update
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Morten Hjorth-Jensen
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@@ -0,0 +1,153 @@
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{
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"metadata": {
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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 exercisesweek47.do.txt -->\n",
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"<!-- dom:TITLE: Exercise week 47 -->"
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"metadata": {
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"editable": true
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},
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"source": [
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"# Exercise week 47\n",
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"**November 18-22, 2024**\n",
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"\n",
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"Date: **Deadline is Friday November 22 at midnight**"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2b90add8",
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"metadata": {
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"editable": true
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},
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"source": [
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"# Overarching aims of the exercises this week\n",
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"\n",
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"The exercise set this week is meant as a summary of many of the\n",
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"central elements in various machine learning algorithms, with a slight\n",
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"bias towards deep learning methods and their training. You don't need to answer all questions.\n",
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"\n",
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"The last weekly exercise (week 48) is a general course survey."
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]
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},
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{
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"cell_type": "markdown",
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"id": "b4f3ae78",
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"metadata": {
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"editable": true
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},
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"source": [
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"## Exercise 1: Linear and logistic regression methods\n",
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"\n",
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"1. What is the main difference between ordinary least squares and Ridge regression?\n",
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"\n",
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"2. Which kind of data set would you use logistic regression for?\n",
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"\n",
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"3. In linear regression you assume that your output is described by a continuous non-stochastic function $f(x)$. Which is the equivalent function in logistic regression?\n",
|
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"\n",
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"4. Can you find an analytic solution to a logistic regression type of problem?\n",
|
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"\n",
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"5. What kind of cost function would you use in logistic regression?"
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "markdown",
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||||
"id": "755cfd27",
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||||
"metadata": {
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||||
"editable": true
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},
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||||
"source": [
|
||||
"## Exercise 2: Deep learning\n",
|
||||
"\n",
|
||||
"1. What is an activation function and discuss the use of an activation function? Explain three different types of activation functions?\n",
|
||||
"\n",
|
||||
"2. Describe the architecture of a typical feed forward Neural Network (NN). \n",
|
||||
"\n",
|
||||
"3. You are using a deep neural network for a prediction task. After training your model, you notice that it is strongly overfitting the training set and that the performance on the test isn’t good. What can you do to reduce overfitting?\n",
|
||||
"\n",
|
||||
"4. How would you know if your model is suffering from the problem of exploding Gradients?\n",
|
||||
"\n",
|
||||
"5. Can you name and explain a few hyperparameters used for training a neural network?\n",
|
||||
"\n",
|
||||
"6. Describe the architecture of a typical Convolutional Neural Network (CNN)\n",
|
||||
"\n",
|
||||
"7. What is the vanishing gradient problem in Neural Networks and how to fix it?\n",
|
||||
"\n",
|
||||
"8. When it comes to training an artificial neural network, what could the reason be for why the cost/loss doesn't decrease in a few epochs?\n",
|
||||
"\n",
|
||||
"9. How does L1/L2 regularization affect a neural network?\n",
|
||||
"\n",
|
||||
"10. What is(are) the advantage(s) of deep learning over traditional methods like linear regression or logistic regression?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
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||||
"id": "85175b87",
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||||
"metadata": {
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||||
"editable": true
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||||
},
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||||
"source": [
|
||||
"## Exercise 3: Decision trees and ensemble methods\n",
|
||||
"\n",
|
||||
"1. Mention some pros and cons when using decision trees\n",
|
||||
"\n",
|
||||
"2. How do we grow a tree? And which are the main parameters? \n",
|
||||
"\n",
|
||||
"3. Mention some of the benefits with using ensemble methods (like bagging, random forests and boosting methods)?\n",
|
||||
"\n",
|
||||
"4. Why would you prefer a random forest instead of using Bagging to grow a forest?\n",
|
||||
"\n",
|
||||
"5. What is the basic philosophy behind boosting methods?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
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||||
"id": "fbfdfe68",
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||||
"metadata": {
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||||
"editable": true
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},
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||||
"source": [
|
||||
"## Exercise 4: Optimization part\n",
|
||||
"\n",
|
||||
"1. Which is the basic mathematical root-finding method behind essentially all gradient descent approaches(stochastic and non-stochastic)? \n",
|
||||
"\n",
|
||||
"2. And why don't we use it? Or stated differently, why do we introduce the learning rate as a parameter?\n",
|
||||
"\n",
|
||||
"3. What might happen if you set the momentum hyperparameter too close to 1 (e.g., 0.9999) when using an optimizer for the learning rate?\n",
|
||||
"\n",
|
||||
"4. Why should we use stochastic gradient descent instead of plain gradient descent?\n",
|
||||
"\n",
|
||||
"5. Which parameters would you need to tune when use a stochastic gradient descent approach?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "92fc1b0c",
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||||
"metadata": {
|
||||
"editable": true
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||||
},
|
||||
"source": [
|
||||
"## Exercise 5: Analysis of results\n",
|
||||
"1. How do you assess overfitting and underfitting?\n",
|
||||
"\n",
|
||||
"2. Why do we divide the data in test and train and/or eventually validation sets?\n",
|
||||
"\n",
|
||||
"3. Why would you use resampling methods in the data analysis? Mention some widely popular resampling methods."
