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87<a href="_caffe_support_8md.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#160;#Caffe layers supported by the Arm NN SDK</div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;This reference guide provides a list of Caffe layers the Arm NN SDK currently supports.</div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;</div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;Although some other neural networks might work, Arm tests the Arm NN SDK with Caffe implementations of the following neural networks: </div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;</div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;- AlexNet.</div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;- Cifar10.</div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;- Inception-BN.</div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;- Resnet_50, Resnet_101 and Resnet_152.</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;- VGG_CNN_S, VGG_16 and VGG_19.</div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;- Yolov1_tiny.</div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160;- Lenet.</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;- MobileNetv1.</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;The Arm NN SDK supports the following machine learning layers for Caffe networks:</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;- BatchNorm, in inference mode.</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;- Convolution, excluding the Dilation Size, Weight Filler, Bias Filler, Engine, Force nd_im2col, and Axis parameters.</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160; Caffe doesn&#39;t support depthwise convolution, the equivalent layer is implemented through the notion of groups. ArmNN supports groups this way:</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160; - when group=1, it is a normal conv2d</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160; - when group=#input_channels, we can replace it by a depthwise convolution</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160; - when group&gt;1 &amp;&amp; group&lt;#input_channels, we need to split the input into the given number of groups, apply a separate convolution and then merge the results</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;- Concat, along the channel dimension only.</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;- Dropout, in inference mode.</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;- Eltwise, excluding the coeff parameter.</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;- Inner Product, excluding the Weight Filler, Bias Filler, Engine, and Axis parameters.</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;- Input.</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;- LRN, excluding the Engine parameter.</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;- Pooling, excluding the Stochastic Pooling and Engine parameters.</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160;- ReLU.</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160;- Scale.</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160;- Softmax, excluding the Axis and Engine parameters.</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;- Split.</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160;More machine learning layers will be supported in future releases.</div></div><!-- fragment --></div><!-- contents -->
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