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Morten Hjorth-Jensen
2023-11-21 06:16:54 +01:00
parent a854a3e9c0
commit 06b09eb681
173 changed files with 13894 additions and 3718 deletions
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doc/LectureNotes/_build/html/week43.html
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@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
Week 45, Recurrent Neural Networks
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="week46.html">
Week 46: Decision Trees, Ensemble methods and Random Forests
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="week47.html">
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
</a>
</li>
</ul>
<p aria-level="2" class="caption" role="heading">
<span class="caption-text">
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
</li>
<li class="toctree-l1">
<a class="reference internal" href="project2.html">
Project 2 on Machine Learning, deadline November 13 (Midnight)
Project 2 on Machine Learning, deadline November 17 (Midnight)
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="project3.html">
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
</a>
</li>
</ul>
@@ -1888,8 +1903,9 @@ Accuracy score on data set: 0.5
Learning rate = 0.0001
Lambda = 0.001
Accuracy score on data set: 0.5
Learning rate = 0.0001
</pre></div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.0001
Lambda = 0.01
Accuracy score on data set: 0.5
@@ -1900,9 +1916,8 @@ Accuracy score on data set: 0.5
Learning rate = 0.0001
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 = 0.0001
Learning rate = 0.0001
Lambda = 10.0
Accuracy score on data set: 0.5
@@ -6374,8 +6389,9 @@ case.</p>
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<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
[----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</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
</pre></div>
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<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
@@ -18843,8 +18859,9 @@ 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
predictions = (n_inputs) = (1437,)
</pre></div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>predictions = (n_inputs) = (1437,)
prediction for image 0: 8
correct label for image 0: 6
</pre></div>