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<title>Algorithms_in_C++: machine_learning::neural_network::NeuralNetwork Class Reference</title>
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<div id="projectname">Algorithms_in_C++<span id="projectnumber"> 1.0.0</span>
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<div id="projectbrief">Set of algorithms implemented in C++.</div>
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@@ -98,6 +98,12 @@ $(document).ready(function(){initNavTree('d4/df4/classmachine__learning_1_1neura
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<div class="headertitle"><div class="title">machine_learning::neural_network::NeuralNetwork Class Reference</div></div>
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Collaboration diagram for machine_learning::neural_network::NeuralNetwork:</div>
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<div class="center"><iframe scrolling="no" frameborder="0" src="../../d7/d59/classmachine__learning_1_1neural__network_1_1_neural_network__coll__graph.svg" width="320" height="483"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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<table class="memberdecls">
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a id="pub-methods" name="pub-methods"></a>
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Public Member Functions</h2></td></tr>
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@@ -192,30 +198,30 @@ Private Attributes</h2></td></tr>
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</dd>
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<div class="fragment"><div class="line"><a id="l00258" name="l00258"></a><span class="lineno"> 258</span> {</div>
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<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>
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<div class="line"><a id="l00260" name="l00260"></a><span class="lineno"> 260</span> <span class="keywordflow">if</span> (config.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/begin.html">begin</a>()->second != <span class="stringliteral">"none"</span>) {</div>
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<div class="line"><a id="l00261" name="l00261"></a><span class="lineno"> 261</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><a id="l00262" name="l00262"></a><span class="lineno"> 262</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a></div>
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<div class="line"><a id="l00263" name="l00263"></a><span class="lineno"> 263</span> << <span class="stringliteral">"First layer can't have activation other than none got "</span></div>
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<div class="line"><a id="l00264" name="l00264"></a><span class="lineno"> 264</span> << config.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/begin.html">begin</a>()->second;</div>
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<div class="line"><a id="l00265" name="l00265"></a><span class="lineno"> 265</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
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<div class="line"><a id="l00266" name="l00266"></a><span class="lineno"> 266</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/program/exit.html">std::exit</a>(EXIT_FAILURE);</div>
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<div class="line"><a id="l00267" name="l00267"></a><span class="lineno"> 267</span> }</div>
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<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>
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<div class="line"><a id="l00269" name="l00269"></a><span class="lineno"> 269</span> <span class="keywordflow">if</span> (config.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>() <= 1) {</div>
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<div class="line"><a id="l00270" name="l00270"></a><span class="lineno"> 270</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><a id="l00271" name="l00271"></a><span class="lineno"> 271</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"Invalid size of network, "</span>;</div>
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<div class="line"><a id="l00272" name="l00272"></a><span class="lineno"> 272</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"Atleast two layers are required"</span>;</div>
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<div class="line"><a id="l00273" name="l00273"></a><span class="lineno"> 273</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/program/exit.html">std::exit</a>(EXIT_FAILURE);</div>
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<div class="line"><a id="l00274" name="l00274"></a><span class="lineno"> 274</span> }</div>
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<div class="line"><a id="l00275" name="l00275"></a><span class="lineno"> 275</span> <span class="comment">// Reconstructing all pretrained layers</span></div>
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<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 < config.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); i++) {</div>
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<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(neural_network::layers::DenseLayer(</div>
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<div class="line"><a id="l00278" name="l00278"></a><span class="lineno"> 278</span> config[i].first, config[i].second, kernels[i]));</div>
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<div class="line"><a id="l00279" name="l00279"></a><span class="lineno"> 279</span> }</div>
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<div class="line"><a id="l00280" name="l00280"></a><span class="lineno"> 280</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">"INFO: Network constructed successfully"</span> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
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<div class="line"><a id="l00281" name="l00281"></a><span class="lineno"> 281</span> }</div>
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<div class="fragment"><div class="line"><span class="lineno"> 258</span> {</div>
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<div class="line"><span class="lineno"> 259</span> <span class="comment">// First layer should not have activation</span></div>
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<div class="line"><span class="lineno"> 260</span> <span class="keywordflow">if</span> (config.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/begin.html">begin</a>()->second != <span class="stringliteral">"none"</span>) {</div>
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<div class="line"><span class="lineno"> 261</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><span class="lineno"> 262</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a></div>
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<div class="line"><span class="lineno"> 263</span> << <span class="stringliteral">"First layer can't have activation other than none got "</span></div>
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<div class="line"><span class="lineno"> 264</span> << config.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/begin.html">begin</a>()->second;</div>
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<div class="line"><span class="lineno"> 265</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
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<div class="line"><span class="lineno"> 266</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/program/exit.html">std::exit</a>(EXIT_FAILURE);</div>
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<div class="line"><span class="lineno"> 267</span> }</div>
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<div class="line"><span class="lineno"> 268</span> <span class="comment">// Network should have atleast two layers</span></div>
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<div class="line"><span class="lineno"> 269</span> <span class="keywordflow">if</span> (config.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>() <= 1) {</div>
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<div class="line"><span class="lineno"> 270</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><span class="lineno"> 271</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"Invalid size of network, "</span>;</div>
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<div class="line"><span class="lineno"> 272</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"Atleast two layers are required"</span>;</div>
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<div class="line"><span class="lineno"> 273</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/program/exit.html">std::exit</a>(EXIT_FAILURE);</div>
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<div class="line"><span class="lineno"> 274</span> }</div>
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<div class="line"><span class="lineno"> 275</span> <span class="comment">// Reconstructing all pretrained layers</span></div>
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<div class="line"><span class="lineno"> 276</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < config.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); i++) {</div>
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<div class="line"><span class="lineno"> 277</span> <a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.emplace_back(neural_network::layers::DenseLayer(</div>
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<div class="line"><span class="lineno"> 278</span> config[i].first, config[i].second, kernels[i]));</div>
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<div class="line"><span class="lineno"> 279</span> }</div>
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<div class="line"><span class="lineno"> 280</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">"INFO: Network constructed successfully"</span> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
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<div class="line"><span class="lineno"> 281</span> }</div>
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<div class="ttc" id="abasic_ostream_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a></div></div>
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<div class="ttc" id="aendl_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a></div><div class="ttdeci">T endl(T... args)</div></div>
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@@ -286,36 +292,36 @@ Here is the call graph for this function:</div>
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<div class="fragment"><div class="line"><a id="l00314" name="l00314"></a><span class="lineno"> 314</span> {</div>
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<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>
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<div class="line"><a id="l00316" name="l00316"></a><span class="lineno"> 316</span> <span class="keywordflow">if</span> (config.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/begin.html">begin</a>()->second != <span class="stringliteral">"none"</span>) {</div>
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<div class="line"><a id="l00317" name="l00317"></a><span class="lineno"> 317</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><a id="l00318" name="l00318"></a><span class="lineno"> 318</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a></div>
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<div class="line"><a id="l00319" name="l00319"></a><span class="lineno"> 319</span> << <span class="stringliteral">"First layer can't have activation other than none got "</span></div>
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<div class="line"><a id="l00320" name="l00320"></a><span class="lineno"> 320</span> << config.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/begin.html">begin</a>()->second;</div>
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<div class="line"><a id="l00321" name="l00321"></a><span class="lineno"> 321</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
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<div class="line"><a id="l00322" name="l00322"></a><span class="lineno"> 322</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/program/exit.html">std::exit</a>(EXIT_FAILURE);</div>
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<div class="line"><a id="l00323" name="l00323"></a><span class="lineno"> 323</span> }</div>
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<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>