|
||||
]
|
||||
}
|
||||
],
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||||
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"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
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Load Diff
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
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|
||||
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|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
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<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
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|
||||
|
||||
@@ -254,6 +254,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
@@ -768,7 +771,7 @@ We use <strong>Pandas</strong> to compute the correlation matrix.</p>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/8bccd564c8ef11fa6a628c29733db8aa23d9dce879b7da0104d7ba24f157d2d5.png" src="_images/8bccd564c8ef11fa6a628c29733db8aa23d9dce879b7da0104d7ba24f157d2d5.png" />
|
||||
<img alt="_images/43f947d18886acb70f69ec0139f7bb4e05b3971b9df3b0dab14aec06ae48e1c5.png" src="_images/43f947d18886acb70f69ec0139f7bb4e05b3971b9df3b0dab14aec06ae48e1c5.png" />
|
||||
<img alt="_images/56152218b294d7d9b026739262de2604c6af03d70f67eadede1bd7045aea96d2.png" src="_images/56152218b294d7d9b026739262de2604c6af03d70f67eadede1bd7045aea96d2.png" />
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
@@ -847,9 +850,7 @@ applications. This will be discussed later this semester (<a class="reference ex
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(426, 30)
|
||||
(143, 30)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[1. 0.86666667 1. 0.85714286 1. 0.85714286
|
||||
[1. 0.86666667 1. 0.85714286 1. 0.85714286
|
||||
1. 0.92857143 0.92857143 1. ]
|
||||
Test set accuracy with Logistic Regression: 0.94
|
||||
</pre></div>
|
||||
|
||||
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
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|
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|
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<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
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|
||||
|
||||
@@ -254,6 +254,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
@@ -727,7 +730,7 @@ 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/ac78b4c9fff962e3b699429550b7c321effb3032e3444a63d30a8efd19ebb5d6.png" src="_images/ac78b4c9fff962e3b699429550b7c321effb3032e3444a63d30a8efd19ebb5d6.png" />
|
||||
<img alt="_images/7208bf883e220945a23bf4a64d7c474c22032a9479d0230f3f7627fde6c870e3.png" src="_images/7208bf883e220945a23bf4a64d7c474c22032a9479d0230f3f7627fde6c870e3.png" />
|
||||
</div>
|
||||
</div>
|
||||
<p>This example serves several aims. It allows us to demonstrate several
|
||||
@@ -812,7 +815,7 @@ to be dominated by outliers.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/1ce5de7d3b7bb6c52facf4e858e6c4b7eb6a4371cc0e4454c2f6e913d60da064.png" src="_images/1ce5de7d3b7bb6c52facf4e858e6c4b7eb6a4371cc0e4454c2f6e913d60da064.png" />
|
||||
<img alt="_images/daf635918e102d6af1572d0f6fcb2bcd9c93425cd41e91dc85022a813e3b1da5.png" src="_images/daf635918e102d6af1572d0f6fcb2bcd9c93425cd41e91dc85022a813e3b1da5.png" />
|
||||
</div>
|
||||
</div>
|
||||
<p>Depending on the parameter in front of the normal distribution, we may
|
||||
@@ -859,16 +862,16 @@ 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:
|
||||
[2.04161185]
|
||||
[1.97386121]
|
||||
Coefficient beta :
|
||||
[[4.82942403]]
|
||||
Mean squared error: 0.25
|
||||
Variance score: 0.87
|
||||
[[5.12574106]]
|
||||
Mean squared error: 0.20
|
||||
Variance score: 0.92
|
||||
Mean squared log error: 0.01
|
||||
Mean absolute error: 0.40
|
||||
Mean absolute error: 0.36
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/544b198f663ddf96819b62f0d11bd4bb1b0916b7f81b07edba1299dabca63229.png" src="_images/544b198f663ddf96819b62f0d11bd4bb1b0916b7f81b07edba1299dabca63229.png" />
|
||||
<img alt="_images/1168b639886622ca16f51897f200d03b2de325bfc3f6688b4b75ff4473160f59.png" src="_images/1168b639886622ca16f51897f200d03b2de325bfc3f6688b4b75ff4473160f59.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
|
||||
@@ -964,7 +967,7 @@ a linear <span class="math notranslate nohighlight">\(x\)</span>-dependence we s
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/5b8216a39dc2cc82a708564495577a73182745d197edb3712c33ea439726bbd7.png" src="_images/5b8216a39dc2cc82a708564495577a73182745d197edb3712c33ea439726bbd7.png" />
|
||||
<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
|
||||
</pre></div>
|
||||
</div>
|
||||
|
||||
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
|
||||
|
||||
@@ -254,6 +254,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
@@ -1111,7 +1114,7 @@ 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_11460/953065564.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_58742/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1445,7 +1448,7 @@ 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_11460/953065564.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_58742/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1454,7 +1457,7 @@ 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_11460/953065564.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_58742/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1463,227 +1466,34 @@ 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_11460/953065564.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_58742/953065564.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_11460/953065564.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_11460/953065564.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_11460/953065564.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_11460/953065564.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_11460/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.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_11460/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.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_11460/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.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_11460/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.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_11460/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.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_11460/953065564.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_11460/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.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_11460/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.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_11460/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.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_11460/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.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_11460/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.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_11460/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.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_11460/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.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 = 1.0
|
||||
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_11460/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/1630775253.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 = 10.0
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
<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>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">8</span> <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">lmbd</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">lmbd_vals</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">9</span> <span class="n">dnn</span> <span class="o">=</span> <span class="n">NeuralNetwork</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train_onehot</span><span class="p">,</span> <span class="n">eta</span><span class="o">=</span><span class="n">eta</span><span class="p">,</span> <span class="n">lmbd</span><span class="o">=</span><span class="n">lmbd</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">10</span> <span class="n">n_hidden_neurons</span><span class="o">=</span><span class="n">n_hidden_neurons</span><span class="p">,</span> <span class="n">n_categories</span><span class="o">=</span><span class="n">n_categories</span><span class="p">)</span>
