update
This commit is contained in:
Morten Hjorth-Jensen
@@ -6,11 +6,11 @@ edge [fontname="helvetica"] ;
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0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
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2 [label="worst concave points <= 0.135\ngini = 0.031\nsamples = 253\nvalue = [[249, 4]\n[4, 249]]", fillcolor="#e78946"] ;
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1 -> 2 ;
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3 [label="area error <= 48.975\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e5833c"] ;
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3 [label="radius error <= 0.643\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e5833c"] ;
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2 -> 3 ;
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4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139"] ;
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3 -> 4 ;
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5 [label="radius error <= 0.688\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ;
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5 [label="worst area <= 566.55\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ;
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6 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139"] ;
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@@ -34,7 +34,7 @@ edge [fontname="helvetica"] ;
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16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139"] ;
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17 [label="area error <= 23.16\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ;
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17 [label="radius error <= 0.251\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ;
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18 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
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@@ -48,10 +48,10 @@ edge [fontname="helvetica"] ;
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23 [label="gini = 0.0\nsamples = 6\nvalue = [[6, 0]\n[0, 6]]", fillcolor="#e58139"] ;
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21 -> 23 ;
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24 [label="mean smoothness <= 0.079\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ;
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24 [label="fractal dimension error <= 0.013\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ;
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20 -> 24 ;
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25 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
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25 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139"] ;
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26 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139"] ;
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}
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@@ -0,0 +1,229 @@
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{
|
||||
"cells": [
|
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{
|
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"cell_type": "markdown",
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"id": "30071d16",
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"metadata": {
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"editable": true
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},
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"source": [
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"<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
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"doconce format html exercisesweek48.do.txt -->\n",
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||||
"<!-- dom:TITLE: Exercises week 48 -->"
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]
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||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "268eaea0",
|
||||
"metadata": {
|
||||
"editable": true
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||||
},
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"source": [
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"# Exercises week 48\n",
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"**November 25-29, 2024**\n",
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"\n",
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||||
"Date: **Deadline is Friday November 29 at midnight**"
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]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1e04cbee",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
"source": [
|
||||
"# Overarching aims of the exercises this week\n",
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||||
"\n",
|
||||
"The exercise this week is a simple course survey and feedback. This\n",
|
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"is important for us in order to improve our teaching material, the\n",
|
||||
"active learning format and anything else related to a succesful\n",
|
||||
"mastering of central machine learning methods and their applications."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "78363328",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
"source": [
|
||||
"### Why did you choose this course?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d4950ede",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
"source": [
|
||||
"### What was your programming knowledge before you started?\n",
|
||||
"\n",
|
||||
"And do you feel this course added to your programming competences and skills?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "95e23f0c",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
"source": [
|
||||
"### How do you judge your own level of knowledge on machine learning before and after this course?\n",
|
||||
"\n",
|
||||
"Here you can discuss your level of skill/knowledge at the start of course\n",
|
||||
"and at the end of the course and how these matched the level of\n",
|
||||
"skill/knowledge needed to complete the projects."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "db819721",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
"source": [
|
||||
"### Did the projects and the teaching material allow you to deepen your insights about Machine Learning methods?\n",
|
||||
"\n",
|
||||
"Feel free to comment here."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "61d153a4",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
"source": [
|
||||
"### Project based teaching and active learning\n",
|
||||
"\n",
|
||||
"This is a project based course and we as teachers would like to keep\n",
|
||||
"it as it is since we see very clearly that people who attend this\n",
|
||||
"course have a very good learning outcome. Project based courses are\n",
|
||||
"however demanding (and expensive seen from the university admin) when\n",
|
||||
"it comes to proper feedback and evaluations. Feel free to discuss\n",
|
||||
"whether you found a project-based useful. Feel also free to comment upon things we can improve upon or\n",
|
||||
"alternative ways to assess whether the learning outcomes have been\n",
|
||||
"achieved. Would you for example prefer a standard 4 hours written exam\n",
|
||||
"be something you would prefer? Or other alternatives to projects? We\n",
|
||||
"would very much value your thoughts here since projects are an\n",
|
||||
"essential part of this course."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ee1af3ff",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
"source": [
|
||||
"### Usefulness of the weekly exercises\n",
|
||||
"\n",
|
||||
"Did the weekly exercises help in getting started with the projects?\n",
|
||||
"How relevant where they for solving the projects? Feel free to\n",
|
||||
"elaborate"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "46a35899",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
"source": [
|
||||
"### Lab sessions and lectures\n",
|
||||
"\n",
|
||||
"Was there a good link between lectures and lab sessions?\n",
|
||||
"Feel\n",
|
||||
"free to comment."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "f6524f03",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
"source": [
|
||||
"### How would you improve this course?\n",
|
||||
"\n",
|
||||
"Are there topics which are missing, topics which could have been\n",
|
||||
"omitted and/or discussed in more depth? Feel free to add your comments\n",
|
||||
"here such as how to improve to teaching material and more."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ab89b772",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
"source": [
|
||||
"## Then some basic questions"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "9811e1b4",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
"source": [
|
||||
"### Which is your preferred information chanel, Canvas, Discord, mail or other?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "6a3741fa",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
"source": [
|
||||
"### Was the weekly update with plans etc useful?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a51df0b9",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
"source": [
|
||||
"### Was it easy to access the course material?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "762d5153",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
"source": [
|
||||
"### Which resources and tools did you use? Jupyter-notebooks, GitHub, the various textbooks we have recommended, etc etc"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "2bf11aae",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
"source": [
|
||||
"### If you did not attend the lectures or the lab sessions, which resources did you use?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3f92f5ff",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
"source": [
|
||||
"### Any other topics, impressions, ideas etc you would like to share with us?"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@@ -257,6 +257,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
<li class="toctree-l1"><a class="reference internal" href="week48.html">Week 48: Gradient boosting and summary of course</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek48.html">Exercises week 48</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
@@ -257,6 +257,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
<li class="toctree-l1"><a class="reference internal" href="week48.html">Week 48: Gradient boosting and summary of course</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek48.html">Exercises week 48</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
@@ -730,7 +733,7 @@ Thereafter we wish to apply it to data which were not included in the training.
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/7208bf883e220945a23bf4a64d7c474c22032a9479d0230f3f7627fde6c870e3.png" src="_images/7208bf883e220945a23bf4a64d7c474c22032a9479d0230f3f7627fde6c870e3.png" />
|
||||
<img alt="_images/01d44d8b3777894a56df96cdebd9411c2a9b9484e40698be0bb84652c7d0f7cf.png" src="_images/01d44d8b3777894a56df96cdebd9411c2a9b9484e40698be0bb84652c7d0f7cf.png" />
|
||||
</div>
|
||||
</div>
|
||||
<p>This example serves several aims. It allows us to demonstrate several
|
||||
@@ -815,7 +818,7 @@ to be dominated by outliers.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/daf635918e102d6af1572d0f6fcb2bcd9c93425cd41e91dc85022a813e3b1da5.png" src="_images/daf635918e102d6af1572d0f6fcb2bcd9c93425cd41e91dc85022a813e3b1da5.png" />
|
||||
<img alt="_images/326bc2bdd25593d0947bbe11be557f7c976fd425b872153c8c8317ebbb51c5ae.png" src="_images/326bc2bdd25593d0947bbe11be557f7c976fd425b872153c8c8317ebbb51c5ae.png" />
|
||||
</div>
|
||||
</div>
|
||||
<p>Depending on the parameter in front of the normal distribution, we may
|
||||
@@ -862,16 +865,16 @@ example of the functionality of <strong>Scikit-Learn</strong>.</p>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The intercept alpha:
|
||||
[1.97386121]
|
||||
[2.00949975]
|
||||
Coefficient beta :
|
||||
[[5.12574106]]
|
||||
Mean squared error: 0.20
|
||||
Variance score: 0.92
|
||||
Mean squared log error: 0.01
|
||||
Mean absolute error: 0.36
|
||||
[[5.07244691]]
|
||||
