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gerrit-public.fairphone.software / platform / external / ComputeLibrary / 8140e1e155d3430992fa46e04ef8938ff09ffd2d / . / documentation / graph__lenet_8cpp.xhtml
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117<a href="#func-members">Functions</a> </div>
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119<div class="title">graph_lenet.cpp File Reference</div> </div>
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121<div class="contents">
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame^]122<div class="textblock"><code>#include &quot;<a class="el" href="_graph_8h_source.xhtml">arm_compute/graph/Graph.h</a>&quot;</code><br />
123<code>#include &quot;<a class="el" href="_nodes_8h_source.xhtml">arm_compute/graph/Nodes.h</a>&quot;</code><br />
124<code>#include &quot;<a class="el" href="_toolchain_support_8h_source.xhtml">support/ToolchainSupport.h</a>&quot;</code><br />
125<code>#include &quot;<a class="el" href="_graph_utils_8h_source.xhtml">utils/GraphUtils.h</a>&quot;</code><br />
126<code>#include &quot;<a class="el" href="utils_2_utils_8h_source.xhtml">utils/Utils.h</a>&quot;</code><br />
127<code>#include &lt;cstdlib&gt;</code><br />
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]128</div>
129<p><a href="graph__lenet_8cpp_source.xhtml">Go to the source code of this file.</a></p>
130<table class="memberdecls">
131<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
132Functions</h2></td></tr>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]133<tr class="memitem:a8b6f84d005166799e5a371a3d3e072b3"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="graph__lenet_8cpp.xhtml#a8b6f84d005166799e5a371a3d3e072b3">main_graph_lenet</a> (int argc, const char **argv)</td></tr>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame^]134<tr class="memdesc:a8b6f84d005166799e5a371a3d3e072b3"><td class="mdescLeft">&#160;</td><td class="mdescRight">Example demonstrating how to implement LeNet's network using the Compute Library's graph API. <a href="#a8b6f84d005166799e5a371a3d3e072b3">More...</a><br /></td></tr>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]135<tr class="separator:a8b6f84d005166799e5a371a3d3e072b3"><td class="memSeparator" colspan="2">&#160;</td></tr>
136<tr class="memitem:a217dbf8b442f20279ea00b898af96f52"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="graph__lenet_8cpp.xhtml#a217dbf8b442f20279ea00b898af96f52">main</a> (int argc, const char **argv)</td></tr>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame^]137<tr class="memdesc:a217dbf8b442f20279ea00b898af96f52"><td class="mdescLeft">&#160;</td><td class="mdescRight">Main program for LeNet. <a href="#a217dbf8b442f20279ea00b898af96f52">More...</a><br /></td></tr>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]138<tr class="separator:a217dbf8b442f20279ea00b898af96f52"><td class="memSeparator" colspan="2">&#160;</td></tr>
139</table>
140<h2 class="groupheader">Function Documentation</h2>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]141<a class="anchor" id="a217dbf8b442f20279ea00b898af96f52"></a>
142<div class="memitem">
143<div class="memproto">
144 <table class="memname">
145 <tr>
146 <td class="memname">int main </td>
147 <td>(</td>
148 <td class="paramtype">int&#160;</td>
149 <td class="paramname"><em>argc</em>, </td>
150 </tr>
151 <tr>
152 <td class="paramkey"></td>
153 <td></td>
154 <td class="paramtype">const char **&#160;</td>
155 <td class="paramname"><em>argv</em>&#160;</td>
156 </tr>
157 <tr>
158 <td></td>
159 <td>)</td>
160 <td></td><td></td>
161 </tr>
162 </table>
163</div><div class="memdoc">
164
165<p>Main program for LeNet. </p>
166<dl class="params"><dt>Parameters</dt><dd>
167 <table class="params">
168 <tr><td class="paramdir">[in]</td><td class="paramname">argc</td><td>Number of arguments </td></tr>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame^]169 <tr><td class="paramdir">[in]</td><td class="paramname">argv</td><td>Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] batches ) </td></tr>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]170 </table>
171 </dd>
172</dl>
173
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame^]174<p>Definition at line <a class="el" href="graph__lenet_8cpp_source.xhtml#l00135">135</a> of file <a class="el" href="graph__lenet_8cpp_source.xhtml">graph_lenet.cpp</a>.</p>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]175
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame^]176<p>References <a class="el" href="graph__lenet_8cpp_source.xhtml#l00064">main_graph_lenet()</a>, and <a class="el" href="utils_2_utils_8cpp_source.xhtml#l00069">arm_compute::utils::run_example()</a>.</p>
177<div class="fragment"><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160;{</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1utils.xhtml#a4c9395db2c8b8d0c336656a7b58fca3e">arm_compute::utils::run_example</a>(argc, argv, <a class="code" href="graph__lenet_8cpp.xhtml#a8b6f84d005166799e5a371a3d3e072b3">main_graph_lenet</a>);</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1utils_xhtml_a4c9395db2c8b8d0c336656a7b58fca3e"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml#a4c9395db2c8b8d0c336656a7b58fca3e">arm_compute::utils::run_example</a></div><div class="ttdeci">int run_example(int argc, const char **argv, example &amp;func)</div><div class="ttdoc">Run an example and handle the potential exceptions it throws. </div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8cpp_source.xhtml#l00069">Utils.cpp:69</a></div></div>