|
||||
<span class="ne">---> </span><span class="mi">11</span> <span class="n">dnn</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
|
||||
<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="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="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="ne">KeyboardInterrupt</span>:
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1729,22 +1539,6 @@ Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</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_11460/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11460/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/f06d0e9eeee0b990dceecc688a16c5ff028476e6e3dd080fdf20884e56a094bd.png" src="_images/f06d0e9eeee0b990dceecc688a16c5ff028476e6e3dd080fdf20884e56a094bd.png" />
|
||||
<img alt="_images/35be2ace9d03a262c51a7163cf278f73639f8f94608ddf8716d48232aaf7213d.png" src="_images/35be2ace9d03a262c51a7163cf278f73639f8f94608ddf8716d48232aaf7213d.png" />
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="scikit-learn-implementation">
|
||||
@@ -1780,327 +1574,6 @@ performance overall.</p>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.18333333333333332
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.18611111111111112
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.13055555555555556
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.24444444444444444
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.23333333333333334
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.12777777777777777
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.1527777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.0001
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.9111111111111111
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.0001
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.8888888888888888
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.0001
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.8722222222222222
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.0001
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.8305555555555556
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.0001
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.8888888888888888
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.0001
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.8805555555555555
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.0001
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.8944444444444445
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.001
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.975
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.001
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.9777777777777777
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.001
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.9805555555555555
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.001
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.9861111111111112
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.001
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.9805555555555555
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.001
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.9777777777777777
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.001
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.9444444444444444
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.9861111111111112
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.9888888888888889
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.9888888888888889
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.9861111111111112
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.9888888888888889
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.9722222222222222
|
||||
|
||||
Learning rate = 0.01
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.9527777777777777
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.1
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.9027777777777778
|
||||
|
||||
Learning rate = 0.1
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.8583333333333333
|
||||
</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: 0.8722222222222222
|
||||
|
||||
Learning rate = 0.1
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.9055555555555556
|
||||
</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: 0.8805555555555555
|
||||
|
||||
Learning rate = 0.1
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.8722222222222222
|
||||
</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: 0.8666666666666667
|
||||
|
||||
Learning rate = 1.0
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.08611111111111111
|
||||
|
||||
Learning rate = 1.0
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.10555555555555556
|
||||
</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: 0.10555555555555556
|
||||
|
||||
Learning rate = 1.0
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.17777777777777778
|
||||
|
||||
Learning rate = 1.0
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.08333333333333333
|
||||
</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: 0.08888888888888889
|
||||
|
||||
Learning rate = 1.0
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.09444444444444444
|
||||
|
||||
Learning rate = 10.0
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.17222222222222222
|
||||
</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: 0.11666666666666667
|
||||
|
||||
Learning rate = 10.0
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.10555555555555556
|
||||
|
||||
Learning rate = 10.0
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.1388888888888889
|
||||
|
||||
Learning rate = 10.0
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.11388888888888889
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.10555555555555556
|
||||
|
||||
Learning rate = 10.0
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.09444444444444444
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="id1">
|
||||
@@ -2144,10 +1617,6 @@ Accuracy score on test set: 0.09444444444444444
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/96aef53b7c1771338b8f26da1aba3a6a8520fe7f4ca53d6567d5c27a3020d7b8.png" src="_images/96aef53b7c1771338b8f26da1aba3a6a8520fe7f4ca53d6567d5c27a3020d7b8.png" />
|
||||
<img alt="_images/f62cdcfda8dab5a042cb6a0fc32e7bfebd1d09efeab7b32826e7097ab7876cef.png" src="_images/f62cdcfda8dab5a042cb6a0fc32e7bfebd1d09efeab7b32826e7097ab7876cef.png" />
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="building-neural-networks-in-tensorflow-and-keras">
|
||||
@@ -2186,14 +1655,6 @@ and/or if you use <strong>anaconda</strong>, just write (or install from the gra
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span> <span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">12</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
|
||||
<span class="n">conda</span> <span class="n">create</span> <span class="o">-</span><span class="n">n</span> <span class="n">tf</span> <span class="n">tensorflow</span>
|
||||
<span class="o">^</span>
|
||||
<span class="ne">SyntaxError</span>: invalid syntax
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<p>To install the current release of GPU TensorFlow</p>
|
||||
<div class="cell docutils container">
|
||||
|
||||
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
|
||||
|
||||
@@ -254,6 +254,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
|
||||
|
||||
@@ -254,6 +254,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
@@ -1109,35 +1112,6 @@ labels = (n_inputs) = (1797,)
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
|
||||
<span class="ne">NotFoundError</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">4</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
|
||||