Mean squared error: 0.30
|
||||
Variance score: 0.88
|
||||
Mean squared log error: 0.02
|
||||
Mean absolute error: 0.42
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/1168b639886622ca16f51897f200d03b2de325bfc3f6688b4b75ff4473160f59.png" src="_images/1168b639886622ca16f51897f200d03b2de325bfc3f6688b4b75ff4473160f59.png" />
|
||||
<img alt="_images/b39b8ed8a4aed5f4a94d2137c267e12d64d23f66d1c50c3e3ad1ae8cd8b9ff5e.png" src="_images/b39b8ed8a4aed5f4a94d2137c267e12d64d23f66d1c50c3e3ad1ae8cd8b9ff5e.png" />
|
||||
</div>
|
||||
</div>
|
||||
<p>The function <strong>coef</strong> gives us the parameter <span class="math notranslate nohighlight">\(\beta\)</span> of our fit while <strong>intercept</strong> yields
|
||||
@@ -967,8 +970,8 @@ a linear <span class="math notranslate nohighlight">\(x\)</span>-dependence we s
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/2e42f972e6bd064ce2062209501a6f469d0ebd090cf62a0159f888f2deaa3de7.png" src="_images/2e42f972e6bd064ce2062209501a6f469d0ebd090cf62a0159f888f2deaa3de7.png" />
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.004999999999999993
|
||||
<img alt="_images/99bd4e87bbb54a5fb018ef916c872e3158711414d535c467a50d947ace53c954.png" src="_images/99bd4e87bbb54a5fb018ef916c872e3158711414d535c467a50d947ace53c954.png" />
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.005000000000000009
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -257,6 +257,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
<li class="toctree-l1"><a class="reference internal" href="week48.html">Week 48: Gradient boosting and summary of course</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek48.html">Exercises week 48</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
@@ -935,8 +938,9 @@ probability that image 0 is in category 0,1,2,...,9 =
|
||||
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>
|
||||
</div>
|
||||
<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>
|
||||
@@ -1114,7 +1118,7 @@ the <em>Hadamard product</em>, meaning element-wise multiplication.</p>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Old accuracy on training data: 0.1440501043841336
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58742/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1448,7 +1452,7 @@ Lambda = 10.0
|
||||
Accuracy score on test set: 0.19166666666666668
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58742/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1457,7 +1461,7 @@ Lambda = 1e-05
|
||||
Accuracy score on test set: 0.10555555555555556
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58742/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1466,10 +1470,211 @@ Lambda = 0.0001
|
||||
Accuracy score on test set: 0.08611111111111111
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58742/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.1
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.10555555555555556
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.1
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.08888888888888889
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.1
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.08611111111111111
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.1
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.08888888888888889
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.1
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.09166666666666666
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1.0
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1.0
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1.0
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1.0
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1.0
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1.0
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.10555555555555556
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1.0
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
|
||||
<span class="ne">KeyboardInterrupt</span><span class="g g-Whitespace"> </span>Traceback (most recent call last)
|
||||
<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">8</span><span class="p">],</span> <span class="n">line</span> <span class="mi">11</span>
|
||||
@@ -1480,18 +1685,17 @@ Accuracy score on test set: 0.08611111111111111
|
||||
<span class="g g-Whitespace"> </span><span class="mi">13</span> <span class="n">DNN_numpy</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">dnn</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">15</span> <span class="n">test_predict</span> <span class="o">=</span> <span class="n">dnn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">Cell In[6], line 98,</span> in <span class="ni">NeuralNetwork.train</span><span class="nt">(self)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">95</span> <span class="bp">self</span><span class="o">.</span><span class="n">X_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">X_data_full</span><span class="p">[</span><span class="n">chosen_datapoints</span><span class="p">]</span>
|
||||
<span class="nn">Cell In[6], line 99,</span> in <span class="ni">NeuralNetwork.train</span><span class="nt">(self)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">96</span> <span class="bp">self</span><span class="o">.</span><span class="n">Y_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">Y_data_full</span><span class="p">[</span><span class="n">chosen_datapoints</span><span class="p">]</span>
|
||||
<span class="ne">---> </span><span class="mi">98</span> <span class="bp">self</span><span class="o">.</span><span class="n">feed_forward</span><span class="p">()</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">99</span> <span class="bp">self</span><span class="o">.</span><span class="n">backpropagation</span><span class="p">()</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">98</span> <span class="bp">self</span><span class="o">.</span><span class="n">feed_forward</span><span class="p">()</span>
|
||||
<span class="ne">---> </span><span class="mi">99</span> <span class="bp">self</span><span class="o">.</span><span class="n">backpropagation</span><span class="p">()</span>
|
||||
|
||||
<span class="nn">Cell In[6], line 38,</span> in <span class="ni">NeuralNetwork.feed_forward</span><span class="nt">(self)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">36</span> <span class="k">def</span> <span class="nf">feed_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">37</span> <span class="c1"># feed-forward for training</span>
|
||||
<span class="ne">---> </span><span class="mi">38</span> <span class="bp">self</span><span class="o">.</span><span class="n">z_h</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X_data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_weights</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_bias</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">39</span> <span class="bp">self</span><span class="o">.</span><span class="n">a_h</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">z_h</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">41</span> <span class="bp">self</span><span class="o">.</span><span class="n">z_o</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">a_h</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_weights</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_bias</span>
|
||||
<span class="nn">Cell In[6], line 64,</span> in <span class="ni">NeuralNetwork.backpropagation</span><span class="nt">(self)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">61</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_weights_gradient</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">a_h</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">error_output</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">62</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_bias_gradient</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">error_output</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="ne">---> </span><span class="mi">64</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_weights_gradient</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X_data</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">error_hidden</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">65</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_bias_gradient</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">error_hidden</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">67</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lmbd</span> <span class="o">></span> <span class="mf">0.0</span><span class="p">:</span>
|
||||
|
||||
<span class="ne">KeyboardInterrupt</span>:
|
||||
</pre></div>
|
||||
|
||||
@@ -257,6 +257,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
<li class="toctree-l1"><a class="reference internal" href="week48.html">Week 48: Gradient boosting and summary of course</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek48.html">Exercises week 48</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
@@ -257,6 +257,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
<li class="toctree-l1"><a class="reference internal" href="week48.html">Week 48: Gradient boosting and summary of course</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek48.html">Exercises week 48</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
@@ -257,6 +257,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
<li class="toctree-l1"><a class="reference internal" href="week48.html">Week 48: Gradient boosting and summary of course</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek48.html">Exercises week 48</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
@@ -558,7 +561,7 @@ systems such as automatic translation and speech-to-text.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/29361dbec77d5254822f2cf57bdddb701f65d5a7a34bf4d781aba818766c3cbf.png" src="_images/29361dbec77d5254822f2cf57bdddb701f65d5a7a34bf4d781aba818766c3cbf.png" />
|
||||
<img alt="_images/d213fd49b73afa7db6fe63b54689d9b6c4aff1adac65c06e412ebb9574c86d39.png" src="_images/d213fd49b73afa7db6fe63b54689d9b6c4aff1adac65c06e412ebb9574c86d39.png" />
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
|
||||
super().__init__(**kwargs)
|
||||
</pre></div>
|
||||
@@ -584,360 +587,402 @@ systems such as automatic translation and speech-to-text.</p>
|
||||
</div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 1/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 2s - 41ms/step - loss: 0.5362
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 2s - 48ms/step - loss: 0.5174
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 2/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 8ms/step - loss: 0.4106
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.4188
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 3/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 8ms/step - loss: 0.4015
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.4065
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 4/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 8ms/step - loss: 0.3978
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 8ms/step - loss: 0.4041
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 5/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3953
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 8ms/step - loss: 0.4032
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 6/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3941
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.4008
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 7/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3899
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.4000
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 8/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3917
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3989
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 9/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 8ms/step - loss: 0.3893
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3963
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 10/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3903
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3964
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 11/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3860