178<div class="ttc" id="graph__lenet_8cpp_xhtml_a8b6f84d005166799e5a371a3d3e072b3"><div class="ttname"><a href="graph__lenet_8cpp.xhtml#a8b6f84d005166799e5a371a3d3e072b3">main_graph_lenet</a></div><div class="ttdeci">void main_graph_lenet(int argc, const char **argv)</div><div class="ttdoc">Example demonstrating how to implement LeNet&amp;#39;s network using the Compute Library&amp;#39;s graph API...</div><div class="ttdef"><b>Definition:</b> <a href="graph__lenet_8cpp_source.xhtml#l00064">graph_lenet.cpp:64</a></div></div>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]179</div><!-- fragment -->
180</div>
181</div>
182<a class="anchor" id="a8b6f84d005166799e5a371a3d3e072b3"></a>
183<div class="memitem">
184<div class="memproto">
185 <table class="memname">
186 <tr>
187 <td class="memname">void main_graph_lenet </td>
188 <td>(</td>
189 <td class="paramtype">int&#160;</td>
190 <td class="paramname"><em>argc</em>, </td>
191 </tr>
192 <tr>
193 <td class="paramkey"></td>
194 <td></td>
195 <td class="paramtype">const char **&#160;</td>
196 <td class="paramname"><em>argv</em>&#160;</td>
197 </tr>
198 <tr>
199 <td></td>
200 <td>)</td>
201 <td></td><td></td>
202 </tr>
203 </table>
204</div><div class="memdoc">
205
206<p>Example demonstrating how to implement LeNet's network using the Compute Library's graph API. </p>
207<dl class="params"><dt>Parameters</dt><dd>
208 <table class="params">
209 <tr><td class="paramdir">[in]</td><td class="paramname">argc</td><td>Number of arguments </td></tr>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame^]210 <tr><td class="paramdir">[in]</td><td class="paramname">argv</td><td>Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] batches ) </td></tr>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]211 </table>
212 </dd>
213</dl>
214<p>Path to the trainable data</p>
215<p>Number of batches </p>
216
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame^]217<p>Definition at line <a class="el" href="graph__lenet_8cpp_source.xhtml#l00064">64</a> of file <a class="el" href="graph__lenet_8cpp_source.xhtml">graph_lenet.cpp</a>.</p>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]218
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame^]219<p>References <a class="el" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a041485a3394541feee82a34d40249d70">arm_compute::graph::ActivationLayer</a>, <a class="el" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047aa252659b59a03bc61e5ec827ab4448b7">arm_compute::graph::ConvolutionLayer</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, <a class="el" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a658061ff1dac70c02116fae6c044da1a">arm_compute::graph::FullyConnectedLayer</a>, <a class="el" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">arm_compute::MAX</a>, <a class="el" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047aea068ae5aae640d018c4300bc7619575">arm_compute::graph::PoolingLayer</a>, <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::RELU</a>, <a class="el" href="classarm__compute_1_1graph_1_1_graph.xhtml#a13a43e6d814de94978c515cb084873b1">Graph::run()</a>, <a class="el" href="_graph_utils_8h_source.xhtml#l00230">arm_compute::graph_utils::set_target_hint()</a>, and <a class="el" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a4a9567bc4a6c28a527c973010eaf9a25">arm_compute::graph::SoftmaxLayer</a>.</p>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]220
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame^]221<p>Referenced by <a class="el" href="graph__lenet_8cpp_source.xhtml#l00135">main()</a>.</p>
222<div class="fragment"><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; std::string data_path; </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batches = 4; </div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; <span class="comment">// Set target. 0 (NEON), 1 (OpenCL). By default it is NEON</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160; <a class="code" href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833b">TargetHint</a> target_hint = <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a9216738b309b6b230b7ba8bca5ba7477">set_target_hint</a>(argc &gt; 1 ? std::strtol(argv[1], <span class="keyword">nullptr</span>, 10) : 0);</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; <span class="keywordflow">if</span>(argc &lt; 2)</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="comment">// Print help</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: &quot;</span> &lt;&lt; argv[0] &lt;&lt; <span class="stringliteral">&quot; [target] [path_to_data] [batches]\n\n&quot;</span>;</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; std::cout &lt;&lt; <span class="stringliteral">&quot;No data folder provided: using random values\n\n&quot;</span>;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; }</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 2)</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; {</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: &quot;</span> &lt;&lt; argv[0] &lt;&lt; <span