<span class="ne">----> </span><span class="mi">1</span> <span class="kn">from</span> <span class="nn">tensorflow.keras</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">models</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">2</span> <span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="kn">import</span> <span class="n">Input</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">3</span> <span class="kn">from</span> <span class="nn">tensorflow.keras.models</span> <span class="kn">import</span> <span class="n">Sequential</span> <span class="c1">#This allows appending layers to existing models</span>
|
||||
|
||||
<span class="n">File</span> <span class="o">~/</span><span class="n">miniforge3</span><span class="o">/</span><span class="n">envs</span><span class="o">/</span><span class="n">myenv</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python3</span><span class="mf">.9</span><span class="o">/</span><span class="n">site</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">tensorflow</span><span class="o">/</span><span class="fm">__init__</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">440</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">438</span> <span class="n">_plugin_dir</span> <span class="o">=</span> <span class="n">_os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">_s</span><span class="p">,</span> <span class="s1">'tensorflow-plugins'</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">439</span> <span class="k">if</span> <span class="n">_os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">_plugin_dir</span><span class="p">):</span>
|
||||
<span class="ne">--> </span><span class="mi">440</span> <span class="n">_ll</span><span class="o">.</span><span class="n">load_library</span><span class="p">(</span><span class="n">_plugin_dir</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">441</span> <span class="c1"># Load Pluggable Device Library</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">442</span> <span class="n">_ll</span><span class="o">.</span><span class="n">load_pluggable_device_library</span><span class="p">(</span><span class="n">_plugin_dir</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/framework/load_library.py:151,</span> in <span class="ni">load_library</span><span class="nt">(library_location)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">148</span> <span class="n">kernel_libraries</span> <span class="o">=</span> <span class="p">[</span><span class="n">library_location</span><span class="p">]</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">150</span> <span class="k">for</span> <span class="n">lib</span> <span class="ow">in</span> <span class="n">kernel_libraries</span><span class="p">:</span>
|
||||
<span class="ne">--> </span><span class="mi">151</span> <span class="n">py_tf</span><span class="o">.</span><span class="n">TF_LoadLibrary</span><span class="p">(</span><span class="n">lib</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">153</span> <span class="k">else</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">154</span> <span class="k">raise</span> <span class="ne">OSError</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">155</span> <span class="n">errno</span><span class="o">.</span><span class="n">ENOENT</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">156</span> <span class="s1">'The file or folder to load kernel libraries from does not exist.'</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">157</span> <span class="n">library_location</span><span class="p">)</span>
|
||||
|
||||
<span class="ne">NotFoundError</span>: dlopen(/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): symbol not found in flat namespace '_TF_GetInputPropertiesList'
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
@@ -1192,6 +1166,71 @@ labels = (n_inputs) = (1797,)
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<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/convolutional/base_conv.py:107: 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__(activity_regularizer=activity_regularizer, **kwargs)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
|
||||
<span class="ne">ValueError</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">6</span><span class="p">],</span> <span class="n">line</span> <span class="mi">5</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">3</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">eta</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">eta_vals</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">4</span> <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">lmbd</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">lmbd_vals</span><span class="p">):</span>
|
||||
<span class="ne">----> </span><span class="mi">5</span> <span class="n">CNN</span> <span class="o">=</span> <span class="n">create_convolutional_neural_network_keras</span><span class="p">(</span><span class="n">input_shape</span><span class="p">,</span> <span class="n">receptive_field</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">6</span> <span class="n">n_filters</span><span class="p">,</span> <span class="n">n_neurons_connected</span><span class="p">,</span> <span class="n">n_categories</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">7</span> <span class="n">eta</span><span class="p">,</span> <span class="n">lmbd</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">8</span> <span class="n">CNN</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">9</span> <span class="n">scores</span> <span class="o">=</span> <span class="n">CNN</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">Y_test</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">Cell In[5], line 12,</span> in <span class="ni">create_convolutional_neural_network_keras</span><span class="nt">(input_shape, receptive_field, n_filters, n_neurons_connected, n_categories, eta, lmbd)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">9</span> <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">n_neurons_connected</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">,</span> <span class="n">kernel_regularizer</span><span class="o">=</span><span class="n">regularizers</span><span class="o">.</span><span class="n">l2</span><span class="p">(</span><span class="n">lmbd</span><span class="p">)))</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">10</span> <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">n_categories</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'softmax'</span><span class="p">,</span> <span class="n">kernel_regularizer</span><span class="o">=</span><span class="n">regularizers</span><span class="o">.</span><span class="n">l2</span><span class="p">(</span><span class="n">lmbd</span><span class="p">)))</span>
|
||||
<span class="ne">---> </span><span class="mi">12</span> <span class="n">sgd</span> <span class="o">=</span> <span class="n">optimizers</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="n">eta</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">13</span> <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">'categorical_crossentropy'</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">sgd</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">])</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">15</span> <span class="k">return</span> <span class="n">model</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/src/optimizers/sgd.py:60,</span> in <span class="ni">SGD.__init__</span><span class="nt">(self, learning_rate, momentum, nesterov, weight_decay, clipnorm, clipvalue, global_clipnorm, use_ema, ema_momentum, ema_overwrite_frequency, loss_scale_factor, gradient_accumulation_steps, name, **kwargs)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">43</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">44</span> <span class="bp">self</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">45</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span>
|
||||
<span class="p">(</span><span class="o">...</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">58</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">59</span> <span class="p">):</span>
|
||||