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<div class="line"><a id="l00325" name="l00325"></a><span class="lineno"> 325</span> <span class="keywordflow">if</span> (config.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>() <= 1) {</div>
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<div class="line"><a id="l00326" name="l00326"></a><span class="lineno"> 326</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><a id="l00327" name="l00327"></a><span class="lineno"> 327</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"Invalid size of network, "</span>;</div>
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<div class="line"><a id="l00328" name="l00328"></a><span class="lineno"> 328</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"Atleast two layers are required"</span>;</div>
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<div class="line"><a id="l00329" name="l00329"></a><span class="lineno"> 329</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/program/exit.html">std::exit</a>(EXIT_FAILURE);</div>
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<div class="line"><a id="l00330" name="l00330"></a><span class="lineno"> 330</span> }</div>
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<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>
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<div class="line"><a id="l00332" name="l00332"></a><span class="lineno"> 332</span> <span class="comment">// as kernel.</span></div>
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<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(neural_network::layers::DenseLayer(</div>
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<div class="line"><a id="l00334" name="l00334"></a><span class="lineno"> 334</span> config[0].first, config[0].second,</div>
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<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>
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<div class="line"><a id="l00336" name="l00336"></a><span class="lineno"> 336</span> <span class="comment">// Creating remaining layers</span></div>
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<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 < config.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); i++) {</div>
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<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(neural_network::layers::DenseLayer(</div>
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<div class="line"><a id="l00339" name="l00339"></a><span class="lineno"> 339</span> config[i].first, config[i].second,</div>
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<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>
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<div class="line"><a id="l00341" name="l00341"></a><span class="lineno"> 341</span> }</div>
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<div class="line"><a id="l00342" name="l00342"></a><span class="lineno"> 342</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">"INFO: Network constructed successfully"</span> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
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<div class="line"><a id="l00343" name="l00343"></a><span class="lineno"> 343</span> }</div>
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<div class="fragment"><div class="line"><span class="lineno"> 314</span> {</div>
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<div class="line"><span class="lineno"> 315</span> <span class="comment">// First layer should not have activation</span></div>
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<div class="line"><span class="lineno"> 316</span> <span class="keywordflow">if</span> (config.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/begin.html">begin</a>()->second != <span class="stringliteral">"none"</span>) {</div>
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<div class="line"><span class="lineno"> 317</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><span class="lineno"> 318</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a></div>
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<div class="line"><span class="lineno"> 319</span> << <span class="stringliteral">"First layer can't have activation other than none got "</span></div>
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<div class="line"><span class="lineno"> 320</span> << config.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/begin.html">begin</a>()->second;</div>
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<div class="line"><span class="lineno"> 321</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><span class="lineno"> 322</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/program/exit.html">std::exit</a>(EXIT_FAILURE);</div>
|
||||
<div class="line"><span class="lineno"> 323</span> }</div>
|
||||
<div class="line"><span class="lineno"> 324</span> <span class="comment">// Network should have atleast two layers</span></div>
|
||||
<div class="line"><span class="lineno"> 325</span> <span class="keywordflow">if</span> (config.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>() <= 1) {</div>
|
||||
<div class="line"><span class="lineno"> 326</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
|
||||
<div class="line"><span class="lineno"> 327</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"Invalid size of network, "</span>;</div>
|
||||
<div class="line"><span class="lineno"> 328</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"Atleast two layers are required"</span>;</div>
|
||||
<div class="line"><span class="lineno"> 329</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/program/exit.html">std::exit</a>(EXIT_FAILURE);</div>
|
||||
<div class="line"><span class="lineno"> 330</span> }</div>
|
||||
<div class="line"><span class="lineno"> 331</span> <span class="comment">// Separately creating first layer so it can have unit matrix</span></div>
|
||||
<div class="line"><span class="lineno"> 332</span> <span class="comment">// as kernel.</span></div>
|
||||
<div class="line"><span class="lineno"> 333</span> <a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.push_back(neural_network::layers::DenseLayer(</div>
|
||||
<div class="line"><span class="lineno"> 334</span> config[0].first, config[0].second,</div>
|
||||
<div class="line"><span class="lineno"> 335</span> {config[0].first, config[0].first}, <span class="keyword">false</span>));</div>
|
||||
<div class="line"><span class="lineno"> 336</span> <span class="comment">// Creating remaining layers</span></div>
|
||||
<div class="line"><span class="lineno"> 337</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 1; i < config.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); i++) {</div>
|
||||
<div class="line"><span class="lineno"> 338</span> <a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.push_back(neural_network::layers::DenseLayer(</div>
|
||||
<div class="line"><span class="lineno"> 339</span> config[i].first, config[i].second,</div>
|
||||
<div class="line"><span class="lineno"> 340</span> {config[i - 1].first, config[i].first}, <span class="keyword">true</span>));</div>
|
||||
<div class="line"><span class="lineno"> 341</span> }</div>
|
||||
<div class="line"><span class="lineno"> 342</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">"INFO: Network constructed successfully"</span> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><span class="lineno"> 343</span> }</div>
|
||||
</div><!-- fragment --><div class="dynheader">
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||||
Here is the call graph for this function:</div>
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||||
<div class="dyncontent">
|
||||
@@ -441,17 +447,17 @@ Here is the call graph for this function:</div>
|
||||
</table>
|
||||
</dd>
|
||||
</dl>
|
||||
<div class="fragment"><div class="line"><a id="l00289" name="l00289"></a><span class="lineno"> 289</span> {</div>
|
||||
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno"> 290</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::vector<std::valarray<double></a>>> details;</div>
|
||||
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno"> 291</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::valarray<double></a>> current_pass = X;</div>
|
||||
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno"> 292</span> details.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/emplace_back.html">emplace_back</a>(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> &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, <a class="code hl_function" href="../../d4/d18/composite__simpson__rule_8cpp.html#a6d8df83a6f26ce24a75d3b358b7f5b8a">l</a>.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, <a class="code hl_function" href="../../d4/d18/composite__simpson__rule_8cpp.html#a6d8df83a6f26ce24a75d3b358b7f5b8a">l</a>.activation_function);</div>
|
||||
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno"> 296</span> details.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/emplace_back.html">emplace_back</a>(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 class="fragment"><div class="line"><span class="lineno"> 289</span> {</div>
|
||||
<div class="line"><span class="lineno"> 290</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::vector<std::valarray<double></a>>> details;</div>
|
||||
<div class="line"><span class="lineno"> 291</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::valarray<double></a>> current_pass = X;</div>
|
||||
<div class="line"><span class="lineno"> 292</span> details.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/emplace_back.html">emplace_back</a>(X);</div>
|
||||
<div class="line"><span class="lineno"> 293</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> &l : <a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>) {</div>
|
||||
<div class="line"><span class="lineno"> 294</span> current_pass = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a5342906d42b80fc6b6b3ad17bf00fcb9">multiply</a>(current_pass, <a class="code hl_function" href="../../d4/d18/composite__simpson__rule_8cpp.html#a6d8df83a6f26ce24a75d3b358b7f5b8a">l</a>.kernel);</div>
|
||||
<div class="line"><span class="lineno"> 295</span> current_pass = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#ad0bdc88e5f1be47c46c0f0c8ebf754bb">apply_function</a>(current_pass, <a class="code hl_function" href="../../d4/d18/composite__simpson__rule_8cpp.html#a6d8df83a6f26ce24a75d3b358b7f5b8a">l</a>.activation_function);</div>
|
||||
<div class="line"><span class="lineno"> 296</span> details.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/emplace_back.html">emplace_back</a>(current_pass);</div>
|
||||
<div class="line"><span class="lineno"> 297</span> }</div>
|
||||
<div class="line"><span class="lineno"> 298</span> <span class="keywordflow">return</span> details;</div>
|
||||
<div class="line"><span class="lineno"> 299</span> }</div>
|
||||
<div class="ttc" id="acomposite__simpson__rule_8cpp_html_a6d8df83a6f26ce24a75d3b358b7f5b8a"><div class="ttname"><a href="../../d4/d18/composite__simpson__rule_8cpp.html#a6d8df83a6f26ce24a75d3b358b7f5b8a">numerical_methods::simpson_method::l</a></div><div class="ttdeci">double l(double x)</div><div class="ttdoc">Another test function.</div><div class="ttdef"><b>Definition:</b> composite_simpson_rule.cpp:119</div></div>
|
||||
<div class="ttc" id="aemplace_back_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/container/vector/emplace_back.html">std::vector::emplace_back</a></div><div class="ttdeci">T emplace_back(T... args)</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< std::valarray< T > > multiply(const std::vector< std::valarray< T > > &A, const std::vector< std::valarray< T > > &B)</div><div class="ttdef"><b>Definition:</b> vector_ops.hpp:460</div></div>
|
||||
@@ -496,16 +502,16 @@ Here is the call graph for this function:</div>
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||||
</dd>
|
||||
</dl>
|
||||
<dl class="section return"><dt>Returns</dt><dd>returns predicted values as vector </dd></dl>
|
||||
<div class="fragment"><div class="line"><a id="l00465" name="l00465"></a><span class="lineno"> 465</span> {</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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::vector<std::valarray<double></a>>> predicted_batch(</div>
|
||||
<div class="line"><a id="l00468" name="l00468"></a><span class="lineno"> 468</span> X.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>());</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 < X.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); 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-><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 class="fragment"><div class="line"><span class="lineno"> 465</span> {</div>
|
||||
