<tr style="height: 56px;">
<td style="padding-left: 0.5em;">
<div id="projectname">Compute Library
-  <span id="projectnumber">17.09</span>
+  <span id="projectnumber">17.10</span>
</div>
</td>
</tr>
<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor"></span><span class="preprocessor">#error "This example needs to be built with -DARM_COMPUTE_CL"</span></div>
<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor"></span><span class="preprocessor">#endif </span><span class="comment">/* ARM_COMPUTE_CL */</span><span class="preprocessor"></span></div>
<div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor"></span></div>
-<div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_graph_8h.xhtml">arm_compute/graph/Graph.h</a>"</span></div>
-<div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="_nodes_8h.xhtml">arm_compute/graph/Nodes.h</a>"</span></div>
-<div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="_c_l_scheduler_8h.xhtml">arm_compute/runtime/CL/CLScheduler.h</a>"</span></div>
-<div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include "<a class="code" href="_scheduler_8h.xhtml">arm_compute/runtime/Scheduler.h</a>"</span></div>
-<div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="_toolchain_support_8h.xhtml">support/ToolchainSupport.h</a>"</span></div>
-<div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include "<a class="code" href="_graph_utils_8h.xhtml">utils/GraphUtils.h</a>"</span></div>
-<div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include "<a class="code" href="utils_2_utils_8h.xhtml">utils/Utils.h</a>"</span></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="preprocessor">#include <cstdlib></span></div>
-<div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor">#include <iostream></span></div>
-<div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="preprocessor">#include <memory></span></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">using namespace </span>arm_compute::graph;</div>
-<div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="keyword">using namespace </span>arm_compute::graph_utils;</div>
-<div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </div>
-<div class="line"><a name="l00052"></a><span class="lineno"><a class="line" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0"> 52</a></span> std::unique_ptr<ITensorAccessor> <a class="code" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(<span class="keyword">const</span> std::string &path, <span class="keyword">const</span> std::string &data_file)</div>
-<div class="line"><a name="l00053"></a><span class="lineno"> 53</span> {</div>
-<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordflow">if</span>(path.empty())</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">return</span> arm_compute::support::cpp14::make_unique<DummyAccessor>();</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>  <span class="keywordflow">else</span></div>
-<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  {</div>
-<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<NumPyBinLoader>(path + data_file);</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> </div>
-<div class="line"><a name="l00069"></a><span class="lineno"><a class="line" href="graph__lenet_8cpp.xhtml#a8b6f84d005166799e5a371a3d3e072b3"> 69</a></span> <span class="keywordtype">void</span> <a class="code" href="graph__lenet_8cpp.xhtml#a8b6f84d005166799e5a371a3d3e072b3">main_graph_lenet</a>(<span class="keywordtype">int</span> argc, <span class="keyword">const</span> <span class="keywordtype">char</span> **argv)</div>
-<div class="line"><a name="l00070"></a><span class="lineno"> 70</span> {</div>
-<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  std::string data_path; </div>
-<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batches = 4; </div>
-<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="comment">// Parse arguments</span></div>
-<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordflow">if</span>(argc < 2)</div>
-<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  {</div>
-<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="comment">// Print help</span></div>
-<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" [path_to_data] [batches]\n\n"</span>;</div>
-<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</span>;</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">else</span> <span class="keywordflow">if</span>(argc == 2)</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="comment">//Do something with argv[1]</span></div>
-<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  data_path = argv[1];</div>
-<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" [path_to_data] [batches]\n\n"</span>;</div>
-<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  std::cout << <span class="stringliteral">"No number of batches where specified, thus will use the default : "</span> << batches << <span class="stringliteral">"\n\n"</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="keywordflow">else</span></div>
-<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  {</div>
-<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="comment">//Do something with argv[1] and argv[2]</span></div>
-<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  data_path = argv[1];</div>
-<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  batches = std::strtol(argv[2], <span class="keyword">nullptr</span>, 0);</div>
-<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  }</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">// Check if OpenCL is available and initialize the scheduler</span></div>
-<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute.xhtml#aa4f4d7a58287017588fc338965873f14">arm_compute::opencl_is_available</a>())</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>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a60f9a6836b628a7171914c4afe43b4a7">arm_compute::CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a46ecf9ef0fe80ba2ed35acfc29856b7d">default_init</a>();</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>  <a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml">Graph</a> graph;</div>
-<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  graph.<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml#afaff225242efe5b2c39c932cfdd0f459">set_info_enablement</a>(<span class="keyword">true</span>);</div>