<span class="ne">---> </span><span class="mi">60</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">61</span> <span class="n">learning_rate</span><span class="o">=</span><span class="n">learning_rate</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">62</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">63</span> <span class="n">weight_decay</span><span class="o">=</span><span class="n">weight_decay</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">64</span> <span class="n">clipnorm</span><span class="o">=</span><span class="n">clipnorm</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">65</span> <span class="n">clipvalue</span><span class="o">=</span><span class="n">clipvalue</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">66</span> <span class="n">global_clipnorm</span><span class="o">=</span><span class="n">global_clipnorm</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">67</span> <span class="n">use_ema</span><span class="o">=</span><span class="n">use_ema</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">68</span> <span class="n">ema_momentum</span><span class="o">=</span><span class="n">ema_momentum</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">69</span> <span class="n">ema_overwrite_frequency</span><span class="o">=</span><span class="n">ema_overwrite_frequency</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">70</span> <span class="n">loss_scale_factor</span><span class="o">=</span><span class="n">loss_scale_factor</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">71</span> <span class="n">gradient_accumulation_steps</span><span class="o">=</span><span class="n">gradient_accumulation_steps</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">72</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">73</span> <span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">74</span> <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">momentum</span><span class="p">,</span> <span class="nb">float</span><span class="p">)</span> <span class="ow">or</span> <span class="n">momentum</span> <span class="o"><</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">momentum</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">75</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"`momentum` must be a float between [0, 1]."</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/src/backend/tensorflow/optimizer.py:23,</span> in <span class="ni">TFOptimizer.__init__</span><span class="nt">(self, *args, **kwargs)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">22</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="ne">---> </span><span class="mi">23</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">24</span> <span class="bp">self</span><span class="o">.</span><span class="n">_distribution_strategy</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">distribute</span><span class="o">.</span><span class="n">get_strategy</span><span class="p">()</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/src/optimizers/base_optimizer.py:90,</span> in <span class="ni">BaseOptimizer.__init__</span><span class="nt">(self, learning_rate, weight_decay, clipnorm, clipvalue, global_clipnorm, use_ema, ema_momentum, ema_overwrite_frequency, loss_scale_factor, gradient_accumulation_steps, name, **kwargs)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">86</span> <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">87</span> <span class="s2">"Argument `decay` is no longer supported and will be ignored."</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">88</span> <span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">89</span> <span class="k">if</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<span class="ne">---> </span><span class="mi">90</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Argument(s) not recognized: </span><span class="si">{</span><span class="n">kwargs</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">92</span> <span class="k">if</span> <span class="n">name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">93</span> <span class="n">name</span> <span class="o">=</span> <span class="n">auto_name</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
|
||||
|
||||
<span class="ne">ValueError</span>: Argument(s) not recognized: {'lr': 1e-05}
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
|
||||
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
|
||||
|
||||
@@ -254,6 +254,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
@@ -555,33 +558,501 @@ 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" />
|
||||
<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>
|
||||
</div>
|
||||
<div class="output text_html"><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold">Model: "sequential"</span>
|
||||
</pre>
|
||||
</div><div class="output text_html"><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
|
||||
┃<span style="font-weight: bold"> Layer (type) </span>┃<span style="font-weight: bold"> Output Shape </span>┃<span style="font-weight: bold"> Param # </span>┃
|
||||
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
|
||||
│ simple_rnn (<span style="color: #0087ff; text-decoration-color: #0087ff">SimpleRNN</span>) │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>) │ <span style="color: #00af00; text-decoration-color: #00af00">1,184</span> │
|
||||
├─────────────────────────────────┼────────────────────────┼───────────────┤
|
||||
│ dense (<span style="color: #0087ff; text-decoration-color: #0087ff">Dense</span>) │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">8</span>) │ <span style="color: #00af00; text-decoration-color: #00af00">264</span> │
|
||||
├─────────────────────────────────┼────────────────────────┼───────────────┤
|
||||
│ dense_1 (<span style="color: #0087ff; text-decoration-color: #0087ff">Dense</span>) │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">1</span>) │ <span style="color: #00af00; text-decoration-color: #00af00">9</span> │
|
||||
└─────────────────────────────────┴────────────────────────┴───────────────┘
|
||||
</pre>
|
||||
</div><div class="output text_html"><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Total params: </span><span style="color: #00af00; text-decoration-color: #00af00">1,457</span> (5.69 KB)
|
||||
</pre>
|
||||
</div><div class="output text_html"><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Trainable params: </span><span style="color: #00af00; text-decoration-color: #00af00">1,457</span> (5.69 KB)
|
||||
</pre>
|
||||
</div><div class="output text_html"><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Non-trainable params: </span><span style="color: #00af00; text-decoration-color: #00af00">0</span> (0.00 B)
|
||||
</pre>
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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
|
||||
</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 traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
|
||||
<span class="ne">NotFoundError</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">7</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">5</span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">6</span> <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
||||
<span class="ne">----> </span><span class="mi">7</span> <span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">8</span> <span class="kn">from</span> <span class="nn">tensorflow.keras</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">models</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">9</span> <span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="kn">import</span> <span class="n">Input</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>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">55</span> <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">'mean_squared_error'</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">'rmsprop'</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">56</span> <span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
|
||||
<span class="ne">---> </span><span class="mi">58</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">trainX</span><span class="p">,</span><span class="n">trainY</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">59</span> <span class="n">trainPredict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">trainX</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">60</span> <span class="n">testPredict</span><span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">testX</span><span class="p">)</span>
