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<div class="headertitle"><div class="title">neural_network.cpp</div></div>
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<a href="../../d2/d58/neural__network_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno"> 1</span> </div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno"> 30</span><span class="preprocessor">#include &lt;algorithm&gt;</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno"> 31</span><span class="preprocessor">#include &lt;cassert&gt;</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno"> 32</span><span class="preprocessor">#include &lt;chrono&gt;</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span><span class="preprocessor">#include &lt;cmath&gt;</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span><span class="preprocessor">#include &lt;fstream&gt;</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno"> 35</span><span class="preprocessor">#include &lt;iostream&gt;</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno"> 36</span><span class="preprocessor">#include &lt;sstream&gt;</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno"> 37</span><span class="preprocessor">#include &lt;string&gt;</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno"> 38</span><span class="preprocessor">#include &lt;valarray&gt;</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno"> 39</span><span class="preprocessor">#include &lt;vector&gt;</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno"> 40</span> </div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno"> 41</span><span class="preprocessor">#include &quot;<a class="code" href="../../d8/d95/vector__ops_8hpp.html">vector_ops.hpp</a>&quot;</span> <span class="comment">// Custom header file for vector operations</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno"> 42</span> </div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"> 46</span><span class="keyword">namespace </span><a class="code hl_namespace" href="../../d8/d77/namespacemachine__learning.html">machine_learning</a> {</div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno"> 50</span><span class="keyword">namespace </span><a class="code hl_namespace" href="../../d0/d2e/namespaceneural__network.html">neural_network</a> {</div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno"> 54</span><span class="keyword">namespace </span><a class="code hl_namespace" href="../../d5/d39/namespaceactivations.html">activations</a> {</div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno"><a class="line" href="../../d2/d58/neural__network_8cpp.html#a23aa9d32bcbcd65cfc85f0a41e2afadc"> 60</a></span><span class="keywordtype">double</span> <a class="code hl_function" href="../../d2/d58/neural__network_8cpp.html#a23aa9d32bcbcd65cfc85f0a41e2afadc">sigmoid</a>(<span class="keyword">const</span> <span class="keywordtype">double</span> &amp;x) { <span class="keywordflow">return</span> 1.0 / (1.0 + std::exp(-x)); }</div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"> 61</span> </div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"><a class="line" href="../../d2/d58/neural__network_8cpp.html#a76eb66212d577f948a457b6e29d87c46"> 67</a></span><span class="keywordtype">double</span> <a class="code hl_function" href="../../d2/d58/neural__network_8cpp.html#a76eb66212d577f948a457b6e29d87c46">dsigmoid</a>(<span class="keyword">const</span> <span class="keywordtype">double</span> &amp;x) { <span class="keywordflow">return</span> x * (1 - x); }</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"> 68</span> </div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"><a class="line" href="../../d2/d58/neural__network_8cpp.html#af8f264600754602b6a9ea19cc690e50e"> 74</a></span><span class="keywordtype">double</span> <a class="code hl_function" href="../../d2/d58/neural__network_8cpp.html#af8f264600754602b6a9ea19cc690e50e">relu</a>(<span class="keyword">const</span> <span class="keywordtype">double</span> &amp;x) { <span class="keywordflow">return</span> std::max(0.0, x); }</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"> 75</span> </div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"><a class="line" href="../../d2/d58/neural__network_8cpp.html#aa69e95a34054d7989bf446f96b2ffaf9"> 81</a></span><span class="keywordtype">double</span> <a class="code hl_function" href="../../d2/d58/neural__network_8cpp.html#aa69e95a34054d7989bf446f96b2ffaf9">drelu</a>(<span class="keyword">const</span> <span class="keywordtype">double</span> &amp;x) { <span class="keywordflow">return</span> x &gt;= 0.0 ? 1.0 : 0.0; }</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno"> 82</span> </div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"><a class="line" href="../../d2/d58/neural__network_8cpp.html#a371aa7dd5d5add0143d1756bb0a1b32f"> 88</a></span><span class="keywordtype">double</span> <a class="code hl_function" href="../../d2/d58/neural__network_8cpp.html#a371aa7dd5d5add0143d1756bb0a1b32f">tanh</a>(<span class="keyword">const</span> <span class="keywordtype">double</span> &amp;x) { <span class="keywordflow">return</span> 2 / (1 + std::exp(-2 * x)) - 1; }</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno"> 89</span> </div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"><a class="line" href="../../d2/d58/neural__network_8cpp.html#a2a5e874b9774aa5362dbcf288828b95c"> 95</a></span><span class="keywordtype">double</span> <a class="code hl_function" href="../../d2/d58/neural__network_8cpp.html#a2a5e874b9774aa5362dbcf288828b95c">dtanh</a>(<span class="keyword">const</span> <span class="keywordtype">double</span> &amp;x) { <span class="keywordflow">return</span> 1 - x * x; }</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno"> 96</span>} <span class="comment">// namespace activations</span></div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"> 100</span><span class="keyword">namespace </span><a class="code hl_namespace" href="../../d3/d17/namespaceutil__functions.html">util_functions</a> {</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"><a class="line" href="../../d2/d58/neural__network_8cpp.html#a45d3e30406712ada3d9713ece3c1b153"> 106</a></span><span class="keywordtype">double</span> <a class="code hl_function" href="../../d2/d58/neural__network_8cpp.html#a45d3e30406712ada3d9713ece3c1b153">square</a>(<span class="keyword">const</span> <span class="keywordtype">double</span> &amp;x) { <span class="keywordflow">return</span> x * x; }</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno"><a class="line" href="../../d2/d58/neural__network_8cpp.html#a32c00da08f2cf641dd336270f6e3c407"> 112</a></span><span class="keywordtype">double</span> <a class="code hl_function" href="../../d2/d58/neural__network_8cpp.html#a32c00da08f2cf641dd336270f6e3c407">identity_function</a>(<span class="keyword">const</span> <span class="keywordtype">double</span> &amp;x) { <span class="keywordflow">return</span> x; }</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"> 113</span>} <span class="comment">// namespace util_functions</span></div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno"> 118</span><span class="keyword">namespace </span><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a> {</div>
<div class="foldopen" id="foldopen00125" data-start="{" data-end="};">
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"><a class="line" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html"> 125</a></span><span class="keyword">class </span><a class="code hl_class" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">DenseLayer</a> {</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"> 126</span> <span class="keyword">public</span>:</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"> 127</span> <span class="comment">// To store activation function and it&#39;s derivative</span></div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"> 128</span> double (*activation_function)(<span class="keyword">const</span> <span class="keywordtype">double</span> &amp;);</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"> 129</span> double (*dactivation_function)(<span class="keyword">const</span> <span class="keywordtype">double</span> &amp;);</div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"> 130</span> <span class="keywordtype">int</span> neurons; <span class="comment">// To store number of neurons (used in summary)</span></div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"> 131</span> std::string activation; <span class="comment">// To store activation name (used in summary)</span></div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"> 132</span> std::vector&lt;std::valarray&lt;double&gt;&gt; kernel; <span class="comment">// To store kernel (aka weights)</span></div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"> 133</span> </div>
<div class="foldopen" id="foldopen00141" data-start="{" data-end="}">
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno"><a class="line" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a11046825be0b6dbb73fbe834aa49200e"> 141</a></span> <a class="code hl_function" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a11046825be0b6dbb73fbe834aa49200e">DenseLayer</a>(<span class="keyword">const</span> <span class="keywordtype">int</span> &amp;neurons, <span class="keyword">const</span> std::string &amp;activation,</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"> 142</span> <span class="keyword">const</span> std::pair&lt;size_t, size_t&gt; &amp;kernel_shape,</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno"> 143</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> &amp;random_kernel) {</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno"> 144</span> <span class="comment">// Choosing activation (and it&#39;s derivative)</span></div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno"> 145</span> <span class="keywordflow">if</span> (activation == <span class="stringliteral">&quot;sigmoid&quot;</span>) {</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno"> 146</span> activation_function = neural_network::activations::sigmoid;</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"> 147</span> dactivation_function = neural_network::activations::sigmoid;</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno"> 148</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (activation == <span class="stringliteral">&quot;relu&quot;</span>) {</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"> 149</span> activation_function = neural_network::activations::relu;</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"> 150</span> dactivation_function = neural_network::activations::drelu;</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno"> 151</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (activation == <span class="stringliteral">&quot;tanh&quot;</span>) {</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno"> 152</span> activation_function = neural_network::activations::tanh;</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno"> 153</span> dactivation_function = neural_network::activations::dtanh;</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno"> 154</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (activation == <span class="stringliteral">&quot;none&quot;</span>) {</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno"> 155</span> <span class="comment">// Set identity function in casse of none is supplied</span></div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno"> 156</span> activation_function =</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"> 157</span> neural_network::util_functions::identity_function;</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno"> 158</span> dactivation_function =</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno"> 159</span> neural_network::util_functions::identity_function;</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno"> 160</span> } <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno"> 161</span> <span class="comment">// If supplied activation is invalid</span></div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno"> 162</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;ERROR (&quot;</span> &lt;&lt; __func__ &lt;&lt; <span class="stringliteral">&quot;) : &quot;</span>;</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno"> 163</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;Invalid argument. Expected {none, sigmoid, relu, &quot;</span></div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno"> 164</span> <span class="stringliteral">&quot;tanh} got &quot;</span>;</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno"> 165</span> std::cerr &lt;&lt; activation &lt;&lt; std::endl;</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno"> 166</span> std::exit(EXIT_FAILURE);</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno"> 167</span> }</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno"> 168</span> this-&gt;activation = activation; <span class="comment">// Setting activation name</span></div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno"> 169</span> this-&gt;neurons = neurons; <span class="comment">// Setting number of neurons</span></div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno"> 170</span> <span class="comment">// Initialize kernel according to flag</span></div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno"> 171</span> <span class="keywordflow">if</span> (random_kernel) {</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno"> 172</span> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#abee7b35403af3612222d3b7a53074905">uniform_random_initialization</a>(kernel, kernel_shape, -1.0, 1.0);</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno"> 173</span> } <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno"> 174</span> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a8dd3f1ffbc2f26a3c88da1b1f8b7e9c4">unit_matrix_initialization</a>(kernel, kernel_shape);</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno"> 175</span> }</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno"> 176</span> }</div>
</div>
<div class="foldopen" id="foldopen00183" data-start="{" data-end="}">