<div class="line"><span class="lineno"> 466</span> <span class="comment">// Store predicted values</span></div>
|
||||
<div class="line"><span class="lineno"> 467</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::vector<std::valarray<double></a>>> predicted_batch(</div>
|
||||
<div class="line"><span class="lineno"> 468</span> X.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>());</div>
|
||||
<div class="line"><span class="lineno"> 469</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < X.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); i++) { <span class="comment">// For every sample</span></div>
|
||||
<div class="line"><span class="lineno"> 470</span> <span class="comment">// Push predicted values</span></div>
|
||||
<div class="line"><span class="lineno"> 471</span> predicted_batch[i] = this-><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"><span class="lineno"> 472</span> }</div>
|
||||
<div class="line"><span class="lineno"> 473</span> <span class="keywordflow">return</span> predicted_batch; <span class="comment">// Return predicted values</span></div>
|
||||
<div class="line"><span class="lineno"> 474</span> }</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< std::valarray< double > > single_predict(const std::vector< std::valarray< double > > &X)</div><div class="ttdef"><b>Definition:</b> neural_network.cpp:451</div></div>
|
||||
</div><!-- fragment --><div class="dynheader">
|
||||
Here is the call graph for this function:</div>
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||||
@@ -556,29 +562,29 @@ Here is the call graph for this function:</div>
|
||||
</table>
|
||||
</dd>
|
||||
</dl>
|
||||
<div class="fragment"><div class="line"><a id="l00607" name="l00607"></a><span class="lineno"> 607</span> {</div>
|
||||
<div class="line"><a id="l00608" name="l00608"></a><span class="lineno"> 608</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">"INFO: Evaluation Started"</span> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</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">// intialize 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 < X.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); 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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::valarray<double></a>> pred =</div>
|
||||
<div class="line"><a id="l00613" name="l00613"></a><span class="lineno"> 613</span> this-><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.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); <span class="comment">// Averaging accuracy</span></div>
|
||||
<div class="line"><a id="l00624" name="l00624"></a><span class="lineno"> 624</span> loss /= X.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); <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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">"Evaluation: Loss: "</span> << loss;</div>
|
||||
<div class="line"><a id="l00627" name="l00627"></a><span class="lineno"> 627</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">", Accuracy: "</span> << acc << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</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 class="fragment"><div class="line"><span class="lineno"> 607</span> {</div>
|
||||
<div class="line"><span class="lineno"> 608</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">"INFO: Evaluation Started"</span> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><span class="lineno"> 609</span> <span class="keywordtype">double</span> acc = 0, loss = 0; <span class="comment">// intialize performance metrics with zero</span></div>
|
||||
<div class="line"><span class="lineno"> 610</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < X.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); i++) { <span class="comment">// For every sample in input</span></div>
|
||||
<div class="line"><span class="lineno"> 611</span> <span class="comment">// Get predictions</span></div>
|
||||
<div class="line"><span class="lineno"> 612</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::valarray<double></a>> pred =</div>
|
||||
<div class="line"><span class="lineno"> 613</span> this-><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"><span class="lineno"> 614</span> <span class="comment">// If predicted class is correct</span></div>
|
||||
<div class="line"><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"><span class="lineno"> 616</span> acc += 1; <span class="comment">// Increment accuracy</span></div>
|
||||
<div class="line"><span class="lineno"> 617</span> }</div>
|
||||
<div class="line"><span class="lineno"> 618</span> <span class="comment">// Calculating loss - Mean Squared Error</span></div>
|
||||
<div class="line"><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"><span class="lineno"> 620</span> neural_network::util_functions::square) *</div>
|
||||
<div class="line"><span class="lineno"> 621</span> 0.5);</div>
|
||||
<div class="line"><span class="lineno"> 622</span> }</div>
|
||||
<div class="line"><span class="lineno"> 623</span> acc /= X.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); <span class="comment">// Averaging accuracy</span></div>
|
||||
<div class="line"><span class="lineno"> 624</span> loss /= X.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); <span class="comment">// Averaging loss</span></div>
|
||||
<div class="line"><span class="lineno"> 625</span> <span class="comment">// Prinitng performance of the model</span></div>
|
||||
<div class="line"><span class="lineno"> 626</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">"Evaluation: Loss: "</span> << loss;</div>
|
||||
<div class="line"><span class="lineno"> 627</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">", Accuracy: "</span> << acc << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><span class="lineno"> 628</span> <span class="keywordflow">return</span>;</div>
|
||||
<div class="line"><span class="lineno"> 629</span> }</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< std::valarray< T > > &A)</div><div class="ttdef"><b>Definition:</b> vector_ops.hpp:307</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< std::valarray< T > > &A)</div><div class="ttdef"><b>Definition:</b> vector_ops.hpp:232</div></div>
|
||||
</div><!-- fragment --><div class="dynheader">
|
||||
@@ -644,14 +650,14 @@ Here is the call graph for this function:</div>
|
||||
</table>
|
||||
</dd>
|
||||
</dl>
|
||||
<div class="fragment"><div class="line"><a id="l00639" name="l00639"></a><span class="lineno"> 639</span> {</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-><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-><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 class="fragment"><div class="line"><span class="lineno"> 639</span> {</div>
|
||||
<div class="line"><span class="lineno"> 640</span> <span class="comment">// Getting training data from csv file</span></div>
|
||||
<div class="line"><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"><span class="lineno"> 642</span> this-><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"><span class="lineno"> 643</span> <span class="comment">// Evaluating model</span></div>
|
||||
<div class="line"><span class="lineno"> 644</span> this-><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"><span class="lineno"> 645</span> <span class="keywordflow">return</span>;</div>
|
||||
<div class="line"><span class="lineno"> 646</span> }</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< std::vector< std::vector< std::valarray< double > > >, std::vector< std::vector< std::valarray< double > > > > get_XY_from_csv(const std::string &file_name, const bool &last_label, const bool &normalize, const int &slip_lines=1)</div><div class="ttdef"><b>Definition:</b> neural_network.cpp:382</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< std::vector< std::valarray< double > > > &X, const std::vector< std::vector< std::valarray< double > > > &Y)</div><div class="ttdef"><b>Definition:</b> neural_network.cpp:606</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> hash_search.cpp:24</div></div>
|
||||
@@ -732,93 +738,93 @@ Here is the call graph for this function:</div>
|
||||
</table>
|
||||
</dd>
|
||||
</dl>
|
||||
<div class="fragment"><div class="line"><a id="l00488" name="l00488"></a><span class="lineno"> 488</span> {</div>
|
||||
<div class="line"><a id="l00489" name="l00489"></a><span class="lineno"> 489</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::vector<std::valarray<double></a>>> 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.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>() != Y.size()) {</div>
|
||||
<div class="line"><a id="l00492" name="l00492"></a><span class="lineno"> 492</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
|
||||
<div class="line"><a id="l00493" name="l00493"></a><span class="lineno"> 493</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"X and Y in fit have different sizes"</span> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><a id="l00494" name="l00494"></a><span class="lineno"> 494</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/program/exit.html">std::exit</a>(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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">"INFO: Training Started"</span> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</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 <= 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> (<a class="code hl_function" href="../../d5/d91/namespacesorting.html#a7bfe11bd4703eacd1dab93f25ec639c5">shuffle</a>) {</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> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/chrono/high_resolution_clock/now.html">std::chrono::high_resolution_clock::now</a>(); <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">// Intialize 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 < X.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>();</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 < <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/algorithm/min.html">std::min</a>(X.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(), batch_start + batch_size); i++) {</div>
|
||||
<div class="line"><a id="l00511" name="l00511"></a><span class="lineno"> 511</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::valarray<double></a>> 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-><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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::vector<std::valarray<double></a>>> gradients;</div>
|
||||
<div class="line"><a id="l00517" name="l00517"></a><span class="lineno"> 517</span> gradients.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/resize.html">resize</a>(this-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.size());</div>
|
||||
<div class="line"><a id="l00518" name="l00518"></a><span class="lineno"> 518</span> <span class="comment">// First intialize 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 < gradients.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); 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-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>[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-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.size() - 1; j >= 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-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>[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-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>[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-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.size() - 1; j >= 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-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>[j].kernel = this-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>[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> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/chrono/high_resolution_clock/now.html">std::chrono::high_resolution_clock::now</a>(); <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<std::chrono::microseconds>(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.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); <span class="comment">// Averaging loss</span></div>
|
||||
<div class="line"><a id="l00563" name="l00563"></a><span class="lineno"> 563</span> acc /= X.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); <span class="comment">// Averaging accuracy</span></div>
|
||||
<div class="line"><a id="l00564" name="l00564"></a><span class="lineno"> 564</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a>.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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">"Training: Epoch "</span> << epoch << <span class="charliteral">'/'</span> << epochs;</div>
|
||||
