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| <div class="title">LeNet5Network.h</div> </div> |
| </div><!--header--> |
| <div class="contents"> |
| <a href="_le_net5_network_8h.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">#ifndef __ARM_COMPUTE_TEST_MODEL_OBJECTS_LENET5_H__</span></div> |
| <div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor"></span><span class="preprocessor">#define __ARM_COMPUTE_TEST_MODEL_OBJECTS_LENET5_H__</span></div> |
| <div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor"></span></div> |
| <div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="_assets_library_8h.xhtml">tests/AssetsLibrary.h</a>"</span></div> |
| <div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_globals_8h.xhtml">tests/Globals.h</a>"</span></div> |
| <div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="tests_2_utils_8h.xhtml">tests/Utils.h</a>"</span></div> |
| <div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div> |
| <div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include <memory></span></div> |
| <div class="line"><a name="l00032"></a><span class="lineno"> 32</span> </div> |
| <div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="keyword">using namespace </span>arm_compute;</div> |
| <div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="keyword">using namespace </span>arm_compute::test;</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>arm_compute</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>test</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>networks</div> |
| <div class="line"><a name="l00041"></a><span class="lineno"> 41</span> {</div> |
| <div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="keyword">template</span> <<span class="keyword">typename</span> TensorType,</div> |
| <div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keyword">typename</span> <a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>,</div> |
| <div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keyword">typename</span> ActivationLayerFunction,</div> |
| <div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keyword">typename</span> ConvolutionLayerFunction,</div> |
| <div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">typename</span> FullyConnectedLayerFunction,</div> |
| <div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keyword">typename</span> PoolingLayerFunction,</div> |
| <div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keyword">typename</span> SoftmaxLayerFunction></div> |
| <div class="line"><a name="l00050"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml"> 50</a></span> <span class="keyword">class </span><a class="code" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml">LeNet5Network</a></div> |
| <div class="line"><a name="l00051"></a><span class="lineno"> 51</span> {</div> |
| <div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="keyword">public</span>:</div> |
| <div class="line"><a name="l00053"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#ae3ad9a7ab50bae85c5020f337adc7869"> 53</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#ae3ad9a7ab50bae85c5020f337adc7869">init</a>(<span class="keywordtype">int</span> batches)</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>  _batches = batches;</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="comment">// Initialize input, output, weights and biases</span></div> |
| <div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  input.allocator()->init(<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>(28<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 28<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div> |
| <div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  output.allocator()->init(<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>(10<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div> |
| <div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  w[0].allocator()->init(<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>(5<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 5<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 20<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div> |
| <div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  b[0].allocator()->init(<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>(20<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div> |
| <div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  w[1].allocator()->init(<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>(5<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 5<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 20<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 50<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div> |
| <div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  b[1].allocator()->init(<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>(50<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div> |
| <div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  w[2].allocator()->init(<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>(800<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 500<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div> |
| <div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  b[2].allocator()->init(<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>(500<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div> |
| <div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  w[3].allocator()->init(<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>(500<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 10<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div> |
| <div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  b[3].allocator()->init(<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>(10<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div> |
| <div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  }</div> |
| <div class="line"><a name="l00069"></a><span class="lineno"> 69</span> </div> |
| <div class="line"><a name="l00071"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#a7740c7ab195c03ac140f1f75f633470f"> 71</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#a7740c7ab195c03ac140f1f75f633470f">build</a>()</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="comment">// Initialize intermediate tensors</span></div> |
| <div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="comment">// Layer 1</span></div> |
| <div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  conv1_out.allocator()->init(<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>(24<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 24<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 20<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div> |