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno"><a class="line" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#af136ec31dbd35b1be2eb9a057677c704"> 183</a></span> <a class="code hl_function" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#af136ec31dbd35b1be2eb9a057677c704">DenseLayer</a>(<span class="keyword">const</span> <span class="keywordtype">int</span> &amp;neurons, <span class="keyword">const</span> std::string &amp;activation,</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno"> 184</span> <span class="keyword">const</span> std::vector&lt;std::valarray&lt;double&gt;&gt; &amp;kernel) {</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno"> 185</span> <span class="comment">// Choosing activation (and it&#39;s derivative)</span></div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno"> 186</span> <span class="keywordflow">if</span> (activation == <span class="stringliteral">&quot;sigmoid&quot;</span>) {</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno"> 187</span> activation_function = neural_network::activations::sigmoid;</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno"> 188</span> dactivation_function = neural_network::activations::sigmoid;</div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno"> 189</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (activation == <span class="stringliteral">&quot;relu&quot;</span>) {</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno"> 190</span> activation_function = neural_network::activations::relu;</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno"> 191</span> dactivation_function = neural_network::activations::drelu;</div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno"> 192</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (activation == <span class="stringliteral">&quot;tanh&quot;</span>) {</div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno"> 193</span> activation_function = neural_network::activations::tanh;</div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"> 194</span> dactivation_function = neural_network::activations::dtanh;</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno"> 195</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (activation == <span class="stringliteral">&quot;none&quot;</span>) {</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno"> 196</span> <span class="comment">// Set identity function in casse of none is supplied</span></div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno"> 197</span> activation_function =</div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno"> 198</span> neural_network::util_functions::identity_function;</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"> 199</span> dactivation_function =</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno"> 200</span> neural_network::util_functions::identity_function;</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno"> 201</span> } <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno"> 202</span> <span class="comment">// If supplied activation is invalid</span></div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno"> 203</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;ERROR (&quot;</span> &lt;&lt; __func__ &lt;&lt; <span class="stringliteral">&quot;) : &quot;</span>;</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno"> 204</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;Invalid argument. Expected {none, sigmoid, relu, &quot;</span></div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno"> 205</span> <span class="stringliteral">&quot;tanh} got &quot;</span>;</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno"> 206</span> std::cerr &lt;&lt; activation &lt;&lt; std::endl;</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno"> 207</span> std::exit(EXIT_FAILURE);</div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno"> 208</span> }</div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno"> 209</span> this-&gt;activation = activation; <span class="comment">// Setting activation name</span></div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno"> 210</span> this-&gt;neurons = neurons; <span class="comment">// Setting number of neurons</span></div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno"> 211</span> this-&gt;kernel = kernel; <span class="comment">// Setting supplied kernel values</span></div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"> 212</span> }</div>
</div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno"> 213</span> </div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno"><a class="line" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a2871146feaaa453558239df67b21e0d2"> 219</a></span> <a class="code hl_function" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a2871146feaaa453558239df67b21e0d2">DenseLayer</a>(<span class="keyword">const</span> <a class="code hl_class" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">DenseLayer</a> &amp;layer) = <span class="keywordflow">default</span>;</div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno"> 220</span> </div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno"><a class="line" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#ac9cda9453c4a0caf5bae7f9213b019a0"> 224</a></span> <a class="code hl_function" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#ac9cda9453c4a0caf5bae7f9213b019a0">~DenseLayer</a>() = <span class="keywordflow">default</span>;</div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno"> 225</span> </div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno"><a class="line" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#ae077132526d2863e46aa77cb0f7d6aa2"> 229</a></span> <a class="code hl_class" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">DenseLayer</a> &amp;<a class="code hl_function" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#ae077132526d2863e46aa77cb0f7d6aa2">operator=</a>(<span class="keyword">const</span> <a class="code hl_class" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">DenseLayer</a> &amp;layer) = <span class="keywordflow">default</span>;</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno"> 230</span> </div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno"><a class="line" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a6c859e3737aa88b29854df0347b29f4e"> 234</a></span> <a class="code hl_function" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a6c859e3737aa88b29854df0347b29f4e">DenseLayer</a>(<a class="code hl_class" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">DenseLayer</a> &amp;&amp;) = <span class="keywordflow">default</span>;</div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno"> 235</span> </div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno"><a class="line" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a19aaccad279b22dbbb6c55e5697b4114"> 239</a></span> <a class="code hl_class" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">DenseLayer</a> &amp;<a class="code hl_function" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a19aaccad279b22dbbb6c55e5697b4114">operator=</a>(<a class="code hl_class" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">DenseLayer</a> &amp;&amp;) = <span class="keywordflow">default</span>;</div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno"> 240</span>};</div>
</div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno"> 241</span>} <span class="comment">// namespace layers</span></div>
<div class="foldopen" id="foldopen00247" data-start="{" data-end="};">
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html"> 247</a></span><span class="keyword">class </span><a class="code hl_class" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">NeuralNetwork</a> {</div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno"> 248</span> <span class="keyword">private</span>:</div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno"> 249</span> std::vector&lt;neural_network::layers::DenseLayer&gt; <a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>; <span class="comment">// To store layers</span></div>
<div class="foldopen" id="foldopen00256" data-start="{" data-end="}">
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a4c4c6f63ab965317f9471518ee931b89"> 256</a></span> <a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a4c4c6f63ab965317f9471518ee931b89">NeuralNetwork</a>(</div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno"> 257</span> <span class="keyword">const</span> std::vector&lt;std::pair&lt;int, std::string&gt;&gt; &amp;config,</div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno"> 258</span> <span class="keyword">const</span> std::vector&lt;std::vector&lt;std::valarray&lt;double&gt;&gt;&gt; &amp;kernels) {</div>
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno"> 259</span> <span class="comment">// First layer should not have activation</span></div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno"> 260</span> <span class="keywordflow">if</span> (config.begin()-&gt;second != <span class="stringliteral">&quot;none&quot;</span>) {</div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno"> 261</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;ERROR (&quot;</span> &lt;&lt; __func__ &lt;&lt; <span class="stringliteral">&quot;) : &quot;</span>;</div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno"> 262</span> std::cerr</div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno"> 263</span> &lt;&lt; <span class="stringliteral">&quot;First layer can&#39;t have activation other than none got &quot;</span></div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno"> 264</span> &lt;&lt; config.begin()-&gt;second;</div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno"> 265</span> std::cerr &lt;&lt; std::endl;</div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno"> 266</span> std::exit(EXIT_FAILURE);</div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno"> 267</span> }</div>
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno"> 268</span> <span class="comment">// Network should have atleast two layers</span></div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno"> 269</span> <span class="keywordflow">if</span> (config.size() &lt;= 1) {</div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno"> 270</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;ERROR (&quot;</span> &lt;&lt; __func__ &lt;&lt; <span class="stringliteral">&quot;) : &quot;</span>;</div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno"> 271</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;Invalid size of network, &quot;</span>;</div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno"> 272</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;Atleast two layers are required&quot;</span>;</div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno"> 273</span> std::exit(EXIT_FAILURE);</div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno"> 274</span> }</div>
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno"> 275</span> <span class="comment">// Reconstructing all pretrained layers</span></div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno"> 276</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; config.size(); i++) {</div>
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno"> 277</span> <a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.emplace_back(<a class="code hl_class" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">neural_network::layers::DenseLayer</a>(</div>
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno"> 278</span> config[i].first, config[i].second, kernels[i]));</div>
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno"> 279</span> }</div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno"> 280</span> std::cout &lt;&lt; <span class="stringliteral">&quot;INFO: Network constructed successfully&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno"> 281</span> }</div>
</div>
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno"> 288</span> std::vector&lt;std::vector&lt;std::valarray&lt;double&gt;&gt;&gt;</div>
<div class="foldopen" id="foldopen00289" data-start="{" data-end="}">
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a361a45f3c3d8347d79103bf182d0570b"> 289</a></span> <a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a361a45f3c3d8347d79103bf182d0570b">__detailed_single_prediction</a>(<span class="keyword">const</span> std::vector&lt;std::valarray&lt;double&gt;&gt; &amp;X) {</div>
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno"> 290</span> std::vector&lt;std::vector&lt;std::valarray&lt;double&gt;&gt;&gt; details;</div>
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno"> 291</span> std::vector&lt;std::valarray&lt;double&gt;&gt; current_pass = X;</div>
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno"> 292</span> details.emplace_back(X);</div>
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno"> 293</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> &amp;l : <a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>) {</div>
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno"> 294</span> current_pass = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a5342906d42b80fc6b6b3ad17bf00fcb9">multiply</a>(current_pass, l.kernel);</div>
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno"> 295</span> current_pass = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#ad0bdc88e5f1be47c46c0f0c8ebf754bb">apply_function</a>(current_pass, l.activation_function);</div>
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno"> 296</span> details.emplace_back(current_pass);</div>
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno"> 297</span> }</div>
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno"> 298</span> <span class="keywordflow">return</span> details;</div>
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno"> 299</span> }</div>
</div>
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno"> 300</span> </div>
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno"> 301</span> <span class="keyword">public</span>:</div>
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#ae7cf126a3a8f9d20c81b21584d061a08"> 306</a></span> <a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#ae7cf126a3a8f9d20c81b21584d061a08">NeuralNetwork</a>() = <span class="keywordflow">default</span>;</div>
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno"> 307</span> </div>
<div class="foldopen" id="foldopen00313" data-start="{" data-end="}">
<div class="line"><a id="l00313" name="l00313"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a62151b0398a2536be60d950e10ffe9a8"> 313</a></span> <span class="keyword">explicit</span> <a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a62151b0398a2536be60d950e10ffe9a8">NeuralNetwork</a>(</div>
<div class="line"><a id="l00314" name="l00314"></a><span class="lineno"> 314</span> <span class="keyword">const</span> std::vector&lt;std::pair&lt;int, std::string&gt;&gt; &amp;config) {</div>
<div class="line"><a id="l00315" name="l00315"></a><span class="lineno"> 315</span> <span class="comment">// First layer should not have activation</span></div>
<div class="line"><a id="l00316" name="l00316"></a><span class="lineno"> 316</span> <span class="keywordflow">if</span> (config.begin()-&gt;second != <span class="stringliteral">&quot;none&quot;</span>) {</div>
<div class="line"><a id="l00317" name="l00317"></a><span class="lineno"> 317</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;ERROR (&quot;</span> &lt;&lt; __func__ &lt;&lt; <span class="stringliteral">&quot;) : &quot;</span>;</div>
<div class="line"><a id="l00318" name="l00318"></a><span class="lineno"> 318</span> std::cerr</div>
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno"> 319</span> &lt;&lt; <span class="stringliteral">&quot;First layer can&#39;t have activation other than none got &quot;</span></div>
<div class="line"><a id="l00320" name="l00320"></a><span class="lineno"> 320</span> &lt;&lt; config.begin()-&gt;second;</div>
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno"> 321</span> std::cerr &lt;&lt; std::endl;</div>
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno"> 322</span> std::exit(EXIT_FAILURE);</div>
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno"> 323</span> }</div>
