</span></div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<T></a> dst{ <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>{ dst_shape }, src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>(), 1, src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a35ccf2eb0c18a15feab2db98b307b78b">fixed_point_position</a>(), src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#ac74736e3863207232a23b7181c1d0f44">quantization_info</a>() };</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> </div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="comment">// Sanity checks</span></div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> num_batch_dimensions = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::max</a>(0, static_cast<int>(dst_shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a0f59f175e7682c7ed5f4ea30ef687834">num_dimensions</a>()) - 1);</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> num_input_dimensions = src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().num_dimensions() - num_batch_dimensions;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> linear_input_size = src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().total_size_lower(num_input_dimensions);</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> </div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(num_batch_dimensions);</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(num_input_dimensions);</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(linear_input_size);</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(weights.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().x() != linear_input_size);</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(weights.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().y() != bias.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().x());</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(weights.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().y() != dst.shape().x());</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> </div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="comment">// Compute reference</span></div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> cols_weights = weights.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().x();</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> rows_weights = weights.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().y();</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> num_batches = dst_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a91d8061f66e7f8bc56da91d965f04376">total_size_upper</a>(1);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> </div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> k = 0; k < num_batches; ++k)</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  {</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> offset_in = k * cols_weights;</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> offset_out = k * rows_weights;</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> </div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  vector_matrix_multiply<T>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>,</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  weights,</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  bias,</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>,</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  offset_in,</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  offset_out,</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  cols_weights,</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  rows_weights,</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  src.fixed_point_position());</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  }</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> </div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>;</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> }</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> </div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> <span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<float></a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aec801fd424adad36e632d433eb113c01">fully_connected_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<float></a> &src, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<float></a> &weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<float></a> &bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &dst_shape);</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> <span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<half></a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aec801fd424adad36e632d433eb113c01">fully_connected_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<half></a> &src, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<half></a> &weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<half></a> &bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &dst_shape);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> <span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<qint8_t></a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aec801fd424adad36e632d433eb113c01">fully_connected_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<qint8_t></a> &src, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<qint8_t></a> &weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<qint8_t></a> &bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &dst_shape);</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> <span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<qint16_t></a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aec801fd424adad36e632d433eb113c01">fully_connected_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<qint16_t></a> &src, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<qint16_t></a> &weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<qint16_t></a> &bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &dst_shape);</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> <span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<uint8_t></a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aec801fd424adad36e632d433eb113c01">fully_connected_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<uint8_t></a> &src, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<uint8_t></a> &weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<int32_t></a> &bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &dst_shape);</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> } <span class="comment">// namespace reference</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> } <span class="comment">// namespace validation</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> } <span class="comment">// namespace test</span></div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> } <span class="comment">// namespace arm_compute</span></div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a5bab95cbeb5c6bf05049df7afd32d823"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a5bab95cbeb5c6bf05049df7afd32d823">arm_compute::test::validation::asymm_rounding_divide_by_pow2</a></div><div class="ttdeci">int32_t asymm_rounding_divide_by_pow2(int32_t x, int exponent)</div><div class="ttdoc">Rounded to nearest division by a power-of-two. </div><div class="ttdef"><b>Definition:</b> <a href="_utils_quantized_asymm_8h_source.xhtml#l00036">UtilsQuantizedAsymm.h:36</a></div></div>
121 <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div>