<div class="line"><a id="l00567" name="l00567"></a><span class="lineno"> 567</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">", Loss: "</span> << loss;</div>
|
||||
<div class="line"><a id="l00568" name="l00568"></a><span class="lineno"> 568</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">", Accuracy: "</span> << acc;</div>
|
||||
<div class="line"><a id="l00569" name="l00569"></a><span class="lineno"> 569</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">", Taken time: "</span> << duration.count() / 1e6</div>
|
||||
<div class="line"><a id="l00570" name="l00570"></a><span class="lineno"> 570</span> << <span class="stringliteral">" seconds"</span>;</div>
|
||||
<div class="line"><a id="l00571" name="l00571"></a><span class="lineno"> 571</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</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 class="fragment"><div class="line"><span class="lineno"> 488</span> {</div>
|
||||
<div class="line"><span class="lineno"> 489</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::vector<std::valarray<double></a>>> X = X_, Y = Y_;</div>
|
||||
<div class="line"><span class="lineno"> 490</span> <span class="comment">// Both label and input data should have same size</span></div>
|
||||
<div class="line"><span class="lineno"> 491</span> <span class="keywordflow">if</span> (X.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>() != Y.size()) {</div>
|
||||
<div class="line"><span class="lineno"> 492</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
|
||||
<div class="line"><span class="lineno"> 493</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"X and Y in fit have different sizes"</span> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><span class="lineno"> 494</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/program/exit.html">std::exit</a>(EXIT_FAILURE);</div>
|
||||
<div class="line"><span class="lineno"> 495</span> }</div>
|
||||
<div class="line"><span class="lineno"> 496</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">"INFO: Training Started"</span> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><span class="lineno"> 497</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> epoch = 1; epoch <= epochs; epoch++) { <span class="comment">// For every epoch</span></div>
|
||||
<div class="line"><span class="lineno"> 498</span> <span class="comment">// Shuffle X and Y if flag is set</span></div>
|
||||
<div class="line"><span class="lineno"> 499</span> <span class="keywordflow">if</span> (<a class="code hl_function" href="../../d5/d91/namespacesorting.html#a7bfe11bd4703eacd1dab93f25ec639c5">shuffle</a>) {</div>
|
||||
<div class="line"><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"><span class="lineno"> 501</span> }</div>
|
||||
<div class="line"><span class="lineno"> 502</span> <span class="keyword">auto</span> start =</div>
|
||||
<div class="line"><span class="lineno"> 503</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/chrono/high_resolution_clock/now.html">std::chrono::high_resolution_clock::now</a>(); <span class="comment">// Start clock</span></div>
|
||||
<div class="line"><span class="lineno"> 504</span> <span class="keywordtype">double</span> loss = 0,</div>
|
||||
<div class="line"><span class="lineno"> 505</span> acc = 0; <span class="comment">// Intialize performance metrics with zero</span></div>
|
||||
<div class="line"><span class="lineno"> 506</span> <span class="comment">// For each starting index of batch</span></div>
|
||||
<div class="line"><span class="lineno"> 507</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> batch_start = 0; batch_start < X.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>();</div>
|
||||
<div class="line"><span class="lineno"> 508</span> batch_start += batch_size) {</div>
|
||||
<div class="line"><span class="lineno"> 509</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = batch_start;</div>
|
||||
<div class="line"><span class="lineno"> 510</span> i < <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/algorithm/min.html">std::min</a>(X.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(), batch_start + batch_size); i++) {</div>
|
||||
<div class="line"><span class="lineno"> 511</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::valarray<double></a>> grad, cur_error,</div>
|
||||
<div class="line"><span class="lineno"> 512</span> predicted;</div>
|
||||
<div class="line"><span class="lineno"> 513</span> <span class="keyword">auto</span> <a class="code hl_namespace" href="../../d5/d39/namespaceactivations.html">activations</a> = this-><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"><span class="lineno"> 514</span> <span class="comment">// Gradients vector to store gradients for all layers</span></div>
|
||||
<div class="line"><span class="lineno"> 515</span> <span class="comment">// They will be averaged and applied to kernel</span></div>
|
||||
<div class="line"><span class="lineno"> 516</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::vector<std::valarray<double></a>>> gradients;</div>
|
||||
<div class="line"><span class="lineno"> 517</span> gradients.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/resize.html">resize</a>(this-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.size());</div>
|
||||
<div class="line"><span class="lineno"> 518</span> <span class="comment">// First intialize gradients to zero</span></div>
|
||||
<div class="line"><span class="lineno"> 519</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < gradients.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); i++) {</div>
|
||||
<div class="line"><span class="lineno"> 520</span> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#ac1bdaa2a724b4ce6a6bb371a5dbe2e7e">zeroes_initialization</a>(</div>
|
||||
<div class="line"><span class="lineno"> 521</span> gradients[i], <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(this-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>[i].kernel));</div>
|
||||
<div class="line"><span class="lineno"> 522</span> }</div>
|
||||
<div class="line"><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"><span class="lineno"> 524</span> cur_error = predicted - Y[i]; <span class="comment">// Absoulute error</span></div>
|
||||
<div class="line"><span class="lineno"> 525</span> <span class="comment">// Calculating loss with MSE</span></div>
|
||||
<div class="line"><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"><span class="lineno"> 527</span> cur_error, neural_network::util_functions::square));</div>
|
||||
<div class="line"><span class="lineno"> 528</span> <span class="comment">// If prediction is correct</span></div>
|
||||
<div class="line"><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"><span class="lineno"> 530</span> acc += 1;</div>
|
||||
<div class="line"><span class="lineno"> 531</span> }</div>
|
||||
<div class="line"><span class="lineno"> 532</span> <span class="comment">// For every layer (except first) starting from last one</span></div>
|
||||
<div class="line"><span class="lineno"> 533</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = this-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.size() - 1; j >= 1; j--) {</div>
|
||||
<div class="line"><span class="lineno"> 534</span> <span class="comment">// Backpropogating errors</span></div>
|
||||
<div class="line"><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"><span class="lineno"> 536</span> cur_error,</div>
|
||||
<div class="line"><span class="lineno"> 537</span> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#ad0bdc88e5f1be47c46c0f0c8ebf754bb">apply_function</a>(</div>
|
||||
<div class="line"><span class="lineno"> 538</span> <a class="code hl_namespace" href="../../d5/d39/namespaceactivations.html">activations</a>[j + 1],</div>
|
||||
<div class="line"><span class="lineno"> 539</span> this-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>[j].dactivation_function));</div>
|
||||
<div class="line"><span class="lineno"> 540</span> <span class="comment">// Calculating gradient for current layer</span></div>
|
||||
<div class="line"><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"><span class="lineno"> 542</span> <span class="comment">// Change error according to current kernel values</span></div>
|
||||
<div class="line"><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"><span class="lineno"> 544</span> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a89fde571b38f9483576594f66572958a">transpose</a>(this-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>[j].kernel));</div>
|
||||
<div class="line"><span class="lineno"> 545</span> <span class="comment">// Adding gradient values to collection of gradients</span></div>
|
||||
<div class="line"><span class="lineno"> 546</span> gradients[j] = gradients[j] + grad / double(batch_size);</div>
|
||||
<div class="line"><span class="lineno"> 547</span> }</div>
|
||||
<div class="line"><span class="lineno"> 548</span> <span class="comment">// Applying gradients</span></div>
|
||||
<div class="line"><span class="lineno"> 549</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = this-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.size() - 1; j >= 1; j--) {</div>
|
||||
<div class="line"><span class="lineno"> 550</span> <span class="comment">// Updating kernel (aka weights)</span></div>
|
||||
<div class="line"><span class="lineno"> 551</span> this-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>[j].kernel = this-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>[j].kernel -</div>
|
||||
<div class="line"><span class="lineno"> 552</span> gradients[j] * learning_rate;</div>
|
||||
<div class="line"><span class="lineno"> 553</span> }</div>
|
||||
<div class="line"><span class="lineno"> 554</span> }</div>
|
||||
<div class="line"><span class="lineno"> 555</span> }</div>
|
||||
<div class="line"><span class="lineno"> 556</span> <span class="keyword">auto</span> stop =</div>
|
||||
<div class="line"><span class="lineno"> 557</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/chrono/high_resolution_clock/now.html">std::chrono::high_resolution_clock::now</a>(); <span class="comment">// Stoping the clock</span></div>
|
||||
<div class="line"><span class="lineno"> 558</span> <span class="comment">// Calculate time taken by epoch</span></div>
|
||||
<div class="line"><span class="lineno"> 559</span> <span class="keyword">auto</span> duration =</div>
|
||||
<div class="line"><span class="lineno"> 560</span> std::chrono::duration_cast<std::chrono::microseconds>(stop -</div>
|
||||
<div class="line"><span class="lineno"> 561</span> start);</div>
|
||||
<div class="line"><span class="lineno"> 562</span> loss /= X.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); <span class="comment">// Averaging loss</span></div>
|
||||
<div class="line"><span class="lineno"> 563</span> acc /= X.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); <span class="comment">// Averaging accuracy</span></div>
|
||||
<div class="line"><span class="lineno"> 564</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a>.precision(4); <span class="comment">// set output precision to 4</span></div>
|
||||
<div class="line"><span class="lineno"> 565</span> <span class="comment">// Printing training stats</span></div>
|
||||
<div class="line"><span class="lineno"> 566</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">"Training: Epoch "</span> << epoch << <span class="charliteral">'/'</span> << epochs;</div>
|
||||
<div class="line"><span class="lineno"> 567</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">", Loss: "</span> << loss;</div>
|
||||
<div class="line"><span class="lineno"> 568</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">", Accuracy: "</span> << acc;</div>
|
||||
<div class="line"><span class="lineno"> 569</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">", Taken time: "</span> << duration.count() / 1e6</div>
|
||||
<div class="line"><span class="lineno"> 570</span> << <span class="stringliteral">" seconds"</span>;</div>
|
||||
<div class="line"><span class="lineno"> 571</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><span class="lineno"> 572</span> }</div>
|
||||
<div class="line"><span class="lineno"> 573</span> <span class="keywordflow">return</span>;</div>
|
||||
<div class="line"><span class="lineno"> 574</span> }</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< std::vector< std::valarray< double > > > __detailed_single_prediction(const std::vector< std::valarray< double > > &X)</div><div class="ttdef"><b>Definition:</b> neural_network.cpp:289</div></div>
|
||||
<div class="ttc" id="amin_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/algorithm/min.html">std::min</a></div><div class="ttdeci">T min(T... args)</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>
|
||||
@@ -921,15 +927,15 @@ Here is the call graph for this function:</div>
|
||||
</table>
|
||||
</dd>