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno"> 324</span> <span class="comment">// Network should have atleast two layers</span></div>
<div class="line"><a id="l00325" name="l00325"></a><span class="lineno"> 325</span> <span class="keywordflow">if</span> (config.size() &lt;= 1) {</div>
<div class="line"><a id="l00326" name="l00326"></a><span class="lineno"> 326</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;ERROR (&quot;</span> &lt;&lt; __func__ &lt;&lt; <span class="stringliteral">&quot;) : &quot;</span>;</div>
<div class="line"><a id="l00327" name="l00327"></a><span class="lineno"> 327</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;Invalid size of network, &quot;</span>;</div>
<div class="line"><a id="l00328" name="l00328"></a><span class="lineno"> 328</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;Atleast two layers are required&quot;</span>;</div>
<div class="line"><a id="l00329" name="l00329"></a><span class="lineno"> 329</span> std::exit(EXIT_FAILURE);</div>
<div class="line"><a id="l00330" name="l00330"></a><span class="lineno"> 330</span> }</div>
<div class="line"><a id="l00331" name="l00331"></a><span class="lineno"> 331</span> <span class="comment">// Separately creating first layer so it can have unit matrix</span></div>
<div class="line"><a id="l00332" name="l00332"></a><span class="lineno"> 332</span> <span class="comment">// as kernel.</span></div>
<div class="line"><a id="l00333" name="l00333"></a><span class="lineno"> 333</span> <a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.push_back(<a class="code hl_class" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">neural_network::layers::DenseLayer</a>(</div>
<div class="line"><a id="l00334" name="l00334"></a><span class="lineno"> 334</span> config[0].first, config[0].second,</div>
<div class="line"><a id="l00335" name="l00335"></a><span class="lineno"> 335</span> {config[0].first, config[0].first}, <span class="keyword">false</span>));</div>
<div class="line"><a id="l00336" name="l00336"></a><span class="lineno"> 336</span> <span class="comment">// Creating remaining layers</span></div>
<div class="line"><a id="l00337" name="l00337"></a><span class="lineno"> 337</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 1; i &lt; config.size(); i++) {</div>
<div class="line"><a id="l00338" name="l00338"></a><span class="lineno"> 338</span> <a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.push_back(<a class="code hl_class" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">neural_network::layers::DenseLayer</a>(</div>
<div class="line"><a id="l00339" name="l00339"></a><span class="lineno"> 339</span> config[i].first, config[i].second,</div>
<div class="line"><a id="l00340" name="l00340"></a><span class="lineno"> 340</span> {config[i - 1].first, config[i].first}, <span class="keyword">true</span>));</div>
<div class="line"><a id="l00341" name="l00341"></a><span class="lineno"> 341</span> }</div>
<div class="line"><a id="l00342" name="l00342"></a><span class="lineno"> 342</span> std::cout &lt;&lt; <span class="stringliteral">&quot;INFO: Network constructed successfully&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00343" name="l00343"></a><span class="lineno"> 343</span> }</div>
</div>
<div class="line"><a id="l00344" name="l00344"></a><span class="lineno"> 344</span> </div>
<div class="line"><a id="l00350" name="l00350"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a176b955c90ae57d7dbc3c63f27c84c75"> 350</a></span> <a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a176b955c90ae57d7dbc3c63f27c84c75">NeuralNetwork</a>(<span class="keyword">const</span> <a class="code hl_class" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">NeuralNetwork</a> &amp;model) = <span class="keywordflow">default</span>;</div>
<div class="line"><a id="l00351" name="l00351"></a><span class="lineno"> 351</span> </div>
<div class="line"><a id="l00355" name="l00355"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a8973f687738ddd76f93b5562feae4027"> 355</a></span> <a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a8973f687738ddd76f93b5562feae4027">~NeuralNetwork</a>() = <span class="keywordflow">default</span>;</div>
<div class="line"><a id="l00356" name="l00356"></a><span class="lineno"> 356</span> </div>
<div class="line"><a id="l00360" name="l00360"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a58a9614e4c6d4ca672d3358e99a3404f"> 360</a></span> <a class="code hl_class" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">NeuralNetwork</a> &amp;<a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a58a9614e4c6d4ca672d3358e99a3404f">operator=</a>(<span class="keyword">const</span> <a class="code hl_class" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">NeuralNetwork</a> &amp;model) = <span class="keywordflow">default</span>;</div>
<div class="line"><a id="l00361" name="l00361"></a><span class="lineno"> 361</span> </div>
<div class="line"><a id="l00365" name="l00365"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a173bb71780af6953ec2e307a4c74b025"> 365</a></span> <a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a173bb71780af6953ec2e307a4c74b025">NeuralNetwork</a>(<a class="code hl_class" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">NeuralNetwork</a> &amp;&amp;) = <span class="keywordflow">default</span>;</div>
<div class="line"><a id="l00366" name="l00366"></a><span class="lineno"> 366</span> </div>
<div class="line"><a id="l00370" name="l00370"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a2c49bfebf9b859d5ceb26035d3003601"> 370</a></span> <a class="code hl_class" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">NeuralNetwork</a> &amp;<a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a2c49bfebf9b859d5ceb26035d3003601">operator=</a>(<a class="code hl_class" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">NeuralNetwork</a> &amp;&amp;) = <span class="keywordflow">default</span>;</div>
<div class="line"><a id="l00371" name="l00371"></a><span class="lineno"> 371</span> </div>
<div class="line"><a id="l00380" name="l00380"></a><span class="lineno"> 380</span> std::pair&lt;std::vector&lt;std::vector&lt;std::valarray&lt;double&gt;&gt;&gt;,</div>
<div class="line"><a id="l00381" name="l00381"></a><span class="lineno"> 381</span> std::vector&lt;std::vector&lt;std::valarray&lt;double&gt;&gt;&gt;&gt;</div>
<div class="foldopen" id="foldopen00382" data-start="{" data-end="}">
<div class="line"><a id="l00382" name="l00382"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a650c677fd6512665741ddd9b7983275d"> 382</a></span> <a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a650c677fd6512665741ddd9b7983275d">get_XY_from_csv</a>(<span class="keyword">const</span> std::string &amp;file_name, <span class="keyword">const</span> <span class="keywordtype">bool</span> &amp;last_label,</div>
<div class="line"><a id="l00383" name="l00383"></a><span class="lineno"> 383</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> &amp;normalize, <span class="keyword">const</span> <span class="keywordtype">int</span> &amp;slip_lines = 1) {</div>
<div class="line"><a id="l00384" name="l00384"></a><span class="lineno"> 384</span> std::ifstream in_file; <span class="comment">// Ifstream to read file</span></div>
<div class="line"><a id="l00385" name="l00385"></a><span class="lineno"> 385</span> in_file.open(file_name.c_str(), std::ios::in); <span class="comment">// Open file</span></div>
<div class="line"><a id="l00386" name="l00386"></a><span class="lineno"> 386</span> <span class="comment">// If there is any problem in opening file</span></div>
<div class="line"><a id="l00387" name="l00387"></a><span class="lineno"> 387</span> <span class="keywordflow">if</span> (!in_file.is_open()) {</div>
<div class="line"><a id="l00388" name="l00388"></a><span class="lineno"> 388</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;ERROR (&quot;</span> &lt;&lt; __func__ &lt;&lt; <span class="stringliteral">&quot;) : &quot;</span>;</div>
<div class="line"><a id="l00389" name="l00389"></a><span class="lineno"> 389</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;Unable to open file: &quot;</span> &lt;&lt; file_name &lt;&lt; std::endl;</div>
<div class="line"><a id="l00390" name="l00390"></a><span class="lineno"> 390</span> std::exit(EXIT_FAILURE);</div>
<div class="line"><a id="l00391" name="l00391"></a><span class="lineno"> 391</span> }</div>
<div class="line"><a id="l00392" name="l00392"></a><span class="lineno"> 392</span> std::vector&lt;std::vector&lt;std::valarray&lt;double&gt;&gt;&gt; X,</div>
<div class="line"><a id="l00393" name="l00393"></a><span class="lineno"> 393</span> Y; <span class="comment">// To store X and Y</span></div>
<div class="line"><a id="l00394" name="l00394"></a><span class="lineno"> 394</span> std::string line; <span class="comment">// To store each line</span></div>
<div class="line"><a id="l00395" name="l00395"></a><span class="lineno"> 395</span> <span class="comment">// Skip lines</span></div>
<div class="line"><a id="l00396" name="l00396"></a><span class="lineno"> 396</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; slip_lines; i++) {</div>
<div class="line"><a id="l00397" name="l00397"></a><span class="lineno"> 397</span> std::getline(in_file, line, <span class="charliteral">&#39;\n&#39;</span>); <span class="comment">// Ignore line</span></div>
<div class="line"><a id="l00398" name="l00398"></a><span class="lineno"> 398</span> }</div>
<div class="line"><a id="l00399" name="l00399"></a><span class="lineno"> 399</span> <span class="comment">// While file has information</span></div>
<div class="line"><a id="l00400" name="l00400"></a><span class="lineno"> 400</span> <span class="keywordflow">while</span> (!in_file.eof() &amp;&amp; std::getline(in_file, line, <span class="charliteral">&#39;\n&#39;</span>)) {</div>
<div class="line"><a id="l00401" name="l00401"></a><span class="lineno"> 401</span> std::valarray&lt;double&gt; x_data,</div>
<div class="line"><a id="l00402" name="l00402"></a><span class="lineno"> 402</span> y_data; <span class="comment">// To store single sample and label</span></div>
<div class="line"><a id="l00403" name="l00403"></a><span class="lineno"> 403</span> std::stringstream ss(line); <span class="comment">// Constructing stringstream from line</span></div>
<div class="line"><a id="l00404" name="l00404"></a><span class="lineno"> 404</span> std::string token; <span class="comment">// To store each token in line (seprated by &#39;,&#39;)</span></div>
<div class="line"><a id="l00405" name="l00405"></a><span class="lineno"> 405</span> <span class="keywordflow">while</span> (std::getline(ss, token, <span class="charliteral">&#39;,&#39;</span>)) { <span class="comment">// For each token</span></div>
<div class="line"><a id="l00406" name="l00406"></a><span class="lineno"> 406</span> <span class="comment">// Insert numerical value of token in x_data</span></div>
<div class="line"><a id="l00407" name="l00407"></a><span class="lineno"> 407</span> x_data = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a496302e3371aa7b478cb7d5917904bdd">insert_element</a>(x_data, std::stod(token));</div>
<div class="line"><a id="l00408" name="l00408"></a><span class="lineno"> 408</span> }</div>
<div class="line"><a id="l00409" name="l00409"></a><span class="lineno"> 409</span> <span class="comment">// If label is in last column</span></div>
<div class="line"><a id="l00410" name="l00410"></a><span class="lineno"> 410</span> <span class="keywordflow">if</span> (last_label) {</div>
<div class="line"><a id="l00411" name="l00411"></a><span class="lineno"> 411</span> y_data.resize(this-&gt;layers.back().neurons);</div>
<div class="line"><a id="l00412" name="l00412"></a><span class="lineno"> 412</span> <span class="comment">// If task is classification</span></div>
<div class="line"><a id="l00413" name="l00413"></a><span class="lineno"> 413</span> <span class="keywordflow">if</span> (y_data.size() &gt; 1) {</div>
<div class="line"><a id="l00414" name="l00414"></a><span class="lineno"> 414</span> y_data[x_data[x_data.size() - 1]] = 1;</div>
<div class="line"><a id="l00415" name="l00415"></a><span class="lineno"> 415</span> }</div>
<div class="line"><a id="l00416" name="l00416"></a><span class="lineno"> 416</span> <span class="comment">// If task is regrssion (of single value)</span></div>
<div class="line"><a id="l00417" name="l00417"></a><span class="lineno"> 417</span> <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00418" name="l00418"></a><span class="lineno"> 418</span> y_data[0] = x_data[x_data.size() - 1];</div>
<div class="line"><a id="l00419" name="l00419"></a><span class="lineno"> 419</span> }</div>
<div class="line"><a id="l00420" name="l00420"></a><span class="lineno"> 420</span> x_data = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#ae10178b082f0205c326550877d998e5d">pop_back</a>(x_data); <span class="comment">// Remove label from x_data</span></div>
<div class="line"><a id="l00421" name="l00421"></a><span class="lineno"> 421</span> } <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00422" name="l00422"></a><span class="lineno"> 422</span> y_data.resize(this-&gt;layers.back().neurons);</div>
<div class="line"><a id="l00423" name="l00423"></a><span class="lineno"> 423</span> <span class="comment">// If task is classification</span></div>
<div class="line"><a id="l00424" name="l00424"></a><span class="lineno"> 424</span> <span class="keywordflow">if</span> (y_data.size() &gt; 1) {</div>
<div class="line"><a id="l00425" name="l00425"></a><span class="lineno"> 425</span> y_data[x_data[x_data.size() - 1]] = 1;</div>
<div class="line"><a id="l00426" name="l00426"></a><span class="lineno"> 426</span> }</div>
<div class="line"><a id="l00427" name="l00427"></a><span class="lineno"> 427</span> <span class="comment">// If task is regrssion (of single value)</span></div>
<div class="line"><a id="l00428" name="l00428"></a><span class="lineno"> 428</span> <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00429" name="l00429"></a><span class="lineno"> 429</span> y_data[0] = x_data[x_data.size() - 1];</div>
<div class="line"><a id="l00430" name="l00430"></a><span class="lineno"> 430</span> }</div>
<div class="line"><a id="l00431" name="l00431"></a><span class="lineno"> 431</span> x_data = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a912cf68863063a38d6e63545be5eb093">pop_front</a>(x_data); <span class="comment">// Remove label from x_data</span></div>
<div class="line"><a id="l00432" name="l00432"></a><span class="lineno"> 432</span> }</div>
<div class="line"><a id="l00433" name="l00433"></a><span class="lineno"> 433</span> <span class="comment">// Push collected X_data and y_data in X and Y</span></div>
<div class="line"><a id="l00434" name="l00434"></a><span class="lineno"> 434</span> X.push_back({x_data});</div>
<div class="line"><a id="l00435" name="l00435"></a><span class="lineno"> 435</span> Y.push_back({y_data});</div>
<div class="line"><a id="l00436" name="l00436"></a><span class="lineno"> 436</span> }</div>
<div class="line"><a id="l00437" name="l00437"></a><span class="lineno"> 437</span> <span class="comment">// Normalize training data if flag is set</span></div>
<div class="line"><a id="l00438" name="l00438"></a><span class="lineno"> 438</span> <span class="keywordflow">if</span> (normalize) {</div>