|
||||
|
||||
<span class="n">File</span> <span class="o">~/</span><span class="n">miniforge3</span><span class="o">/</span><span class="n">envs</span><span class="o">/</span><span class="n">myenv</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python3</span><span class="mf">.9</span><span class="o">/</span><span class="n">site</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">tensorflow</span><span class="o">/</span><span class="fm">__init__</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">440</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">438</span> <span class="n">_plugin_dir</span> <span class="o">=</span> <span class="n">_os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">_s</span><span class="p">,</span> <span class="s1">'tensorflow-plugins'</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">439</span> <span class="k">if</span> <span class="n">_os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">_plugin_dir</span><span class="p">):</span>
|
||||
<span class="ne">--> </span><span class="mi">440</span> <span class="n">_ll</span><span class="o">.</span><span class="n">load_library</span><span class="p">(</span><span class="n">_plugin_dir</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">441</span> <span class="c1"># Load Pluggable Device Library</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">442</span> <span class="n">_ll</span><span class="o">.</span><span class="n">load_pluggable_device_library</span><span class="p">(</span><span class="n">_plugin_dir</span><span class="p">)</span>
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py:117,</span> in <span class="ni">filter_traceback.<locals>.error_handler</span><span class="nt">(*args, **kwargs)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">115</span> <span class="n">filtered_tb</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">116</span> <span class="k">try</span><span class="p">:</span>
|
||||
<span class="ne">--> </span><span class="mi">117</span> <span class="k">return</span> <span class="n">fn</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">118</span> <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">119</span> <span class="n">filtered_tb</span> <span class="o">=</span> <span class="n">_process_traceback_frames</span><span class="p">(</span><span class="n">e</span><span class="o">.</span><span class="n">__traceback__</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/framework/load_library.py:151,</span> in <span class="ni">load_library</span><span class="nt">(library_location)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">148</span> <span class="n">kernel_libraries</span> <span class="o">=</span> <span class="p">[</span><span class="n">library_location</span><span class="p">]</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">150</span> <span class="k">for</span> <span class="n">lib</span> <span class="ow">in</span> <span class="n">kernel_libraries</span><span class="p">:</span>
|
||||
<span class="ne">--> </span><span class="mi">151</span> <span class="n">py_tf</span><span class="o">.</span><span class="n">TF_LoadLibrary</span><span class="p">(</span><span class="n">lib</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">153</span> <span class="k">else</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">154</span> <span class="k">raise</span> <span class="ne">OSError</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">155</span> <span class="n">errno</span><span class="o">.</span><span class="n">ENOENT</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">156</span> <span class="s1">'The file or folder to load kernel libraries from does not exist.'</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">157</span> <span class="n">library_location</span><span class="p">)</span>
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/src/backend/tensorflow/trainer.py:320,</span> in <span class="ni">TensorFlowTrainer.fit</span><span class="nt">(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">318</span> <span class="k">for</span> <span class="n">step</span><span class="p">,</span> <span class="n">iterator</span> <span class="ow">in</span> <span class="n">epoch_iterator</span><span class="o">.</span><span class="n">enumerate_epoch</span><span class="p">():</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">319</span> <span class="n">callbacks</span><span class="o">.</span><span class="n">on_train_batch_begin</span><span class="p">(</span><span class="n">step</span><span class="p">)</span>
|
||||
<span class="ne">--> </span><span class="mi">320</span> <span class="n">logs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_function</span><span class="p">(</span><span class="n">iterator</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">321</span> <span class="n">callbacks</span><span class="o">.</span><span class="n">on_train_batch_end</span><span class="p">(</span><span class="n">step</span><span class="p">,</span> <span class="n">logs</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">322</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">stop_training</span><span class="p">:</span>
|
||||
|
||||
<span class="ne">NotFoundError</span>: dlopen(/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): symbol not found in flat namespace '_TF_GetInputPropertiesList'
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py:150,</span> in <span class="ni">filter_traceback.<locals>.error_handler</span><span class="nt">(*args, **kwargs)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">148</span> <span class="n">filtered_tb</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">149</span> <span class="k">try</span><span class="p">:</span>
|
||||
<span class="ne">--> </span><span class="mi">150</span> <span class="k">return</span> <span class="n">fn</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">151</span> <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">152</span> <span class="n">filtered_tb</span> <span class="o">=</span> <span class="n">_process_traceback_frames</span><span class="p">(</span><span class="n">e</span><span class="o">.</span><span class="n">__traceback__</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:833,</span> in <span class="ni">Function.__call__</span><span class="nt">(self, *args, **kwds)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">830</span> <span class="n">compiler</span> <span class="o">=</span> <span class="s2">"xla"</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jit_compile</span> <span class="k">else</span> <span class="s2">"nonXla"</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">832</span> <span class="k">with</span> <span class="n">OptionalXlaContext</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jit_compile</span><span class="p">):</span>
|
||||
<span class="ne">--> </span><span class="mi">833</span> <span class="n">result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwds</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">835</span> <span class="n">new_tracing_count</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">experimental_get_tracing_count</span><span class="p">()</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">836</span> <span class="n">without_tracing</span> <span class="o">=</span> <span class="p">(</span><span class="n">tracing_count</span> <span class="o">==</span> <span class="n">new_tracing_count</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:878,</span> in <span class="ni">Function._call</span><span class="nt">(self, *args, **kwds)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">875</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lock</span><span class="o">.</span><span class="n">release</span><span class="p">()</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">876</span> <span class="c1"># In this case we have not created variables on the first call. So we can</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">877</span> <span class="c1"># run the first trace but we should fail if variables are created.</span>
|
||||
