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<div class="title">naive_convolution.hpp</div> </div>
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<a href="naive__convolution_8hpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#ifndef MLPACK_METHODS_ANN_CONVOLUTION_RULES_NAIVE_CONVOLUTION_HPP</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#define MLPACK_METHODS_ANN_CONVOLUTION_RULES_NAIVE_CONVOLUTION_HPP</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> </div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <<a class="code" href="prereqs_8hpp.html">mlpack/prereqs.hpp</a>></span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include "<a class="code" href="border__modes_8hpp.html">border_modes.hpp</a>"</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> </div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="keyword">namespace </span><a class="code" href="namespacemlpack.html">mlpack</a> {</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="keyword">namespace </span>ann {</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="keyword">template</span><<span class="keyword">typename</span> BorderMode = FullConvolution></div><div class="line"><a name="l00035"></a><span class="lineno"><a class="line" href="classmlpack_1_1ann_1_1NaiveConvolution.html"> 35</a></span> <span class="keyword">class </span><a class="code" href="classmlpack_1_1ann_1_1NaiveConvolution.html">NaiveConvolution</a></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="keyword">public</span>:</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="comment">/*</span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="comment"> * Perform a convolution (valid mode).</span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="comment"> *</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="comment"> * @param input Input used to perform the convolution.</span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="comment"> * @param filter Filter used to perform the conolution.</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="comment"> * @param output Output data that contains the results of the convolution.</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="comment"> * @param dW Stride of filter application in the x direction.</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="comment"> * @param dH Stride of filter application in the y direction.</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="comment"> */</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">template</span><<span class="keyword">typename</span> eT, <span class="keyword">typename</span> Border = BorderMode></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keyword">static</span> <span class="keyword">typename</span> std::enable_if<</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  std::is_same<Border, ValidConvolution>::value, <span class="keywordtype">void</span>>::type</div><div class="line"><a name="l00050"></a><span class="lineno"><a class="line" href="classmlpack_1_1ann_1_1NaiveConvolution.html#a2b7b5586c1039450b156b33be87098a2"> 50</a></span>  <a class="code" href="classmlpack_1_1ann_1_1NaiveConvolution.html#a2b7b5586c1039450b156b33be87098a2">Convolution</a>(<span class="keyword">const</span> arma::Mat<eT>& input,</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keyword">const</span> arma::Mat<eT>& filter,</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  arma::Mat<eT>& output,</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dW = 1,</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dH = 1)</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>  output = arma::zeros<arma::Mat<eT> >((input.n_rows - filter.n_rows + 1) /</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  dW, (input.n_cols - filter.n_cols + 1) / dH);</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="comment">// It seems to be about 3.5 times faster to use pointers instead of</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="comment">// filter(ki, kj) * input(leftInput + ki, topInput + kj) and output(i, j).</span></div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  eT* outputPtr = output.memptr();</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> </div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j < output.n_cols; ++j)</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  {</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < output.n_rows; ++i, outputPtr++)</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  {</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keyword">const</span> eT* kernelPtr = filter.memptr();</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> kj = 0; kj < filter.n_cols; ++kj)</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  {</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keyword">const</span> eT* inputPtr = input.colptr(kj + j * dW) + i * dH;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> ki = 0; ki < filter.n_rows; ++ki, ++kernelPtr, ++inputPtr)</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  *outputPtr += *kernelPtr * (*inputPtr);</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  }</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  }</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  }</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  }</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">/*</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> <span class="comment"> * Perform a convolution (full mode).</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> <span class="comment"> *</span></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> <span class="comment"> * @param input Input used to perform the convolution.</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> <span class="comment"> * @param filter Filter used to perform the conolution.</span></div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> <span class="comment"> * @param output Output data that contains the results of the convolution.