<div class="line"><a id="l00439" name="l00439"></a><span class="lineno"> 439</span> <span class="comment">// Scale data between 0 and 1 using min-max scaler</span></div>
<div class="line"><a id="l00440" name="l00440"></a><span class="lineno"> 440</span> X = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#ac332d152078e96311e43ac5e7183ea26">minmax_scaler</a>(X, 0.01, 1.0);</div>
<div class="line"><a id="l00441" name="l00441"></a><span class="lineno"> 441</span> }</div>
<div class="line"><a id="l00442" name="l00442"></a><span class="lineno"> 442</span> in_file.close(); <span class="comment">// Closing file</span></div>
<div class="line"><a id="l00443" name="l00443"></a><span class="lineno"> 443</span> <span class="keywordflow">return</span> make_pair(X, Y); <span class="comment">// Return pair of X and Y</span></div>
<div class="line"><a id="l00444" name="l00444"></a><span class="lineno"> 444</span> }</div>
</div>
<div class="line"><a id="l00445" name="l00445"></a><span class="lineno"> 445</span> </div>
<div class="foldopen" id="foldopen00451" data-start="{" data-end="}">
<div class="line"><a id="l00451" name="l00451"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a3b9eac1824d365dce715fb17c33cb96f"> 451</a></span> std::vector&lt;std::valarray&lt;double&gt;&gt; <a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a3b9eac1824d365dce715fb17c33cb96f">single_predict</a>(</div>
<div class="line"><a id="l00452" name="l00452"></a><span class="lineno"> 452</span> <span class="keyword">const</span> std::vector&lt;std::valarray&lt;double&gt;&gt; &amp;X) {</div>
<div class="line"><a id="l00453" name="l00453"></a><span class="lineno"> 453</span> <span class="comment">// Get activations of all layers</span></div>
<div class="line"><a id="l00454" name="l00454"></a><span class="lineno"> 454</span> <span class="keyword">auto</span> <a class="code hl_namespace" href="../../d5/d39/namespaceactivations.html">activations</a> = this-&gt;<a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a361a45f3c3d8347d79103bf182d0570b">__detailed_single_prediction</a>(X);</div>
<div class="line"><a id="l00455" name="l00455"></a><span class="lineno"> 455</span> <span class="comment">// Return activations of last layer (actual predicted values)</span></div>
<div class="line"><a id="l00456" name="l00456"></a><span class="lineno"> 456</span> <span class="keywordflow">return</span> <a class="code hl_namespace" href="../../d5/d39/namespaceactivations.html">activations</a>.back();</div>
<div class="line"><a id="l00457" name="l00457"></a><span class="lineno"> 457</span> }</div>
</div>
<div class="line"><a id="l00458" name="l00458"></a><span class="lineno"> 458</span> </div>
<div class="foldopen" id="foldopen00464" data-start="{" data-end="}">
<div class="line"><a id="l00464" name="l00464"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a88bf9023ab3d4cdb61cf707c7cdfc86b"> 464</a></span> std::vector&lt;std::vector&lt;std::valarray&lt;double&gt;&gt;&gt; <a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a88bf9023ab3d4cdb61cf707c7cdfc86b">batch_predict</a>(</div>
<div class="line"><a id="l00465" name="l00465"></a><span class="lineno"> 465</span> <span class="keyword">const</span> std::vector&lt;std::vector&lt;std::valarray&lt;double&gt;&gt;&gt; &amp;X) {</div>
<div class="line"><a id="l00466" name="l00466"></a><span class="lineno"> 466</span> <span class="comment">// Store predicted values</span></div>
<div class="line"><a id="l00467" name="l00467"></a><span class="lineno"> 467</span> std::vector&lt;std::vector&lt;std::valarray&lt;double&gt;&gt;&gt; predicted_batch(</div>
<div class="line"><a id="l00468" name="l00468"></a><span class="lineno"> 468</span> X.size());</div>
<div class="line"><a id="l00469" name="l00469"></a><span class="lineno"> 469</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; X.size(); i++) { <span class="comment">// For every sample</span></div>
<div class="line"><a id="l00470" name="l00470"></a><span class="lineno"> 470</span> <span class="comment">// Push predicted values</span></div>
<div class="line"><a id="l00471" name="l00471"></a><span class="lineno"> 471</span> predicted_batch[i] = this-&gt;<a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a3b9eac1824d365dce715fb17c33cb96f">single_predict</a>(X[i]);</div>
<div class="line"><a id="l00472" name="l00472"></a><span class="lineno"> 472</span> }</div>
<div class="line"><a id="l00473" name="l00473"></a><span class="lineno"> 473</span> <span class="keywordflow">return</span> predicted_batch; <span class="comment">// Return predicted values</span></div>
<div class="line"><a id="l00474" name="l00474"></a><span class="lineno"> 474</span> }</div>
</div>
<div class="line"><a id="l00475" name="l00475"></a><span class="lineno"> 475</span> </div>
<div class="foldopen" id="foldopen00485" data-start="{" data-end="}">
<div class="line"><a id="l00485" name="l00485"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a2be1b52bb9f57486f9a436f35c9089c0"> 485</a></span> <span class="keywordtype">void</span> <a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a2be1b52bb9f57486f9a436f35c9089c0">fit</a>(<span class="keyword">const</span> std::vector&lt;std::vector&lt;std::valarray&lt;double&gt;&gt;&gt; &amp;X_,</div>
<div class="line"><a id="l00486" name="l00486"></a><span class="lineno"> 486</span> <span class="keyword">const</span> std::vector&lt;std::vector&lt;std::valarray&lt;double&gt;&gt;&gt; &amp;Y_,</div>
<div class="line"><a id="l00487" name="l00487"></a><span class="lineno"> 487</span> <span class="keyword">const</span> <span class="keywordtype">int</span> &amp;epochs = 100, <span class="keyword">const</span> <span class="keywordtype">double</span> &amp;learning_rate = 0.01,</div>
<div class="line"><a id="l00488" name="l00488"></a><span class="lineno"> 488</span> <span class="keyword">const</span> <span class="keywordtype">size_t</span> &amp;batch_size = 32, <span class="keyword">const</span> <span class="keywordtype">bool</span> &amp;shuffle = <span class="keyword">true</span>) {</div>
<div class="line"><a id="l00489" name="l00489"></a><span class="lineno"> 489</span> std::vector&lt;std::vector&lt;std::valarray&lt;double&gt;&gt;&gt; X = X_, Y = Y_;</div>
<div class="line"><a id="l00490" name="l00490"></a><span class="lineno"> 490</span> <span class="comment">// Both label and input data should have same size</span></div>
<div class="line"><a id="l00491" name="l00491"></a><span class="lineno"> 491</span> <span class="keywordflow">if</span> (X.size() != Y.size()) {</div>
<div class="line"><a id="l00492" name="l00492"></a><span class="lineno"> 492</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;ERROR (&quot;</span> &lt;&lt; __func__ &lt;&lt; <span class="stringliteral">&quot;) : &quot;</span>;</div>
<div class="line"><a id="l00493" name="l00493"></a><span class="lineno"> 493</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;X and Y in fit have different sizes&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00494" name="l00494"></a><span class="lineno"> 494</span> std::exit(EXIT_FAILURE);</div>
<div class="line"><a id="l00495" name="l00495"></a><span class="lineno"> 495</span> }</div>
<div class="line"><a id="l00496" name="l00496"></a><span class="lineno"> 496</span> std::cout &lt;&lt; <span class="stringliteral">&quot;INFO: Training Started&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00497" name="l00497"></a><span class="lineno"> 497</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> epoch = 1; epoch &lt;= epochs; epoch++) { <span class="comment">// For every epoch</span></div>
<div class="line"><a id="l00498" name="l00498"></a><span class="lineno"> 498</span> <span class="comment">// Shuffle X and Y if flag is set</span></div>
<div class="line"><a id="l00499" name="l00499"></a><span class="lineno"> 499</span> <span class="keywordflow">if</span> (shuffle) {</div>
<div class="line"><a id="l00500" name="l00500"></a><span class="lineno"> 500</span> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#af801bf30591ca6b2c38ff4fed0ded23f">equal_shuffle</a>(X, Y);</div>
<div class="line"><a id="l00501" name="l00501"></a><span class="lineno"> 501</span> }</div>
<div class="line"><a id="l00502" name="l00502"></a><span class="lineno"> 502</span> <span class="keyword">auto</span> start =</div>
<div class="line"><a id="l00503" name="l00503"></a><span class="lineno"> 503</span> std::chrono::high_resolution_clock::now(); <span class="comment">// Start clock</span></div>
<div class="line"><a id="l00504" name="l00504"></a><span class="lineno"> 504</span> <span class="keywordtype">double</span> loss = 0,</div>
<div class="line"><a id="l00505" name="l00505"></a><span class="lineno"> 505</span> acc = 0; <span class="comment">// Initialize performance metrics with zero</span></div>
<div class="line"><a id="l00506" name="l00506"></a><span class="lineno"> 506</span> <span class="comment">// For each starting index of batch</span></div>
<div class="line"><a id="l00507" name="l00507"></a><span class="lineno"> 507</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> batch_start = 0; batch_start &lt; X.size();</div>
<div class="line"><a id="l00508" name="l00508"></a><span class="lineno"> 508</span> batch_start += batch_size) {</div>
<div class="line"><a id="l00509" name="l00509"></a><span class="lineno"> 509</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = batch_start;</div>
<div class="line"><a id="l00510" name="l00510"></a><span class="lineno"> 510</span> i &lt; std::min(X.size(), batch_start + batch_size); i++) {</div>
<div class="line"><a id="l00511" name="l00511"></a><span class="lineno"> 511</span> std::vector&lt;std::valarray&lt;double&gt;&gt; grad, cur_error,</div>
<div class="line"><a id="l00512" name="l00512"></a><span class="lineno"> 512</span> predicted;</div>
<div class="line"><a id="l00513" name="l00513"></a><span class="lineno"> 513</span> <span class="keyword">auto</span> <a class="code hl_namespace" href="../../d5/d39/namespaceactivations.html">activations</a> = this-&gt;<a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a361a45f3c3d8347d79103bf182d0570b">__detailed_single_prediction</a>(X[i]);</div>
<div class="line"><a id="l00514" name="l00514"></a><span class="lineno"> 514</span> <span class="comment">// Gradients vector to store gradients for all layers</span></div>
<div class="line"><a id="l00515" name="l00515"></a><span class="lineno"> 515</span> <span class="comment">// They will be averaged and applied to kernel</span></div>
<div class="line"><a id="l00516" name="l00516"></a><span class="lineno"> 516</span> std::vector&lt;std::vector&lt;std::valarray&lt;double&gt;&gt;&gt; gradients;</div>
<div class="line"><a id="l00517" name="l00517"></a><span class="lineno"> 517</span> gradients.resize(this-&gt;layers.size());</div>
<div class="line"><a id="l00518" name="l00518"></a><span class="lineno"> 518</span> <span class="comment">// First initialize gradients to zero</span></div>
<div class="line"><a id="l00519" name="l00519"></a><span class="lineno"> 519</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; gradients.size(); i++) {</div>
<div class="line"><a id="l00520" name="l00520"></a><span class="lineno"> 520</span> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#ac1bdaa2a724b4ce6a6bb371a5dbe2e7e">zeroes_initialization</a>(</div>
<div class="line"><a id="l00521" name="l00521"></a><span class="lineno"> 521</span> gradients[i], <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(this-&gt;layers[i].kernel));</div>
<div class="line"><a id="l00522" name="l00522"></a><span class="lineno"> 522</span> }</div>
<div class="line"><a id="l00523" name="l00523"></a><span class="lineno"> 523</span> predicted = <a class="code hl_namespace" href="../../d5/d39/namespaceactivations.html">activations</a>.back(); <span class="comment">// Predicted vector</span></div>
<div class="line"><a id="l00524" name="l00524"></a><span class="lineno"> 524</span> cur_error = predicted - Y[i]; <span class="comment">// Absoulute error</span></div>
<div class="line"><a id="l00525" name="l00525"></a><span class="lineno"> 525</span> <span class="comment">// Calculating loss with MSE</span></div>
<div class="line"><a id="l00526" name="l00526"></a><span class="lineno"> 526</span> loss += <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a6f1c98c016ad34ff3d9f39372161bd35">sum</a>(<a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#ad0bdc88e5f1be47c46c0f0c8ebf754bb">apply_function</a>(</div>
<div class="line"><a id="l00527" name="l00527"></a><span class="lineno"> 527</span> cur_error, neural_network::util_functions::square));</div>
<div class="line"><a id="l00528" name="l00528"></a><span class="lineno"> 528</span> <span class="comment">// If prediction is correct</span></div>
<div class="line"><a id="l00529" name="l00529"></a><span class="lineno"> 529</span> <span class="keywordflow">if</span> (<a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a50480fccfb39de20ca47f1bf51ecb6ec">argmax</a>(predicted) == <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a50480fccfb39de20ca47f1bf51ecb6ec">argmax</a>(Y[i])) {</div>
<div class="line"><a id="l00530" name="l00530"></a><span class="lineno"> 530</span> acc += 1;</div>
<div class="line"><a id="l00531" name="l00531"></a><span class="lineno"> 531</span> }</div>
<div class="line"><a id="l00532" name="l00532"></a><span class="lineno"> 532</span> <span class="comment">// For every layer (except first) starting from last one</span></div>
<div class="line"><a id="l00533" name="l00533"></a><span class="lineno"> 533</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = this-&gt;layers.size() - 1; j &gt;= 1; j--) {</div>
<div class="line"><a id="l00534" name="l00534"></a><span class="lineno"> 534</span> <span class="comment">// Backpropogating errors</span></div>
<div class="line"><a id="l00535" name="l00535"></a><span class="lineno"> 535</span> cur_error = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#acafa3e62b686aebdbad81c4f89913f43">hadamard_product</a>(</div>
<div class="line"><a id="l00536" name="l00536"></a><span class="lineno"> 536</span> cur_error,</div>
<div class="line"><a id="l00537" name="l00537"></a><span class="lineno"> 537</span> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#ad0bdc88e5f1be47c46c0f0c8ebf754bb">apply_function</a>(</div>
<div class="line"><a id="l00538" name="l00538"></a><span class="lineno"> 538</span> <a class="code hl_namespace" href="../../d5/d39/namespaceactivations.html">activations</a>[j + 1],</div>
<div class="line"><a id="l00539" name="l00539"></a><span class="lineno"> 539</span> this-&gt;layers[j].dactivation_function));</div>
<div class="line"><a id="l00540" name="l00540"></a><span class="lineno"> 540</span> <span class="comment">// Calculating gradient for current layer</span></div>
<div class="line"><a id="l00541" name="l00541"></a><span class="lineno"> 541</span> grad = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a5342906d42b80fc6b6b3ad17bf00fcb9">multiply</a>(<a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a89fde571b38f9483576594f66572958a">transpose</a>(<a class="code hl_namespace" href="../../d5/d39/namespaceactivations.html">activations</a>[j]), cur_error);</div>