122 <div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_a9a3e72153aeb3ed212e9c3698774e881"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">arm_compute::test::SimpleTensor::data_type</a></div><div class="ttdeci">DataType data_type() const override</div><div class="ttdoc">Data type of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00337">SimpleTensor.h:337</a></div></div>
123 <div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00328">Error.h:328</a></div></div>
124 <div class="ttc" id="namespacearm__compute_1_1quantization_xhtml_aa7bd9c3a3bcfe392c90d78e29429db26"><div class="ttname"><a href="namespacearm__compute_1_1quantization.xhtml#aa7bd9c3a3bcfe392c90d78e29429db26">arm_compute::quantization::calculate_quantized_multiplier_less_than_one</a></div><div class="ttdeci">arm_compute::Status calculate_quantized_multiplier_less_than_one(double multiplier, int *quant_multiplier, int *right_shift)</div><div class="ttdoc">Calculate quantized representation of multiplier with value less than one. </div></div>
125 <div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_aba5871b3e4a65d057ec1c28fce8b00ba"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">arm_compute::test::SimpleTensor::shape</a></div><div class="ttdeci">TensorShape shape() const override</div><div class="ttdoc">Shape of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00294">SimpleTensor.h:294</a></div></div>
126 <div class="ttc" id="tests_2validation_2_fixed_point_8h_xhtml"><div class="ttname"><a href="tests_2validation_2_fixed_point_8h.xhtml">FixedPoint.h</a></div></div>
127 <div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">This file contains all available output stages for GEMMLowp on OpenCL. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00001">00_introduction.dox:1</a></div></div>
128 <div class="ttc" id="_fully_connected_layer_8h_xhtml"><div class="ttname"><a href="_fully_connected_layer_8h.xhtml">FullyConnectedLayer.h</a></div></div>
129 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_aea27abcd3d58d627282320dfdd213596"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#aea27abcd3d58d627282320dfdd213596">arm_compute::test::validation::asymm_int_mult</a></div><div class="ttdeci">int32_t asymm_int_mult(int32_t a, int32_t b)</div><div class="ttdoc">Multiplication of two integers. </div><div class="ttdef"><b>Definition:</b> <a href="_utils_quantized_asymm_8h_source.xhtml#l00044">UtilsQuantizedAsymm.h:44</a></div></div>
130 <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a91d8061f66e7f8bc56da91d965f04376"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a91d8061f66e7f8bc56da91d965f04376">arm_compute::TensorShape::total_size_upper</a></div><div class="ttdeci">size_t total_size_upper(size_t dimension) const </div><div class="ttdoc">Collapses given dimension and above. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00167">TensorShape.h:167</a></div></div>
131 <div class="ttc" id="_error_8h_xhtml_a6dc630a6ae9cc063b3924bcea8dee9d6"><div class="ttname"><a href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a></div><div class="ttdeci">#define ARM_COMPUTE_UNUSED(...)</div><div class="ttdoc">To avoid unused variables warnings. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00159">Error.h:159</a></div></div>
132 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_aec801fd424adad36e632d433eb113c01"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aec801fd424adad36e632d433eb113c01">arm_compute::test::validation::reference::fully_connected_layer</a></div><div class="ttdeci">SimpleTensor< T > fully_connected_layer(const SimpleTensor< T > &src, const SimpleTensor< T > &weights, const SimpleTensor< TB > &bias, const TensorShape &dst_shape)</div><div class="ttdef"><b>Definition:</b> <a href="validation_2reference_2_fully_connected_layer_8cpp_source.xhtml#l00152">FullyConnectedLayer.cpp:152</a></div></div>
133 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_adbf67dcee294e673cf796f1ed8aeb6a4"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">arm_compute::test::validation::dst</a></div><div class="ttdeci">CLTensor dst</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_8cpp_source.xhtml#l00137">Convolution.cpp:137</a></div></div>
134 <div class="ttc" id="_utils_quantized_asymm_8h_xhtml"><div class="ttname"><a href="_utils_quantized_asymm_8h.xhtml">UtilsQuantizedAsymm.h</a></div></div>
135 <div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">arm_compute::test::SimpleTensor</a></div><div class="ttdoc">Simple tensor object that stores elements in a consecutive chunk of memory. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00059">SimpleTensor.h:59</a></div></div>
136 <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a0f59f175e7682c7ed5f4ea30ef687834"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a0f59f175e7682c7ed5f4ea30ef687834">arm_compute::Dimensions::num_dimensions</a></div><div class="ttdeci">unsigned int num_dimensions() const </div><div class="ttdoc">Returns the effective dimensionality of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00122">Dimensions.h:122</a></div></div>
137 <div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_ad91bb73431b4de1f4946ed949d444849"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">arm_compute::test::fixed_point_arithmetic::detail::max</a></div><div class="ttdeci">fixed_point< T > max(fixed_point< T > x, fixed_point< T > y)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00902">FixedPoint.h:902</a></div></div>
138 <div class="ttc" id="_asymm_helpers_8h_xhtml"><div class="ttname"><a href="_asymm_helpers_8h.xhtml">AsymmHelpers.h</a></div></div>
139 <div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_ac74736e3863207232a23b7181c1d0f44"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#ac74736e3863207232a23b7181c1d0f44">arm_compute::test::SimpleTensor::quantization_info</a></div><div class="ttdeci">QuantizationInfo quantization_info() const override</div><div class="ttdoc">Quantization info in case of asymmetric quantized type. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00312">SimpleTensor.h:312</a></div></div>
140 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7b8004eef325a40dd43eb80755610fff"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">arm_compute::test::validation::b</a></div><div class="ttdeci">CLTensor b</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00122">GEMM.cpp:122</a></div></div>
141 <div class="ttc" id="arm__compute_2core_2_types_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_types_8h.xhtml">Types.h</a></div></div>
142 <div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_a35ccf2eb0c18a15feab2db98b307b78b"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a35ccf2eb0c18a15feab2db98b307b78b">arm_compute::test::SimpleTensor::fixed_point_position</a></div><div class="ttdeci">int fixed_point_position() const override</div><div class="ttdoc">Number of bits for the fractional part. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00306">SimpleTensor.h:306</a></div></div>
143 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a6743f0a130e8311e6f5b1a23df102472"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">arm_compute::test::validation::src</a></div><div class="ttdeci">convolution configure & src</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_8cpp_source.xhtml#l00147">Convolution.cpp:147</a></div></div>
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149 <li class="navelem"><a class="el" href="dir_59425e443f801f1f2fd8bbe4959a3ccf.xhtml">tests</a></li><li class="navelem"><a class="el" href="dir_e7c7b16542faa38cb4655ff1750d3604.xhtml">validation</a></li><li class="navelem"><a class="el" href="dir_46fdb196cebdbffe77dac340cde62f29.xhtml">reference</a></li><li class="navelem"><a class="el" href="validation_2reference_2_fully_connected_layer_8cpp.xhtml">FullyConnectedLayer.cpp</a></li>
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