|
||||
</dl>
|
||||
<div class="fragment"><div class="line"><a id="l00591" name="l00591"></a><span class="lineno"> 591</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-><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-><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> <a class="code hl_function" href="../../d5/d91/namespacesorting.html#a7bfe11bd4703eacd1dab93f25ec639c5">shuffle</a>);</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 class="fragment"><div class="line"><span class="lineno"> 591</span> {</div>
|
||||
<div class="line"><span class="lineno"> 592</span> <span class="comment">// Getting training data from csv file</span></div>
|
||||
<div class="line"><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"><span class="lineno"> 594</span> this-><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"><span class="lineno"> 595</span> <span class="comment">// Fit the model on training data</span></div>
|
||||
<div class="line"><span class="lineno"> 596</span> this-><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"><span class="lineno"> 597</span> <a class="code hl_function" href="../../d5/d91/namespacesorting.html#a7bfe11bd4703eacd1dab93f25ec639c5">shuffle</a>);</div>
|
||||
<div class="line"><span class="lineno"> 598</span> <span class="keywordflow">return</span>;</div>
|
||||
<div class="line"><span class="lineno"> 599</span> }</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< std::vector< std::valarray< double > > > &X_, const std::vector< std::vector< std::valarray< double > > > &Y_, const int &epochs=100, const double &learning_rate=0.01, const size_t &batch_size=32, const bool &shuffle=true)</div><div class="ttdef"><b>Definition:</b> neural_network.cpp:485</div></div>
|
||||
</div><!-- fragment --><div class="dynheader">
|
||||
Here is the call graph for this function:</div>
|
||||
@@ -995,68 +1001,68 @@ Here is the call graph for this function:</div>
|
||||
</dd>
|
||||
</dl>
|
||||
<dl class="section return"><dt>Returns</dt><dd>returns pair of X and Y </dd></dl>
|
||||
<div class="fragment"><div class="line"><a id="l00383" name="l00383"></a><span class="lineno"> 383</span> {</div>
|
||||
<div class="line"><a id="l00384" name="l00384"></a><span class="lineno"> 384</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ifstream.html">std::ifstream</a> 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.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ifstream/open.html">open</a>(file_name.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string/c_str.html">c_str</a>(), 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.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ifstream/is_open.html">is_open</a>()) {</div>
|
||||
<div class="line"><a id="l00388" name="l00388"></a><span class="lineno"> 388</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
|
||||
<div class="line"><a id="l00389" name="l00389"></a><span class="lineno"> 389</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"Unable to open file: "</span> << file_name << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><a id="l00390" name="l00390"></a><span class="lineno"> 390</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/program/exit.html">std::exit</a>(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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::vector<std::valarray<double></a>>> 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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string.html">std::string</a> 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 < slip_lines; i++) {</div>
|
||||
<div class="line"><a id="l00397" name="l00397"></a><span class="lineno"> 397</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string/getline.html">std::getline</a>(in_file, line, <span class="charliteral">'\n'</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.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ios/eof.html">eof</a>() && <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string/getline.html">std::getline</a>(in_file, line, <span class="charliteral">'\n'</span>)) {</div>
|
||||
<div class="line"><a id="l00401" name="l00401"></a><span class="lineno"> 401</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/numeric/valarray.html">std::valarray<double></a> 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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_stringstream.html">std::stringstream</a> 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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string.html">std::string</a> token; <span class="comment">// To store each token in line (seprated by ',')</span></div>
|
||||
<div class="line"><a id="l00405" name="l00405"></a><span class="lineno"> 405</span> <span class="keywordflow">while</span> (<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string/getline.html">std::getline</a>(ss, token, <span class="charliteral">','</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, <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string/stof.html">std::stod</a>(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-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.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() > 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-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.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() > 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.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/push_back.html">push_back</a>({x_data});</div>
|
||||
<div class="line"><a id="l00435" name="l00435"></a><span class="lineno"> 435</span> Y.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/push_back.html">push_back</a>({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.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ifstream/close.html">close</a>(); <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> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/pair/make_pair.html">make_pair</a>(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 class="fragment"><div class="line"><span class="lineno"> 383</span> {</div>
|
||||
<div class="line"><span class="lineno"> 384</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ifstream.html">std::ifstream</a> in_file; <span class="comment">// Ifstream to read file</span></div>
|
||||
<div class="line"><span class="lineno"> 385</span> in_file.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ifstream/open.html">open</a>(file_name.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string/c_str.html">c_str</a>(), std::ios::in); <span class="comment">// Open file</span></div>
|
||||
<div class="line"><span class="lineno"> 386</span> <span class="comment">// If there is any problem in opening file</span></div>
|
||||
<div class="line"><span class="lineno"> 387</span> <span class="keywordflow">if</span> (!in_file.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ifstream/is_open.html">is_open</a>()) {</div>
|
||||
<div class="line"><span class="lineno"> 388</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
|
||||
<div class="line"><span class="lineno"> 389</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"Unable to open file: "</span> << file_name << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><span class="lineno"> 390</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/program/exit.html">std::exit</a>(EXIT_FAILURE);</div>
|
||||
<div class="line"><span class="lineno"> 391</span> }</div>
|
||||
<div class="line"><span class="lineno"> 392</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::vector<std::valarray<double></a>>> X,</div>
|
||||
<div class="line"><span class="lineno"> 393</span> Y; <span class="comment">// To store X and Y</span></div>
|
||||
<div class="line"><span class="lineno"> 394</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string.html">std::string</a> line; <span class="comment">// To store each line</span></div>
|
||||
<div class="line"><span class="lineno"> 395</span> <span class="comment">// Skip lines</span></div>
|
||||
<div class="line"><span class="lineno"> 396</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < slip_lines; i++) {</div>
|
||||
<div class="line"><span class="lineno"> 397</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string/getline.html">std::getline</a>(in_file, line, <span class="charliteral">'\n'</span>); <span class="comment">// Ignore line</span></div>
|
||||
<div class="line"><span class="lineno"> 398</span> }</div>
|
||||
<div class="line"><span class="lineno"> 399</span> <span class="comment">// While file has information</span></div>
|
||||
<div class="line"><span class="lineno"> 400</span> <span class="keywordflow">while</span> (!in_file.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ios/eof.html">eof</a>() && <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string/getline.html">std::getline</a>(in_file, line, <span class="charliteral">'\n'</span>)) {</div>
|
||||
<div class="line"><span class="lineno"> 401</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/numeric/valarray.html">std::valarray<double></a> x_data,</div>
|
||||
<div class="line"><span class="lineno"> 402</span> y_data; <span class="comment">// To store single sample and label</span></div>
|
||||
<div class="line"><span class="lineno"> 403</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_stringstream.html">std::stringstream</a> ss(line); <span class="comment">// Constructing stringstream from line</span></div>
|
||||
<div class="line"><span class="lineno"> 404</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string.html">std::string</a> token; <span class="comment">// To store each token in line (seprated by ',')</span></div>
|
||||
<div class="line"><span class="lineno"> 405</span> <span class="keywordflow">while</span> (<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string/getline.html">std::getline</a>(ss, token, <span class="charliteral">','</span>)) { <span class="comment">// For each token</span></div>
|
||||
<div class="line"><span class="lineno"> 406</span> <span class="comment">// Insert numerical value of token in x_data</span></div>
|
||||
<div class="line"><span class="lineno"> 407</span> x_data = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a496302e3371aa7b478cb7d5917904bdd">insert_element</a>(x_data, <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string/stof.html">std::stod</a>(token));</div>
|
||||
<div class="line"><span class="lineno"> 408</span> }</div>
|
||||
<div class="line"><span class="lineno"> 409</span> <span class="comment">// If label is in last column</span></div>
|
||||
<div class="line"><span class="lineno"> 410</span> <span class="keywordflow">if</span> (last_label) {</div>
|
||||
<div class="line"><span class="lineno"> 411</span> y_data.resize(this-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.back().neurons);</div>
|
||||
<div class="line"><span class="lineno"> 412</span> <span class="comment">// If task is classification</span></div>
|
||||
<div class="line"><span class="lineno"> 413</span> <span class="keywordflow">if</span> (y_data.size() > 1) {</div>
|
||||
<div class="line"><span class="lineno"> 414</span> y_data[x_data[x_data.size() - 1]] = 1;</div>
|
||||
<div class="line"><span class="lineno"> 415</span> }</div>
|
||||
<div class="line"><span class="lineno"> 416</span> <span class="comment">// If task is regrssion (of single value)</span></div>
|
||||
<div class="line"><span class="lineno"> 417</span> <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><span class="lineno"> 418</span> y_data[0] = x_data[x_data.size() - 1];</div>
|
||||
<div class="line"><span class="lineno"> 419</span> }</div>
|
||||
<div class="line"><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"><span class="lineno"> 421</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><span class="lineno"> 422</span> y_data.resize(this-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.back().neurons);</div>
|
||||
<div class="line"><span class="lineno"> 423</span> <span class="comment">// If task is classification</span></div>
|
||||
<div class="line"><span class="lineno"> 424</span> <span class="keywordflow">if</span> (y_data.size() > 1) {</div>
|
||||
<div class="line"><span class="lineno"> 425</span> y_data[x_data[x_data.size() - 1]] = 1;</div>
|
||||
<div class="line"><span class="lineno"> 426</span> }</div>
|
||||
<div class="line"><span class="lineno"> 427</span> <span class="comment">// If task is regrssion (of single value)</span></div>
|
||||
<div class="line"><span class="lineno"> 428</span> <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><span class="lineno"> 429</span> y_data[0] = x_data[x_data.size() - 1];</div>
|
||||
<div class="line"><span class="lineno"> 430</span> }</div>
|
||||
<div class="line"><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"><span class="lineno"> 432</span> }</div>