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3957
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 12/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3843
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3965
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 13/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - 11ms/step - loss: 0.3874
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3965
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 14/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 8ms/step - loss: 0.3868
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3953
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 15/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 8ms/step - loss: 0.3842
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3943
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 16/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 8ms/step - loss: 0.3855
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3938
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 17/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - 13ms/step - loss: 0.3861
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3920
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 18/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 10ms/step - loss: 0.3809
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3935
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 19/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3829
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3949
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 20/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3809
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3922
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 21/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3824
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 9ms/step - loss: 0.3927
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 22/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3804
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 9ms/step - loss: 0.3914
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 23/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3797
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3924
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 24/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3807
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 8ms/step - loss: 0.3885
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 25/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3796
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3896
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 26/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 2s - 31ms/step - loss: 0.3783
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3901
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 27/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 9ms/step - loss: 0.3775
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3885
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 28/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 8ms/step - loss: 0.3789
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3905
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 29/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 8ms/step - loss: 0.3774
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3884
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 30/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 9ms/step - loss: 0.3759
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3904
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 31/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3768
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3889
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 32/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3741
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3874
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 33/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3755
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3899
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 34/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 8ms/step - loss: 0.3733
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3886
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 35/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - 22ms/step - loss: 0.3736
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3886
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 36/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - 10ms/step - loss: 0.3721
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3862
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 37/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 10ms/step - loss: 0.3736
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3873
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 38/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 8ms/step - loss: 0.3728
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3875
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 39/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3719
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3857
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 40/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - 11ms/step - loss: 0.3718
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 9ms/step - loss: 0.3860
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 41/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3704
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 8ms/step - loss: 0.3860
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 42/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 8ms/step - loss: 0.3730
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3850
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 43/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3704
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3863
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 44/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3698
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3836
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 45/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3712
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3844
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 46/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3691
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3841
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 47/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3672
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3828
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 48/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 8ms/step - loss: 0.3657
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3843
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 49/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3678
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3837
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 50/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3634
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3850
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 51/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3667
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3847
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 52/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3685
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3809
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 53/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3669
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3835
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 54/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3654
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3830
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 55/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3635
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3813
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 56/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3643
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3826
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 57/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3649
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3835
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 58/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3617
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 8ms/step - loss: 0.3823
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 59/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3648
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3820
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 60/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3814
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 61/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3825
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 62/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3817
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 63/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3815
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 64/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3796
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 65/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3802
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 66/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - 7ms/step - loss: 0.3805
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 67/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
|
||||
<span class="ne">KeyboardInterrupt</span><span class="g g-Whitespace"> </span>Traceback (most recent call last)
|
||||
<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">line</span> <span class="mi">58</span>
|
||||
|
||||
@@ -257,6 +257,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
<li class="toctree-l1"><a class="reference internal" href="week48.html">Week 48: Gradient boosting and summary of course</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek48.html">Exercises week 48</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
@@ -1185,10 +1188,10 @@ covariance matrix through the <strong>np.linalg.eig()</strong> function.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.07163028969289174
|
||||
3.7656278764040367
|
||||
[[0.7647107 2.29986727]
|
||||
[2.29986727 7.88107866]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.00239782448350092
|
||||
3.945643027765946
|
||||
[[1.04582521 3.00214849]
|
||||
[3.00214849 9.76163974]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1225,10 +1228,10 @@ a more brute force way. Here we scale the mean values for each column of the des
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.08212703190343323
|
||||
2.425866065899094
|
||||
[[1. 0.65333306]
|
||||
[0.65333306 1. ]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.07878080847813602
|
||||
2.1500369042116563
|
||||
[[1. 0.63295205]
|
||||
[0.63295205 1. ]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1258,30 +1261,30 @@ this matrix we easily see that it is a positive definite matrix.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[-0.68481734 -2.74241828]
|
||||
[-1.03710525 -3.82202137]
|
||||
[ 0.71505805 2.54840587]
|
||||
[ 0.4645853 0.98764188]
|
||||
[-1.95392781 -4.51089358]
|
||||
[ 0.79256149 3.37757489]
|
||||
[-0.18745641 0.4457749 ]
|
||||
[ 2.53950612 7.87543978]
|
||||
[ 0.25354074 -0.28008123]
|
||||
[-0.90194489 -3.87942287]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[-0.05862476 0.45533295]
|
||||
[ 0.84689611 4.01381445]
|
||||
[ 0.21778772 0.62032544]
|
||||
[-1.3117471 -2.69182187]
|
||||
[-0.78512935 -3.44198948]
|
||||
[-0.12030798 -0.93994208]
|
||||
[ 0.60559053 2.2457079 ]
|
||||
[ 1.8170149 4.51652722]
|
||||
[-0.44215901 -3.25646952]
|
||||
[-0.76932106 -1.52148501]]
|
||||
0 1
|
||||
0 -0.684817 -2.742418
|
||||
1 -1.037105 -3.822021
|
||||
2 0.715058 2.548406
|
||||
3 0.464585 0.987642
|
||||
4 -1.953928 -4.510894
|
||||
5 0.792561 3.377575
|
||||
6 -0.187456 0.445775
|
||||
7 2.539506 7.875440
|
||||
8 0.253541 -0.280081
|
||||
9 -0.901945 -3.879423
|
||||
0 1
|
||||
0 1.000000 0.972082
|
||||
1 0.972082 1.000000
|