+<div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_logger_8h.xhtml">arm_compute/core/Logger.h</a>"</span></div>
+<div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="_graph_8h.xhtml">arm_compute/graph/Graph.h</a>"</span></div>
+<div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="_nodes_8h.xhtml">arm_compute/graph/Nodes.h</a>"</span></div>
+<div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include "<a class="code" href="_c_l_scheduler_8h.xhtml">arm_compute/runtime/CL/CLScheduler.h</a>"</span></div>
+<div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="_scheduler_8h.xhtml">arm_compute/runtime/Scheduler.h</a>"</span></div>
+<div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include "<a class="code" href="_toolchain_support_8h.xhtml">support/ToolchainSupport.h</a>"</span></div>
+<div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include "<a class="code" href="_graph_utils_8h.xhtml">utils/GraphUtils.h</a>"</span></div>
+<div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include "<a class="code" href="utils_2_utils_8h.xhtml">utils/Utils.h</a>"</span></div>
+<div class="line"><a name="l00036"></a><span class="lineno"> 36</span> </div>
+<div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor">#include <cstdlib></span></div>
+<div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="preprocessor">#include <iostream></span></div>
+<div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="preprocessor">#include <memory></span></div>
+<div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div>
+<div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="keyword">using namespace </span>arm_compute::graph;</div>
+<div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="keyword">using namespace </span>arm_compute::graph_utils;</div>
+<div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div>
+<div class="line"><a name="l00053"></a><span class="lineno"><a class="line" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0"> 53</a></span> std::unique_ptr<ITensorAccessor> <a class="code" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(<span class="keyword">const</span> std::string &path, <span class="keyword">const</span> std::string &data_file)</div>
+<div class="line"><a name="l00054"></a><span class="lineno"> 54</span> {</div>
+<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordflow">if</span>(path.empty())</div>
+<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  {</div>
+<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<DummyAccessor>();</div>
+<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  }</div>
+<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keywordflow">else</span></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>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<NumPyBinLoader>(path + data_file);</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> }</div>
+<div class="line"><a name="l00064"></a><span class="lineno"> 64</span> </div>
+<div class="line"><a name="l00070"></a><span class="lineno"><a class="line" href="graph__lenet_8cpp.xhtml#a8b6f84d005166799e5a371a3d3e072b3"> 70</a></span> <span class="keywordtype">void</span> <a class="code" href="graph__lenet_8cpp.xhtml#a8b6f84d005166799e5a371a3d3e072b3">main_graph_lenet</a>(<span class="keywordtype">int</span> argc, <span class="keyword">const</span> <span class="keywordtype">char</span> **argv)</div>
+<div class="line"><a name="l00071"></a><span class="lineno"> 71</span> {</div>
+<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  std::string data_path; </div>
+<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batches = 4; </div>
+<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="comment">// Parse arguments</span></div>
+<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">if</span>(argc < 2)</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">// Print help</span></div>
+<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" [path_to_data] [batches]\n\n"</span>;</div>
+<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</span>;</div>
+<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  }</div>
+<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 2)</div>
+<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  {</div>
+<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="comment">//Do something with argv[1]</span></div>
+<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  data_path = argv[1];</div>
+<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" [path_to_data] [batches]\n\n"</span>;</div>
+<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  std::cout << <span class="stringliteral">"No number of batches where specified, thus will use the default : "</span> << batches << <span class="stringliteral">"\n\n"</span>;</div>
+<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  }</div>
+<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordflow">else</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">//Do something with argv[1] and argv[2]</span></div>
+<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  data_path = argv[1];</div>
+<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  batches = std::strtol(argv[2], <span class="keyword">nullptr</span>, 0);</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> </div>
+<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="comment">// Check if OpenCL is available and initialize the scheduler</span></div>
+<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <a class="code" href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833b">TargetHint</a> hint = <a class="code" href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833bacaf162e9233294cadf62d2a71a14ca09">TargetHint::NEON</a>;</div>
+<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute.xhtml#aa4f4d7a58287017588fc338965873f14">arm_compute::opencl_is_available</a>())</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>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a60f9a6836b628a7171914c4afe43b4a7">arm_compute::CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a46ecf9ef0fe80ba2ed35acfc29856b7d">default_init</a>();</div>