</span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> <span class="comment"> * @param dW Stride of filter application in the x direction.</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> <span class="comment"> * @param dH Stride of filter application in the y direction.</span></div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> <span class="comment"> */</span></div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keyword">template</span><<span class="keyword">typename</span> eT, <span class="keyword">typename</span> Border = BorderMode></div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keyword">static</span> <span class="keyword">typename</span> std::enable_if<</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  std::is_same<Border, FullConvolution>::value, <span class="keywordtype">void</span>>::type</div><div class="line"><a name="l00090"></a><span class="lineno"><a class="line" href="classmlpack_1_1ann_1_1NaiveConvolution.html#a0880dee911f823f7f366289dc783057c"> 90</a></span>  <a class="code" href="classmlpack_1_1ann_1_1NaiveConvolution.html#a0880dee911f823f7f366289dc783057c">Convolution</a>(<span class="keyword">const</span> arma::Mat<eT>& input,</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keyword">const</span> arma::Mat<eT>& filter,</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  arma::Mat<eT>& output,</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dW = 1,</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dH = 1)</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="keyword">const</span> <span class="keywordtype">size_t</span> outputRows = (input.n_rows + 2 * (filter.n_rows - 1)) * dW;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> outputCols = (input.n_cols + 2 * (filter.n_cols - 1)) * dH;</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">// Pad filter and input to the working output shape.</span></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  arma::Mat<eT> inputPadded = arma::zeros<arma::Mat<eT> >(outputRows,</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  outputCols);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  inputPadded.submat(filter.n_rows - 1, filter.n_cols - 1,</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  filter.n_rows - 1 + input.n_rows - 1,</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  filter.n_cols - 1 + input.n_cols - 1) = input;</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>  <a class="code" href="classmlpack_1_1ann_1_1NaiveConvolution.html#a2b7b5586c1039450b156b33be87098a2">NaiveConvolution<ValidConvolution>::Convolution</a>(inputPadded, filter,</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  output, 1, 1);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  }</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> </div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="comment">/*</span></div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> <span class="comment"> * Perform a convolution using 3rd order tensors.</span></div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> <span class="comment"> *</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> <span class="comment"> * @param input Input used to perform the convolution.</span></div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> <span class="comment"> * @param filter Filter used to perform the conolution.</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> <span class="comment"> * @param output Output data that contains the results of the convolution.</span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> <span class="comment"> * @param dW Stride of filter application in the x direction.</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> <span class="comment"> * @param dH Stride of filter application in the y direction.</span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> <span class="comment"> */</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keyword">template</span><<span class="keyword">typename</span> eT></div><div class="line"><a name="l00120"></a><span class="lineno"><a class="line" href="classmlpack_1_1ann_1_1NaiveConvolution.html#a4e7889db36bd7f7d6f30ccd7be5ccacb"> 120</a></span>  <span class="keyword">static</span> <span class="keywordtype">void</span> <a class="code" href="classmlpack_1_1ann_1_1NaiveConvolution.html#a4e7889db36bd7f7d6f30ccd7be5ccacb">Convolution</a>(<span class="keyword">const</span> arma::Cube<eT>& input,</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keyword">const</span> arma::Cube<eT>& filter,</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  arma::Cube<eT>& output,</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dW = 1,</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dH = 1)</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>  arma::Mat<eT> convOutput;</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <a class="code" href="classmlpack_1_1ann_1_1NaiveConvolution.html#a2b7b5586c1039450b156b33be87098a2">NaiveConvolution<BorderMode>::Convolution</a>(input.slice(0), filter.slice(0),</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  convOutput, dW, dH);</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>  output = arma::Cube<eT>(convOutput.n_rows, convOutput.n_cols,</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  input.n_slices);</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  output.slice(0) = convOutput;</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> </div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 1; i < input.n_slices; i++)</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>  <a class="code" href="classmlpack_1_1ann_1_1NaiveConvolution.html#a2b7b5586c1039450b156b33be87098a2">NaiveConvolution<BorderMode>::Convolution</a>(input.slice(i), filter.slice(i),</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  output.slice(i), dW, dH);</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  }</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  }</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="comment">/*</span></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> <span class="comment"> * Perform a convolution using dense matrix as input and a 3rd order tensors</span></div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> <span class="comment"> * as filter and output.