||||
0 -0.058625 0.455333
|
||||
1 0.846896 4.013814
|
||||
2 0.217788 0.620325
|
||||
3 -1.311747 -2.691822
|
||||
4 -0.785129 -3.441989
|
||||
5 -0.120308 -0.939942
|
||||
6 0.605591 2.245708
|
||||
7 1.817015 4.516527
|
||||
8 -0.442159 -3.256470
|
||||
9 -0.769321 -1.521485
|
||||
0 1
|
||||
0 1.00000 0.92403
|
||||
1 0.92403 1.00000
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1338,37 +1341,37 @@ this matrix we easily see that it is a positive definite matrix.</p>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 0 1 2 3 4 5 6 7 \
|
||||
0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
|
||||
1 0.0 0.070253 0.070611 0.070163 0.068562 0.067004 0.062136 0.060528
|
||||
2 0.0 0.070611 0.071502 0.070808 0.069529 0.068256 0.062916 0.061526
|
||||
3 0.0 0.070163 0.070808 0.074767 0.073368 0.071996 0.069069 0.067536
|
||||
4 0.0 0.068562 0.069529 0.073368 0.072239 0.071113 0.068033 0.066707
|
||||
5 0.0 0.067004 0.068256 0.071996 0.071113 0.070210 0.067007 0.065872
|
||||
6 0.0 0.062136 0.062916 0.069069 0.068033 0.067007 0.065727 0.064490
|
||||
7 0.0 0.060528 0.061526 0.067536 0.066707 0.065872 0.064490 0.063421
|
||||
8 0.0 0.059023 0.060216 0.066097 0.065457 0.064796 0.063327 0.062411
|
||||
9 0.0 0.057616 0.058984 0.064749 0.064281 0.063778 0.062233 0.061458
|
||||
10 0.0 0.054096 0.054970 0.061945 0.061234 0.060520 0.060261 0.059314
|
||||
11 0.0 0.052750 0.053781 0.060606 0.060054 0.059486 0.059137 0.058322
|
||||
12 0.0 0.051499 0.052672 0.059359 0.058952 0.058518 0.058088 0.057394
|
||||
13 0.0 0.050336 0.051637 0.058198 0.057923 0.057612 0.057109 0.056526
|
||||
14 0.0 0.049255 0.050672 0.057116 0.056962 0.056764 0.056195 0.055714
|
||||
1 0.0 0.081622 0.081406 0.078296 0.079396 0.080527 0.068235 0.069327
|
||||
2 0.0 0.081406 0.081919 0.078612 0.080089 0.081603 0.068822 0.070146
|
||||
3 0.0 0.078296 0.078612 0.080411 0.081717 0.083038 0.073368 0.074578
|
||||
4 0.0 0.079396 0.080089 0.081717 0.083265 0.084831 0.074620 0.075994
|
||||
5 0.0 0.080527 0.081603 0.083038 0.084831 0.086645 0.075870 0.077411
|
||||
6 0.0 0.068235 0.068822 0.073368 0.074620 0.075870 0.069194 0.070300
|
||||
7 0.0 0.069327 0.070146 0.074578 0.075994 0.077411 0.070300 0.071523
|
||||
8 0.0 0.070483 0.071536 0.075839 0.077423 0.079011 0.071441 0.072784
|
||||
9 0.0 0.071702 0.072994 0.077153 0.078908 0.080672 0.072619 0.074087
|
||||
10 0.0 0.059187 0.059821 0.065770 0.066863 0.067945 0.063580 0.064519
|
||||
11 0.0 0.060114 0.060903 0.066767 0.067977 0.069178 0.064472 0.065498
|
||||
12 0.0 0.061096 0.062042 0.067812 0.069142 0.070466 0.065402 0.066517
|
||||
13 0.0 0.062134 0.063240 0.068908 0.070362 0.071812 0.066371 0.067578
|
||||
14 0.0 0.063230 0.064502 0.070057 0.071639 0.073220 0.067381 0.068684
|
||||
|
||||
8 9 10 11 12 13 14
|
||||
0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
|
||||
1 0.059023 0.057616 0.054096 0.052750 0.051499 0.050336 0.049255
|
||||
2 0.060216 0.058984 0.054970 0.053781 0.052672 0.051637 0.050672
|
||||
3 0.066097 0.064749 0.061945 0.060606 0.059359 0.058198 0.057116
|
||||
4 0.065457 0.064281 0.061234 0.060054 0.058952 0.057923 0.056962
|
||||
5 0.064796 0.063778 0.060520 0.059486 0.058518 0.057612 0.056764
|
||||
6 0.063327 0.062233 0.060261 0.059137 0.058088 0.057109 0.056195
|
||||
7 0.062411 0.061458 0.059314 0.058322 0.057394 0.056526 0.055714
|
||||
8 0.061541 0.060718 0.058420 0.057549 0.056732 0.055967 0.055250
|
||||
9 0.060718 0.060014 0.057576 0.056817 0.056103 0.055433 0.054805
|
||||
10 0.058420 0.057576 0.056198 0.055300 0.054459 0.053671 0.052934
|
||||
11 0.057549 0.056817 0.055300 0.054507 0.053763 0.053066 0.052412
|
||||
12 0.056732 0.056103 0.054459 0.053763 0.053109 0.052495 0.051918
|
||||
13 0.055967 0.055433 0.053671 0.053066 0.052495 0.051957 0.051452
|
||||
14 0.055250 0.054805 0.052934 0.052412 0.051918 0.051452 0.051015
|
||||
1 0.070483 0.071702 0.059187 0.060114 0.061096 0.062134 0.063230
|
||||
2 0.071536 0.072994 0.059821 0.060903 0.062042 0.063240 0.064502
|
||||
3 0.075839 0.077153 0.065770 0.066767 0.067812 0.068908 0.070057
|
||||
4 0.077423 0.078908 0.066863 0.067977 0.069142 0.070362 0.071639
|
||||
5 0.079011 0.080672 0.067945 0.069178 0.070466 0.071812 0.073220
|
||||
6 0.071441 0.072619 0.063580 0.064472 0.065402 0.066371 0.067381
|
||||
7 0.072784 0.074087 0.064519 0.065498 0.066517 0.067578 0.068684
|
||||
8 0.074170 0.075601 0.065481 0.066550 0.067660 0.068817 0.070021
|
||||
9 0.075601 0.077163 0.066469 0.067629 0.068835 0.070089 0.071396
|
||||
10 0.065481 0.066469 0.059543 0.060291 0.061066 0.061870 0.062705
|
||||
11 0.066550 0.067629 0.060291 0.061104 0.061946 0.062820 0.063728
|
||||
12 0.067660 0.068835 0.061066 0.061946 0.062859 0.063805 0.064788
|
||||
13 0.068817 0.070089 0.061870 0.062820 0.063805 0.064827 0.065887
|
||||
14 0.070021 0.071396 0.062705 0.063728 0.064788 0.065887 0.067030
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -257,6 +257,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
<li class="toctree-l1"><a class="reference internal" href="week48.html">Week 48: Gradient boosting and summary of course</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek48.html">Exercises week 48</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
@@ -726,10 +729,10 @@ number <span class="math notranslate nohighlight">\(i\)</span> is left out. Usin
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Runtime: 0.177848 sec
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Runtime: 0.154011 sec
|
||||
Jackknife Statistics :
|
||||
original bias std. error
|
||||
99.9735 99.9635 0.149504
|
||||
100.105 100.095 0.14726
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -948,7 +951,7 @@ theorem.</p>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Bootstrap Statistics :
|
||||
original bias std. error
|
||||
99.9629 14.9658 99.9626 0.148488
|
||||
100.182 15.0891 100.184 0.15218
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -970,7 +973,7 @@ original bias std. error
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/01ac462cedf743599925f73cc6294e533c9fdc93068eb00daba5834da82ae1f3.png" src="_images/01ac462cedf743599925f73cc6294e533c9fdc93068eb00daba5834da82ae1f3.png" />
|
||||
<img alt="_images/1f1e999736d47a2811569158c3eb924dab62b0f520b0f854e48f2e2dbb32e047.png" src="_images/1f1e999736d47a2811569158c3eb924dab62b0f520b0f854e48f2e2dbb32e047.png" />
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
@@ -1155,9 +1158,7 @@ Error: 0.08426840630693411
|
||||
Bias^2: 0.0796891867672603
|
||||
Var: 0.004579219539673834
|
||||
0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 2
|
||||
Polynomial degree: 2
|
||||
Error: 0.10398646080125035
|
||||
Bias^2: 0.1007711427354898
|
||||
Var: 0.0032153180657605116
|
||||
@@ -1189,9 +1190,7 @@ Error: 0.02760977349102253
|
||||
Bias^2: 0.022999498260366312
|
||||
Var: 0.004610275230656212
|
||||
0.02760977349102253 >= 0.022999498260366312 + 0.004610275230656212 = 0.027609773491022525
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 8
|
||||
Polynomial degree: 8
|
||||
Error: 0.017355848195593347
|
||||
Bias^2: 0.010331721306655127
|
||||
Var: 0.007024126888938232
|
||||
@@ -1206,14 +1205,14 @@ Error: 0.021592704588025025
|
||||
Bias^2: 0.010516485576645508
|
||||
Var: 0.011076219011379514
|
||||
0.021592704588025025 >= 0.010516485576645508 + 0.011076219011379514 = 0.021592704588025022
|
||||
Polynomial degree: 11
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 11
|
||||
Error: 0.07160048164233104
|
||||
Bias^2: 0.014436800088904942
|
||||
Var: 0.05716368155342608
|
||||
0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 12
|
||||
Polynomial degree: 12
|
||||
Error: 0.11547777218872497
|
||||
Bias^2: 0.01628578269596628
|
||||
Var: 0.09919198949275869
|
||||
@@ -1540,9 +1539,9 @@ Mean squared error on training data: 0.00060704
|
||||
Mean squared error on test data: 3262.26814548
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
|
||||
plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
|
||||
plt.plot(polynomial, np.log10(testerror), label='Test Error')
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1776,7 +1775,7 @@ cross-validation (LOOCV).</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
|
||||
plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -2665,7 +2664,7 @@ linear system as an equation would reduce this down to
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
|
||||
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -2809,7 +2808,7 @@ with the form utilized in linear regression, viz.</p>
|
||||
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|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
|
||||
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -2849,7 +2848,7 @@ cost function is given by</p>
|
||||
</div>
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||||
</div>
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||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
|
||||
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -2884,7 +2883,7 @@ cost function is given by</p>
|
||||
</div>
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||||
</div>
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||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
|
||||
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
|
||||
</pre></div>
|
||||
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||||
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|
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||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
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@@ -621,13 +624,13 @@ predicting the target features of query instances is as follows:</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>2nd degree coefficients:
|
||||
zero power: -3.6801072677808095
|
||||
first power: 0.14054303349959596
|
||||
second power: -0.0002999281168222194
|
||||
zero power: -1.399819665759832
|
||||
first power: -0.04120453230477482
|
||||
second power: 0.0007267213049602343
|
||||
</pre></div>
|
||||
</div>
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||||
<img alt="_images/928e4d75f6fb1b7a9e9c65e0db52145f075c9aed9b2e97bdef41ed3d957ace12.png" src="_images/928e4d75f6fb1b7a9e9c65e0db52145f075c9aed9b2e97bdef41ed3d957ace12.png" />
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@@ -1486,10 +1489,10 @@ attributes at each step while growing the tree.</p>
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||||
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||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(426, 30)
|
||||
(143, 30)
|
||||
Test set accuracy with Logistic Regression: 0.94
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test set accuracy with Logistic Regression: 0.94
|
||||
Test set accuracy with SVM: 0.63
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test set accuracy with SVM: 0.63
|
||||
Test set accuracy with Decision Trees: 0.90
|
||||
Test set accuracy Logistic Regression with scaled data: 0.96
|
||||
Test set accuracy SVM with scaled data: 0.96
|
||||
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@@ -257,6 +257,9 @@
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||||
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|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
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||||
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@@ -257,6 +257,9 @@
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@@ -617,10 +620,10 @@ covariance matrix through the <strong>np.linalg.eig()</strong> function.</p>
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||||
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|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.08913527419249101
|
||||
3.7127415072708665
|
||||
[[ 1.13025431 3.39215451]
|
||||
[ 3.39215451 11.15061293]]
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||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.02745698767039481
|
||||
3.9527157086177156
|
||||
[[0.80249705 2.35440603]
|
||||
[2.35440603 7.83541057]]
|
||||
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|
||||
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|
||||
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||||
@@ -660,10 +663,10 @@ a more brute force way. Here we scale the mean values for each column of the des
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||||