+<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  hint = <a class="code" href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833ba542f952490e2db695a1d544338a70cda">TargetHint::OPENCL</a>;</div>
+<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  }</div>
<div class="line"><a name="l00103"></a><span class="lineno"> 103</span> </div>
-<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="comment">//conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx</span></div>
-<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  graph << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a0e3ca6c9bf8d16363c1be8fddd2cfcaea542f952490e2db695a1d544338a70cda">Hint::OPENCL</a></div>
-<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  << <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, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>), <a class="code" href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml">DummyAccessor</a>())</div>
-<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div>
-<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  5U, 5U, 20U,</div>
-<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <a class="code" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/conv1_w.npy"</span>),</div>
-<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <a class="code" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/conv1_b.npy"</span>),</div>
-<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div>
-<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  << <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>(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 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="l00113"></a><span class="lineno"> 113</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div>
-<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  5U, 5U, 50U,</div>
-<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <a class="code" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/conv2_w.npy"</span>),</div>
-<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <a class="code" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/conv2_b.npy"</span>),</div>
-<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div>
-<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  << <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>(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 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="l00119"></a><span class="lineno"> 119</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div>
-<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  500U,</div>
-<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <a class="code" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/ip1_w.npy"</span>),</div>
-<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <a class="code" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/ip1_b.npy"</span>))</div>
-<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  << <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>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))</div>
-<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div>
-<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  10U,</div>
-<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <a class="code" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/ip2_w.npy"</span>),</div>
-<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <a class="code" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/ip2_b.npy"</span>))</div>
-<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_softmax_layer.xhtml">SoftmaxLayer</a>()</div>
-<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  << <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="l00130"></a><span class="lineno"> 130</span> </div>
-<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  graph.<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml#a13a43e6d814de94978c515cb084873b1">run</a>();</div>
-<div class="line"><a name="l00132"></a><span class="lineno"> 132</span> }</div>
+<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml">Graph</a> graph;</div>
+<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <a class="code" href="classarm__compute_1_1_logger.xhtml#a6b2499aec6ae645d88670be75dcef769">arm_compute::Logger::get</a>().<a class="code" href="classarm__compute_1_1_logger.xhtml#a8ce097d129855b3ce7f3fcd6c30551ba">set_logger</a>(std::cout, <a class="code" href="namespacearm__compute.xhtml#afb2e0528558bbb0131d2cb41a66c13d7a551b723eafd6a31d444fcb2f5920fbd3">arm_compute::LoggerVerbosity::INFO</a>);</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>  <span class="comment">//conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx</span></div>
+<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  graph << hint</div>
+<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  << <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, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>), <a class="code" href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml">DummyAccessor</a>())</div>
+<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div>
+<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  5U, 5U, 20U,</div>
+<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <a class="code" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/conv1_w.npy"</span>),</div>
+<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <a class="code" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/conv1_b.npy"</span>),</div>
+<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div>
+<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  << <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>(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 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="l00116"></a><span class="lineno"> 116</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div>
+<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  5U, 5U, 50U,</div>
+<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <a class="code" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/conv2_w.npy"</span>),</div>
+<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <a class="code" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/conv2_b.npy"</span>),</div>
+<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div>