class="stringliteral">&quot; &quot;</span> &lt;&lt; argv[1] &lt;&lt; <span class="stringliteral">&quot; [path_to_data] [batches]\n\n&quot;</span>;</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; std::cout &lt;&lt; <span class="stringliteral">&quot;No data folder provided: using random values\n\n&quot;</span>;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; }</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 3)</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; {</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; <span class="comment">//Do something with argv[1]</span></div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; data_path = argv[2];</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: &quot;</span> &lt;&lt; argv[0] &lt;&lt; <span class="stringliteral">&quot; [path_to_data] [batches]\n\n&quot;</span>;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; std::cout &lt;&lt; <span class="stringliteral">&quot;No number of batches where specified, thus will use the default : &quot;</span> &lt;&lt; batches &lt;&lt; <span class="stringliteral">&quot;\n\n&quot;</span>;</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="keywordflow">else</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; {</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; <span class="comment">//Do something with argv[1] and argv[2]</span></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; data_path = argv[2];</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; batches = std::strtol(argv[3], <span class="keyword">nullptr</span>, 0);</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; }</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160;</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml">Graph</a> graph;</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="comment">//conv1 &lt;&lt; pool1 &lt;&lt; conv2 &lt;&lt; pool2 &lt;&lt; fc1 &lt;&lt; act1 &lt;&lt; fc2 &lt;&lt; smx</span></div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; graph &lt;&lt; target_hint</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml">Tensor</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(28U, 28U, 1U, batches), 1, DataType::F32), <a class="code" href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml">DummyAccessor</a>())</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; 5U, 5U, 20U,</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; get_accessor(data_path, <span class="stringliteral">&quot;/cnn_data/lenet_model/conv1_w.npy&quot;</span>),</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; get_accessor(data_path, <span class="stringliteral">&quot;/cnn_data/lenet_model/conv1_b.npy&quot;</span>),</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_pooling_layer.xhtml">PoolingLayer</a>(<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(PoolingType::MAX, 2, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)))</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; 5U, 5U, 50U,</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; get_accessor(data_path, <span class="stringliteral">&quot;/cnn_data/lenet_model/conv2_w.npy&quot;</span>),</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; get_accessor(data_path, <span class="stringliteral">&quot;/cnn_data/lenet_model/conv2_b.npy&quot;</span>),</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_pooling_layer.xhtml">PoolingLayer</a>(<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(PoolingType::MAX, 2, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)))</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; 500U,</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; get_accessor(data_path, <span class="stringliteral">&quot;/cnn_data/lenet_model/ip1_w.npy&quot;</span>),</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; get_accessor(data_path, <span class="stringliteral">&quot;/cnn_data/lenet_model/ip1_b.npy&quot;</span>))</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; 10U,</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; get_accessor(data_path, <span class="stringliteral">&quot;/cnn_data/lenet_model/ip2_w.npy&quot;</span>),</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; get_accessor(data_path, <span class="stringliteral">&quot;/cnn_data/lenet_model/ip2_b.npy&quot;</span>))</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_softmax_layer.xhtml">SoftmaxLayer</a>()</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml">Tensor</a>(<a class="code" href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml">DummyAccessor</a>());</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; graph.<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml#a13a43e6d814de94978c515cb084873b1">run</a>();</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160;}</div><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#l00038">TensorShape.h:38</a></div></div>