<span class="ne">--> </span><span class="mi">878</span> <span class="n">results</span> <span class="o">=</span> <span class="n">tracing_compilation</span><span class="o">.</span><span class="n">call_function</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">879</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwds</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_variable_creation_config</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">880</span> <span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">881</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_created_variables</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">882</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Creating variables on a non-first call to a function"</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">883</span> <span class="s2">" decorated with tf.function."</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:139,</span> in <span class="ni">call_function</span><span class="nt">(args, kwargs, tracing_options)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">137</span> <span class="n">bound_args</span> <span class="o">=</span> <span class="n">function</span><span class="o">.</span><span class="n">function_type</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">138</span> <span class="n">flat_inputs</span> <span class="o">=</span> <span class="n">function</span><span class="o">.</span><span class="n">function_type</span><span class="o">.</span><span class="n">unpack_inputs</span><span class="p">(</span><span class="n">bound_args</span><span class="p">)</span>
|
||||
<span class="ne">--> </span><span class="mi">139</span> <span class="k">return</span> <span class="n">function</span><span class="o">.</span><span class="n">_call_flat</span><span class="p">(</span> <span class="c1"># pylint: disable=protected-access</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">140</span> <span class="n">flat_inputs</span><span class="p">,</span> <span class="n">captured_inputs</span><span class="o">=</span><span class="n">function</span><span class="o">.</span><span class="n">captured_inputs</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">141</span> <span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py:1322,</span> in <span class="ni">ConcreteFunction._call_flat</span><span class="nt">(self, tensor_inputs, captured_inputs)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1318</span> <span class="n">possible_gradient_type</span> <span class="o">=</span> <span class="n">gradients_util</span><span class="o">.</span><span class="n">PossibleTapeGradientTypes</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1319</span> <span class="k">if</span> <span class="p">(</span><span class="n">possible_gradient_type</span> <span class="o">==</span> <span class="n">gradients_util</span><span class="o">.</span><span class="n">POSSIBLE_GRADIENT_TYPES_NONE</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1320</span> <span class="ow">and</span> <span class="n">executing_eagerly</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1321</span> <span class="c1"># No tape is watching; skip to running the function.</span>
|
||||
<span class="ne">-> </span><span class="mi">1322</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_inference_function</span><span class="o">.</span><span class="n">call_preflattened</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1323</span> <span class="n">forward_backward</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_select_forward_and_backward_functions</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1324</span> <span class="n">args</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1325</span> <span class="n">possible_gradient_type</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1326</span> <span class="n">executing_eagerly</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1327</span> <span class="n">forward_function</span><span class="p">,</span> <span class="n">args_with_tangents</span> <span class="o">=</span> <span class="n">forward_backward</span><span class="o">.</span><span class="n">forward</span><span class="p">()</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:216,</span> in <span class="ni">AtomicFunction.call_preflattened</span><span class="nt">(self, args)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">214</span> <span class="k">def</span> <span class="nf">call_preflattened</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">core</span><span class="o">.</span><span class="n">Tensor</span><span class="p">])</span> <span class="o">-></span> <span class="n">Any</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">215</span><span class="w"> </span><span class="sd">"""Calls with flattened tensor inputs and returns the structured output."""</span>
|
||||
<span class="ne">--> </span><span class="mi">216</span> <span class="n">flat_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">call_flat</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">217</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">function_type</span><span class="o">.</span><span class="n">pack_output</span><span class="p">(</span><span class="n">flat_outputs</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:251,</span> in <span class="ni">AtomicFunction.call_flat</span><span class="nt">(self, *args)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">249</span> <span class="k">with</span> <span class="n">record</span><span class="o">.</span><span class="n">stop_recording</span><span class="p">():</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">250</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bound_context</span><span class="o">.</span><span class="n">executing_eagerly</span><span class="p">():</span>
|
||||
<span class="ne">--> </span><span class="mi">251</span> <span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bound_context</span><span class="o">.</span><span class="n">call_function</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">252</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">253</span> <span class="nb">list</span><span class="p">(</span><span class="n">args</span><span class="p">),</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">254</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">function_type</span><span class="o">.</span><span class="n">flat_outputs</span><span class="p">),</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">255</span> <span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">256</span> <span class="k">else</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">257</span> <span class="n">outputs</span> <span class="o">=</span> <span class="n">make_call_op_in_graph</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">258</span> <span class="bp">self</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">259</span> <span class="nb">list</span><span class="p">(</span><span class="n">args</span><span class="p">),</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">260</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bound_context</span><span class="o">.</span><span class="n">function_call_options</span><span class="o">.</span><span class="n">as_attrs</span><span class="p">(),</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">261</span> <span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/context.py:1500,</span> in <span class="ni">Context.call_function</span><span class="nt">(self, name, tensor_inputs, num_outputs)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1498</span> <span class="n">cancellation_context</span> <span class="o">=</span> <span class="n">cancellation</span><span class="o">.</span><span class="n">context</span><span class="p">()</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1499</span> <span class="k">if</span> <span class="n">cancellation_context</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="ne">-> </span><span class="mi">1500</span> <span class="n">outputs</span> <span class="o">=</span> <span class="n">execute</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1501</span> <span class="n">name</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s2">"utf-8"</span><span class="p">),</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1502</span> <span class="n">num_outputs</span><span class="o">=</span><span class="n">num_outputs</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1503</span> <span class="n">inputs</span><span class="o">=</span><span class="n">tensor_inputs</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1504</span> <span class="n">attrs</span><span class="o">=</span><span class="n">attrs</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1505</span> <span class="n">ctx</span><span class="o">=</span><span class="bp">self</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1506</span> <span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1507</span> <span class="k">else</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1508</span> <span class="n">outputs</span> <span class="o">=</span> <span class="n">execute</span><span class="o">.</span><span class="n">execute_with_cancellation</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1509</span> <span class="n">name</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s2">"utf-8"</span><span class="p">),</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1510</span> <span class="n">num_outputs</span><span class="o">=</span><span class="n">num_outputs</span><span class="p">,</span>