+<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  << <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>(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 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="l00122"></a><span class="lineno"> 122</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div>
+<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  500U,</div>
+<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <a class="code" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/ip1_w.npy"</span>),</div>
+<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <a class="code" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/ip1_b.npy"</span>))</div>
+<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  << <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>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))</div>
+<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div>
+<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  10U,</div>
+<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <a class="code" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/ip2_w.npy"</span>),</div>
+<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <a class="code" href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/ip2_b.npy"</span>))</div>
+<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_softmax_layer.xhtml">SoftmaxLayer</a>()</div>
+<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  << <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="l00133"></a><span class="lineno"> 133</span> </div>
-<div class="line"><a name="l00139"></a><span class="lineno"><a class="line" href="graph__lenet_8cpp.xhtml#a217dbf8b442f20279ea00b898af96f52"> 139</a></span> <span class="keywordtype">int</span> <a class="code" href="graph__lenet_8cpp.xhtml#a217dbf8b442f20279ea00b898af96f52">main</a>(<span class="keywordtype">int</span> argc, <span class="keyword">const</span> <span class="keywordtype">char</span> **argv)</div>
-<div class="line"><a name="l00140"></a><span class="lineno"> 140</span> {</div>
-<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <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="l00142"></a><span class="lineno"> 142</span> }</div>
+<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  graph.<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml#a13a43e6d814de94978c515cb084873b1">run</a>();</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> </div>
+<div class="line"><a name="l00142"></a><span class="lineno"><a class="line" href="graph__lenet_8cpp.xhtml#a217dbf8b442f20279ea00b898af96f52"> 142</a></span> <span class="keywordtype">int</span> <a class="code" href="graph__lenet_8cpp.xhtml#a217dbf8b442f20279ea00b898af96f52">main</a>(<span class="keywordtype">int</span> argc, <span class="keyword">const</span> <span class="keywordtype">char</span> **argv)</div>
+<div class="line"><a name="l00143"></a><span class="lineno"> 143</span> {</div>
+<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <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="l00145"></a><span class="lineno"> 145</span> }</div>
+<div class="ttc" id="_logger_8h_xhtml"><div class="ttname"><a href="_logger_8h.xhtml">Logger.h</a></div></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>
-<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#l00036">FullyConnectedLayer.h:36</a></div></div>
+<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#l00037">FullyConnectedLayer.h:37</a></div></div>
<div class="ttc" id="_toolchain_support_8h_xhtml"><div class="ttname"><a href="_toolchain_support_8h.xhtml">ToolchainSupport.h</a></div></div>
<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>
-<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#l00057">GraphUtils.h:57</a></div></div>
+<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#l00060">GraphUtils.h:60</a></div></div>
<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">arm_compute::ActivationLayerInfo::ActivationFunction::RELU</a></div><div class="ttdoc">Rectifier ( ) </div></div>
<div class="ttc" id="utils_2_utils_8h_xhtml"><div class="ttname"><a href="utils_2_utils_8h.xhtml">Utils.h</a></div></div>
-<div class="ttc" id="classarm__compute_1_1graph_1_1_graph_xhtml_afaff225242efe5b2c39c932cfdd0f459"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_graph.xhtml#afaff225242efe5b2c39c932cfdd0f459">arm_compute::graph::Graph::set_info_enablement</a></div><div class="ttdeci">void set_info_enablement(bool is_enabled)</div><div class="ttdoc">Sets whether to enable information print out. </div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F16 per channel </div></div>
<div class="ttc" id="_graph_8h_xhtml"><div class="ttname"><a href="_graph_8h.xhtml">Graph.h</a></div></div>
-<div class="ttc" id="graph__lenet_8cpp_xhtml_acbea98d13e0adbf27ecc036feeb610f0"><div class="ttname"><a href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a></div><div class="ttdeci">std::unique_ptr< ITensorAccessor > get_accessor(const std::string &path, const std::string &data_file)</div><div class="ttdoc">Generates appropriate accessor according to the specified path. </div><div class="ttdef"><b>Definition:</b> <a href="graph__lenet_8cpp_source.xhtml#l00052">graph_lenet.cpp:52</a></div></div>
+<div class="ttc" id="graph__lenet_8cpp_xhtml_acbea98d13e0adbf27ecc036feeb610f0"><div class="ttname"><a href="graph__lenet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a></div><div class="ttdeci">std::unique_ptr< ITensorAccessor > get_accessor(const std::string &path, const std::string &data_file)</div><div class="ttdoc">Generates appropriate accessor according to the specified path. </div><div class="ttdef"><b>Definition:</b> <a href="graph__lenet_8cpp_source.xhtml#l00053">graph_lenet.cpp:53</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_aa4f4d7a58287017588fc338965873f14"><div class="ttname"><a href="namespacearm__compute.xhtml#aa4f4d7a58287017588fc338965873f14">arm_compute::opencl_is_available</a></div><div class="ttdeci">bool opencl_is_available()</div></div>
+<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a8d5e69e9a697c2007e241eb413c9833bacaf162e9233294cadf62d2a71a14ca09"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833bacaf162e9233294cadf62d2a71a14ca09">arm_compute::graph::TargetHint::NEON</a></div><div class="ttdoc">Run node on a NEON capable device. </div></div>