<div class="line"><a id="l00542" name="l00542"></a><span class="lineno"> 542</span> <span class="comment">// Change error according to current kernel values</span></div>
<div class="line"><a id="l00543" name="l00543"></a><span class="lineno"> 543</span> cur_error = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a5342906d42b80fc6b6b3ad17bf00fcb9">multiply</a>(cur_error,</div>
<div class="line"><a id="l00544" name="l00544"></a><span class="lineno"> 544</span> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a89fde571b38f9483576594f66572958a">transpose</a>(this-&gt;layers[j].kernel));</div>
<div class="line"><a id="l00545" name="l00545"></a><span class="lineno"> 545</span> <span class="comment">// Adding gradient values to collection of gradients</span></div>
<div class="line"><a id="l00546" name="l00546"></a><span class="lineno"> 546</span> gradients[j] = gradients[j] + grad / double(batch_size);</div>
<div class="line"><a id="l00547" name="l00547"></a><span class="lineno"> 547</span> }</div>
<div class="line"><a id="l00548" name="l00548"></a><span class="lineno"> 548</span> <span class="comment">// Applying gradients</span></div>
<div class="line"><a id="l00549" name="l00549"></a><span class="lineno"> 549</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = this-&gt;layers.size() - 1; j &gt;= 1; j--) {</div>
<div class="line"><a id="l00550" name="l00550"></a><span class="lineno"> 550</span> <span class="comment">// Updating kernel (aka weights)</span></div>
<div class="line"><a id="l00551" name="l00551"></a><span class="lineno"> 551</span> this-&gt;layers[j].kernel = this-&gt;layers[j].kernel -</div>
<div class="line"><a id="l00552" name="l00552"></a><span class="lineno"> 552</span> gradients[j] * learning_rate;</div>
<div class="line"><a id="l00553" name="l00553"></a><span class="lineno"> 553</span> }</div>
<div class="line"><a id="l00554" name="l00554"></a><span class="lineno"> 554</span> }</div>
<div class="line"><a id="l00555" name="l00555"></a><span class="lineno"> 555</span> }</div>
<div class="line"><a id="l00556" name="l00556"></a><span class="lineno"> 556</span> <span class="keyword">auto</span> stop =</div>
<div class="line"><a id="l00557" name="l00557"></a><span class="lineno"> 557</span> std::chrono::high_resolution_clock::now(); <span class="comment">// Stoping the clock</span></div>
<div class="line"><a id="l00558" name="l00558"></a><span class="lineno"> 558</span> <span class="comment">// Calculate time taken by epoch</span></div>
<div class="line"><a id="l00559" name="l00559"></a><span class="lineno"> 559</span> <span class="keyword">auto</span> duration =</div>
<div class="line"><a id="l00560" name="l00560"></a><span class="lineno"> 560</span> std::chrono::duration_cast&lt;std::chrono::microseconds&gt;(stop -</div>
<div class="line"><a id="l00561" name="l00561"></a><span class="lineno"> 561</span> start);</div>
<div class="line"><a id="l00562" name="l00562"></a><span class="lineno"> 562</span> loss /= X.size(); <span class="comment">// Averaging loss</span></div>
<div class="line"><a id="l00563" name="l00563"></a><span class="lineno"> 563</span> acc /= X.size(); <span class="comment">// Averaging accuracy</span></div>
<div class="line"><a id="l00564" name="l00564"></a><span class="lineno"> 564</span> std::cout.precision(4); <span class="comment">// set output precision to 4</span></div>
<div class="line"><a id="l00565" name="l00565"></a><span class="lineno"> 565</span> <span class="comment">// Printing training stats</span></div>
<div class="line"><a id="l00566" name="l00566"></a><span class="lineno"> 566</span> std::cout &lt;&lt; <span class="stringliteral">&quot;Training: Epoch &quot;</span> &lt;&lt; epoch &lt;&lt; <span class="charliteral">&#39;/&#39;</span> &lt;&lt; epochs;</div>
<div class="line"><a id="l00567" name="l00567"></a><span class="lineno"> 567</span> std::cout &lt;&lt; <span class="stringliteral">&quot;, Loss: &quot;</span> &lt;&lt; loss;</div>
<div class="line"><a id="l00568" name="l00568"></a><span class="lineno"> 568</span> std::cout &lt;&lt; <span class="stringliteral">&quot;, Accuracy: &quot;</span> &lt;&lt; acc;</div>
<div class="line"><a id="l00569" name="l00569"></a><span class="lineno"> 569</span> std::cout &lt;&lt; <span class="stringliteral">&quot;, Taken time: &quot;</span> &lt;&lt; duration.count() / 1e6</div>
<div class="line"><a id="l00570" name="l00570"></a><span class="lineno"> 570</span> &lt;&lt; <span class="stringliteral">&quot; seconds&quot;</span>;</div>
<div class="line"><a id="l00571" name="l00571"></a><span class="lineno"> 571</span> std::cout &lt;&lt; std::endl;</div>
<div class="line"><a id="l00572" name="l00572"></a><span class="lineno"> 572</span> }</div>
<div class="line"><a id="l00573" name="l00573"></a><span class="lineno"> 573</span> <span class="keywordflow">return</span>;</div>
<div class="line"><a id="l00574" name="l00574"></a><span class="lineno"> 574</span> }</div>
</div>
<div class="line"><a id="l00575" name="l00575"></a><span class="lineno"> 575</span> </div>
<div class="foldopen" id="foldopen00587" data-start="{" data-end="}">
<div class="line"><a id="l00587" name="l00587"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a5172a6791b9bd24f4232bab8d6b81fff"> 587</a></span> <span class="keywordtype">void</span> <a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a5172a6791b9bd24f4232bab8d6b81fff">fit_from_csv</a>(<span class="keyword">const</span> std::string &amp;file_name, <span class="keyword">const</span> <span class="keywordtype">bool</span> &amp;last_label,</div>
<div class="line"><a id="l00588" name="l00588"></a><span class="lineno"> 588</span> <span class="keyword">const</span> <span class="keywordtype">int</span> &amp;epochs, <span class="keyword">const</span> <span class="keywordtype">double</span> &amp;learning_rate,</div>
<div class="line"><a id="l00589" name="l00589"></a><span class="lineno"> 589</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> &amp;normalize, <span class="keyword">const</span> <span class="keywordtype">int</span> &amp;slip_lines = 1,</div>
<div class="line"><a id="l00590" name="l00590"></a><span class="lineno"> 590</span> <span class="keyword">const</span> <span class="keywordtype">size_t</span> &amp;batch_size = 32,</div>
<div class="line"><a id="l00591" name="l00591"></a><span class="lineno"> 591</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> &amp;shuffle = <span class="keyword">true</span>) {</div>
<div class="line"><a id="l00592" name="l00592"></a><span class="lineno"> 592</span> <span class="comment">// Getting training data from csv file</span></div>
<div class="line"><a id="l00593" name="l00593"></a><span class="lineno"> 593</span> <span class="keyword">auto</span> <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a> =</div>
<div class="line"><a id="l00594" name="l00594"></a><span class="lineno"> 594</span> this-&gt;<a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a650c677fd6512665741ddd9b7983275d">get_XY_from_csv</a>(file_name, last_label, normalize, slip_lines);</div>
<div class="line"><a id="l00595" name="l00595"></a><span class="lineno"> 595</span> <span class="comment">// Fit the model on training data</span></div>
<div class="line"><a id="l00596" name="l00596"></a><span class="lineno"> 596</span> this-&gt;<a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a2be1b52bb9f57486f9a436f35c9089c0">fit</a>(<a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>.first, <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>.second, epochs, learning_rate, batch_size,</div>
<div class="line"><a id="l00597" name="l00597"></a><span class="lineno"> 597</span> shuffle);</div>
<div class="line"><a id="l00598" name="l00598"></a><span class="lineno"> 598</span> <span class="keywordflow">return</span>;</div>
<div class="line"><a id="l00599" name="l00599"></a><span class="lineno"> 599</span> }</div>
</div>
<div class="line"><a id="l00600" name="l00600"></a><span class="lineno"> 600</span> </div>
<div class="foldopen" id="foldopen00606" data-start="{" data-end="}">
<div class="line"><a id="l00606" name="l00606"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#aec648ea4f40bd71123b5f907a681dd8e"> 606</a></span> <span class="keywordtype">void</span> <a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#aec648ea4f40bd71123b5f907a681dd8e">evaluate</a>(<span class="keyword">const</span> std::vector&lt;std::vector&lt;std::valarray&lt;double&gt;&gt;&gt; &amp;X,</div>
<div class="line"><a id="l00607" name="l00607"></a><span class="lineno"> 607</span> <span class="keyword">const</span> std::vector&lt;std::vector&lt;std::valarray&lt;double&gt;&gt;&gt; &amp;Y) {</div>
<div class="line"><a id="l00608" name="l00608"></a><span class="lineno"> 608</span> std::cout &lt;&lt; <span class="stringliteral">&quot;INFO: Evaluation Started&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00609" name="l00609"></a><span class="lineno"> 609</span> <span class="keywordtype">double</span> acc = 0, loss = 0; <span class="comment">// initialize performance metrics with zero</span></div>
<div class="line"><a id="l00610" name="l00610"></a><span class="lineno"> 610</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; X.size(); i++) { <span class="comment">// For every sample in input</span></div>
<div class="line"><a id="l00611" name="l00611"></a><span class="lineno"> 611</span> <span class="comment">// Get predictions</span></div>
<div class="line"><a id="l00612" name="l00612"></a><span class="lineno"> 612</span> std::vector&lt;std::valarray&lt;double&gt;&gt; pred =</div>
<div class="line"><a id="l00613" name="l00613"></a><span class="lineno"> 613</span> this-&gt;<a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a3b9eac1824d365dce715fb17c33cb96f">single_predict</a>(X[i]);</div>
<div class="line"><a id="l00614" name="l00614"></a><span class="lineno"> 614</span> <span class="comment">// If predicted class is correct</span></div>
<div class="line"><a id="l00615" name="l00615"></a><span class="lineno"> 615</span> <span class="keywordflow">if</span> (<a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a50480fccfb39de20ca47f1bf51ecb6ec">argmax</a>(pred) == <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a50480fccfb39de20ca47f1bf51ecb6ec">argmax</a>(Y[i])) {</div>
<div class="line"><a id="l00616" name="l00616"></a><span class="lineno"> 616</span> acc += 1; <span class="comment">// Increment accuracy</span></div>
<div class="line"><a id="l00617" name="l00617"></a><span class="lineno"> 617</span> }</div>
<div class="line"><a id="l00618" name="l00618"></a><span class="lineno"> 618</span> <span class="comment">// Calculating loss - Mean Squared Error</span></div>
<div class="line"><a id="l00619" name="l00619"></a><span class="lineno"> 619</span> loss += <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a6f1c98c016ad34ff3d9f39372161bd35">sum</a>(<a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#ad0bdc88e5f1be47c46c0f0c8ebf754bb">apply_function</a>((Y[i] - pred),</div>
<div class="line"><a id="l00620" name="l00620"></a><span class="lineno"> 620</span> neural_network::util_functions::square) *</div>
<div class="line"><a id="l00621" name="l00621"></a><span class="lineno"> 621</span> 0.5);</div>
<div class="line"><a id="l00622" name="l00622"></a><span class="lineno"> 622</span> }</div>
<div class="line"><a id="l00623" name="l00623"></a><span class="lineno"> 623</span> acc /= X.size(); <span class="comment">// Averaging accuracy</span></div>
<div class="line"><a id="l00624" name="l00624"></a><span class="lineno"> 624</span> loss /= X.size(); <span class="comment">// Averaging loss</span></div>
<div class="line"><a id="l00625" name="l00625"></a><span class="lineno"> 625</span> <span class="comment">// Prinitng performance of the model</span></div>
<div class="line"><a id="l00626" name="l00626"></a><span class="lineno"> 626</span> std::cout &lt;&lt; <span class="stringliteral">&quot;Evaluation: Loss: &quot;</span> &lt;&lt; loss;</div>
<div class="line"><a id="l00627" name="l00627"></a><span class="lineno"> 627</span> std::cout &lt;&lt; <span class="stringliteral">&quot;, Accuracy: &quot;</span> &lt;&lt; acc &lt;&lt; std::endl;</div>
<div class="line"><a id="l00628" name="l00628"></a><span class="lineno"> 628</span> <span class="keywordflow">return</span>;</div>
<div class="line"><a id="l00629" name="l00629"></a><span class="lineno"> 629</span> }</div>
</div>
<div class="line"><a id="l00630" name="l00630"></a><span class="lineno"> 630</span> </div>
<div class="foldopen" id="foldopen00638" data-start="{" data-end="}">
<div class="line"><a id="l00638" name="l00638"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a36494e26ff36d6e15c1022bb9a1ee848"> 638</a></span> <span class="keywordtype">void</span> <a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a36494e26ff36d6e15c1022bb9a1ee848">evaluate_from_csv</a>(<span class="keyword">const</span> std::string &amp;file_name, <span class="keyword">const</span> <span class="keywordtype">bool</span> &amp;last_label,</div>
<div class="line"><a id="l00639" name="l00639"></a><span class="lineno"> 639</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> &amp;normalize, <span class="keyword">const</span> <span class="keywordtype">int</span> &amp;slip_lines = 1) {</div>
<div class="line"><a id="l00640" name="l00640"></a><span class="lineno"> 640</span> <span class="comment">// Getting training data from csv file</span></div>
<div class="line"><a id="l00641" name="l00641"></a><span class="lineno"> 641</span> <span class="keyword">auto</span> <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a> =</div>
<div class="line"><a id="l00642" name="l00642"></a><span class="lineno"> 642</span> this-&gt;<a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a650c677fd6512665741ddd9b7983275d">get_XY_from_csv</a>(file_name, last_label, normalize, slip_lines);</div>
<div class="line"><a id="l00643" name="l00643"></a><span class="lineno"> 643</span> <span class="comment">// Evaluating model</span></div>
<div class="line"><a id="l00644" name="l00644"></a><span class="lineno"> 644</span> this-&gt;<a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#aec648ea4f40bd71123b5f907a681dd8e">evaluate</a>(<a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>.first, <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>.second);</div>
<div class="line"><a id="l00645" name="l00645"></a><span class="lineno"> 645</span> <span class="keywordflow">return</span>;</div>
<div class="line"><a id="l00646" name="l00646"></a><span class="lineno"> 646</span> }</div>
</div>
<div class="line"><a id="l00647" name="l00647"></a><span class="lineno"> 647</span> </div>
<div class="foldopen" id="foldopen00652" data-start="{" data-end="}">
<div class="line"><a id="l00652" name="l00652"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a4f14e473bb0722c6490b9dc8da5982aa"> 652</a></span> <span class="keywordtype">void</span> <a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a4f14e473bb0722c6490b9dc8da5982aa">save_model</a>(<span class="keyword">const</span> std::string &amp;_file_name) {</div>
<div class="line"><a id="l00653" name="l00653"></a><span class="lineno"> 653</span> std::string file_name = _file_name;</div>
<div class="line"><a id="l00654" name="l00654"></a><span class="lineno"> 654</span> <span class="comment">// Adding &quot;.model&quot; extension if it is not already there in name</span></div>