</span></div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> <span class="comment"> *</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> <span class="comment"> * @param input Input used to perform the convolution.</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> <span class="comment"> * @param filter Filter used to perform the conolution.</span></div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> <span class="comment"> * @param output Output data that contains the results of the convolution.</span></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="comment"> * @param dW Stride of filter application in the x direction.</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> <span class="comment"> * @param dH Stride of filter application in the y direction.</span></div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> <span class="comment"> */</span></div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keyword">template</span><<span class="keyword">typename</span> eT></div><div class="line"><a name="l00152"></a><span class="lineno"><a class="line" href="classmlpack_1_1ann_1_1NaiveConvolution.html#ae211bec9445cd27180e9b408735cd664"> 152</a></span>  <span class="keyword">static</span> <span class="keywordtype">void</span> <a class="code" href="classmlpack_1_1ann_1_1NaiveConvolution.html#ae211bec9445cd27180e9b408735cd664">Convolution</a>(<span class="keyword">const</span> arma::Mat<eT>& input,</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keyword">const</span> arma::Cube<eT>& filter,</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  arma::Cube<eT>& output,</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dW = 1,</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dH = 1)</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  {</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  arma::Mat<eT> convOutput;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <a class="code" href="classmlpack_1_1ann_1_1NaiveConvolution.html#a2b7b5586c1039450b156b33be87098a2">NaiveConvolution<BorderMode>::Convolution</a>(input, filter.slice(0),</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  convOutput, dW, dH);</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> </div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  output = arma::Cube<eT>(convOutput.n_rows, convOutput.n_cols,</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  filter.n_slices);</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  output.slice(0) = convOutput;</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>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 1; i < filter.n_slices; i++)</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  {</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="classmlpack_1_1ann_1_1NaiveConvolution.html#a2b7b5586c1039450b156b33be87098a2">NaiveConvolution<BorderMode>::Convolution</a>(input, filter.slice(i),</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  output.slice(i), dW, dH);</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  }</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>  <span class="comment">/*</span></div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> <span class="comment"> * Perform a convolution using a 3rd order tensors as input and output and a</span></div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> <span class="comment"> * dense matrix as filter.</span></div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> <span class="comment"> *</span></div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> <span class="comment"> * @param input Input used to perform the convolution.</span></div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> <span class="comment"> * @param filter Filter used to perform the conolution.</span></div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> <span class="comment"> * @param output Output data that contains the results of the convolution.</span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> <span class="comment"> * @param dW Stride of filter application in the x direction.</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> <span class="comment"> * @param dH Stride of filter application in the y direction.</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> <span class="comment"> */</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keyword">template</span><<span class="keyword">typename</span> eT></div><div class="line"><a name="l00184"></a><span class="lineno"><a class="line" href="classmlpack_1_1ann_1_1NaiveConvolution.html#a353ea31d9ebf7a23f33d2bf7cedc10bd"> 184</a></span>  <span class="keyword">static</span> <span class="keywordtype">void</span> <a class="code" href="classmlpack_1_1ann_1_1NaiveConvolution.html#a353ea31d9ebf7a23f33d2bf7cedc10bd">Convolution</a>(<span class="keyword">const</span> arma::Cube<eT>& input,</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keyword">const</span> arma::Mat<eT>& filter,</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  arma::Cube<eT>& output,</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dW = 1,</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dH = 1)</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  {</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  arma::Mat<eT> convOutput;</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <a class="code" href="classmlpack_1_1ann_1_1NaiveConvolution.html#a2b7b5586c1039450b156b33be87098a2">NaiveConvolution<BorderMode>::Convolution</a>(input.slice(0), filter,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  