<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#l00511">Types.h:511</a></div></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 &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>
<div class="ttc" id="_c_l_scheduler_8h_xhtml"><div class="ttname"><a href="_c_l_scheduler_8h.xhtml">CLScheduler.h</a></div></div>
<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#l00406">Types.h:406</a></div></div>
<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>
<div class="ttc" id="_scheduler_8h_xhtml"><div class="ttname"><a href="_scheduler_8h.xhtml">Scheduler.h</a></div></div>
-<div class="ttc" id="graph__lenet_8cpp_xhtml_a217dbf8b442f20279ea00b898af96f52"><div class="ttname"><a href="graph__lenet_8cpp.xhtml#a217dbf8b442f20279ea00b898af96f52">main</a></div><div class="ttdeci">int main(int argc, const char **argv)</div><div class="ttdoc">Main program for LeNet. </div><div class="ttdef"><b>Definition:</b> <a href="graph__lenet_8cpp_source.xhtml#l00139">graph_lenet.cpp:139</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a8d5e69e9a697c2007e241eb413c9833ba542f952490e2db695a1d544338a70cda"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833ba542f952490e2db695a1d544338a70cda">arm_compute::graph::TargetHint::OPENCL</a></div><div class="ttdoc">Run node on an OpenCL capable device (GPU) </div></div>
+<div class="ttc" id="graph__lenet_8cpp_xhtml_a217dbf8b442f20279ea00b898af96f52"><div class="ttname"><a href="graph__lenet_8cpp.xhtml#a217dbf8b442f20279ea00b898af96f52">main</a></div><div class="ttdeci">int main(int argc, const char **argv)</div><div class="ttdoc">Main program for LeNet. </div><div class="ttdef"><b>Definition:</b> <a href="graph__lenet_8cpp_source.xhtml#l00142">graph_lenet.cpp:142</a></div></div>
<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#l00041">Graph.h:41</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a0e3ca6c9bf8d16363c1be8fddd2cfcaea542f952490e2db695a1d544338a70cda"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a0e3ca6c9bf8d16363c1be8fddd2cfcaea542f952490e2db695a1d544338a70cda">arm_compute::graph::Hint::OPENCL</a></div><div class="ttdoc">Run node on an OpenCL capable device (GPU) </div></div>
-<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's network using the Compute Library's graph API...</div><div class="ttdef"><b>Definition:</b> <a href="graph__lenet_8cpp_source.xhtml#l00069">graph_lenet.cpp:69</a></div></div>
-<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#l00036">PoolingLayer.h:36</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_afb2e0528558bbb0131d2cb41a66c13d7a551b723eafd6a31d444fcb2f5920fbd3"><div class="ttname"><a href="namespacearm__compute.xhtml#afb2e0528558bbb0131d2cb41a66c13d7a551b723eafd6a31d444fcb2f5920fbd3">arm_compute::LoggerVerbosity::INFO</a></div><div class="ttdoc">Log info. </div></div>
+<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's network using the Compute Library's graph API...</div><div class="ttdef"><b>Definition:</b> <a href="graph__lenet_8cpp_source.xhtml#l00070">graph_lenet.cpp:70</a></div></div>
+<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>
+<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">< Execution hint to the graph executor </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00050">Types.h:50</a></div></div>
<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's metadata. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00042">TensorInfo.h:42</a></div></div>
-<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#l00036">ActivationLayer.h:36</a></div></div>
+<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>
<div class="ttc" id="_nodes_8h_xhtml"><div class="ttname"><a href="_nodes_8h.xhtml">Nodes.h</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5"><div class="ttname"><a href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">arm_compute::NonLinearFilterFunction::MAX</a></div><div class="ttdoc">Non linear dilate. </div></div>
-<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#l00036">ConvolutionLayer.h:36</a></div></div>
+<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#l00041">ConvolutionLayer.h:41</a></div></div>
<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#l00445">Types.h:445</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a46ecf9ef0fe80ba2ed35acfc29856b7d"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a46ecf9ef0fe80ba2ed35acfc29856b7d">arm_compute::CLScheduler::default_init</a></div><div class="ttdeci">void default_init(ICLTuner *cl_tuner=nullptr)</div><div class="ttdoc">Initialises the context and command queue used by the scheduler to default values and sets a default ...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8h_source.xhtml#l00061">CLScheduler.h:61</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_logger_xhtml_a6b2499aec6ae645d88670be75dcef769"><div class="ttname"><a href="classarm__compute_1_1_logger.xhtml#a6b2499aec6ae645d88670be75dcef769">arm_compute::Logger::get</a></div><div class="ttdeci">static Logger & get()</div></div>
+<div class="ttc" id="classarm__compute_1_1_logger_xhtml_a8ce097d129855b3ce7f3fcd6c30551ba"><div class="ttname"><a href="classarm__compute_1_1_logger.xhtml#a8ce097d129855b3ce7f3fcd6c30551ba">arm_compute::Logger::set_logger</a></div><div class="ttdeci">void set_logger(std::ostream &ostream, LoggerVerbosity verbosity)</div></div>
+<div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a46ecf9ef0fe80ba2ed35acfc29856b7d"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a46ecf9ef0fe80ba2ed35acfc29856b7d">arm_compute::CLScheduler::default_init</a></div><div class="ttdeci">void default_init(ICLTuner *cl_tuner=nullptr)</div><div class="ttdoc">Initialises the context and command queue used by the scheduler to default values and sets a default ...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8h_source.xhtml#l00083">CLScheduler.h:83</a></div></div>
<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#l00038">Tensor.h:38</a></div></div>
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<li class="navelem"><a class="el" href="dir_1253bad92dedae5edd993ead924afb7b.xhtml">examples</a></li><li class="navelem"><a class="el" href="graph__lenet_8cpp.xhtml">graph_lenet.cpp</a></li>
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