|
||||
<div class="line"><span class="lineno"> 433</span> <span class="comment">// Push collected X_data and y_data in X and Y</span></div>
|
||||
<div class="line"><span class="lineno"> 434</span> X.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/push_back.html">push_back</a>({x_data});</div>
|
||||
<div class="line"><span class="lineno"> 435</span> Y.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/push_back.html">push_back</a>({y_data});</div>
|
||||
<div class="line"><span class="lineno"> 436</span> }</div>
|
||||
<div class="line"><span class="lineno"> 437</span> <span class="comment">// Normalize training data if flag is set</span></div>
|
||||
<div class="line"><span class="lineno"> 438</span> <span class="keywordflow">if</span> (normalize) {</div>
|
||||
<div class="line"><span class="lineno"> 439</span> <span class="comment">// Scale data between 0 and 1 using min-max scaler</span></div>
|
||||
<div class="line"><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"><span class="lineno"> 441</span> }</div>
|
||||
<div class="line"><span class="lineno"> 442</span> in_file.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ifstream/close.html">close</a>(); <span class="comment">// Closing file</span></div>
|
||||
<div class="line"><span class="lineno"> 443</span> <span class="keywordflow">return</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/pair/make_pair.html">make_pair</a>(X, Y); <span class="comment">// Return pair of X and Y</span></div>
|
||||
<div class="line"><span class="lineno"> 444</span> }</div>
|
||||
<div class="ttc" id="abasic_ifstream_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/io/basic_ifstream.html">std::ifstream</a></div></div>
|
||||
<div class="ttc" id="abasic_string_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/string/basic_string.html">std::string</a></div></div>
|
||||
<div class="ttc" id="abasic_stringstream_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/io/basic_stringstream.html">std::stringstream</a></div></div>
|
||||
@@ -1073,7 +1079,7 @@ Here is the call graph for this function:</div>
|
||||
<div class="ttc" id="aopen_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/io/basic_ifstream/open.html">std::ifstream::open</a></div><div class="ttdeci">T open(T... args)</div></div>
|
||||
<div class="ttc" id="apush_back_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/container/vector/push_back.html">std::vector::push_back</a></div><div class="ttdeci">T push_back(T... args)</div></div>
|
||||
<div class="ttc" id="astof_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/string/basic_string/stof.html">std::stod</a></div><div class="ttdeci">T stod(T... args)</div></div>
|
||||
<div class="ttc" id="avalarray_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/numeric/valarray.html">std::valarray</a></div></div>
|
||||
<div class="ttc" id="avalarray_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/numeric/valarray.html">std::valarray< double ></a></div></div>
|
||||
</div><!-- fragment --><div class="dynheader">
|
||||
Here is the call graph for this function:</div>
|
||||
<div class="dyncontent">
|
||||
@@ -1113,43 +1119,43 @@ Here is the call graph for this function:</div>
|
||||
</dd>
|
||||
</dl>
|
||||
<dl class="section return"><dt>Returns</dt><dd>instance of <a class="el" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">NeuralNetwork</a> class with pretrained weights </dd></dl>
|
||||
<div class="fragment"><div class="line"><a id="l00732" name="l00732"></a><span class="lineno"> 732</span> {</div>
|
||||
<div class="line"><a id="l00733" name="l00733"></a><span class="lineno"> 733</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ifstream.html">std::ifstream</a> 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.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ifstream/open.html">open</a>(file_name.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string/c_str.html">c_str</a>()); <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.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ifstream/is_open.html">is_open</a>()) {</div>
|
||||
<div class="line"><a id="l00737" name="l00737"></a><span class="lineno"> 737</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
|
||||
<div class="line"><a id="l00738" name="l00738"></a><span class="lineno"> 738</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"Unable to open file: "</span> << file_name << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><a id="l00739" name="l00739"></a><span class="lineno"> 739</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/program/exit.html">std::exit</a>(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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::pair<int, std::string></a>> config; <span class="comment">// To store config</span></div>
|
||||
<div class="line"><a id="l00742" name="l00742"></a><span class="lineno"> 742</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::vector<std::valarray<double></a>>></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 >> 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 < 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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string.html">std::string</a> 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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::valarray<double></a>> kernel;</div>
|
||||
<div class="line"><a id="l00752" name="l00752"></a><span class="lineno"> 752</span> in_file >> neurons >> activation >> shape_a >> 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 < shape_a; r++) {</div>
|
||||
<div class="line"><a id="l00754" name="l00754"></a><span class="lineno"> 754</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/numeric/valarray.html">std::valarray<double></a> 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 < shape_b; c++) {</div>
|
||||
<div class="line"><a id="l00756" name="l00756"></a><span class="lineno"> 756</span> in_file >> 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.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/push_back.html">push_back</a>(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.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/emplace_back.html">emplace_back</a>(<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/pair/make_pair.html">make_pair</a>(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.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/emplace_back.html">emplace_back</a>(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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">"INFO: Model loaded successfully"</span> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><a id="l00765" name="l00765"></a><span class="lineno"> 765</span> in_file.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ifstream/close.html">close</a>(); <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 class="fragment"><div class="line"><span class="lineno"> 732</span> {</div>
|
||||
<div class="line"><span class="lineno"> 733</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ifstream.html">std::ifstream</a> in_file; <span class="comment">// Ifstream to read file</span></div>
|
||||
<div class="line"><span class="lineno"> 734</span> in_file.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ifstream/open.html">open</a>(file_name.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string/c_str.html">c_str</a>()); <span class="comment">// Openinig file</span></div>
|
||||
<div class="line"><span class="lineno"> 735</span> <span class="comment">// If there is any problem in opening file</span></div>
|
||||
<div class="line"><span class="lineno"> 736</span> <span class="keywordflow">if</span> (!in_file.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ifstream/is_open.html">is_open</a>()) {</div>
|
||||
<div class="line"><span class="lineno"> 737</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
|
||||
<div class="line"><span class="lineno"> 738</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"Unable to open file: "</span> << file_name << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><span class="lineno"> 739</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/program/exit.html">std::exit</a>(EXIT_FAILURE);</div>
|
||||
<div class="line"><span class="lineno"> 740</span> }</div>
|
||||
<div class="line"><span class="lineno"> 741</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::pair<int, std::string></a>> config; <span class="comment">// To store config</span></div>
|
||||
<div class="line"><span class="lineno"> 742</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::vector<std::valarray<double></a>>></div>
|
||||
<div class="line"><span class="lineno"> 743</span> kernels; <span class="comment">// To store pretrained kernels</span></div>
|
||||
<div class="line"><span class="lineno"> 744</span> <span class="comment">// Loading model from saved file format</span></div>
|
||||
<div class="line"><span class="lineno"> 745</span> <span class="keywordtype">size_t</span> total_layers = 0;</div>
|
||||
<div class="line"><span class="lineno"> 746</span> in_file >> total_layers;</div>
|
||||
<div class="line"><span class="lineno"> 747</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < total_layers; i++) {</div>
|
||||
<div class="line"><span class="lineno"> 748</span> <span class="keywordtype">int</span> neurons = 0;</div>
|
||||
<div class="line"><span class="lineno"> 749</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string.html">std::string</a> activation;</div>
|
||||
<div class="line"><span class="lineno"> 750</span> <span class="keywordtype">size_t</span> shape_a = 0, shape_b = 0;</div>
|
||||
<div class="line"><span class="lineno"> 751</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector<std::valarray<double></a>> kernel;</div>
|
||||
<div class="line"><span class="lineno"> 752</span> in_file >> neurons >> activation >> shape_a >> shape_b;</div>
|
||||
<div class="line"><span class="lineno"> 753</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> r = 0; r < shape_a; r++) {</div>
|
||||
<div class="line"><span class="lineno"> 754</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/numeric/valarray.html">std::valarray<double></a> row(shape_b);</div>
|
||||
<div class="line"><span class="lineno"> 755</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> c = 0; c < shape_b; c++) {</div>
|
||||
<div class="line"><span class="lineno"> 756</span> in_file >> row[c];</div>
|
||||
<div class="line"><span class="lineno"> 757</span> }</div>
|
||||
<div class="line"><span class="lineno"> 758</span> kernel.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/push_back.html">push_back</a>(row);</div>
|
||||
<div class="line"><span class="lineno"> 759</span> }</div>
|
||||
<div class="line"><span class="lineno"> 760</span> config.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/emplace_back.html">emplace_back</a>(<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/pair/make_pair.html">make_pair</a>(neurons, activation));</div>
|
||||
<div class="line"><span class="lineno"> 761</span> ;</div>
|
||||
<div class="line"><span class="lineno"> 762</span> kernels.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/emplace_back.html">emplace_back</a>(kernel);</div>
|
||||
<div class="line"><span class="lineno"> 763</span> }</div>
|
||||
<div class="line"><span class="lineno"> 764</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">"INFO: Model loaded successfully"</span> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><span class="lineno"> 765</span> in_file.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ifstream/close.html">close</a>(); <span class="comment">// Closing file</span></div>
|
||||
<div class="line"><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"><span class="lineno"> 767</span> config, kernels); <span class="comment">// Return instance of NeuralNetwork class</span></div>
|
||||
<div class="line"><span class="lineno"> 768</span> }</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><!-- fragment --><div class="dynheader">
|
||||
Here is the call graph for this function:</div>
|
||||
@@ -1244,7 +1250,11 @@ Here is the call graph for this function:</div>
|
||||
</dd>
|
||||
</dl>
|
||||
<p >Format in which model is saved:</p>
|
||||
<p >total_layers neurons(1st neural_network::layers::DenseLayer) activation_name(1st <a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">neural_network::layers::DenseLayer</a>) kernel_shape(1st <a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">neural_network::layers::DenseLayer</a>) kernel_values neurons(Nth neural_network::layers::DenseLayer) activation_name(Nth <a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">neural_network::layers::DenseLayer</a>) kernel_shape(Nth <a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">neural_network::layers::DenseLayer</a>) kernel_value</p>