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||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.09335279187105122
|
||||
2.2108106787032815
|
||||
[[1. 0.66771869]
|
||||
[0.66771869 1. ]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.07408022521552643
|
||||
1.9519435372439522
|
||||
[[1. 0.56323995]
|
||||
[0.56323995 1. ]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -692,30 +695,30 @@ this matrix we easily see that it is a positive definite matrix.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[ 0.35582913 0.96196912]
|
||||
[ 0.20039135 1.07902642]
|
||||
[-0.47708415 -1.39752266]
|
||||
[-0.0118395 0.55664957]
|
||||
[-0.18990703 1.30646904]
|
||||
[ 0.22370063 -0.76173777]
|
||||
[-0.25925579 -1.7132548 ]
|
||||
[-0.56061802 -1.9921751 ]
|
||||
[-1.14025916 -4.44655399]
|
||||
[ 1.85904253 6.40713017]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[ 0.53340467 1.72530188]
|
||||
[-0.56167889 -3.2544002 ]
|
||||
[ 1.85988539 5.87145508]
|
||||
[-1.23561281 -4.57354138]
|
||||
[ 0.52739566 0.93489572]
|
||||
[ 0.89775088 2.87036267]
|
||||
[-0.71834014 -0.65667853]
|
||||
[ 0.38699758 1.30537358]
|
||||
[-1.48772565 -3.9664681 ]
|
||||
[-0.20207669 -0.25630072]]
|
||||
0 1
|
||||
0 0.355829 0.961969
|
||||
1 0.200391 1.079026
|
||||
2 -0.477084 -1.397523
|
||||
3 -0.011839 0.556650
|
||||
4 -0.189907 1.306469
|
||||
5 0.223701 -0.761738
|
||||
6 -0.259256 -1.713255
|
||||
7 -0.560618 -1.992175
|
||||
8 -1.140259 -4.446554
|
||||
9 1.859043 6.407130
|
||||
0 0.533405 1.725302
|
||||
1 -0.561679 -3.254400
|
||||
2 1.859885 5.871455
|
||||
3 -1.235613 -4.573541
|
||||
4 0.527396 0.934896
|
||||
5 0.897751 2.870363
|
||||
6 -0.718340 -0.656679
|
||||
7 0.386998 1.305374
|
||||
8 -1.487726 -3.966468
|
||||
9 -0.202077 -0.256301
|
||||
0 1
|
||||
0 1.000000 0.946278
|
||||
1 0.946278 1.000000
|
||||
0 1.000000 0.966262
|
||||
1 0.966262 1.000000
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -772,37 +775,37 @@ this matrix we easily see that it is a positive definite matrix.</p>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 0 1 2 3 4 5 6 7 \
|
||||
0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
|
||||
1 0.0 0.075894 0.073179 0.073456 0.072746 0.071970 0.064485 0.064123
|
||||
2 0.0 0.073179 0.070833 0.070597 0.070031 0.069409 0.061930 0.061649
|
||||
3 0.0 0.073456 0.070597 0.076555 0.075647 0.074674 0.070295 0.069812
|
||||
4 0.0 0.072746 0.070031 0.075647 0.074821 0.073930 0.069398 0.068968
|
||||
5 0.0 0.071970 0.069409 0.074674 0.073930 0.073126 0.068445 0.068069
|
||||
6 0.0 0.064485 0.061930 0.070295 0.069398 0.068445 0.066551 0.066050
|
||||
7 0.0 0.064123 0.061649 0.069812 0.068968 0.068069 0.066050 0.065589
|
||||
8 0.0 0.063763 0.061372 0.069330 0.068539 0.067694 0.065550 0.065128
|
||||
9 0.0 0.063403 0.061097 0.068844 0.068107 0.067318 0.065047 0.064664
|
||||
10 0.0 0.055942 0.053732 0.062913 0.062085 0.061210 0.060946 0.060463
|
||||
11 0.0 0.055680 0.053525 0.062569 0.061780 0.060945 0.060582 0.060130
|
||||
12 0.0 0.055429 0.053330 0.062237 0.061487 0.060692 0.060229 0.059808
|
||||
13 0.0 0.055190 0.053147 0.061915 0.061205 0.060449 0.059887 0.059497
|
||||
14 0.0 0.054960 0.052974 0.061603 0.060933 0.060217 0.059554 0.059195
|
||||
1 0.0 0.073438 0.077471 0.075458 0.076186 0.076594 0.069080 0.068939
|
||||
2 0.0 0.077471 0.083242 0.080919 0.082408 0.083412 0.074516 0.074729
|
||||
3 0.0 0.075458 0.080919 0.083069 0.084410 0.085279 0.079308 0.079408
|
||||
4 0.0 0.076186 0.082408 0.084410 0.086139 0.087327 0.080792 0.081105
|
||||
5 0.0 0.076594 0.083412 0.085279 0.087327 0.088786 0.081792 0.082290
|
||||
6 0.0 0.069080 0.074516 0.079308 0.080792 0.081792 0.077847 0.078070
|
||||
7 0.0 0.068939 0.074729 0.079408 0.081105 0.082290 0.078070 0.078429
|
||||
8 0.0 0.068716 0.074791 0.079363 0.081242 0.082589 0.078134 0.078618
|
||||
9 0.0 0.068441 0.074753 0.079219 0.081258 0.082750 0.078091 0.078687
|
||||
10 0.0 0.061939 0.066921 0.073159 0.074603 0.075599 0.073240 0.073520
|
||||
11 0.0 0.061580 0.066741 0.072883 0.074456 0.075571 0.073054 0.073431
|
||||
12 0.0 0.061213 0.066524 0.072574 0.074264 0.075487 0.072829 0.073296
|
||||
13 0.0 0.060849 0.066290 0.072252 0.074047 0.075371 0.072584 0.073135
|
||||
14 0.0 0.060497 0.066053 0.071930 0.073822 0.075239 0.072334 0.072965
|
||||
|
||||
8 9 10 11 12 13 14
|
||||
0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
|
||||
1 0.063763 0.063403 0.055942 0.055680 0.055429 0.055190 0.054960
|
||||
2 0.061372 0.061097 0.053732 0.053525 0.053330 0.053147 0.052974
|
||||
3 0.069330 0.068844 0.062913 0.062569 0.062237 0.061915 0.061603
|
||||
4 0.068539 0.068107 0.062085 0.061780 0.061487 0.061205 0.060933
|
||||
5 0.067694 0.067318 0.061210 0.060945 0.060692 0.060449 0.060217
|
||||
6 0.065550 0.065047 0.060946 0.060582 0.060229 0.059887 0.059554
|
||||
7 0.065128 0.064664 0.060463 0.060130 0.059808 0.059497 0.059195
|
||||
8 0.064706 0.064282 0.059981 0.059679 0.059388 0.059108 0.058837
|
||||
9 0.064282 0.063898 0.059497 0.059226 0.058967 0.058718 0.058478
|
||||
10 0.059981 0.059497 0.056837 0.056475 0.056124 0.055783 0.055452
|
||||
11 0.059679 0.059226 0.056475 0.056139 0.055814 0.055499 0.055194
|
||||
12 0.059388 0.058967 0.056124 0.055814 0.055515 0.055226 0.054947
|
||||
13 0.059108 0.058718 0.055783 0.055499 0.055226 0.054963 0.054709
|
||||
14 0.058837 0.058478 0.055452 0.055194 0.054947 0.054709 0.054481
|
||||
1 0.068716 0.068441 0.061939 0.061580 0.061213 0.060849 0.060497
|
||||
2 0.074791 0.074753 0.066921 0.066741 0.066524 0.066290 0.066053
|
||||
3 0.079363 0.079219 0.073159 0.072883 0.072574 0.072252 0.071930
|
||||
4 0.081242 0.081258 0.074603 0.074456 0.074264 0.074047 0.073822
|
||||
5 0.082589 0.082750 0.075599 0.075571 0.075487 0.075371 0.075239
|
||||
6 0.078134 0.078091 0.073240 0.073054 0.072829 0.072584 0.072334
|
||||
7 0.078618 0.078687 0.073520 0.073431 0.073296 0.073135 0.072965
|
||||
8 0.078919 0.079094 0.073650 0.073652 0.073602 0.073523 0.073430
|
||||
9 0.079094 0.079368 0.073677 0.073765 0.073796 0.073795 0.073777
|
||||
10 0.073650 0.073677 0.069911 0.069801 0.069653 0.069484 0.069308
|
||||
11 0.073652 0.073765 0.069801 0.069768 0.069692 0.069593 0.069484
|
||||
12 0.073602 0.073796 0.069653 0.069692 0.069686 0.069654 0.069611
|
||||
13 0.073523 0.073795 0.069484 0.069593 0.069654 0.069688 0.069709
|
||||
14 0.073430 0.073777 0.069308 0.069484 0.069611 0.069709 0.069791
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -991,10 +994,10 @@ We can write our own code or simply use either the functionaly of <strong>numpy<
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 0 1
|
||||
0 3.946263 1.971035
|
||||
1 1.971035 1.988524
|
||||
[[3.94626291 1.97103474]
|
||||
[1.97103474 1.98852413]]
|
||||
0 4.066103 2.050297
|
||||
1 2.050297 2.019626
|
||||
[[4.06610301 2.0502966 ]
|
||||
[2.0502966 2.01962561]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1021,11 +1024,11 @@ Our own code here is not very elegant and asks for obvious improvements. It is t
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Centered covariance using own code
|
||||
[[3.94626291 1.97103474]
|
||||
[1.97103474 1.98852413]]
|
||||
[[4.06610301 2.0502966 ]
|
||||
[2.0502966 2.01962561]]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/6ca0e6a8c6122c37cbb752b49917fd1e67a1de46298e8c2247a990d083fdb3c7.png" src="_images/6ca0e6a8c6122c37cbb752b49917fd1e67a1de46298e8c2247a990d083fdb3c7.png" />
|
||||
<img alt="_images/6221ee55bb26dba5cef5540ef9967df02eb119abda11c5c5903f7762c8c9f763.png" src="_images/6221ee55bb26dba5cef5540ef9967df02eb119abda11c5c5903f7762c8c9f763.png" />
|
||||
</div>
|
||||
</div>
|
||||
<p>Depending on the number of points <span class="math notranslate nohighlight">\(n\)</span>, we will get results that are close to the covariance values defined above.
|
||||
@@ -1082,16 +1085,16 @@ questions.</p>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Eigenvalues of Covariance matrix
|
||||
5.168112312789667
|
||||
0.7666747242371026
|
||||
5.3343122336302145
|
||||
0.7514163800957537
|
||||
First eigenvector
|
||||
[0.84993979 0.52687982]
|
||||
[0.85045481 0.5260481 ]
|
||||
Second eigenvector
|
||||
[-0.52687982 0.84993979]
|
||||
[-0.5260481 0.85045481]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Eigenvector of largest eigenvalue
|
||||
[0.84993979 0.52687982]
|
||||
[0.85045481 0.5260481 ]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
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|
||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
|
||||
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|
||||
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||||
|
||||
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||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
|
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|
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||||
|
||||
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||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
|
||||
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|
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||||
|
||||
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|
||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
|
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||||
|
||||
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||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
|
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
|
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||||
|
||||
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||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
|
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|
||||
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||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
|
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|
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|
||||
|
||||
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|
||||
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|
||||
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|
||||
|
||||
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|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek48.html">Exercises week 48</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
@@ -599,18 +602,18 @@ regression.</p>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
|
||||
[[4.0010275 ]
|
||||
[3.10085547]]
|
||||
Eigenvalues of Hessian Matrix:[0.32462943 4.22813513]
|
||||
[[4.41622083]
|
||||
[2.74692745]]
|
||||
Eigenvalues of Hessian Matrix:[0.26594878 4.45190996]
|
||||
theta from own gd
|
||||
[[4.0010275 ]
|
||||
[3.10085547]]
|
||||
[[4.41622083]
|
||||
[2.74692745]]
|
||||
theta from own sdg
|
||||
[[4.08051993]
|
||||
[3.13522576]]
|
||||
[[4.38222303]
|
||||
[2.74411787]]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/28af7ec857f6b0c73278974d8a0f02963d4300b7ede7129fc2e95b8d68a8492b.png" src="_images/28af7ec857f6b0c73278974d8a0f02963d4300b7ede7129fc2e95b8d68a8492b.png" />
|
||||
<img alt="_images/74585d2c5d042ce0364bf21b5a8e9bc5b5504011eaa4a30c445a4415a4c1df98.png" src="_images/74585d2c5d042ce0364bf21b5a8e9bc5b5504011eaa4a30c445a4415a4c1df98.png" />
|
||||
</div>
|
||||
</div>
|
||||
<p>In the above code, we have use replacement in setting up the
|
||||
@@ -729,17 +732,17 @@ first example shows results with ordinary leats squares.</p>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
|
||||
[[3.8679219 ]
|
||||
[3.12497024]]
|
||||
Eigenvalues of Hessian Matrix:[0.31887646 4.68712364]
|
||||
[[4.32147704]
|
||||
[2.78166226]]
|
||||
Eigenvalues of Hessian Matrix:[0.32306518 4.38577204]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>theta from own gd
|
||||
[[3.8679219 ]
|
||||
[3.12497024]]
|
||||
[[4.32147704]
|
||||
[2.78166226]]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/634d0baa356c54adef6974350a5d0f31fb981ef2c1199e2bf8abc3dbbd76d38b.png" src="_images/634d0baa356c54adef6974350a5d0f31fb981ef2c1199e2bf8abc3dbbd76d38b.png" />
|
||||
<img alt="_images/c61bca31ee6787e0e5d1512e4bf365143d789096f02da0a858ff7e7c3834630c.png" src="_images/c61bca31ee6787e0e5d1512e4bf365143d789096f02da0a858ff7e7c3834630c.png" />
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
@@ -807,73 +810,73 @@ Eigenvalues of Hessian Matrix:[0.31887646 4.68712364]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
|
||||
[[4.]
|
||||
[3.]]