<div class="line"><a id="l00655" name="l00655"></a><span class="lineno"> 655</span> <span class="keywordflow">if</span> (file_name.find(<span class="stringliteral">&quot;.model&quot;</span>) == file_name.npos) {</div>
<div class="line"><a id="l00656" name="l00656"></a><span class="lineno"> 656</span> file_name += <span class="stringliteral">&quot;.model&quot;</span>;</div>
<div class="line"><a id="l00657" name="l00657"></a><span class="lineno"> 657</span> }</div>
<div class="line"><a id="l00658" name="l00658"></a><span class="lineno"> 658</span> std::ofstream out_file; <span class="comment">// Ofstream to write in file</span></div>
<div class="line"><a id="l00659" name="l00659"></a><span class="lineno"> 659</span> <span class="comment">// Open file in out|trunc mode</span></div>
<div class="line"><a id="l00660" name="l00660"></a><span class="lineno"> 660</span> out_file.open(file_name.c_str(),</div>
<div class="line"><a id="l00661" name="l00661"></a><span class="lineno"> 661</span> std::ofstream::out | std::ofstream::trunc);</div>
<div class="line"><a id="l00662" name="l00662"></a><span class="lineno"> 662</span> <span class="comment">// If there is any problem in opening file</span></div>
<div class="line"><a id="l00663" name="l00663"></a><span class="lineno"> 663</span> <span class="keywordflow">if</span> (!out_file.is_open()) {</div>
<div class="line"><a id="l00664" name="l00664"></a><span class="lineno"> 664</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;ERROR (&quot;</span> &lt;&lt; __func__ &lt;&lt; <span class="stringliteral">&quot;) : &quot;</span>;</div>
<div class="line"><a id="l00665" name="l00665"></a><span class="lineno"> 665</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;Unable to open file: &quot;</span> &lt;&lt; file_name &lt;&lt; std::endl;</div>
<div class="line"><a id="l00666" name="l00666"></a><span class="lineno"> 666</span> std::exit(EXIT_FAILURE);</div>
<div class="line"><a id="l00667" name="l00667"></a><span class="lineno"> 667</span> }</div>
<div class="line"><a id="l00707" name="l00707"></a><span class="lineno"> 707</span> <span class="comment">// Saving model in the same format</span></div>
<div class="line"><a id="l00708" name="l00708"></a><span class="lineno"> 708</span> out_file &lt;&lt; <a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.size();</div>
<div class="line"><a id="l00709" name="l00709"></a><span class="lineno"> 709</span> out_file &lt;&lt; std::endl;</div>
<div class="line"><a id="l00710" name="l00710"></a><span class="lineno"> 710</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> &amp;layer : this-&gt;layers) {</div>
<div class="line"><a id="l00711" name="l00711"></a><span class="lineno"> 711</span> out_file &lt;&lt; layer.neurons &lt;&lt; <span class="charliteral">&#39; &#39;</span> &lt;&lt; layer.activation &lt;&lt; std::endl;</div>
<div class="line"><a id="l00712" name="l00712"></a><span class="lineno"> 712</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(layer.kernel);</div>
<div class="line"><a id="l00713" name="l00713"></a><span class="lineno"> 713</span> out_file &lt;&lt; shape.first &lt;&lt; <span class="charliteral">&#39; &#39;</span> &lt;&lt; shape.second &lt;&lt; std::endl;</div>
<div class="line"><a id="l00714" name="l00714"></a><span class="lineno"> 714</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> &amp;row : layer.kernel) {</div>
<div class="line"><a id="l00715" name="l00715"></a><span class="lineno"> 715</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> &amp;val : row) {</div>
<div class="line"><a id="l00716" name="l00716"></a><span class="lineno"> 716</span> out_file &lt;&lt; val &lt;&lt; <span class="charliteral">&#39; &#39;</span>;</div>
<div class="line"><a id="l00717" name="l00717"></a><span class="lineno"> 717</span> }</div>
<div class="line"><a id="l00718" name="l00718"></a><span class="lineno"> 718</span> out_file &lt;&lt; std::endl;</div>
<div class="line"><a id="l00719" name="l00719"></a><span class="lineno"> 719</span> }</div>
<div class="line"><a id="l00720" name="l00720"></a><span class="lineno"> 720</span> }</div>
<div class="line"><a id="l00721" name="l00721"></a><span class="lineno"> 721</span> std::cout &lt;&lt; <span class="stringliteral">&quot;INFO: Model saved successfully with name : &quot;</span>;</div>
<div class="line"><a id="l00722" name="l00722"></a><span class="lineno"> 722</span> std::cout &lt;&lt; file_name &lt;&lt; std::endl;</div>
<div class="line"><a id="l00723" name="l00723"></a><span class="lineno"> 723</span> out_file.close(); <span class="comment">// Closing file</span></div>
<div class="line"><a id="l00724" name="l00724"></a><span class="lineno"> 724</span> <span class="keywordflow">return</span>;</div>
<div class="line"><a id="l00725" name="l00725"></a><span class="lineno"> 725</span> }</div>
</div>
<div class="line"><a id="l00726" name="l00726"></a><span class="lineno"> 726</span> </div>
<div class="foldopen" id="foldopen00732" data-start="{" data-end="}">
<div class="line"><a id="l00732" name="l00732"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a58ed20abf6ce3744535bd8b5bb9e741b"> 732</a></span> <a class="code hl_class" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">NeuralNetwork</a> <a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a58ed20abf6ce3744535bd8b5bb9e741b">load_model</a>(<span class="keyword">const</span> std::string &amp;file_name) {</div>
<div class="line"><a id="l00733" name="l00733"></a><span class="lineno"> 733</span> std::ifstream in_file; <span class="comment">// Ifstream to read file</span></div>
<div class="line"><a id="l00734" name="l00734"></a><span class="lineno"> 734</span> in_file.open(file_name.c_str()); <span class="comment">// Openinig file</span></div>
<div class="line"><a id="l00735" name="l00735"></a><span class="lineno"> 735</span> <span class="comment">// If there is any problem in opening file</span></div>
<div class="line"><a id="l00736" name="l00736"></a><span class="lineno"> 736</span> <span class="keywordflow">if</span> (!in_file.is_open()) {</div>
<div class="line"><a id="l00737" name="l00737"></a><span class="lineno"> 737</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;ERROR (&quot;</span> &lt;&lt; __func__ &lt;&lt; <span class="stringliteral">&quot;) : &quot;</span>;</div>
<div class="line"><a id="l00738" name="l00738"></a><span class="lineno"> 738</span> std::cerr &lt;&lt; <span class="stringliteral">&quot;Unable to open file: &quot;</span> &lt;&lt; file_name &lt;&lt; std::endl;</div>
<div class="line"><a id="l00739" name="l00739"></a><span class="lineno"> 739</span> std::exit(EXIT_FAILURE);</div>
<div class="line"><a id="l00740" name="l00740"></a><span class="lineno"> 740</span> }</div>
<div class="line"><a id="l00741" name="l00741"></a><span class="lineno"> 741</span> std::vector&lt;std::pair&lt;int, std::string&gt;&gt; config; <span class="comment">// To store config</span></div>
<div class="line"><a id="l00742" name="l00742"></a><span class="lineno"> 742</span> std::vector&lt;std::vector&lt;std::valarray&lt;double&gt;&gt;&gt;</div>
<div class="line"><a id="l00743" name="l00743"></a><span class="lineno"> 743</span> kernels; <span class="comment">// To store pretrained kernels</span></div>
<div class="line"><a id="l00744" name="l00744"></a><span class="lineno"> 744</span> <span class="comment">// Loading model from saved file format</span></div>
<div class="line"><a id="l00745" name="l00745"></a><span class="lineno"> 745</span> <span class="keywordtype">size_t</span> total_layers = 0;</div>
<div class="line"><a id="l00746" name="l00746"></a><span class="lineno"> 746</span> in_file &gt;&gt; total_layers;</div>
<div class="line"><a id="l00747" name="l00747"></a><span class="lineno"> 747</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; total_layers; i++) {</div>
<div class="line"><a id="l00748" name="l00748"></a><span class="lineno"> 748</span> <span class="keywordtype">int</span> neurons = 0;</div>
<div class="line"><a id="l00749" name="l00749"></a><span class="lineno"> 749</span> std::string activation;</div>
<div class="line"><a id="l00750" name="l00750"></a><span class="lineno"> 750</span> <span class="keywordtype">size_t</span> shape_a = 0, shape_b = 0;</div>
<div class="line"><a id="l00751" name="l00751"></a><span class="lineno"> 751</span> std::vector&lt;std::valarray&lt;double&gt;&gt; kernel;</div>
<div class="line"><a id="l00752" name="l00752"></a><span class="lineno"> 752</span> in_file &gt;&gt; neurons &gt;&gt; activation &gt;&gt; shape_a &gt;&gt; shape_b;</div>
<div class="line"><a id="l00753" name="l00753"></a><span class="lineno"> 753</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> r = 0; r &lt; shape_a; r++) {</div>
<div class="line"><a id="l00754" name="l00754"></a><span class="lineno"> 754</span> std::valarray&lt;double&gt; row(shape_b);</div>
<div class="line"><a id="l00755" name="l00755"></a><span class="lineno"> 755</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> c = 0; c &lt; shape_b; c++) {</div>
<div class="line"><a id="l00756" name="l00756"></a><span class="lineno"> 756</span> in_file &gt;&gt; row[c];</div>
<div class="line"><a id="l00757" name="l00757"></a><span class="lineno"> 757</span> }</div>
<div class="line"><a id="l00758" name="l00758"></a><span class="lineno"> 758</span> kernel.push_back(row);</div>
<div class="line"><a id="l00759" name="l00759"></a><span class="lineno"> 759</span> }</div>
<div class="line"><a id="l00760" name="l00760"></a><span class="lineno"> 760</span> config.emplace_back(make_pair(neurons, activation));</div>
<div class="line"><a id="l00761" name="l00761"></a><span class="lineno"> 761</span> ;</div>
<div class="line"><a id="l00762" name="l00762"></a><span class="lineno"> 762</span> kernels.emplace_back(kernel);</div>
<div class="line"><a id="l00763" name="l00763"></a><span class="lineno"> 763</span> }</div>
<div class="line"><a id="l00764" name="l00764"></a><span class="lineno"> 764</span> std::cout &lt;&lt; <span class="stringliteral">&quot;INFO: Model loaded successfully&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00765" name="l00765"></a><span class="lineno"> 765</span> in_file.close(); <span class="comment">// Closing file</span></div>
<div class="line"><a id="l00766" name="l00766"></a><span class="lineno"> 766</span> <span class="keywordflow">return</span> <a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#ae7cf126a3a8f9d20c81b21584d061a08">NeuralNetwork</a>(</div>
<div class="line"><a id="l00767" name="l00767"></a><span class="lineno"> 767</span> config, kernels); <span class="comment">// Return instance of NeuralNetwork class</span></div>
<div class="line"><a id="l00768" name="l00768"></a><span class="lineno"> 768</span> }</div>
</div>
<div class="line"><a id="l00769" name="l00769"></a><span class="lineno"> 769</span> </div>
<div class="foldopen" id="foldopen00773" data-start="{" data-end="}">
<div class="line"><a id="l00773" name="l00773"></a><span class="lineno"><a class="line" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a61d30113d13304c664057118b92a5931"> 773</a></span> <span class="keywordtype">void</span> <a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a61d30113d13304c664057118b92a5931">summary</a>() {</div>
<div class="line"><a id="l00774" name="l00774"></a><span class="lineno"> 774</span> <span class="comment">// Printing Summary</span></div>
<div class="line"><a id="l00775" name="l00775"></a><span class="lineno"> 775</span> std::cout</div>
<div class="line"><a id="l00776" name="l00776"></a><span class="lineno"> 776</span> &lt;&lt; <span class="stringliteral">&quot;===============================================================&quot;</span></div>
<div class="line"><a id="l00777" name="l00777"></a><span class="lineno"> 777</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00778" name="l00778"></a><span class="lineno"> 778</span> std::cout &lt;&lt; <span class="stringliteral">&quot;\t\t+ MODEL SUMMARY +\t\t\n&quot;</span>;</div>
<div class="line"><a id="l00779" name="l00779"></a><span class="lineno"> 779</span> std::cout</div>
<div class="line"><a id="l00780" name="l00780"></a><span class="lineno"> 780</span> &lt;&lt; <span class="stringliteral">&quot;===============================================================&quot;</span></div>
<div class="line"><a id="l00781" name="l00781"></a><span class="lineno"> 781</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00782" name="l00782"></a><span class="lineno"> 782</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 1; i &lt;= <a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.size(); i++) { <span class="comment">// For every layer</span></div>
<div class="line"><a id="l00783" name="l00783"></a><span class="lineno"> 783</span> std::cout &lt;&lt; i &lt;&lt; <span class="stringliteral">&quot;)&quot;</span>;</div>
<div class="line"><a id="l00784" name="l00784"></a><span class="lineno"> 784</span> std::cout &lt;&lt; <span class="stringliteral">&quot; Neurons : &quot;</span></div>
<div class="line"><a id="l00785" name="l00785"></a><span class="lineno"> 785</span> &lt;&lt; <a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>[i - 1].neurons; <span class="comment">// number of neurons</span></div>
<div class="line"><a id="l00786" name="l00786"></a><span class="lineno"> 786</span> std::cout &lt;&lt; <span class="stringliteral">&quot;, Activation : &quot;</span></div>
<div class="line"><a id="l00787" name="l00787"></a><span class="lineno"> 787</span> &lt;&lt; <a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>[i - 1].activation; <span class="comment">// activation</span></div>
<div class="line"><a id="l00788" name="l00788"></a><span class="lineno"> 788</span> std::cout &lt;&lt; <span class="stringliteral">&quot;, kernel Shape : &quot;</span></div>
<div class="line"><a id="l00789" name="l00789"></a><span class="lineno"> 789</span> &lt;&lt; <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(<a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>[i - 1].kernel); <span class="comment">// kernel shape</span></div>
<div class="line"><a id="l00790" name="l00790"></a><span class="lineno"> 790</span> std::cout &lt;&lt; std::endl;</div>
<div class="line"><a id="l00791" name="l00791"></a><span class="lineno"> 791</span> }</div>
<div class="line"><a id="l00792" name="l00792"></a><span class="lineno"> 792</span> std::cout</div>
<div class="line"><a id="l00793" name="l00793"></a><span class="lineno"> 793</span> &lt;&lt; <span class="stringliteral">&quot;===============================================================&quot;</span></div>
<div class="line"><a id="l00794" name="l00794"></a><span class="lineno"> 794</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00795" name="l00795"></a><span class="lineno"> 795</span> <span class="keywordflow">return</span>;</div>
<div class="line"><a id="l00796" name="l00796"></a><span class="lineno"> 796</span> }</div>
</div>
<div class="line"><a id="l00797" name="l00797"></a><span class="lineno"> 797</span>};</div>
</div>
<div class="line"><a id="l00798" name="l00798"></a><span class="lineno"> 798</span>} <span class="comment">// namespace neural_network</span></div>
<div class="line"><a id="l00799" name="l00799"></a><span class="lineno"> 799</span>} <span class="comment">// namespace machine_learning</span></div>