| <div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  pool1_out.allocator()->init(<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>(12<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 12<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 20<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div> |
| <div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="comment">// Layer 2</span></div> |
| <div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  conv2_out.allocator()->init(<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>(8<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 8<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 50<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div> |
| <div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  pool2_out.allocator()->init(<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>(4<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 4<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 50<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div> |
| <div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="comment">// Layer 3</span></div> |
| <div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  fc1_out.allocator()->init(<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>(500<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div> |
| <div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  act1_out.allocator()->init(<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>(500<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div> |
| <div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="comment">// Layer 6</span></div> |
| <div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  fc2_out.allocator()->init(<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>(10<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div> |
| <div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div> |
| <div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="comment">// Configure Layers</span></div> |
| <div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  conv1.configure(&input, &w[0], &b[0], &conv1_out, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div> |
| <div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  pool1.configure(&conv1_out, &pool1_out, <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="l00089"></a><span class="lineno"> 89</span>  conv2.configure(&pool1_out, &w[1], &b[1], &conv2_out, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div> |
| <div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  pool2.configure(&conv2_out, &pool2_out, <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="l00091"></a><span class="lineno"> 91</span>  fc1.configure(&pool2_out, &w[2], &b[2], &fc1_out);</div> |
| <div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  act1.configure(&fc1_out, &act1_out, <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="l00093"></a><span class="lineno"> 93</span>  fc2.configure(&act1_out, &w[3], &b[3], &fc2_out);</div> |
| <div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  smx.configure(&fc2_out, &output);</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> </div> |
| <div class="line"><a name="l00097"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#acaefe811b78a2fdc4a0dba0c4029c3ef"> 97</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#acaefe811b78a2fdc4a0dba0c4029c3ef">allocate</a>()</div> |
| <div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  {</div> |
| <div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="comment">// Allocate tensors</span></div> |
| <div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  input.allocator()->allocate();</div> |
| <div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  output.allocator()->allocate();</div> |
| <div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &wi : w)</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>  wi.allocator()->allocate();</div> |
| <div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  }</div> |
| <div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &bi : b)</div> |
| <div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  {</div> |
| <div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  bi.allocator()->allocate();</div> |
| <div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  }</div> |
| <div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  conv1_out.allocator()->allocate();</div> |
| <div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  pool1_out.allocator()->allocate();</div> |
| <div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  conv2_out.allocator()->allocate();</div> |
| <div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  pool2_out.allocator()->allocate();</div> |
| <div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  fc1_out.allocator()->allocate();</div> |
| <div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  act1_out.allocator()->allocate();</div> |
| <div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  fc2_out.allocator()->allocate();</div> |
| <div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  }</div> |
| <div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div> |
| <div class="line"><a name="l00120"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#a3b778cda9ac3fad08e7217edbcb942e0"> 120</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#a3b778cda9ac3fad08e7217edbcb942e0">fill_random</a>()</div> |
| <div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  {</div> |
| <div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  std::uniform_real_distribution<> distribution(-1, 1);</div> |
| <div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>(input), distribution, 0);</div> |
| <div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < w.size(); ++i)</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>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>(w[i]), distribution, i + 1);</div> |
| <div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>(b[i]), distribution, i + 10);</div> |
| <div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  }</div> |
| <div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  }</div> |
| <div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div> |
| <div class="line"><a name="l00136"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#aab0a3920e581535eeb32febaf20dca50"> 136</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#aab0a3920e581535eeb32febaf20dca50">fill</a>(std::vector<std::string> weights, std::vector<std::string> biases)</div> |
| <div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  {</div> |
| <div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(weights.size() != w.size());</div> |