223<div class="ttc" id="classarm__compute_1_1graph_1_1_fully_connected_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">arm_compute::graph::FullyConnectedLayer</a></div><div class="ttdoc">Fully connected layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_fully_connected_layer_8h_source.xhtml#l00038">FullyConnectedLayer.h:38</a></div></div>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]224<div class="ttc" id="classarm__compute_1_1graph_1_1_graph_xhtml_a13a43e6d814de94978c515cb084873b1"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_graph.xhtml#a13a43e6d814de94978c515cb084873b1">arm_compute::graph::Graph::run</a></div><div class="ttdeci">void run()</div><div class="ttdoc">Executes the graph. </div></div>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame^]225<div class="ttc" id="classarm__compute_1_1graph__utils_1_1_dummy_accessor_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml">arm_compute::graph_utils::DummyAccessor</a></div><div class="ttdoc">Dummy accessor class. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00063">GraphUtils.h:63</a></div></div>
226<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml">arm_compute::ActivationLayerInfo</a></div><div class="ttdoc">Activation Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00650">Types.h:650</a></div></div>
227<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_a9216738b309b6b230b7ba8bca5ba7477"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#a9216738b309b6b230b7ba8bca5ba7477">arm_compute::graph_utils::set_target_hint</a></div><div class="ttdeci">graph::TargetHint set_target_hint(int target)</div><div class="ttdoc">Utility function to return the TargetHint. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00230">GraphUtils.h:230</a></div></div>
228<div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00460">Types.h:460</a></div></div>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]229<div class="ttc" id="classarm__compute_1_1graph_1_1_softmax_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_softmax_layer.xhtml">arm_compute::graph::SoftmaxLayer</a></div><div class="ttdoc">Softmax layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_softmax_layer_8h_source.xhtml#l00036">SoftmaxLayer.h:36</a></div></div>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame^]230<div class="ttc" id="classarm__compute_1_1graph_1_1_graph_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_graph.xhtml">arm_compute::graph::Graph</a></div><div class="ttdoc">Graph class. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_8h_source.xhtml#l00043">Graph.h:43</a></div></div>
Kaizenbf8b01d2017-10-12 14:26:51 +0100[diff] [blame]231<div class="ttc" id="classarm__compute_1_1graph_1_1_pooling_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_pooling_layer.xhtml">arm_compute::graph::PoolingLayer</a></div><div class="ttdoc">Pooling layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_pooling_layer_8h_source.xhtml#l00037">PoolingLayer.h:37</a></div></div>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame^]232<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a8d5e69e9a697c2007e241eb413c9833b"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833b">arm_compute::graph::TargetHint</a></div><div class="ttdeci">TargetHint</div><div class="ttdoc">&lt; Execution hint to the graph executor </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00076">Types.h:76</a></div></div>
233<div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor&amp;#39;s metadata. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00044">TensorInfo.h:44</a></div></div>
Kaizenbf8b01d2017-10-12 14:26:51 +0100[diff] [blame]234<div class="ttc" id="classarm__compute_1_1graph_1_1_activation_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">arm_compute::graph::ActivationLayer</a></div><div class="ttdoc">Activation Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_activation_layer_8h_source.xhtml#l00037">ActivationLayer.h:37</a></div></div>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame^]235<div class="ttc" id="classarm__compute_1_1graph_1_1_convolution_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">arm_compute::graph::ConvolutionLayer</a></div><div class="ttdoc">Convolution layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_convolution_layer_8h_source.xhtml#l00042">ConvolutionLayer.h:42</a></div></div>
236<div class="ttc" id="classarm__compute_1_1_pooling_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pooling_layer_info.xhtml">arm_compute::PoolingLayerInfo</a></div><div class="ttdoc">Pooling Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00553">Types.h:553</a></div></div>
237<div class="ttc" id="classarm__compute_1_1graph_1_1_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_tensor.xhtml">arm_compute::graph::Tensor</a></div><div class="ttdoc">Tensor class. </div><div class="ttdef"><b>Definition:</b> <a href="graph_2_tensor_8h_source.xhtml#l00039">Tensor.h:39</a></div></div>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]238</div><!-- fragment -->
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Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame^]246 <li class="navelem"><a class="el" href="dir_d28a4824dc47e487b107a5db32ef43c4.xhtml">examples</a></li><li class="navelem"><a class="el" href="graph__lenet_8cpp.xhtml">graph_lenet.cpp</a></li>
247 <li class="footer">Generated on Thu Dec 14 2017 23:48:33 for Compute Library by
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]248 <a href="http://www.doxygen.org/index.html">
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame^]249 <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.11 </li>
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