|
||||
Eigenvalues of Hessian Matrix:[0.3011187 4.49781862]
|
||||
0 [-10.84111032] [-12.09572749]
|
||||
1 [-0.48072858] [0.39646149]
|
||||
2 [-0.4485449] [0.3699193]
|
||||
3 [-0.41851584] [0.34515405]
|
||||
4 [-0.39049716] [0.32204677]
|
||||
5 [-0.36435426] [0.30048647]
|
||||
6 [-0.33996158] [0.28036959]
|
||||
7 [-0.31720192] [0.26159948]
|
||||
8 [-0.29596598] [0.24408599]
|
||||
9 [-0.27615173] [0.22774499]
|
||||
10 [-0.257664] [0.21249799]
|
||||
11 [-0.24041398] [0.19827173]
|
||||
12 [-0.22431882] [0.18499789]
|
||||
13 [-0.20930118] [0.1726127]
|
||||
14 [-0.19528895] [0.16105668]
|
||||
15 [-0.1822148] [0.1502743]
|
||||
16 [-0.17001593] [0.14021378]
|
||||
17 [-0.15863375] [0.13082678]
|
||||
18 [-0.14801358] [0.12206823]
|
||||
19 [-0.13810441] [0.11389604]
|
||||
20 [-0.12885864] [0.10627096]
|
||||
21 [-0.12023184] [0.09915636]
|
||||
22 [-0.1121826] [0.09251807]
|
||||
23 [-0.10467223] [0.08632419]
|
||||
24 [-0.09766466] [0.08054499]
|
||||
25 [-0.09112623] [0.07515268]
|
||||
26 [-0.08502554] [0.07012138]
|
||||
27 [-0.07933327] [0.06542692]
|
||||
28 [-0.07402209] [0.06104673]
|
||||
29 [-0.06906648] [0.05695979]
|
||||
Eigenvalues of Hessian Matrix:[0.26136301 4.38259367]
|
||||
0 [-12.42926102] [-13.7797646]
|
||||
1 [-0.35774662] [0.30560116]
|
||||
2 [-0.33641182] [0.28737615]
|
||||
3 [-0.31634936] [0.27023801]
|
||||
4 [-0.29748336] [0.25412193]
|
||||
5 [-0.27974246] [0.23896696]
|
||||
6 [-0.26305957] [0.22471578]
|
||||
7 [-0.24737159] [0.2113145]
|
||||
8 [-0.23261919] [0.19871242]
|
||||
9 [-0.21874657] [0.18686188]
|
||||
10 [-0.20570127] [0.17571808]
|
||||
11 [-0.19343394] [0.16523885]
|
||||
12 [-0.1818982] [0.15538456]
|
||||
13 [-0.17105041] [0.14611795]
|
||||
14 [-0.16084954] [0.13740398]
|
||||
15 [-0.15125702] [0.12920967]
|
||||
16 [-0.14223657] [0.12150404]
|
||||
17 [-0.13375406] [0.11425795]
|
||||
18 [-0.12577742] [0.107444]
|
||||
19 [-0.11827649] [0.1010364]
|
||||
20 [-0.11122288] [0.09501094]
|
||||
21 [-0.10458992] [0.08934481]
|
||||
22 [-0.09835253] [0.08401658]
|
||||
23 [-0.09248712] [0.07900612]
|
||||
24 [-0.08697151] [0.07429446]
|
||||
25 [-0.08178482] [0.06986379]
|
||||
26 [-0.07690745] [0.06569735]
|
||||
27 [-0.07232095] [0.06177939]
|
||||
28 [-0.06800798] [0.05809507]
|
||||
29 [-0.06395221] [0.05463048]
|
||||
theta from own gd
|
||||
[[3.78598925]
|
||||
[3.17649673]]
|
||||
0 [-0.06444264] [0.05314647]
|
||||
1 [-0.06012835] [0.04958843]
|
||||
2 [-0.05480861] [0.04520119]
|
||||
3 [-0.04954337] [0.0408589]
|
||||
4 [-0.04464699] [0.0368208]
|
||||
5 [-0.04018906] [0.0331443]
|
||||
6 [-0.03616111] [0.02982242]
|
||||
7 [-0.03253183] [0.02682931]
|
||||
8 [-0.02926511] [0.02413522]
|
||||
9 [-0.02632586] [0.02171119]
|
||||
10 [-0.02368163] [0.01953047]
|
||||
11 [-0.02130293] [0.01756873]
|
||||
12 [-0.01916314] [0.01580402]
|
||||
13 [-0.01723827] [0.01421657]
|
||||
14 [-0.01550675] [0.01278856]
|
||||
15 [-0.01394915] [0.011504]
|
||||
16 [-0.01254801] [0.01034846]
|
||||
17 [-0.0112876] [0.009309]
|
||||
18 [-0.0101538] [0.00837394]
|
||||
19 [-0.00913389] [0.00753281]
|
||||
20 [-0.00821642] [0.00677616]
|
||||
21 [-0.00739111] [0.00609552]
|
||||
22 [-0.0066487] [0.00548325]
|
||||
23 [-0.00598086] [0.00493247]
|
||||
24 [-0.0053801] [0.00443702]
|
||||
25 [-0.00483969] [0.00399134]
|
||||
26 [-0.00435356] [0.00359042]
|
||||
27 [-0.00391626] [0.00322978]
|
||||
28 [-0.00352289] [0.00290536]
|
||||
29 [-0.00316902] [0.00261353]
|
||||
[[3.76990501]
|
||||
[3.19655615]]
|
||||
0 [-0.06013832] [0.05137251]
|
||||
1 [-0.05655187] [0.04830882]
|
||||
2 [-0.05210338] [0.04450874]
|
||||
3 [-0.04766156] [0.04071437]
|
||||
4 [-0.04348664] [0.03714799]
|
||||
5 [-0.03964077] [0.0338627]
|
||||
6 [-0.03612297] [0.03085765]
|
||||
7 [-0.03291338] [0.02811589]
|
||||
8 [-0.02998766] [0.02561663]
|
||||
9 [-0.02732158] [0.02333916]
|
||||
10 [-0.02489239] [0.02126405]
|
||||
11 [-0.02267913] [0.0193734]
|
||||
12 [-0.02066265] [0.01765085]
|
||||
13 [-0.01882546] [0.01608144]
|
||||
14 [-0.01715161] [0.01465158]
|
||||
15 [-0.01562659] [0.01334885]
|
||||
16 [-0.01423717] [0.01216195]
|
||||
17 [-0.01297129] [0.01108058]
|
||||
18 [-0.01181796] [0.01009536]
|
||||
19 [-0.01076718] [0.00919775]
|
||||
20 [-0.00980983] [0.00837994]
|
||||
21 [-0.0089376] [0.00763484]
|
||||
22 [-0.00814292] [0.006956]
|
||||
23 [-0.0074189] [0.00633751]
|
||||
24 [-0.00675926] [0.00577402]
|
||||
25 [-0.00615826] [0.00526063]
|
||||
26 [-0.00561071] [0.00479289]
|
||||
27 [-0.00511184] [0.00436673]
|
||||
28 [-0.00465732] [0.00397847]
|
||||
29 [-0.00424322] [0.00362473]
|
||||
theta from own gd wth momentum
|
||||
[[3.99053294]
|
||||
[3.00780757]]
|
||||
[[3.98520854]
|
||||
[3.01263545]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -926,17 +929,17 @@ theta from own gd wth momentum
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
|
||||
[[4.1739262 ]
|
||||
[2.85239235]]
|
||||
Eigenvalues of Hessian Matrix:[0.27968902 4.49424386]
|
||||
0 [-13.16403338] [-14.97007223]
|
||||
1 [7.61543606e-16] [2.78435018e-15]
|
||||
2 [-4.23272528e-16] [-4.70203152e-16]
|
||||
3 [-4.23272528e-16] [-4.70203152e-16]
|
||||
4 [-4.23272528e-16] [-4.70203152e-16]
|
||||
[[3.96856022]
|
||||
[2.98116672]]
|
||||
Eigenvalues of Hessian Matrix:[0.3407072 4.46918198]
|
||||
0 [-14.47784776] [-17.95282048]
|
||||
1 [2.49106291e-15] [-6.96409595e-15]
|
||||
2 [8.32667268e-17] [8.72456212e-17]
|
||||
3 [8.32667268e-17] [8.72456212e-17]
|
||||
4 [8.32667268e-17] [8.72456212e-17]
|
||||
beta from own Newton code
|
||||
[[4.1739262 ]
|
||||
[2.85239235]]
|
||||
[[3.96856022]
|
||||
[2.98116672]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1025,18 +1028,18 @@ beta from own Newton code
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
|
||||
[[4.50455587]
|
||||
[2.69978846]]
|
||||
Eigenvalues of Hessian Matrix:[0.35172802 4.35427615]
|
||||
[[4.20603792]
|
||||
[2.82345148]]
|
||||
Eigenvalues of Hessian Matrix:[0.26589296 4.18756348]
|
||||
theta from own gd
|
||||
[[4.50455587]
|
||||
[2.69978846]]
|
||||
[[4.20603792]
|
||||
[2.82345148]]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/c8d1a6036144492783b21bafb5df5462fba5688ed1014961e0a3ecd4909a9caf.png" src="_images/c8d1a6036144492783b21bafb5df5462fba5688ed1014961e0a3ecd4909a9caf.png" />
|
||||
<img alt="_images/5cfc76ca7e820abfb8e15070adf2fa59d6dcfb6d702ef48441625e67b326e24c.png" src="_images/5cfc76ca7e820abfb8e15070adf2fa59d6dcfb6d702ef48441625e67b326e24c.png" />
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>theta from own sdg
|
||||
[[4.51550231]
|
||||
[2.6728048 ]]
|
||||
[[4.20773884]
|
||||
[2.81706996]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1118,15 +1121,15 @@ theta from own gd
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
|
||||
[[3.8301107 ]
|
||||
[3.31610195]]
|
||||
Eigenvalues of Hessian Matrix:[0.31047897 4.32701817]
|
||||
[[3.79928328]
|
||||
[3.09772988]]
|
||||
Eigenvalues of Hessian Matrix:[0.33500183 3.88706629]
|
||||
theta from own gd
|
||||
[[3.82958153]
|
||||
[3.31655284]]
|
||||
[[3.79937084]
|
||||
[3.09764763]]
|
||||
theta from own sdg with momentum
|
||||
[[3.82977678]
|
||||
[3.25067525]]
|
||||
[[3.77947186]
|
||||
[3.08314421]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1201,9 +1204,9 @@ theta from own sdg with momentum
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>theta from own AdaGrad
|
||||
[[1.99978312]
|
||||
[3.00126504]
|
||||
[3.99880408]]
|
||||
[[2.00000205]
|
||||
[2.99998905]
|
||||
[4.00001059]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1285,9 +1288,9 @@ theta from own sdg with momentum
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>theta from own RMSprop
|
||||
[[1.99827523]
|
||||
[2.99976195]
|
||||
[3.99585312]]
|
||||
[[1.99957289]
|
||||
[3.00371103]
|
||||
[3.99595959]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1373,9 +1376,9 @@ theta from own sdg with momentum
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>theta from own ADAM
|
||||
[[2.00000058]
|
||||
[2.99998203]
|
||||
[4.00001412]]
|
||||
[[2.00001509]
|
||||
[2.99992841]
|
||||
[4.00006811]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1448,7 +1451,7 @@ It provides composable transformations of Python+NumPy programs: differentiate,
|
||||
return asarray(x, dtype=self.dtype)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[<matplotlib.lines.Line2D at 0x11d60e820>]
|
||||
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[<matplotlib.lines.Line2D at 0x139592670>]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/02e94753795fba95a52acc4488a9ab82172ba372cbb7c1cc79a3e88c3ba03d69.png" src="_images/02e94753795fba95a52acc4488a9ab82172ba372cbb7c1cc79a3e88c3ba03d69.png" />
|
||||
@@ -1483,7 +1486,7 @@ It provides composable transformations of Python+NumPy programs: differentiate,
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span><matplotlib.collections.PathCollection at 0x107a83760>
|
||||
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span><matplotlib.collections.PathCollection at 0x1394faca0>
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/60f34a975702165b9275820e0dda6259f453a92c5ad587b37191bb47a9c67ae9.png" src="_images/60f34a975702165b9275820e0dda6259f453a92c5ad587b37191bb47a9c67ae9.png" />
|
||||
|
||||
@@ -257,6 +257,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