<div class="line"><a id="l00800" name="l00800"></a><span class="lineno"> 800</span> </div>
<div class="foldopen" id="foldopen00805" data-start="{" data-end="}">
<div class="line"><a id="l00805" name="l00805"></a><span class="lineno"><a class="line" href="../../d2/d58/neural__network_8cpp.html#aa8dca7b867074164d5f45b0f3851269d"> 805</a></span><span class="keyword">static</span> <span class="keywordtype">void</span> <a class="code hl_function" href="../../d2/d58/neural__network_8cpp.html#aa8dca7b867074164d5f45b0f3851269d">test</a>() {</div>
<div class="line"><a id="l00806" name="l00806"></a><span class="lineno"> 806</span> <span class="comment">// Creating network with 3 layers for &quot;iris.csv&quot;</span></div>
<div class="line"><a id="l00807" name="l00807"></a><span class="lineno"> 807</span> <a class="code hl_class" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">machine_learning::neural_network::NeuralNetwork</a> myNN =</div>
<div class="line"><a id="l00808" name="l00808"></a><span class="lineno"> 808</span> <a class="code hl_class" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">machine_learning::neural_network::NeuralNetwork</a>({</div>
<div class="line"><a id="l00809" name="l00809"></a><span class="lineno"> 809</span> {4, <span class="stringliteral">&quot;none&quot;</span>}, <span class="comment">// First layer with 3 neurons and &quot;none&quot; as activation</span></div>
<div class="line"><a id="l00810" name="l00810"></a><span class="lineno"> 810</span> {6,</div>
<div class="line"><a id="l00811" name="l00811"></a><span class="lineno"> 811</span> <span class="stringliteral">&quot;relu&quot;</span>}, <span class="comment">// Second layer with 6 neurons and &quot;relu&quot; as activation</span></div>
<div class="line"><a id="l00812" name="l00812"></a><span class="lineno"> 812</span> {3, <span class="stringliteral">&quot;sigmoid&quot;</span>} <span class="comment">// Third layer with 3 neurons and &quot;sigmoid&quot; as</span></div>
<div class="line"><a id="l00813" name="l00813"></a><span class="lineno"> 813</span> <span class="comment">// activation</span></div>
<div class="line"><a id="l00814" name="l00814"></a><span class="lineno"> 814</span> });</div>
<div class="line"><a id="l00815" name="l00815"></a><span class="lineno"> 815</span> <span class="comment">// Printing summary of model</span></div>
<div class="line"><a id="l00816" name="l00816"></a><span class="lineno"> 816</span> myNN.<a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a61d30113d13304c664057118b92a5931">summary</a>();</div>
<div class="line"><a id="l00817" name="l00817"></a><span class="lineno"> 817</span> <span class="comment">// Training Model</span></div>
<div class="line"><a id="l00818" name="l00818"></a><span class="lineno"> 818</span> myNN.<a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a5172a6791b9bd24f4232bab8d6b81fff">fit_from_csv</a>(<span class="stringliteral">&quot;iris.csv&quot;</span>, <span class="keyword">true</span>, 100, 0.3, <span class="keyword">false</span>, 2, 32, <span class="keyword">true</span>);</div>
<div class="line"><a id="l00819" name="l00819"></a><span class="lineno"> 819</span> <span class="comment">// Testing predictions of model</span></div>
<div class="line"><a id="l00820" name="l00820"></a><span class="lineno"> 820</span> assert(<a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a50480fccfb39de20ca47f1bf51ecb6ec">machine_learning::argmax</a>(</div>
<div class="line"><a id="l00821" name="l00821"></a><span class="lineno"> 821</span> myNN.<a class="code hl_function" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a3b9eac1824d365dce715fb17c33cb96f">single_predict</a>({{5, 3.4, 1.6, 0.4}})) == 0);</div>
<div class="line"><a id="l00822" name="l00822"></a><span class="lineno"> 822</span> assert(<a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a50480fccfb39de20ca47f1bf51ecb6ec">machine_learning::argmax</a>(</div>
<div class="line"><a id="l00823" name="l00823"></a><span class="lineno"> 823</span> myNN.single_predict({{6.4, 2.9, 4.3, 1.3}})) == 1);</div>
<div class="line"><a id="l00824" name="l00824"></a><span class="lineno"> 824</span> assert(<a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a50480fccfb39de20ca47f1bf51ecb6ec">machine_learning::argmax</a>(</div>
<div class="line"><a id="l00825" name="l00825"></a><span class="lineno"> 825</span> myNN.single_predict({{6.2, 3.4, 5.4, 2.3}})) == 2);</div>
<div class="line"><a id="l00826" name="l00826"></a><span class="lineno"> 826</span> <span class="keywordflow">return</span>;</div>
<div class="line"><a id="l00827" name="l00827"></a><span class="lineno"> 827</span>}</div>
</div>
<div class="line"><a id="l00828" name="l00828"></a><span class="lineno"> 828</span> </div>
<div class="foldopen" id="foldopen00833" data-start="{" data-end="}">
<div class="line"><a id="l00833" name="l00833"></a><span class="lineno"><a class="line" href="../../d2/d58/neural__network_8cpp.html#ae66f6b31b5ad750f1fe042a706a4e3d4"> 833</a></span><span class="keywordtype">int</span> <a class="code hl_function" href="../../d2/d58/neural__network_8cpp.html#ae66f6b31b5ad750f1fe042a706a4e3d4">main</a>() {</div>
<div class="line"><a id="l00834" name="l00834"></a><span class="lineno"> 834</span> <span class="comment">// Testing</span></div>
<div class="line"><a id="l00835" name="l00835"></a><span class="lineno"> 835</span> <a class="code hl_function" href="../../d2/d58/neural__network_8cpp.html#aa8dca7b867074164d5f45b0f3851269d">test</a>();</div>
<div class="line"><a id="l00836" name="l00836"></a><span class="lineno"> 836</span> <span class="keywordflow">return</span> 0;</div>
<div class="line"><a id="l00837" name="l00837"></a><span class="lineno"> 837</span>}</div>
</div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">machine_learning::neural_network::NeuralNetwork</a></div><div class="ttdef"><b>Definition</b> <a href="#l00247">neural_network.cpp:247</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a173bb71780af6953ec2e307a4c74b025"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a173bb71780af6953ec2e307a4c74b025">machine_learning::neural_network::NeuralNetwork::NeuralNetwork</a></div><div class="ttdeci">NeuralNetwork(NeuralNetwork &amp;&amp;)=default</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a176b955c90ae57d7dbc3c63f27c84c75"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a176b955c90ae57d7dbc3c63f27c84c75">machine_learning::neural_network::NeuralNetwork::NeuralNetwork</a></div><div class="ttdeci">NeuralNetwork(const NeuralNetwork &amp;model)=default</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a2be1b52bb9f57486f9a436f35c9089c0"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a2be1b52bb9f57486f9a436f35c9089c0">machine_learning::neural_network::NeuralNetwork::fit</a></div><div class="ttdeci">void fit(const std::vector&lt; std::vector&lt; std::valarray&lt; double &gt; &gt; &gt; &amp;X_, const std::vector&lt; std::vector&lt; std::valarray&lt; double &gt; &gt; &gt; &amp;Y_, const int &amp;epochs=100, const double &amp;learning_rate=0.01, const size_t &amp;batch_size=32, const bool &amp;shuffle=true)</div><div class="ttdef"><b>Definition</b> <a href="#l00485">neural_network.cpp:485</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a2c49bfebf9b859d5ceb26035d3003601"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a2c49bfebf9b859d5ceb26035d3003601">machine_learning::neural_network::NeuralNetwork::operator=</a></div><div class="ttdeci">NeuralNetwork &amp; operator=(NeuralNetwork &amp;&amp;)=default</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a361a45f3c3d8347d79103bf182d0570b"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a361a45f3c3d8347d79103bf182d0570b">machine_learning::neural_network::NeuralNetwork::__detailed_single_prediction</a></div><div class="ttdeci">std::vector&lt; std::vector&lt; std::valarray&lt; double &gt; &gt; &gt; __detailed_single_prediction(const std::vector&lt; std::valarray&lt; double &gt; &gt; &amp;X)</div><div class="ttdef"><b>Definition</b> <a href="#l00289">neural_network.cpp:289</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a36494e26ff36d6e15c1022bb9a1ee848"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a36494e26ff36d6e15c1022bb9a1ee848">machine_learning::neural_network::NeuralNetwork::evaluate_from_csv</a></div><div class="ttdeci">void evaluate_from_csv(const std::string &amp;file_name, const bool &amp;last_label, const bool &amp;normalize, const int &amp;slip_lines=1)</div><div class="ttdef"><b>Definition</b> <a href="#l00638">neural_network.cpp:638</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a3b9eac1824d365dce715fb17c33cb96f"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a3b9eac1824d365dce715fb17c33cb96f">machine_learning::neural_network::NeuralNetwork::single_predict</a></div><div class="ttdeci">std::vector&lt; std::valarray&lt; double &gt; &gt; single_predict(const std::vector&lt; std::valarray&lt; double &gt; &gt; &amp;X)</div><div class="ttdef"><b>Definition</b> <a href="#l00451">neural_network.cpp:451</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a4c4c6f63ab965317f9471518ee931b89"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a4c4c6f63ab965317f9471518ee931b89">machine_learning::neural_network::NeuralNetwork::NeuralNetwork</a></div><div class="ttdeci">NeuralNetwork(const std::vector&lt; std::pair&lt; int, std::string &gt; &gt; &amp;config, const std::vector&lt; std::vector&lt; std::valarray&lt; double &gt; &gt; &gt; &amp;kernels)</div><div class="ttdef"><b>Definition</b> <a href="#l00256">neural_network.cpp:256</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a4f14e473bb0722c6490b9dc8da5982aa"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a4f14e473bb0722c6490b9dc8da5982aa">machine_learning::neural_network::NeuralNetwork::save_model</a></div><div class="ttdeci">void save_model(const std::string &amp;_file_name)</div><div class="ttdef"><b>Definition</b> <a href="#l00652">neural_network.cpp:652</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a5172a6791b9bd24f4232bab8d6b81fff"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a5172a6791b9bd24f4232bab8d6b81fff">machine_learning::neural_network::NeuralNetwork::fit_from_csv</a></div><div class="ttdeci">void fit_from_csv(const std::string &amp;file_name, const bool &amp;last_label, const int &amp;epochs, const double &amp;learning_rate, const bool &amp;normalize, const int &amp;slip_lines=1, const size_t &amp;batch_size=32, const bool &amp;shuffle=true)</div><div class="ttdef"><b>Definition</b> <a href="#l00587">neural_network.cpp:587</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a58a9614e4c6d4ca672d3358e99a3404f"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a58a9614e4c6d4ca672d3358e99a3404f">machine_learning::neural_network::NeuralNetwork::operator=</a></div><div class="ttdeci">NeuralNetwork &amp; operator=(const NeuralNetwork &amp;model)=default</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a58ed20abf6ce3744535bd8b5bb9e741b"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a58ed20abf6ce3744535bd8b5bb9e741b">machine_learning::neural_network::NeuralNetwork::load_model</a></div><div class="ttdeci">NeuralNetwork load_model(const std::string &amp;file_name)</div><div class="ttdef"><b>Definition</b> <a href="#l00732">neural_network.cpp:732</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a61d30113d13304c664057118b92a5931"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a61d30113d13304c664057118b92a5931">machine_learning::neural_network::NeuralNetwork::summary</a></div><div class="ttdeci">void summary()</div><div class="ttdef"><b>Definition</b> <a href="#l00773">neural_network.cpp:773</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a62151b0398a2536be60d950e10ffe9a8"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a62151b0398a2536be60d950e10ffe9a8">machine_learning::neural_network::NeuralNetwork::NeuralNetwork</a></div><div class="ttdeci">NeuralNetwork(const std::vector&lt; std::pair&lt; int, std::string &gt; &gt; &amp;config)</div><div class="ttdef"><b>Definition</b> <a href="#l00313">neural_network.cpp:313</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a650c677fd6512665741ddd9b7983275d"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a650c677fd6512665741ddd9b7983275d">machine_learning::neural_network::NeuralNetwork::get_XY_from_csv</a></div><div class="ttdeci">std::pair&lt; std::vector&lt; std::vector&lt; std::valarray&lt; double &gt; &gt; &gt;, std::vector&lt; std::vector&lt; std::valarray&lt; double &gt; &gt; &gt; &gt; get_XY_from_csv(const std::string &amp;file_name, const bool &amp;last_label, const bool &amp;normalize, const int &amp;slip_lines=1)</div><div class="ttdef"><b>Definition</b> <a href="#l00382">neural_network.cpp:382</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a88bf9023ab3d4cdb61cf707c7cdfc86b"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a88bf9023ab3d4cdb61cf707c7cdfc86b">machine_learning::neural_network::NeuralNetwork::batch_predict</a></div><div class="ttdeci">std::vector&lt; std::vector&lt; std::valarray&lt; double &gt; &gt; &gt; batch_predict(const std::vector&lt; std::vector&lt; std::valarray&lt; double &gt; &gt; &gt; &amp;X)</div><div class="ttdef"><b>Definition</b> <a href="#l00464">neural_network.cpp:464</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a8973f687738ddd76f93b5562feae4027"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a8973f687738ddd76f93b5562feae4027">machine_learning::neural_network::NeuralNetwork::~NeuralNetwork</a></div><div class="ttdeci">~NeuralNetwork()=default</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_ae7cf126a3a8f9d20c81b21584d061a08"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#ae7cf126a3a8f9d20c81b21584d061a08">machine_learning::neural_network::NeuralNetwork::NeuralNetwork</a></div><div class="ttdeci">NeuralNetwork()=default</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_aec648ea4f40bd71123b5f907a681dd8e"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#aec648ea4f40bd71123b5f907a681dd8e">machine_learning::neural_network::NeuralNetwork::evaluate</a></div><div class="ttdeci">void evaluate(const std::vector&lt; std::vector&lt; std::valarray&lt; double &gt; &gt; &gt; &amp;X, const std::vector&lt; std::vector&lt; std::valarray&lt; double &gt; &gt; &gt; &amp;Y)</div><div class="ttdef"><b>Definition</b> <a href="#l00606">neural_network.cpp:606</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_html"><div class="ttname"><a href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a></div><div class="ttdef"><b>Definition</b> <a href="#l00125">neural_network.cpp:125</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_html_a11046825be0b6dbb73fbe834aa49200e"><div class="ttname"><a href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a11046825be0b6dbb73fbe834aa49200e">machine_learning::neural_network::layers::DenseLayer::DenseLayer</a></div><div class="ttdeci">DenseLayer(const int &amp;neurons, const std::string &amp;activation, const std::pair&lt; size_t, size_t &gt; &amp;kernel_shape, const bool &amp;random_kernel)</div><div class="ttdef"><b>Definition</b> <a href="#l00141">neural_network.cpp:141</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_html_a19aaccad279b22dbbb6c55e5697b4114"><div class="ttname"><a href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a19aaccad279b22dbbb6c55e5697b4114">machine_learning::neural_network::layers::DenseLayer::operator=</a></div><div class="ttdeci">DenseLayer &amp; operator=(DenseLayer &amp;&amp;)=default</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_html_a2871146feaaa453558239df67b21e0d2"><div class="ttname"><a href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a2871146feaaa453558239df67b21e0d2">machine_learning::neural_network::layers::DenseLayer::DenseLayer</a></div><div class="ttdeci">DenseLayer(const DenseLayer &amp;layer)=default</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_html_a6c859e3737aa88b29854df0347b29f4e"><div class="ttname"><a href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a6c859e3737aa88b29854df0347b29f4e">machine_learning::neural_network::layers::DenseLayer::DenseLayer</a></div><div class="ttdeci">DenseLayer(DenseLayer &amp;&amp;)=default</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_html_ac9cda9453c4a0caf5bae7f9213b019a0"><div class="ttname"><a href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#ac9cda9453c4a0caf5bae7f9213b019a0">machine_learning::neural_network::layers::DenseLayer::~DenseLayer</a></div><div class="ttdeci">~DenseLayer()=default</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_html_ae077132526d2863e46aa77cb0f7d6aa2"><div class="ttname"><a href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#ae077132526d2863e46aa77cb0f7d6aa2">machine_learning::neural_network::layers::DenseLayer::operator=</a></div><div class="ttdeci">DenseLayer &amp; operator=(const DenseLayer &amp;layer)=default</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_html_af136ec31dbd35b1be2eb9a057677c704"><div class="ttname"><a href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#af136ec31dbd35b1be2eb9a057677c704">machine_learning::neural_network::layers::DenseLayer::DenseLayer</a></div><div class="ttdeci">DenseLayer(const int &amp;neurons, const std::string &amp;activation, const std::vector&lt; std::valarray&lt; double &gt; &gt; &amp;kernel)</div><div class="ttdef"><b>Definition</b> <a href="#l00183">neural_network.cpp:183</a></div></div>