|
||||
<p >total_layers neurons(1st neural_network::layers::DenseLayer) activation_name(1st
|
||||
neural_network::layers::DenseLayer) kernel_shape(1st
|
||||
neural_network::layers::DenseLayer) kernel_values neurons(Nth neural_network::layers::DenseLayer) activation_name(Nth
|
||||
neural_network::layers::DenseLayer) kernel_shape(Nth
|
||||
neural_network::layers::DenseLayer) kernel_value</p>
|
||||
<p >For Example, pretrained model with 3 layers: </p><pre>
|
||||
3
|
||||
4 none
|
||||
@@ -1267,80 +1277,80 @@ Here is the call graph for this function:</div>
|
||||
-2.01336 -0.0219682 1.44145
|
||||
1.72853 -0.465264 -0.705373
|
||||
-0.908409 -0.740547 0.376416
|
||||
</pre><div class="fragment"><div class="line"><a id="l00652" name="l00652"></a><span class="lineno"> 652</span> {</div>
|
||||
<div class="line"><a id="l00653" name="l00653"></a><span class="lineno"> 653</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string.html">std::string</a> file_name = _file_name;</div>
|
||||
<div class="line"><a id="l00654" name="l00654"></a><span class="lineno"> 654</span> <span class="comment">// Adding ".model" 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.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string/find.html">find</a>(<span class="stringliteral">".model"</span>) == file_name.npos) {</div>
|
||||
<div class="line"><a id="l00656" name="l00656"></a><span class="lineno"> 656</span> file_name += <span class="stringliteral">".model"</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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ofstream.html">std::ofstream</a> 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.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ofstream/open.html">open</a>(file_name.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string/c_str.html">c_str</a>(),</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.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ofstream/is_open.html">is_open</a>()) {</div>
|
||||
<div class="line"><a id="l00664" name="l00664"></a><span class="lineno"> 664</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
|
||||
<div class="line"><a id="l00665" name="l00665"></a><span class="lineno"> 665</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"Unable to open file: "</span> << file_name << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><a id="l00666" name="l00666"></a><span class="lineno"> 666</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/program/exit.html">std::exit</a>(EXIT_FAILURE);</div>
|
||||
<div class="line"><a id="l00667" name="l00667"></a><span class="lineno"> 667</span> }<span class="comment"></span></div>
|
||||
<div class="line"><a id="l00668" name="l00668"></a><span class="lineno"> 668</span><span class="comment"> /**</span></div>
|
||||
<div class="line"><a id="l00669" name="l00669"></a><span class="lineno"> 669</span><span class="comment"> Format in which model is saved:</span></div>
|
||||
<div class="line"><a id="l00670" name="l00670"></a><span class="lineno"> 670</span><span class="comment"></span> </div>
|
||||
<div class="line"><a id="l00671" name="l00671"></a><span class="lineno"> 671</span><span class="comment"> total_layers</span></div>
|
||||
<div class="line"><a id="l00672" name="l00672"></a><span class="lineno"> 672</span><span class="comment"> neurons(1st neural_network::layers::DenseLayer) activation_name(1st</span></div>
|
||||
<div class="line"><a id="l00673" name="l00673"></a><span class="lineno"> 673</span><span class="comment"> neural_network::layers::DenseLayer) kernel_shape(1st</span></div>
|
||||
<div class="line"><a id="l00674" name="l00674"></a><span class="lineno"> 674</span><span class="comment"> neural_network::layers::DenseLayer) kernel_values</span></div>
|
||||
<div class="line"><a id="l00675" name="l00675"></a><span class="lineno"> 675</span><span class="comment"> .</span></div>
|
||||
<div class="line"><a id="l00676" name="l00676"></a><span class="lineno"> 676</span><span class="comment"> .</span></div>
|
||||
<div class="line"><a id="l00677" name="l00677"></a><span class="lineno"> 677</span><span class="comment"> .</span></div>
|
||||
<div class="line"><a id="l00678" name="l00678"></a><span class="lineno"> 678</span><span class="comment"> neurons(Nth neural_network::layers::DenseLayer) activation_name(Nth</span></div>
|
||||
<div class="line"><a id="l00679" name="l00679"></a><span class="lineno"> 679</span><span class="comment"> neural_network::layers::DenseLayer) kernel_shape(Nth</span></div>
|
||||
<div class="line"><a id="l00680" name="l00680"></a><span class="lineno"> 680</span><span class="comment"> neural_network::layers::DenseLayer) kernel_value</span></div>
|
||||
<div class="line"><a id="l00681" name="l00681"></a><span class="lineno"> 681</span><span class="comment"></span> </div>
|
||||
<div class="line"><a id="l00682" name="l00682"></a><span class="lineno"> 682</span><span class="comment"> For Example, pretrained model with 3 layers:</span></div>
|
||||
<div class="line"><a id="l00683" name="l00683"></a><span class="lineno"> 683</span><span class="comment"> <pre></span></div>
|
||||
<div class="line"><a id="l00684" name="l00684"></a><span class="lineno"> 684</span><span class="comment"> 3</span></div>
|
||||
<div class="line"><a id="l00685" name="l00685"></a><span class="lineno"> 685</span><span class="comment"> 4 none</span></div>
|
||||
<div class="line"><a id="l00686" name="l00686"></a><span class="lineno"> 686</span><span class="comment"> 4 4</span></div>
|
||||
<div class="line"><a id="l00687" name="l00687"></a><span class="lineno"> 687</span><span class="comment"> 1 0 0 0</span></div>
|
||||
<div class="line"><a id="l00688" name="l00688"></a><span class="lineno"> 688</span><span class="comment"> 0 1 0 0</span></div>
|
||||
<div class="line"><a id="l00689" name="l00689"></a><span class="lineno"> 689</span><span class="comment"> 0 0 1 0</span></div>
|
||||
<div class="line"><a id="l00690" name="l00690"></a><span class="lineno"> 690</span><span class="comment"> 0 0 0 1</span></div>
|
||||
<div class="line"><a id="l00691" name="l00691"></a><span class="lineno"> 691</span><span class="comment"> 6 relu</span></div>
|
||||
<div class="line"><a id="l00692" name="l00692"></a><span class="lineno"> 692</span><span class="comment"> 4 6</span></div>
|
||||
<div class="line"><a id="l00693" name="l00693"></a><span class="lineno"> 693</span><span class="comment"> -1.88963 -3.61165 1.30757 -0.443906 -2.41039 -2.69653</span></div>
|
||||
<div class="line"><a id="l00694" name="l00694"></a><span class="lineno"> 694</span><span class="comment"> -0.684753 0.0891452 0.795294 -2.39619 2.73377 0.318202</span></div>
|
||||
<div class="line"><a id="l00695" name="l00695"></a><span class="lineno"> 695</span><span class="comment"> -2.91451 -4.43249 -0.804187 2.51995 -6.97524 -1.07049</span></div>
|
||||
<div class="line"><a id="l00696" name="l00696"></a><span class="lineno"> 696</span><span class="comment"> -0.571531 -1.81689 -1.24485 1.92264 -2.81322 1.01741</span></div>
|
||||
<div class="line"><a id="l00697" name="l00697"></a><span class="lineno"> 697</span><span class="comment"> 3 sigmoid</span></div>
|
||||
<div class="line"><a id="l00698" name="l00698"></a><span class="lineno"> 698</span><span class="comment"> 6 3</span></div>
|
||||
<div class="line"><a id="l00699" name="l00699"></a><span class="lineno"> 699</span><span class="comment"> 0.390267 -0.391703 -0.0989607</span></div>
|
||||
<div class="line"><a id="l00700" name="l00700"></a><span class="lineno"> 700</span><span class="comment"> 0.499234 -0.564539 -0.28097</span></div>
|
||||
<div class="line"><a id="l00701" name="l00701"></a><span class="lineno"> 701</span><span class="comment"> 0.553386 -0.153974 -1.92493</span></div>
|
||||
<div class="line"><a id="l00702" name="l00702"></a><span class="lineno"> 702</span><span class="comment"> -2.01336 -0.0219682 1.44145</span></div>
|
||||
<div class="line"><a id="l00703" name="l00703"></a><span class="lineno"> 703</span><span class="comment"> 1.72853 -0.465264 -0.705373</span></div>
|
||||
<div class="line"><a id="l00704" name="l00704"></a><span class="lineno"> 704</span><span class="comment"> -0.908409 -0.740547 0.376416</span></div>
|
||||
<div class="line"><a id="l00705" name="l00705"></a><span class="lineno"> 705</span><span class="comment"> </pre></span></div>
|
||||
<div class="line"><a id="l00706" name="l00706"></a><span class="lineno"> 706</span><span class="comment"> */</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 << <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 << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</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> &layer : this-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>) {</div>
|
||||
<div class="line"><a id="l00711" name="l00711"></a><span class="lineno"> 711</span> out_file << layer.neurons << <span class="charliteral">' '</span> << layer.activation << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</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 << shape.first << <span class="charliteral">' '</span> << shape.second << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</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> &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> &val : row) {</div>
|
||||
<div class="line"><a id="l00716" name="l00716"></a><span class="lineno"> 716</span> out_file << val << <span class="charliteral">' '</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 << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">"INFO: Model saved successfully with name : "</span>;</div>
|
||||
<div class="line"><a id="l00722" name="l00722"></a><span class="lineno"> 722</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << file_name << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><a id="l00723" name="l00723"></a><span class="lineno"> 723</span> out_file.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ofstream/close.html">close</a>(); <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>
|
||||
</pre><div class="fragment"><div class="line"><span class="lineno"> 652</span> {</div>
|
||||
<div class="line"><span class="lineno"> 653</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string.html">std::string</a> file_name = _file_name;</div>
|
||||
<div class="line"><span class="lineno"> 654</span> <span class="comment">// Adding ".model" extension if it is not already there in name</span></div>
|
||||
<div class="line"><span class="lineno"> 655</span> <span class="keywordflow">if</span> (file_name.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string/find.html">find</a>(<span class="stringliteral">".model"</span>) == file_name.npos) {</div>
|
||||
<div class="line"><span class="lineno"> 656</span> file_name += <span class="stringliteral">".model"</span>;</div>
|
||||
<div class="line"><span class="lineno"> 657</span> }</div>
|
||||
<div class="line"><span class="lineno"> 658</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ofstream.html">std::ofstream</a> out_file; <span class="comment">// Ofstream to write in file</span></div>
|
||||
<div class="line"><span class="lineno"> 659</span> <span class="comment">// Open file in out|trunc mode</span></div>
|
||||
<div class="line"><span class="lineno"> 660</span> out_file.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ofstream/open.html">open</a>(file_name.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/string/basic_string/c_str.html">c_str</a>(),</div>
|
||||
<div class="line"><span class="lineno"> 661</span> std::ofstream::out | std::ofstream::trunc);</div>
|
||||
<div class="line"><span class="lineno"> 662</span> <span class="comment">// If there is any problem in opening file</span></div>
|
||||
<div class="line"><span class="lineno"> 663</span> <span class="keywordflow">if</span> (!out_file.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ofstream/is_open.html">is_open</a>()) {</div>
|
||||
<div class="line"><span class="lineno"> 664</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
|
||||
<div class="line"><span class="lineno"> 665</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> << <span class="stringliteral">"Unable to open file: "</span> << file_name << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><span class="lineno"> 666</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/program/exit.html">std::exit</a>(EXIT_FAILURE);</div>
|
||||
<div class="line"><span class="lineno"> 667</span> }<span class="comment"></span></div>
|
||||
<div class="line"><span class="lineno"> 668</span><span class="comment"> /**</span></div>
|
||||
<div class="line"><span class="lineno"> 669</span><span class="comment"> Format in which model is saved:</span></div>
|
||||
<div class="line"><span class="lineno"> 670</span><span class="comment"></span> </div>