| <div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(biases.size() != b.size());</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">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < weights.size(); ++i)</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>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill_layer_data(<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>(w[i]), weights[i]);</div> |
| <div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill_layer_data(<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>(b[i]), biases[i]);</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>  }</div> |
| <div class="line"><a name="l00147"></a><span class="lineno"> 147</span> </div> |
| <div class="line"><a name="l00152"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#a3a41262ce9aed70a248ecefae646013b"> 152</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#a3a41262ce9aed70a248ecefae646013b">feed</a>(std::string name)</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>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill_layer_data(<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>(input), name);</div> |
| <div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  }</div> |
| <div class="line"><a name="l00156"></a><span class="lineno"> 156</span> </div> |
| <div class="line"><a name="l00161"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#a1466ef70729f3c8b5da5ebfec3f53f26"> 161</a></span>  std::vector<unsigned int> <a class="code" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#a1466ef70729f3c8b5da5ebfec3f53f26">get_classifications</a>()</div> |
| <div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  {</div> |
| <div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  std::vector<unsigned int> classified_labels;</div> |
| <div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a> output_accessor(output);</div> |
| <div class="line"><a name="l00165"></a><span class="lineno"> 165</span> </div> |
| <div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window;</div> |
| <div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(0, 1, 1));</div> |
| <div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> d = 1; d < output_accessor.<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a0f59f175e7682c7ed5f4ea30ef687834">num_dimensions</a>(); ++d)</div> |
| <div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  {</div> |
| <div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(d, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(0, output_accessor.<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>()[d], 1));</div> |
| <div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  }</div> |
| <div class="line"><a name="l00172"></a><span class="lineno"> 172</span> </div> |
| <div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <a class="code" href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">execute_window_loop</a>(window, [&](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> & <span class="keywordtype">id</span>)</div> |
| <div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  {</div> |
| <div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keywordtype">int</span> max_idx = 0;</div> |
| <div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keywordtype">float</span> val = 0;</div> |
| <div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keyword">const</span> <span class="keywordtype">void</span> *<span class="keyword">const</span> out_ptr = output_accessor(<span class="keywordtype">id</span>);</div> |
| <div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> l = 0; l < output_accessor.<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#afb5cd37bb08f1029691590372e6330f0">x</a>(); ++l)</div> |
| <div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  {</div> |
| <div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keywordtype">float</span> curr_val = <span class="keyword">reinterpret_cast<</span><span class="keyword">const </span><span class="keywordtype">float</span> *<span class="keyword">></span>(out_ptr)[l];</div> |
| <div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keywordflow">if</span>(curr_val > val)</div> |
| <div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  {</div> |
| <div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  max_idx = l;</div> |
| <div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  val = curr_val;</div> |
| <div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  }</div> |
| <div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  }</div> |
| <div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  classified_labels.push_back(max_idx);</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>  <span class="keywordflow">return</span> classified_labels;</div> |
| <div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  }</div> |
| <div class="line"><a name="l00191"></a><span class="lineno"> 191</span> </div> |
| <div class="line"><a name="l00193"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#ac8bb3912a3ce86b15842e79d0b421204"> 193</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#ac8bb3912a3ce86b15842e79d0b421204">clear</a>()</div> |
| <div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  {</div> |
| <div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  input.allocator()->free();</div> |
| <div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  output.allocator()->free();</div> |
| <div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &wi : w)</div> |
| <div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  {</div> |
| <div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  wi.allocator()->free();</div> |
| <div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  }</div> |
| <div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &bi : b)</div> |
| <div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  {</div> |
| <div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  bi.allocator()->free();</div> |
| <div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  }</div> |
| <div class="line"><a name="l00205"></a><span class="lineno"> 205</span> </div> |
| <div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  conv1_out.allocator()->free();</div> |
| <div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  pool1_out.allocator()->free();</div> |
| <div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  conv2_out.allocator()->free();</div> |
| <div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  pool2_out.allocator()->free();</div> |
| <div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  fc1_out.allocator()->free();</div> |
| <div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  act1_out.allocator()->free();</div> |