<li class="toctree-l1"><a class="reference internal" href="week48.html">Week 48: Gradient boosting and summary of course</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek48.html">Exercises week 48</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
@@ -257,6 +257,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
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Applied Data Analysis and Machine Learning
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">From Regression to Support Vector Machines</span></p>
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<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week48.html">Week 48: Gradient boosting and summary of course</a></li>
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<li class="toctree-l1 current active"><a class="current reference internal" href="#">Exercises week 48</a></li>
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<div id="jb-print-docs-body" class="onlyprint">
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<h1>Exercises week 48</h1>
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<!-- Table of contents -->
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<ul class="visible nav section-nav flex-column">
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<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Exercises week 48</a></li>
|
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<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#overarching-aims-of-the-exercises-this-week">Overarching aims of the exercises this week</a><ul class="visible nav section-nav flex-column">
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#why-did-you-choose-this-course">Why did you choose this course?</a></li>
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#what-was-your-programming-knowledge-before-you-started">What was your programming knowledge before you started?</a></li>
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#how-do-you-judge-your-own-level-of-knowledge-on-machine-learning-before-and-after-this-course">How do you judge your own level of knowledge on machine learning before and after this course?</a></li>
|
||||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#did-the-projects-and-the-teaching-material-allow-you-to-deepen-your-insights-about-machine-learning-methods">Did the projects and the teaching material allow you to deepen your insights about Machine Learning methods?</a></li>
|
||||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#project-based-teaching-and-active-learning">Project based teaching and active learning</a></li>
|
||||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#usefulness-of-the-weekly-exercises">Usefulness of the weekly exercises</a></li>
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#lab-sessions-and-lectures">Lab sessions and lectures</a></li>
|
||||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#how-would-you-improve-this-course">How would you improve this course?</a></li>
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#then-some-basic-questions">Then some basic questions</a><ul class="nav section-nav flex-column">
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#which-is-your-preferred-information-chanel-canvas-discord-mail-or-other">Which is your preferred information chanel, Canvas, Discord, mail or other?</a></li>
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#was-the-weekly-update-with-plans-etc-useful">Was the weekly update with plans etc useful?</a></li>
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#was-it-easy-to-access-the-course-material">Was it easy to access the course material?</a></li>
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#which-resources-and-tools-did-you-use-jupyter-notebooks-github-the-various-textbooks-we-have-recommended-etc-etc">Which resources and tools did you use? Jupyter-notebooks, GitHub, the various textbooks we have recommended, etc etc</a></li>
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#if-you-did-not-attend-the-lectures-or-the-lab-sessions-which-resources-did-you-use">If you did not attend the lectures or the lab sessions, which resources did you use?</a></li>
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#any-other-topics-impressions-ideas-etc-you-would-like-to-share-with-us">Any other topics, impressions, ideas etc you would like to share with us?</a></li>
|
||||
</ul>
|
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</li>
|
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</ul>
|
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</li>
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<div id="searchbox"></div>
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<article class="bd-article">
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|
||||
<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)
|
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doconce format html exercisesweek48.do.txt -->
|
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<!-- dom:TITLE: Exercises week 48 --><section class="tex2jax_ignore mathjax_ignore" id="exercises-week-48">
|
||||
<h1>Exercises week 48<a class="headerlink" href="#exercises-week-48" title="Link to this heading">#</a></h1>
|
||||
<p><strong>November 25-29, 2024</strong></p>
|
||||
<p>Date: <strong>Deadline is Friday November 29 at midnight</strong></p>
|
||||
</section>
|
||||
<section class="tex2jax_ignore mathjax_ignore" id="overarching-aims-of-the-exercises-this-week">
|
||||
<h1>Overarching aims of the exercises this week<a class="headerlink" href="#overarching-aims-of-the-exercises-this-week" title="Link to this heading">#</a></h1>
|
||||
<p>The exercise this week is a simple course survey and feedback. This
|
||||
is important for us in order to improve our teaching material, the
|
||||
active learning format and anything else related to a succesful
|
||||
mastering of central machine learning methods and their applications.</p>
|
||||
<section id="why-did-you-choose-this-course">
|
||||
<h2>Why did you choose this course?<a class="headerlink" href="#why-did-you-choose-this-course" title="Link to this heading">#</a></h2>
|
||||
</section>
|
||||
<section id="what-was-your-programming-knowledge-before-you-started">
|
||||
<h2>What was your programming knowledge before you started?<a class="headerlink" href="#what-was-your-programming-knowledge-before-you-started" title="Link to this heading">#</a></h2>
|
||||
<p>And do you feel this course added to your programming competences and skills?</p>
|
||||
</section>
|
||||
<section id="how-do-you-judge-your-own-level-of-knowledge-on-machine-learning-before-and-after-this-course">
|
||||
<h2>How do you judge your own level of knowledge on machine learning before and after this course?<a class="headerlink" href="#how-do-you-judge-your-own-level-of-knowledge-on-machine-learning-before-and-after-this-course" title="Link to this heading">#</a></h2>
|
||||
<p>Here you can discuss your level of skill/knowledge at the start of course
|
||||
and at the end of the course and how these matched the level of
|
||||
skill/knowledge needed to complete the projects.</p>
|
||||
</section>
|
||||
<section id="did-the-projects-and-the-teaching-material-allow-you-to-deepen-your-insights-about-machine-learning-methods">
|
||||
<h2>Did the projects and the teaching material allow you to deepen your insights about Machine Learning methods?<a class="headerlink" href="#did-the-projects-and-the-teaching-material-allow-you-to-deepen-your-insights-about-machine-learning-methods" title="Link to this heading">#</a></h2>
|
||||
<p>Feel free to comment here.</p>
|
||||
</section>
|
||||
<section id="project-based-teaching-and-active-learning">
|
||||
<h2>Project based teaching and active learning<a class="headerlink" href="#project-based-teaching-and-active-learning" title="Link to this heading">#</a></h2>
|
||||
<p>This is a project based course and we as teachers would like to keep
|
||||
it as it is since we see very clearly that people who attend this
|
||||
course have a very good learning outcome. Project based courses are
|
||||
however demanding (and expensive seen from the university admin) when
|
||||
it comes to proper feedback and evaluations. Feel free to discuss
|
||||
whether you found a project-based useful. Feel also free to comment upon things we can improve upon or
|
||||
alternative ways to assess whether the learning outcomes have been
|
||||
achieved. Would you for example prefer a standard 4 hours written exam
|
||||
be something you would prefer? Or other alternatives to projects? We
|
||||
would very much value your thoughts here since projects are an
|
||||
essential part of this course.</p>
|
||||
</section>
|
||||
<section id="usefulness-of-the-weekly-exercises">
|
||||
<h2>Usefulness of the weekly exercises<a class="headerlink" href="#usefulness-of-the-weekly-exercises" title="Link to this heading">#</a></h2>
|
||||
<p>Did the weekly exercises help in getting started with the projects?
|
||||
How relevant where they for solving the projects? Feel free to
|
||||
elaborate</p>
|
||||
</section>
|
||||
<section id="lab-sessions-and-lectures">
|
||||
<h2>Lab sessions and lectures<a class="headerlink" href="#lab-sessions-and-lectures" title="Link to this heading">#</a></h2>
|
||||
<p>Was there a good link between lectures and lab sessions?
