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Morten Hjorth-Jensen
2024-10-21 13:45:48 +02:00
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doc/LectureNotes/_build/html/week35.html
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@@ -343,6 +343,11 @@ const thebe_selector_output = ".output, .cell_output"
Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
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<a class="reference internal" href="exercisesweek43.html">
Exercises week 43
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@@ -1691,7 +1696,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9960490331171794
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9951746722640107
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@@ -1708,7 +1713,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.008128643056242772
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.011011355570628998
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@@ -1723,23 +1728,23 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.0414517 0.01527618 0.00673693 0.03003494 0.03248618 0.00283324
0.03532164 0.07082844 0.05655534 0.00080345 0.00157346 0.004578
0.0581406 0.01563358 0.06919343 0.00761546 0.00914784 0.05549907
0.01149386 0.0110602 0.0271259 0.05232855 0.03351257 0.05334469
0.0089987 0.01442817 0.00573681 0.00930457 0.00443629 0.06034627
0.03612962 0.00485232 0.03143856 0.01643952 0.02448011 0.04006242
0.01018465 0.02483712 0.02375066 0.05793564 0.01317824 0.02079774
0.09129027 0.02990404 0.03601781 0.03065856 0.00210569 0.02038063
0.01455301 0.00303686 0.0016202 0.05321642 0.00512149 0.01263474
0.0260728 0.04836325 0.00520752 0.00059681 0.00067567 0.02206393
0.03484946 0.08516604 0.00777619 0.0021452 0.03843478 0.00952138
0.07036197 0.0482407 0.01746176 0.00458083 0.01525387 0.01671659
0.03032534 0.02015755 0.02109876 0.01873823 0.01950331 0.02336409
0.02234283 0.06037137 0.01105813 0.06082594 0.00288198 0.01532072
0.00473744 0.03710736 0.0211042 0.00896253 0.02036067 0.07259548
0.01074493 0.02028151 0.00893649 0.01709886 0.00821525 0.01655001
0.05105586 0.01908329 0.06069047 0.00213716]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.04642309 0.0279278 0.0228686 0.0650014 0.01290661 0.01519201
0.0016396 0.05684345 0.06249397 0.00778835 0.00518242 0.02159357
0.00421124 0.01267244 0.02280587 0.00184147 0.07626658 0.02041607
0.03238421 0.02937931 0.04215521 0.03179975 0.00508636 0.04983222
0.0705828 0.00555576 0.02882718 0.00046916 0.00861192 0.04145287
0.02255993 0.00567103 0.02731257 0.02307403 0.02643023 0.03374269
0.02728257 0.00045414 0.01269348 0.01433606 0.0031986 0.00813523
0.01677512 0.02132304 0.02971554 0.02671209 0.03110579 0.00701382
0.0281646 0.01167316 0.00049905 0.01368052 0.01667926 0.00935737
0.02496143 0.08648837 0.01394224 0.04394742 0.00582904 0.06185995
0.03162926 0.05276967 0.01028953 0.05735741 0.01571214 0.02287532
0.01947663 0.00861563 0.00414684 0.00858857 0.0036035 0.00523549
0.01608796 0.03520118 0.02959231 0.00068056 0.02717123 0.02989838
0.01203668 0.02622061 0.0109944 0.09258408 0.03607387 0.01775486
0.08931681 0.00514007 0.03450725 0.03465317 0.01712306 0.00757359
0.02069038 0.02243626 0.01508864 0.03202289 0.06776056 0.01054406
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@@ -1808,15 +1813,15 @@ but now splitting the data into a training set and a test set.</p>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 1.93967766 0.96157921 2.62927207 1.64642277 -0.14580867]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 1.99804178 -0.16533342 5.68321093 -0.84401704 0.35781308]
Training R2
0.9957680433056965
0.9960664320362111
Training MSE
0.009277782622954527
0.008377169630073601
Test R2
0.9959177108554428
0.9944299733827195
Test MSE
0.01153294477941533
0.01282028141098116
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