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Morten Hjorth-Jensen
2023-11-08 15:57:33 +01:00
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doc/LectureNotes/_build/html/week43.html
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@@ -55,7 +55,7 @@ const thebe_selector_output = ".output, .cell_output"
<script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
<link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" />
<link rel="next" title="Project 1 on Machine Learning, deadline October 9 (midnight), 2023" href="project1.html" />
<link rel="next" title="Week 44, Convolutional Neural Networks (CNN)" href="week44.html" />
<link rel="prev" title="Exercises weeks 43 and 44" href="exercisesweek43.html" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="docsearch:language" content="None">
@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="week44.html">
Week 44, Convolutional Neural Networks (CNN)
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="week45.html">
Week 45, Recurrent Neural Networks
</a>
</li>
</ul>
<p aria-level="2" class="caption" role="heading">
<span class="caption-text">
@@ -1862,9 +1872,8 @@ Accuracy score on data set: 0.5
Learning rate = 1e-05
Lambda = 1.0
Accuracy score on data set: 0.5
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
Learning rate = 1e-05
Lambda = 10.0
Accuracy score on data set: 0.5
@@ -1891,8 +1900,9 @@ Accuracy score on data set: 0.5
Learning rate = 0.0001
Lambda = 1.0
Accuracy score on data set: 0.5
Learning rate = 0.0001
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.0001
Lambda = 10.0
Accuracy score on data set: 0.5
@@ -6364,9 +6374,8 @@ case.</p>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Adam: Eta=0.001, Lambda=0
</pre></div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
[----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.1000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
@@ -18834,12 +18843,9 @@ This is then passed through the activation:</p>
1.10378326e-04 5.08318298e-09 2.03256632e-04 1.92507116e-03
9.84443254e-01 3.11507992e-04]
probabilities sum up to: 1.0
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>predictions = (n_inputs) = (1437,)
</pre></div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>prediction for image 0: 8
predictions = (n_inputs) = (1437,)
prediction for image 0: 8
correct label for image 0: 6
</pre></div>
</div>
@@ -22318,10 +22324,10 @@ g(x,t) = \sin(\pi x)\cos(\pi t) - \sin(\pi x)\sin(\pi t)
<p class="prev-next-title">Exercises weeks 43 and 44</p>
</div>
</a>
<a class='right-next' id="next-link" href="project1.html" title="next page">
<a class='right-next' id="next-link" href="week44.html" title="next page">
<div class="prev-next-info">
<p class="prev-next-subtitle">next</p>
<p class="prev-next-title">Project 1 on Machine Learning, deadline October 9 (midnight), 2023</p>
<p class="prev-next-title">Week 44, Convolutional Neural Networks (CNN)</p>
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</a>