|
||||
Feel
|
||||
free to comment.</p>
|
||||
</section>
|
||||
<section id="how-would-you-improve-this-course">
|
||||
<h2>How would you improve this course?<a class="headerlink" href="#how-would-you-improve-this-course" title="Link to this heading">#</a></h2>
|
||||
<p>Are there topics which are missing, topics which could have been
|
||||
omitted and/or discussed in more depth? Feel free to add your comments
|
||||
here such as how to improve to teaching material and more.</p>
|
||||
</section>
|
||||
<section id="then-some-basic-questions">
|
||||
<h2>Then some basic questions<a class="headerlink" href="#then-some-basic-questions" title="Link to this heading">#</a></h2>
|
||||
<section id="which-is-your-preferred-information-chanel-canvas-discord-mail-or-other">
|
||||
<h3>Which is your preferred information chanel, Canvas, Discord, mail or other?<a class="headerlink" href="#which-is-your-preferred-information-chanel-canvas-discord-mail-or-other" title="Link to this heading">#</a></h3>
|
||||
</section>
|
||||
<section id="was-the-weekly-update-with-plans-etc-useful">
|
||||
<h3>Was the weekly update with plans etc useful?<a class="headerlink" href="#was-the-weekly-update-with-plans-etc-useful" title="Link to this heading">#</a></h3>
|
||||
</section>
|
||||
<section id="was-it-easy-to-access-the-course-material">
|
||||
<h3>Was it easy to access the course material?<a class="headerlink" href="#was-it-easy-to-access-the-course-material" title="Link to this heading">#</a></h3>
|
||||
</section>
|
||||
<section id="which-resources-and-tools-did-you-use-jupyter-notebooks-github-the-various-textbooks-we-have-recommended-etc-etc">
|
||||
<h3>Which resources and tools did you use? Jupyter-notebooks, GitHub, the various textbooks we have recommended, etc etc<a class="headerlink" href="#which-resources-and-tools-did-you-use-jupyter-notebooks-github-the-various-textbooks-we-have-recommended-etc-etc" title="Link to this heading">#</a></h3>
|
||||
</section>
|
||||
<section id="if-you-did-not-attend-the-lectures-or-the-lab-sessions-which-resources-did-you-use">
|
||||
<h3>If you did not attend the lectures or the lab sessions, which resources did you use?<a class="headerlink" href="#if-you-did-not-attend-the-lectures-or-the-lab-sessions-which-resources-did-you-use" title="Link to this heading">#</a></h3>
|
||||
</section>
|
||||
<section id="any-other-topics-impressions-ideas-etc-you-would-like-to-share-with-us">
|
||||
<h3>Any other topics, impressions, ideas etc you would like to share with us?<a class="headerlink" href="#any-other-topics-impressions-ideas-etc-you-would-like-to-share-with-us" title="Link to this heading">#</a></h3>
|
||||
</section>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
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<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Exercises week 48</a></li>
|
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<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#overarching-aims-of-the-exercises-this-week">Overarching aims of the exercises this week</a><ul class="visible nav section-nav flex-column">
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#why-did-you-choose-this-course">Why did you choose this course?</a></li>
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#what-was-your-programming-knowledge-before-you-started">What was your programming knowledge before you started?</a></li>
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#how-do-you-judge-your-own-level-of-knowledge-on-machine-learning-before-and-after-this-course">How do you judge your own level of knowledge on machine learning before and after this course?</a></li>
|
||||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#did-the-projects-and-the-teaching-material-allow-you-to-deepen-your-insights-about-machine-learning-methods">Did the projects and the teaching material allow you to deepen your insights about Machine Learning methods?</a></li>
|
||||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#project-based-teaching-and-active-learning">Project based teaching and active learning</a></li>
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#usefulness-of-the-weekly-exercises">Usefulness of the weekly exercises</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#lab-sessions-and-lectures">Lab sessions and lectures</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#how-would-you-improve-this-course">How would you improve this course?</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#then-some-basic-questions">Then some basic questions</a><ul class="nav section-nav flex-column">
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#which-resources-and-tools-did-you-use-jupyter-notebooks-github-the-various-textbooks-we-have-recommended-etc-etc">Which resources and tools did you use? Jupyter-notebooks, GitHub, the various textbooks we have recommended, etc etc</a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#if-you-did-not-attend-the-lectures-or-the-lab-sessions-which-resources-did-you-use">If you did not attend the lectures or the lab sessions, which resources did you use?</a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#any-other-topics-impressions-ideas-etc-you-would-like-to-share-with-us">Any other topics, impressions, ideas etc you would like to share with us?</a></li>
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@@ -254,6 +254,9 @@
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<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week48.html">Week 48: Gradient boosting and summary of course</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek48.html">Exercises week 48</a></li>
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
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<ul class="nav bd-sidenav">
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@@ -258,6 +258,9 @@
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<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
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||||
<li class="toctree-l1"><a class="reference internal" href="week48.html">Week 48: Gradient boosting and summary of course</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek48.html">Exercises week 48</a></li>
|
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
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<ul class="nav bd-sidenav">
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@@ -257,6 +257,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
<li class="toctree-l1"><a class="reference internal" href="week48.html">Week 48: Gradient boosting and summary of course</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek48.html">Exercises week 48</a></li>
|
||||
|
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</ul>
|
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
@@ -573,8 +576,8 @@ matrices and vectors.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[-0.61405495 -0.32387761 1.12215626 -0.4519346 0.45982886 0.30950817
|
||||
-0.99310295 0.38292897 1.44400587 -0.28739213]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 1.32917488 0.42655046 -1.04006029 -0.11860214 -0.68913413 0.23546487
|
||||
0.91092242 0.83425723 0.05541192 0.19079267]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -795,26 +798,26 @@ as (recall that we user lowercase letters for vectors and uppercase letters for
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[0.56237933 0.1025416 0.22316615 0.85503611 0.99057991 0.94485713
|
||||
0.03845685 0.74210311 0.91828517 0.29234676]
|
||||
[0.20570617 0.55663962 0.14395076 0.63318471 0.88506797 0.79793482
|
||||
0.21572768 0.70817694 0.98132273 0.47451039]
|
||||
[0.10238488 0.08006058 0.26302678 0.0532921 0.89460147 0.11961481
|
||||
0.90199101 0.17545974 0.89611233 0.76803496]
|
||||
[0.02942421 0.99685226 0.12021078 0.72362232 0.02251816 0.67824444
|
||||
0.70638884 0.32589372 0.39505738 0.5378907 ]
|
||||
[0.88579143 0.45698195 0.61143771 0.59118363 0.92409815 0.68270325
|
||||
0.58619632 0.77509651 0.93481453 0.87482389]
|
||||
[0.40760778 0.62136996 0.7761515 0.76287014 0.81606967 0.17622628
|
||||
0.65211459 0.55721129 0.20955256 0.20757376]
|
||||
[0.59910949 0.22876398 0.59992922 0.0586431 0.71238022 0.59293896
|
||||
0.58318087 0.50099904 0.15835637 0.1221625 ]
|
||||
[0.3510257 0.5364078 0.15260744 0.21019486 0.53797608 0.92789992
|
||||
0.91110946 0.33914178 0.20822698 0.62281444]
|
||||
[0.88111434 0.05117772 0.30607722 0.34939146 0.30612395 0.06384183
|
||||
0.23829156 0.11534663 0.44474604 0.49985968]
|
||||
[0.17578147 0.63422131 0.86986521 0.53418362 0.30679815 0.33175971
|
||||
0.83105371 0.97275788 0.37250759 0.05652557]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[0.76374594 0.87699656 0.93752045 0.58920669 0.11762693 0.41835272
|
||||
0.14130145 0.22405983 0.63569519 0.57023469]
|
||||
[0.51205632 0.0815808 0.83997706 0.62319185 0.9918084 0.82551589
|
||||
0.23523437 0.5917984 0.26977294 0.22313112]
|
||||
[0.78875244 0.33827328 0.15734346 0.76351169 0.88571475 0.8744876
|
||||
0.8179527 0.96419308 0.28859292 0.21034124]
|
||||
[0.56604693 0.0014122 0.69927222 0.76825307 0.81056827 0.18710226
|
||||
0.96298663 0.51905411 0.75661166 0.19343497]
|
||||
[0.80954821 0.27400301 0.19608145 0.90678518 0.52612126 0.57201534
|
||||
0.9065678 0.72410901 0.77533887 0.73086608]
|
||||
[0.0583118 0.57305201 0.99949947 0.6534883 0.87204797 0.81128625
|
||||
0.39326284 0.67711227 0.61020809 0.31082354]
|
||||
[0.13856233 0.04308921 0.87608062 0.27598375 0.37282031 0.5906656
|
||||
0.05406637 0.09201716 0.39585666 0.16639711]
|
||||
[0.93928366 0.33845772 0.1675106 0.86044604 0.2131759 0.13239107
|
||||
0.06461985 0.25020484 0.93644206 0.93511011]
|
||||
[0.52539786 0.72253483 0.80209029 0.8317496 0.06071723 0.60162183
|
||||
0.41902867 0.01471716 0.75263199 0.47058817]
|
||||
[0.61513099 0.99809525 0.36655653 0.36519275 0.38100512 0.84195998
|
||||
0.06668051 0.41250502 0.54002942 0.66316435]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -874,13 +877,13 @@ covariance matrix through the <strong>np.linalg.eig()</strong> function.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.07252404965482757
|
||||
3.80181024148491
|
||||
-0.6317631821295671
|
||||
[[ 0.91103528 2.76447289 2.63577152]
|
||||
[ 2.76447289 9.56058946 7.79271587]
|
||||
[ 2.63577152 7.79271587 15.61196303]]
|
||||
[21.62109277 0.08978688 4.37270812]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.10396409632756674
|
||||
4.152389775605299
|
||||
0.1319830915583838
|
||||
[[ 1.1503634 3.6073857 3.74515461]
|
||||
[ 3.6073857 12.39943598 12.04015097]
|
||||
[ 3.74515461 12.04015097 18.79739603]]
|
||||
[29.01518302 0.08664758 3.24536482]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
Binary file not shown.
@@ -66,7 +66,7 @@
|
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@@ -257,6 +257,9 @@
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<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week48.html">Week 48: Gradient boosting and summary of course</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek48.html">Exercises week 48</a></li>
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@@ -953,12 +956,12 @@ of code developers and contributors keeps increasing.</p>
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<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
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@@ -256,6 +256,9 @@
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<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week48.html">Week 48: Gradient boosting and summary of course</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek48.html">Exercises week 48</a></li>
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@@ -255,6 +255,9 @@
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<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek48.html">Exercises week 48</a></li>
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@@ -256,6 +256,9 @@
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<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week48.html">Week 48: Gradient boosting and summary of course</a></li>
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