| <div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  fc2_out.allocator()->free();</div> |
| <div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  }</div> |
| <div class="line"><a name="l00214"></a><span class="lineno"> 214</span> </div> |
| <div class="line"><a name="l00216"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#a13a43e6d814de94978c515cb084873b1"> 216</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#a13a43e6d814de94978c515cb084873b1">run</a>()</div> |
| <div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  {</div> |
| <div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="comment">// Layer 1</span></div> |
| <div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  conv1.run();</div> |
| <div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  pool1.run();</div> |
| <div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="comment">// Layer 2</span></div> |
| <div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  conv2.run();</div> |
| <div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  pool2.run();</div> |
| <div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="comment">// Layer 3</span></div> |
| <div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  fc1.run();</div> |
| <div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  act1.run();</div> |
| <div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="comment">// Layer 4</span></div> |
| <div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  fc2.run();</div> |
| <div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="comment">// Softmax</span></div> |
| <div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  smx.run();</div> |
| <div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  }</div> |
| <div class="line"><a name="l00232"></a><span class="lineno"> 232</span> </div> |
| <div class="line"><a name="l00233"></a><span class="lineno"> 233</span> <span class="keyword">private</span>:</div> |
| <div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> _batches{ 0 };</div> |
| <div class="line"><a name="l00235"></a><span class="lineno"> 235</span> </div> |
| <div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  ActivationLayerFunction act1{};</div> |
| <div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  ConvolutionLayerFunction conv1{}, conv2{};</div> |
| <div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  FullyConnectedLayerFunction fc1{}, fc2{};</div> |
| <div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  PoolingLayerFunction pool1{}, pool2{};</div> |
| <div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  SoftmaxLayerFunction smx{};</div> |
| <div class="line"><a name="l00241"></a><span class="lineno"> 241</span> </div> |
| <div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  TensorType input{}, output{};</div> |
| <div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  std::array<TensorType, 4> w{ {} }, b{ {} };</div> |
| <div class="line"><a name="l00244"></a><span class="lineno"> 244</span> </div> |
| <div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  TensorType conv1_out{}, pool1_out{};</div> |
| <div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  TensorType conv2_out{}, pool2_out{};</div> |
| <div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  TensorType fc1_out{}, act1_out{};</div> |
| <div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  TensorType fc2_out{};</div> |
| <div class="line"><a name="l00249"></a><span class="lineno"> 249</span> };</div> |
| <div class="line"><a name="l00250"></a><span class="lineno"> 250</span> } <span class="comment">// namespace networks</span></div> |
| <div class="line"><a name="l00251"></a><span class="lineno"> 251</span> } <span class="comment">// namespace test</span></div> |
| <div class="line"><a name="l00252"></a><span class="lineno"> 252</span> } <span class="comment">// namespace arm_compute</span></div> |
| <div class="line"><a name="l00253"></a><span class="lineno"> 253</span> <span class="preprocessor">#endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_LENET5_H__</span></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_1test_1_1networks_1_1_le_net5_network_xhtml_a3b778cda9ac3fad08e7217edbcb942e0"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#a3b778cda9ac3fad08e7217edbcb942e0">arm_compute::test::networks::LeNet5Network::fill_random</a></div><div class="ttdeci">void fill_random()</div><div class="ttdoc">Fills the trainable parameters and input with random data. </div><div class="ttdef"><b>Definition:</b> <a href="_le_net5_network_8h_source.xhtml#l00120">LeNet5Network.h:120</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="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="_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#l00124">Error.h:124</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_accessor_xhtml_aba5871b3e4a65d057ec1c28fce8b00ba"><div class="ttname"><a href="classarm__compute_1_1test_1_1_accessor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">arm_compute::test::Accessor::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="_accessor_8h_source.xhtml#l00072">Accessor.h:72</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_1_1_dimension_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window_1_1_dimension.xhtml">arm_compute::Window::Dimension</a></div><div class="ttdoc">Describe one of the image's dimensions with a start, end and step. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00068">Window.h:68</a></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="classarm__compute_1_1_dimensions_xhtml_afb5cd37bb08f1029691590372e6330f0"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#afb5cd37bb08f1029691590372e6330f0">arm_compute::Dimensions::x</a></div><div class="ttdeci">T x() const </div><div class="ttdoc">Alias to access the size of the first dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00081">Dimensions.h:81</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1networks_1_1_le_net5_network_xhtml_a3a41262ce9aed70a248ecefae646013b"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#a3a41262ce9aed70a248ecefae646013b">arm_compute::test::networks::LeNet5Network::feed</a></div><div class="ttdeci">void feed(std::string name)</div><div class="ttdoc">Feed input to network from file. </div><div class="ttdef"><b>Definition:</b> <a href="_le_net5_network_8h_source.xhtml#l00152">LeNet5Network.h:152</a></div></div> |