convOutput, dW, dH);</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> </div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  output = arma::Cube<eT>(convOutput.n_rows, convOutput.n_cols,</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  input.n_slices);</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  output.slice(0) = convOutput;</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> </div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 1; i < input.n_slices; i++)</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  {</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="classmlpack_1_1ann_1_1NaiveConvolution.html#a2b7b5586c1039450b156b33be87098a2">NaiveConvolution<BorderMode>::Convolution</a>(input.slice(i), filter,</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  output.slice(i), dW, dH);</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>  }</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> }; <span class="comment">// class NaiveConvolution</span></div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> </div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> } <span class="comment">// namespace ann</span></div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span> } <span class="comment">// namespace mlpack</span></div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> </div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> <span class="preprocessor">#endif</span></div><div class="ttc" id="classmlpack_1_1ann_1_1NaiveConvolution_html_a0880dee911f823f7f366289dc783057c"><div class="ttname"><a href="classmlpack_1_1ann_1_1NaiveConvolution.html#a0880dee911f823f7f366289dc783057c">mlpack::ann::NaiveConvolution::Convolution</a></div><div class="ttdeci">static std::enable_if< std::is_same< Border, FullConvolution >::value, void >::type Convolution(const arma::Mat< eT > &input, const arma::Mat< eT > &filter, arma::Mat< eT > &output, const size_t dW=1, const size_t dH=1)</div><div class="ttdef"><b>Definition:</b> <a href="naive__convolution_8hpp_source.html#l00090">naive_convolution.hpp:90</a></div></div>
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<div class="ttc" id="namespacemlpack_html"><div class="ttname"><a href="namespacemlpack.html">mlpack</a></div><div class="ttdoc">Linear algebra utility functions, generally performed on matrices or vectors. </div><div class="ttdef"><b>Definition:</b> <a href="binarize_8hpp_source.html#l00018">binarize.hpp:18</a></div></div>
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<div class="ttc" id="prereqs_8hpp_html"><div class="ttname"><a href="prereqs_8hpp.html">prereqs.hpp</a></div><div class="ttdoc">The core includes that mlpack expects; standard C++ includes and Armadillo. </div></div>
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<div class="ttc" id="classmlpack_1_1ann_1_1NaiveConvolution_html_a4e7889db36bd7f7d6f30ccd7be5ccacb"><div class="ttname"><a href="classmlpack_1_1ann_1_1NaiveConvolution.html#a4e7889db36bd7f7d6f30ccd7be5ccacb">mlpack::ann::NaiveConvolution::Convolution</a></div><div class="ttdeci">static void Convolution(const arma::Cube< eT > &input, const arma::Cube< eT > &filter, arma::Cube< eT > &output, const size_t dW=1, const size_t dH=1)</div><div class="ttdef"><b>Definition:</b> <a href="naive__convolution_8hpp_source.html#l00120">naive_convolution.hpp:120</a></div></div>
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<div class="ttc" id="classmlpack_1_1ann_1_1NaiveConvolution_html_ae211bec9445cd27180e9b408735cd664"><div class="ttname"><a href="classmlpack_1_1ann_1_1NaiveConvolution.html#ae211bec9445cd27180e9b408735cd664">mlpack::ann::NaiveConvolution::Convolution</a></div><div class="ttdeci">static void Convolution(const arma::Mat< eT > &input, const arma::Cube< eT > &filter, arma::Cube< eT > &output, const size_t dW=1, const size_t dH=1)</div><div class="ttdef"><b>Definition:</b> <a href="naive__convolution_8hpp_source.html#l00152">naive_convolution.hpp:152</a></div></div>
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<div class="ttc" id="classmlpack_1_1ann_1_1NaiveConvolution_html_a353ea31d9ebf7a23f33d2bf7cedc10bd"><div class="ttname"><a href="classmlpack_1_1ann_1_1NaiveConvolution.html#a353ea31d9ebf7a23f33d2bf7cedc10bd">mlpack::ann::NaiveConvolution::Convolution</a></div><div class="ttdeci">static void Convolution(const arma::Cube< eT > &input, const arma::Mat< eT > &filter, arma::Cube< eT > &output, const size_t dW=1, const size_t dH=1)</div><div class="ttdef"><b>Definition:</b> <a href="naive__convolution_8hpp_source.html#l00184">naive_convolution.hpp:184</a></div></div>
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<div class="ttc" id="classmlpack_1_1ann_1_1NaiveConvolution_html"><div class="ttname"><a href="classmlpack_1_1ann_1_1NaiveConvolution.html">mlpack::ann::NaiveConvolution</a></div><div class="ttdoc">Computes the two-dimensional convolution. </div><div class="ttdef"><b>Definition:</b> <a href="naive__convolution_8hpp_source.html#l00035">naive_convolution.hpp:35</a></div></div>
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<div class="ttc" id="classmlpack_1_1ann_1_1NaiveConvolution_html_a2b7b5586c1039450b156b33be87098a2"><div class="ttname"><a href="classmlpack_1_1ann_1_1NaiveConvolution.html#a2b7b5586c1039450b156b33be87098a2">mlpack::ann::NaiveConvolution::Convolution</a></div><div class="ttdeci">static std::enable_if< std::is_same< Border, ValidConvolution >::value, void >::type Convolution(const arma::Mat< eT > &input, const arma::Mat< eT > &filter, arma::Mat< eT > &output, const size_t dW=1, const size_t dH=1)</div><div class="ttdef"><b>Definition:</b> <a href="naive__convolution_8hpp_source.html#l00050">naive_convolution.hpp:50</a></div></div>
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<div class="ttc" id="border__modes_8hpp_html"><div class="ttname"><a href="border__modes_8hpp.html">border_modes.hpp</a></div></div>
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