<div class="ttc" id="ahash__search_8cpp_html_a6e1a77282bc65ad359d753d25df23243"><div class="ttname"><a href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a></div><div class="ttdeci">int data[MAX]</div><div class="ttdoc">test data</div><div class="ttdef"><b>Definition</b> <a href="../../d1/df3/hash__search_8cpp_source.html#l00024">hash_search.cpp:24</a></div></div>
<div class="ttc" id="anamespaceactivations_html"><div class="ttname"><a href="../../d5/d39/namespaceactivations.html">activations</a></div><div class="ttdoc">Various activation functions used in Neural network.</div></div>
<div class="ttc" id="anamespacelayers_html"><div class="ttname"><a href="../../d5/d2c/namespacelayers.html">layers</a></div><div class="ttdoc">This namespace contains layers used in MLP.</div></div>
<div class="ttc" id="anamespacemachine__learning_html"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html">machine_learning</a></div><div class="ttdoc">A* search algorithm</div></div>
<div class="ttc" id="anamespacemachine__learning_html_a496302e3371aa7b478cb7d5917904bdd"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a496302e3371aa7b478cb7d5917904bdd">machine_learning::insert_element</a></div><div class="ttdeci">std::valarray&lt; T &gt; insert_element(const std::valarray&lt; T &gt; &amp;A, const T &amp;ele)</div><div class="ttdef"><b>Definition</b> <a href="../../d8/d95/vector__ops_8hpp_source.html#l00085">vector_ops.hpp:85</a></div></div>
<div class="ttc" id="anamespacemachine__learning_html_a50480fccfb39de20ca47f1bf51ecb6ec"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a50480fccfb39de20ca47f1bf51ecb6ec">machine_learning::argmax</a></div><div class="ttdeci">size_t argmax(const std::vector&lt; std::valarray&lt; T &gt; &gt; &amp;A)</div><div class="ttdef"><b>Definition</b> <a href="../../d8/d95/vector__ops_8hpp_source.html#l00307">vector_ops.hpp:307</a></div></div>
<div class="ttc" id="anamespacemachine__learning_html_a5342906d42b80fc6b6b3ad17bf00fcb9"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a5342906d42b80fc6b6b3ad17bf00fcb9">machine_learning::multiply</a></div><div class="ttdeci">std::vector&lt; std::valarray&lt; T &gt; &gt; multiply(const std::vector&lt; std::valarray&lt; T &gt; &gt; &amp;A, const std::vector&lt; std::valarray&lt; T &gt; &gt; &amp;B)</div><div class="ttdef"><b>Definition</b> <a href="../../d8/d95/vector__ops_8hpp_source.html#l00460">vector_ops.hpp:460</a></div></div>
<div class="ttc" id="anamespacemachine__learning_html_a6f1c98c016ad34ff3d9f39372161bd35"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a6f1c98c016ad34ff3d9f39372161bd35">machine_learning::sum</a></div><div class="ttdeci">T sum(const std::vector&lt; std::valarray&lt; T &gt; &gt; &amp;A)</div><div class="ttdef"><b>Definition</b> <a href="../../d8/d95/vector__ops_8hpp_source.html#l00232">vector_ops.hpp:232</a></div></div>
<div class="ttc" id="anamespacemachine__learning_html_a89fde571b38f9483576594f66572958a"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a89fde571b38f9483576594f66572958a">machine_learning::transpose</a></div><div class="ttdeci">std::vector&lt; std::valarray&lt; T &gt; &gt; transpose(const std::vector&lt; std::valarray&lt; T &gt; &gt; &amp;A)</div><div class="ttdef"><b>Definition</b> <a href="../../d8/d95/vector__ops_8hpp_source.html#l00382">vector_ops.hpp:382</a></div></div>
<div class="ttc" id="anamespacemachine__learning_html_a8dd3f1ffbc2f26a3c88da1b1f8b7e9c4"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a8dd3f1ffbc2f26a3c88da1b1f8b7e9c4">machine_learning::unit_matrix_initialization</a></div><div class="ttdeci">void unit_matrix_initialization(std::vector&lt; std::valarray&lt; T &gt; &gt; &amp;A, const std::pair&lt; size_t, size_t &gt; &amp;shape)</div><div class="ttdef"><b>Definition</b> <a href="../../d8/d95/vector__ops_8hpp_source.html#l00193">vector_ops.hpp:193</a></div></div>
<div class="ttc" id="anamespacemachine__learning_html_a912cf68863063a38d6e63545be5eb093"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a912cf68863063a38d6e63545be5eb093">machine_learning::pop_front</a></div><div class="ttdeci">std::valarray&lt; T &gt; pop_front(const std::valarray&lt; T &gt; &amp;A)</div><div class="ttdef"><b>Definition</b> <a href="../../d8/d95/vector__ops_8hpp_source.html#l00102">vector_ops.hpp:102</a></div></div>
<div class="ttc" id="anamespacemachine__learning_html_aa4bbf61e65f8cd297255fa94b983d078"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078">machine_learning::get_shape</a></div><div class="ttdeci">std::pair&lt; size_t, size_t &gt; get_shape(const std::vector&lt; std::valarray&lt; T &gt; &gt; &amp;A)</div><div class="ttdef"><b>Definition</b> <a href="../../d8/d95/vector__ops_8hpp_source.html#l00247">vector_ops.hpp:247</a></div></div>
<div class="ttc" id="anamespacemachine__learning_html_abee7b35403af3612222d3b7a53074905"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#abee7b35403af3612222d3b7a53074905">machine_learning::uniform_random_initialization</a></div><div class="ttdeci">void uniform_random_initialization(std::vector&lt; std::valarray&lt; T &gt; &gt; &amp;A, const std::pair&lt; size_t, size_t &gt; &amp;shape, const T &amp;low, const T &amp;high)</div><div class="ttdef"><b>Definition</b> <a href="../../d8/d95/vector__ops_8hpp_source.html#l00166">vector_ops.hpp:166</a></div></div>
<div class="ttc" id="anamespacemachine__learning_html_ac1bdaa2a724b4ce6a6bb371a5dbe2e7e"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#ac1bdaa2a724b4ce6a6bb371a5dbe2e7e">machine_learning::zeroes_initialization</a></div><div class="ttdeci">void zeroes_initialization(std::vector&lt; std::valarray&lt; T &gt; &gt; &amp;A, const std::pair&lt; size_t, size_t &gt; &amp;shape)</div><div class="ttdef"><b>Definition</b> <a href="../../d8/d95/vector__ops_8hpp_source.html#l00213">vector_ops.hpp:213</a></div></div>
<div class="ttc" id="anamespacemachine__learning_html_ac332d152078e96311e43ac5e7183ea26"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#ac332d152078e96311e43ac5e7183ea26">machine_learning::minmax_scaler</a></div><div class="ttdeci">std::vector&lt; std::vector&lt; std::valarray&lt; T &gt; &gt; &gt; minmax_scaler(const std::vector&lt; std::vector&lt; std::valarray&lt; T &gt; &gt; &gt; &amp;A, const T &amp;low, const T &amp;high)</div><div class="ttdef"><b>Definition</b> <a href="../../d8/d95/vector__ops_8hpp_source.html#l00269">vector_ops.hpp:269</a></div></div>
<div class="ttc" id="anamespacemachine__learning_html_acafa3e62b686aebdbad81c4f89913f43"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#acafa3e62b686aebdbad81c4f89913f43">machine_learning::hadamard_product</a></div><div class="ttdeci">std::vector&lt; std::valarray&lt; T &gt; &gt; hadamard_product(const std::vector&lt; std::valarray&lt; T &gt; &gt; &amp;A, const std::vector&lt; std::valarray&lt; T &gt; &gt; &amp;B)</div><div class="ttdef"><b>Definition</b> <a href="../../d8/d95/vector__ops_8hpp_source.html#l00494">vector_ops.hpp:494</a></div></div>
<div class="ttc" id="anamespacemachine__learning_html_ad0bdc88e5f1be47c46c0f0c8ebf754bb"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#ad0bdc88e5f1be47c46c0f0c8ebf754bb">machine_learning::apply_function</a></div><div class="ttdeci">std::vector&lt; std::valarray&lt; T &gt; &gt; apply_function(const std::vector&lt; std::valarray&lt; T &gt; &gt; &amp;A, T(*func)(const T &amp;))</div><div class="ttdef"><b>Definition</b> <a href="../../d8/d95/vector__ops_8hpp_source.html#l00329">vector_ops.hpp:329</a></div></div>
<div class="ttc" id="anamespacemachine__learning_html_ae10178b082f0205c326550877d998e5d"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#ae10178b082f0205c326550877d998e5d">machine_learning::pop_back</a></div><div class="ttdeci">std::valarray&lt; T &gt; pop_back(const std::valarray&lt; T &gt; &amp;A)</div><div class="ttdef"><b>Definition</b> <a href="../../d8/d95/vector__ops_8hpp_source.html#l00119">vector_ops.hpp:119</a></div></div>
<div class="ttc" id="anamespacemachine__learning_html_af801bf30591ca6b2c38ff4fed0ded23f"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#af801bf30591ca6b2c38ff4fed0ded23f">machine_learning::equal_shuffle</a></div><div class="ttdeci">void equal_shuffle(std::vector&lt; std::vector&lt; std::valarray&lt; T &gt; &gt; &gt; &amp;A, std::vector&lt; std::vector&lt; std::valarray&lt; T &gt; &gt; &gt; &amp;B)</div><div class="ttdef"><b>Definition</b> <a href="../../d8/d95/vector__ops_8hpp_source.html#l00136">vector_ops.hpp:136</a></div></div>
<div class="ttc" id="anamespaceneural__network_html"><div class="ttname"><a href="../../d0/d2e/namespaceneural__network.html">neural_network</a></div><div class="ttdoc">Neural Network or Multilayer Perceptron.</div></div>
<div class="ttc" id="anamespaceutil__functions_html"><div class="ttname"><a href="../../d3/d17/namespaceutil__functions.html">util_functions</a></div><div class="ttdoc">Various utility functions used in Neural network.</div></div>
<div class="ttc" id="aneural__network_8cpp_html_a23aa9d32bcbcd65cfc85f0a41e2afadc"><div class="ttname"><a href="../../d2/d58/neural__network_8cpp.html#a23aa9d32bcbcd65cfc85f0a41e2afadc">machine_learning::neural_network::activations::sigmoid</a></div><div class="ttdeci">double sigmoid(const double &amp;x)</div><div class="ttdef"><b>Definition</b> <a href="#l00060">neural_network.cpp:60</a></div></div>
<div class="ttc" id="aneural__network_8cpp_html_a2a5e874b9774aa5362dbcf288828b95c"><div class="ttname"><a href="../../d2/d58/neural__network_8cpp.html#a2a5e874b9774aa5362dbcf288828b95c">machine_learning::neural_network::activations::dtanh</a></div><div class="ttdeci">double dtanh(const double &amp;x)</div><div class="ttdef"><b>Definition</b> <a href="#l00095">neural_network.cpp:95</a></div></div>
<div class="ttc" id="aneural__network_8cpp_html_a32c00da08f2cf641dd336270f6e3c407"><div class="ttname"><a href="../../d2/d58/neural__network_8cpp.html#a32c00da08f2cf641dd336270f6e3c407">machine_learning::neural_network::util_functions::identity_function</a></div><div class="ttdeci">double identity_function(const double &amp;x)</div><div class="ttdef"><b>Definition</b> <a href="#l00112">neural_network.cpp:112</a></div></div>
<div class="ttc" id="aneural__network_8cpp_html_a371aa7dd5d5add0143d1756bb0a1b32f"><div class="ttname"><a href="../../d2/d58/neural__network_8cpp.html#a371aa7dd5d5add0143d1756bb0a1b32f">machine_learning::neural_network::activations::tanh</a></div><div class="ttdeci">double tanh(const double &amp;x)</div><div class="ttdef"><b>Definition</b> <a href="#l00088">neural_network.cpp:88</a></div></div>
<div class="ttc" id="aneural__network_8cpp_html_a45d3e30406712ada3d9713ece3c1b153"><div class="ttname"><a href="../../d2/d58/neural__network_8cpp.html#a45d3e30406712ada3d9713ece3c1b153">machine_learning::neural_network::util_functions::square</a></div><div class="ttdeci">double square(const double &amp;x)</div><div class="ttdef"><b>Definition</b> <a href="#l00106">neural_network.cpp:106</a></div></div>
<div class="ttc" id="aneural__network_8cpp_html_a76eb66212d577f948a457b6e29d87c46"><div class="ttname"><a href="../../d2/d58/neural__network_8cpp.html#a76eb66212d577f948a457b6e29d87c46">machine_learning::neural_network::activations::dsigmoid</a></div><div class="ttdeci">double dsigmoid(const double &amp;x)</div><div class="ttdef"><b>Definition</b> <a href="#l00067">neural_network.cpp:67</a></div></div>
<div class="ttc" id="aneural__network_8cpp_html_aa69e95a34054d7989bf446f96b2ffaf9"><div class="ttname"><a href="../../d2/d58/neural__network_8cpp.html#aa69e95a34054d7989bf446f96b2ffaf9">machine_learning::neural_network::activations::drelu</a></div><div class="ttdeci">double drelu(const double &amp;x)</div><div class="ttdef"><b>Definition</b> <a href="#l00081">neural_network.cpp:81</a></div></div>
<div class="ttc" id="aneural__network_8cpp_html_aa8dca7b867074164d5f45b0f3851269d"><div class="ttname"><a href="../../d2/d58/neural__network_8cpp.html#aa8dca7b867074164d5f45b0f3851269d">test</a></div><div class="ttdeci">static void test()</div><div class="ttdef"><b>Definition</b> <a href="#l00805">neural_network.cpp:805</a></div></div>
<div class="ttc" id="aneural__network_8cpp_html_ae66f6b31b5ad750f1fe042a706a4e3d4"><div class="ttname"><a href="../../d2/d58/neural__network_8cpp.html#ae66f6b31b5ad750f1fe042a706a4e3d4">main</a></div><div class="ttdeci">int main()</div><div class="ttdoc">Main function.</div><div class="ttdef"><b>Definition</b> <a href="#l00833">neural_network.cpp:833</a></div></div>
<div class="ttc" id="aneural__network_8cpp_html_af8f264600754602b6a9ea19cc690e50e"><div class="ttname"><a href="../../d2/d58/neural__network_8cpp.html#af8f264600754602b6a9ea19cc690e50e">machine_learning::neural_network::activations::relu</a></div><div class="ttdeci">double relu(const double &amp;x)</div><div class="ttdef"><b>Definition</b> <a href="#l00074">neural_network.cpp:74</a></div></div>
<div class="ttc" id="avector__ops_8hpp_html"><div class="ttname"><a href="../../d8/d95/vector__ops_8hpp.html">vector_ops.hpp</a></div><div class="ttdoc">Various functions for vectors associated with [NeuralNetwork (aka Multilayer Perceptron)] (https://en...</div></div>
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