| <div class="ttc" id="_globals_8h_xhtml"><div class="ttname"><a href="_globals_8h.xhtml">Globals.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_accessor_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_accessor.xhtml">arm_compute::test::Accessor</a></div><div class="ttdoc">Accessor implementation for Tensor objects. </div><div class="ttdef"><b>Definition:</b> <a href="_accessor_8h_source.xhtml#l00035">Accessor.h:35</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_aa96e81276ee4f87ab386cd05a5539a7d"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">arm_compute::Window::DimX</a></div><div class="ttdeci">static constexpr size_t DimX</div><div class="ttdoc">Alias for dimension 0 also known as X dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00043">Window.h:43</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1networks_1_1_le_net5_network_xhtml_ae3ad9a7ab50bae85c5020f337adc7869"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#ae3ad9a7ab50bae85c5020f337adc7869">arm_compute::test::networks::LeNet5Network::init</a></div><div class="ttdeci">void init(int batches)</div><div class="ttdef"><b>Definition:</b> <a href="_le_net5_network_8h_source.xhtml#l00053">LeNet5Network.h:53</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_xhtml_a71326f0909d77386e29b511e1990a11f"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">arm_compute::test::library</a></div><div class="ttdeci">std::unique_ptr< AssetsLibrary > library</div><div class="ttdef"><b>Definition:</b> <a href="main_8cpp_source.xhtml#l00054">main.cpp:54</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb"><div class="ttname"><a href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">arm_compute::Channel::U</a></div><div class="ttdoc">Cb/U channel. </div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1networks_1_1_le_net5_network_xhtml_acaefe811b78a2fdc4a0dba0c4029c3ef"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#acaefe811b78a2fdc4a0dba0c4029c3ef">arm_compute::test::networks::LeNet5Network::allocate</a></div><div class="ttdeci">void allocate()</div><div class="ttdef"><b>Definition:</b> <a href="_le_net5_network_8h_source.xhtml#l00097">LeNet5Network.h:97</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a6c0dcc38187027dcb89cd9724bc5a823"><div class="ttname"><a href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">arm_compute::execute_window_loop</a></div><div class="ttdeci">void execute_window_loop(const Window &w, L &&lambda_function, Ts &&...iterators)</div><div class="ttdoc">Iterate through the passed window, automatically adjusting the iterators and calling the lambda_funct...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00127">Helpers.inl:127</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1networks_1_1_le_net5_network_xhtml_aab0a3920e581535eeb32febaf20dca50"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#aab0a3920e581535eeb32febaf20dca50">arm_compute::test::networks::LeNet5Network::fill</a></div><div class="ttdeci">void fill(std::vector< std::string > weights, std::vector< std::string > biases)</div><div class="ttdoc">Fills the trainable parameters from binary files. </div><div class="ttdef"><b>Definition:</b> <a href="_le_net5_network_8h_source.xhtml#l00136">LeNet5Network.h:136</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_coordinates_xhtml"><div class="ttname"><a href="classarm__compute_1_1_coordinates.xhtml">arm_compute::Coordinates</a></div><div class="ttdoc">Coordinates of an item. </div><div class="ttdef"><b>Definition:</b> <a href="_coordinates_8h_source.xhtml#l00037">Coordinates.h:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1networks_1_1_le_net5_network_xhtml_a13a43e6d814de94978c515cb084873b1"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#a13a43e6d814de94978c515cb084873b1">arm_compute::test::networks::LeNet5Network::run</a></div><div class="ttdeci">void run()</div><div class="ttdoc">Runs the model. </div><div class="ttdef"><b>Definition:</b> <a href="_le_net5_network_8h_source.xhtml#l00216">LeNet5Network.h:216</a></div></div> |
| <div class="ttc" id="_assets_library_8h_xhtml"><div class="ttname"><a href="_assets_library_8h.xhtml">AssetsLibrary.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_1_window_xhtml_acd3d2bba51cb84d34dd7656ad2375a6e"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">arm_compute::Window::set</a></div><div class="ttdeci">void set(size_t dimension, const Dimension &dim)</div><div class="ttdoc">Set the values of a given dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00040">Window.inl:40</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1networks_1_1_le_net5_network_xhtml_a7740c7ab195c03ac140f1f75f633470f"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#a7740c7ab195c03ac140f1f75f633470f">arm_compute::test::networks::LeNet5Network::build</a></div><div class="ttdeci">void build()</div><div class="ttdoc">Build the model. </div><div class="ttdef"><b>Definition:</b> <a href="_le_net5_network_8h_source.xhtml#l00071">LeNet5Network.h:71</a></div></div> |
| <div class="ttc" id="tests_2_utils_8h_xhtml"><div class="ttname"><a href="tests_2_utils_8h.xhtml">Utils.h</a></div></div> |
| <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#l00109">Dimensions.h:109</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1networks_1_1_le_net5_network_xhtml_a1466ef70729f3c8b5da5ebfec3f53f26"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#a1466ef70729f3c8b5da5ebfec3f53f26">arm_compute::test::networks::LeNet5Network::get_classifications</a></div><div class="ttdeci">std::vector< unsigned int > get_classifications()</div><div class="ttdoc">Get the classification results. </div><div class="ttdef"><b>Definition:</b> <a href="_le_net5_network_8h_source.xhtml#l00161">LeNet5Network.h:161</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_1test_1_1networks_1_1_le_net5_network_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml">arm_compute::test::networks::LeNet5Network</a></div><div class="ttdoc">Lenet5 model object. </div><div class="ttdef"><b>Definition:</b> <a href="_le_net5_network_8h_source.xhtml#l00050">LeNet5Network.h:50</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1networks_1_1_le_net5_network_xhtml_ac8bb3912a3ce86b15842e79d0b421204"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_le_net5_network.xhtml#ac8bb3912a3ce86b15842e79d0b421204">arm_compute::test::networks::LeNet5Network::clear</a></div><div class="ttdeci">void clear()</div><div class="ttdoc">Clear all allocated memory from the tensor objects. </div><div class="ttdef"><b>Definition:</b> <a href="_le_net5_network_8h_source.xhtml#l00193">LeNet5Network.h:193</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_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_window_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml">arm_compute::Window</a></div><div class="ttdoc">Describe a multidimensional execution window. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00039">Window.h:39</a></div></div> |
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