|
||||
<div class="line"><span class="lineno"> 671</span><span class="comment"> total_layers</span></div>
|
||||
<div class="line"><span class="lineno"> 672</span><span class="comment"> neurons(1st neural_network::layers::DenseLayer) activation_name(1st</span></div>
|
||||
<div class="line"><span class="lineno"> 673</span><span class="comment"> neural_network::layers::DenseLayer) kernel_shape(1st</span></div>
|
||||
<div class="line"><span class="lineno"> 674</span><span class="comment"> neural_network::layers::DenseLayer) kernel_values</span></div>
|
||||
<div class="line"><span class="lineno"> 675</span><span class="comment"> .</span></div>
|
||||
<div class="line"><span class="lineno"> 676</span><span class="comment"> .</span></div>
|
||||
<div class="line"><span class="lineno"> 677</span><span class="comment"> .</span></div>
|
||||
<div class="line"><span class="lineno"> 678</span><span class="comment"> neurons(Nth neural_network::layers::DenseLayer) activation_name(Nth</span></div>
|
||||
<div class="line"><span class="lineno"> 679</span><span class="comment"> neural_network::layers::DenseLayer) kernel_shape(Nth</span></div>
|
||||
<div class="line"><span class="lineno"> 680</span><span class="comment"> neural_network::layers::DenseLayer) kernel_value</span></div>
|
||||
<div class="line"><span class="lineno"> 681</span><span class="comment"></span> </div>
|
||||
<div class="line"><span class="lineno"> 682</span><span class="comment"> For Example, pretrained model with 3 layers:</span></div>
|
||||
<div class="line"><span class="lineno"> 683</span><span class="comment"> <pre></span></div>
|
||||
<div class="line"><span class="lineno"> 684</span><span class="comment"> 3</span></div>
|
||||
<div class="line"><span class="lineno"> 685</span><span class="comment"> 4 none</span></div>
|
||||
<div class="line"><span class="lineno"> 686</span><span class="comment"> 4 4</span></div>
|
||||
<div class="line"><span class="lineno"> 687</span><span class="comment"> 1 0 0 0</span></div>
|
||||
<div class="line"><span class="lineno"> 688</span><span class="comment"> 0 1 0 0</span></div>
|
||||
<div class="line"><span class="lineno"> 689</span><span class="comment"> 0 0 1 0</span></div>
|
||||
<div class="line"><span class="lineno"> 690</span><span class="comment"> 0 0 0 1</span></div>
|
||||
<div class="line"><span class="lineno"> 691</span><span class="comment"> 6 relu</span></div>
|
||||
<div class="line"><span class="lineno"> 692</span><span class="comment"> 4 6</span></div>
|
||||
<div class="line"><span class="lineno"> 693</span><span class="comment"> -1.88963 -3.61165 1.30757 -0.443906 -2.41039 -2.69653</span></div>
|
||||
<div class="line"><span class="lineno"> 694</span><span class="comment"> -0.684753 0.0891452 0.795294 -2.39619 2.73377 0.318202</span></div>
|
||||
<div class="line"><span class="lineno"> 695</span><span class="comment"> -2.91451 -4.43249 -0.804187 2.51995 -6.97524 -1.07049</span></div>
|
||||
<div class="line"><span class="lineno"> 696</span><span class="comment"> -0.571531 -1.81689 -1.24485 1.92264 -2.81322 1.01741</span></div>
|
||||
<div class="line"><span class="lineno"> 697</span><span class="comment"> 3 sigmoid</span></div>
|
||||
<div class="line"><span class="lineno"> 698</span><span class="comment"> 6 3</span></div>
|
||||
<div class="line"><span class="lineno"> 699</span><span class="comment"> 0.390267 -0.391703 -0.0989607</span></div>
|
||||
<div class="line"><span class="lineno"> 700</span><span class="comment"> 0.499234 -0.564539 -0.28097</span></div>
|
||||
<div class="line"><span class="lineno"> 701</span><span class="comment"> 0.553386 -0.153974 -1.92493</span></div>
|
||||
<div class="line"><span class="lineno"> 702</span><span class="comment"> -2.01336 -0.0219682 1.44145</span></div>
|
||||
<div class="line"><span class="lineno"> 703</span><span class="comment"> 1.72853 -0.465264 -0.705373</span></div>
|
||||
<div class="line"><span class="lineno"> 704</span><span class="comment"> -0.908409 -0.740547 0.376416</span></div>
|
||||
<div class="line"><span class="lineno"> 705</span><span class="comment"> </pre></span></div>
|
||||
<div class="line"><span class="lineno"> 706</span><span class="comment"> */</span></div>
|
||||
<div class="line"><span class="lineno"> 707</span> <span class="comment">// Saving model in the same format</span></div>
|
||||
<div class="line"><span class="lineno"> 708</span> out_file << <a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>.size();</div>
|
||||
<div class="line"><span class="lineno"> 709</span> out_file << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><span class="lineno"> 710</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> &layer : this-><a class="code hl_namespace" href="../../d5/d2c/namespacelayers.html">layers</a>) {</div>
|
||||
<div class="line"><span class="lineno"> 711</span> out_file << layer.neurons << <span class="charliteral">' '</span> << layer.activation << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><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"><span class="lineno"> 713</span> out_file << shape.first << <span class="charliteral">' '</span> << shape.second << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><span class="lineno"> 714</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> &row : layer.kernel) {</div>
|
||||
<div class="line"><span class="lineno"> 715</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> &val : row) {</div>
|
||||
<div class="line"><span class="lineno"> 716</span> out_file << val << <span class="charliteral">' '</span>;</div>
|
||||
<div class="line"><span class="lineno"> 717</span> }</div>
|
||||
<div class="line"><span class="lineno"> 718</span> out_file << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><span class="lineno"> 719</span> }</div>
|
||||
<div class="line"><span class="lineno"> 720</span> }</div>
|
||||
<div class="line"><span class="lineno"> 721</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">"INFO: Model saved successfully with name : "</span>;</div>
|
||||
<div class="line"><span class="lineno"> 722</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << file_name << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><span class="lineno"> 723</span> out_file.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ofstream/close.html">close</a>(); <span class="comment">// Closing file</span></div>
|
||||
<div class="line"><span class="lineno"> 724</span> <span class="keywordflow">return</span>;</div>
|
||||
<div class="line"><span class="lineno"> 725</span> }</div>
|
||||
<div class="ttc" id="abasic_ofstream_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/io/basic_ofstream.html">std::ofstream</a></div></div>
|
||||
<div class="ttc" id="afind_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/string/basic_string/find.html">std::string::find</a></div><div class="ttdeci">T find(T... args)</div></div>
|
||||
</div><!-- fragment --><div class="dynheader">
|
||||
@@ -1382,12 +1392,12 @@ Here is the call graph for this function:</div>
|
||||
</dd>
|
||||
</dl>
|
||||
<dl class="section return"><dt>Returns</dt><dd>returns predictions as vector </dd></dl>
|
||||
<div class="fragment"><div class="line"><a id="l00452" name="l00452"></a><span class="lineno"> 452</span> {</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-><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 class="fragment"><div class="line"><span class="lineno"> 452</span> {</div>
|
||||
<div class="line"><span class="lineno"> 453</span> <span class="comment">// Get activations of all layers</span></div>
|
||||
<div class="line"><span class="lineno"> 454</span> <span class="keyword">auto</span> <a class="code hl_namespace" href="../../d5/d39/namespaceactivations.html">activations</a> = this-><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"><span class="lineno"> 455</span> <span class="comment">// Return activations of last layer (actual predicted values)</span></div>
|
||||
<div class="line"><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"><span class="lineno"> 457</span> }</div>
|
||||
</div><!-- fragment --><div class="dynheader">
|
||||
Here is the call graph for this function:</div>
|
||||
<div class="dyncontent">
|
||||
@@ -1420,30 +1430,30 @@ Here is the call graph for this function:</div>
|
||||
</table>
|
||||
</div><div class="memdoc">
|
||||
<p >Function to print summary of the network. </p>
|
||||
<div class="fragment"><div class="line"><a id="l00773" name="l00773"></a><span class="lineno"> 773</span> {</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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a></div>
|
||||
<div class="line"><a id="l00776" name="l00776"></a><span class="lineno"> 776</span> << <span class="stringliteral">"==============================================================="</span></div>
|
||||
<div class="line"><a id="l00777" name="l00777"></a><span class="lineno"> 777</span> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><a id="l00778" name="l00778"></a><span class="lineno"> 778</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">"\t\t+ MODEL SUMMARY +\t\t\n"</span>;</div>
|
||||
<div class="line"><a id="l00779" name="l00779"></a><span class="lineno"> 779</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a></div>
|
||||
<div class="line"><a id="l00780" name="l00780"></a><span class="lineno"> 780</span> << <span class="stringliteral">"==============================================================="</span></div>
|
||||
<div class="line"><a id="l00781" name="l00781"></a><span class="lineno"> 781</span> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</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 <= <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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << i << <span class="stringliteral">")"</span>;</div>
|
||||
<div class="line"><a id="l00784" name="l00784"></a><span class="lineno"> 784</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">" Neurons : "</span></div>
|
||||
<div class="line"><a id="l00785" name="l00785"></a><span class="lineno"> 785</span> << <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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">", Activation : "</span></div>
|
||||
<div class="line"><a id="l00787" name="l00787"></a><span class="lineno"> 787</span> << <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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">", kernel Shape : "</span></div>
|
||||
<div class="line"><a id="l00789" name="l00789"></a><span class="lineno"> 789</span> << <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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a></div>
|
||||
<div class="line"><a id="l00793" name="l00793"></a><span class="lineno"> 793</span> << <span class="stringliteral">"==============================================================="</span></div>
|
||||
<div class="line"><a id="l00794" name="l00794"></a><span class="lineno"> 794</span> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</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 class="fragment"><div class="line"><span class="lineno"> 773</span> {</div>
|
||||
<div class="line"><span class="lineno"> 774</span> <span class="comment">// Printing Summary</span></div>
|
||||
<div class="line"><span class="lineno"> 775</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a></div>
|
||||
<div class="line"><span class="lineno"> 776</span> << <span class="stringliteral">"==============================================================="</span></div>
|
||||
<div class="line"><span class="lineno"> 777</span> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><span class="lineno"> 778</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">"\t\t+ MODEL SUMMARY +\t\t\n"</span>;</div>
|
||||
<div class="line"><span class="lineno"> 779</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a></div>
|
||||
<div class="line"><span class="lineno"> 780</span> << <span class="stringliteral">"==============================================================="</span></div>
|
||||
<div class="line"><span class="lineno"> 781</span> << <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
|
||||
<div class="line"><span class="lineno"> 782</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 1; i <= <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"><span class="lineno"> 783</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << i << <span class="stringliteral">")"</span>;</div>
|
||||
<div class="line"><span class="lineno"> 784</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> << <span class="stringliteral">" Neurons : "</span></div>
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</a>
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Before Width: | Height: | Size: 15 KiB After Width: | Height: | Size: 15 KiB |
Reference in New Issue
Block a user