|
||||
<span class="p">(</span><span class="o">...</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1514</span> <span class="n">cancellation_manager</span><span class="o">=</span><span class="n">cancellation_context</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1515</span> <span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/execute.py:53,</span> in <span class="ni">quick_execute</span><span class="nt">(op_name, num_outputs, inputs, attrs, ctx, name)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">51</span> <span class="k">try</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">52</span> <span class="n">ctx</span><span class="o">.</span><span class="n">ensure_initialized</span><span class="p">()</span>
|
||||
<span class="ne">---> </span><span class="mi">53</span> <span class="n">tensors</span> <span class="o">=</span> <span class="n">pywrap_tfe</span><span class="o">.</span><span class="n">TFE_Py_Execute</span><span class="p">(</span><span class="n">ctx</span><span class="o">.</span><span class="n">_handle</span><span class="p">,</span> <span class="n">device_name</span><span class="p">,</span> <span class="n">op_name</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">54</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">attrs</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">55</span> <span class="k">except</span> <span class="n">core</span><span class="o">.</span><span class="n">_NotOkStatusException</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">56</span> <span class="k">if</span> <span class="n">name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
|
||||
<span class="ne">KeyboardInterrupt</span>:
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
|
||||
|
||||
@@ -254,6 +254,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
@@ -1182,10 +1185,10 @@ 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>0.1773607338287572
|
||||
4.407282337374826
|
||||
[[ 1.20040555 3.61585894]
|
||||
[ 3.61585894 11.77930797]]
|
||||
<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]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1222,10 +1225,10 @@ 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>0.08179347557022959
|
||||
1.4467442413216047
|
||||
[[1. 0.59082482]
|
||||
[0.59082482 1. ]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.08212703190343323
|
||||
2.425866065899094
|
||||
[[1. 0.65333306]
|
||||
[0.65333306 1. ]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1255,30 +1258,30 @@ 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>[[-1.84644069 -5.10787354]
|
||||
[ 0.47775601 2.07770305]
|
||||
[-0.54141982 -1.63948037]
|
||||
[ 0.38543167 1.1728405 ]
|
||||
[ 1.26358459 4.0297867 ]
|
||||
[-0.22052391 0.26084085]
|
||||
[-1.0053471 -4.21294424]
|
||||
[ 0.93657669 1.24909665]
|
||||
[-0.24706509 0.23743528]
|
||||
[ 0.79744765 1.93259511]]
|
||||
<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]]
|
||||
0 1
|
||||
0 -1.846441 -5.107874
|
||||
1 0.477756 2.077703
|
||||
2 -0.541420 -1.639480
|
||||
3 0.385432 1.172841
|
||||
4 1.263585 4.029787
|
||||
5 -0.220524 0.260841
|
||||
6 -1.005347 -4.212944
|
||||
7 0.936577 1.249097
|
||||
8 -0.247065 0.237435
|
||||
9 0.797448 1.932595
|
||||
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 0.955977
|
||||
1 0.955977 1.000000
|
||||
0 1.000000 0.972082
|
||||
1 0.972082 1.000000
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1335,37 +1338,37 @@ 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 0.105343 0.095336 0.103486 0.093680 0.085393 0.093763 0.085272
|
||||
2 0.0 0.095336 0.089569 0.097692 0.090745 0.084581 0.091324 0.084771
|
||||
3 0.0 0.103486 0.097692 0.108087 0.100345 0.093449 0.101684 0.094190
|
||||
4 0.0 0.093680 0.090745 0.100345 0.094844 0.089679 0.096260 0.090450
|
||||
5 0.0 0.085393 0.084581 0.093449 0.089679 0.085876 0.091107 0.086639
|
||||
6 0.0 0.093763 0.091324 0.101684 0.096260 0.091107 0.098122 0.092213
|
||||
7 0.0 0.085272 0.084771 0.094190 0.090450 0.086639 0.092213 0.087659
|
||||
8 0.0 0.078085 0.079027 0.087624 0.085195 0.082446 0.086846 0.083375
|
||||
9 0.0 0.071958 0.073975 0.081853 0.080447 0.078544 0.081982 0.079383
|
||||
10 0.0 0.084253 0.084056 0.093748 0.090129 0.086392 0.092146 0.087612
|
||||
11 0.0 0.077103 0.078235 0.087023 0.084667 0.081962 0.086513 0.083050
|
||||
12 0.0 0.071014 0.073137 0.081146 0.079778 0.077897 0.081465 0.078863
|
||||
13 0.0 0.065792 0.068653 0.075988 0.075397 0.074176 0.076936 0.075030
|
||||
14 0.0 0.061284 0.064692 0.071442 0.071464 0.070776 0.072867 0.071529
|
||||
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
|
||||
|
||||
8 9 10 11 12 13 14
|
||||
0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
|
||||
1 0.078085 0.071958 0.084253 0.077103 0.071014 0.065792 0.061284
|
||||
2 0.079027 0.073975 0.084056 0.078235 0.073137 0.068653 0.064692
|
||||
3 0.087624 0.081853 0.093748 0.087023 0.081146 0.075988 0.071442
|
||||
4 0.085195 0.080447 0.090129 0.084667 0.079778 0.075397 0.071464
|
||||
5 0.082446 0.078544 0.086392 0.081962 0.077897 0.074176 0.070776
|
||||
6 0.086846 0.081982 0.092146 0.086513 0.081465 0.076936 0.072867
|
||||
7 0.083375 0.079383 0.087612 0.083050 0.078863 0.075030 0.071529
|
||||
8 0.079972 0.076700 0.083329 0.079643 0.076172 0.072928 0.069915
|
||||
9 0.076700 0.074026 0.079325 0.076360 0.073487 0.070744 0.068152
|
||||
10 0.083329 0.079325 0.087745 0.083150 0.078926 0.075055 0.071515
|
||||
11 0.079643 0.076360 0.083150 0.079436 0.075936 0.072663 0.069621
|
||||
12 0.076172 0.073487 0.078926 0.075936 0.073039 0.070272 0.067658
|
||||
13 0.072928 0.070744 0.075055 0.072663 0.070272 0.067937 0.065691
|
||||
14 0.069915 0.068152 0.071515 0.069621 0.067658 0.065691 0.063765
|
||||
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
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
|
||||
|
||||
@@ -254,6 +254,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
@@ -723,10 +726,10 @@ 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: 0.132623 sec
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Runtime: 0.177848 sec
|
||||
Jackknife Statistics :
|
||||
original bias std. error
|
||||
99.9531 99.9431 0.150263
|
||||
99.9735 99.9635 0.149504
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -945,7 +948,7 @@ 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
|
||||
100.17 15.1243 100.169 0.154869
|
||||
99.9629 14.9658 99.9626 0.148488
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -967,7 +970,7 @@ original bias std. error
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/b3c0231661624694353db29319df18f593d19e1b0512fd194c3234fa9e50f4c4.png" src="_images/b3c0231661624694353db29319df18f593d19e1b0512fd194c3234fa9e50f4c4.png" />
|
||||
<img alt="_images/01ac462cedf743599925f73cc6294e533c9fdc93068eb00daba5834da82ae1f3.png" src="_images/01ac462cedf743599925f73cc6294e533c9fdc93068eb00daba5834da82ae1f3.png" />
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
@@ -1152,14 +1155,14 @@ Error: 0.08426840630693411
|
||||
Bias^2: 0.0796891867672603
|
||||
Var: 0.004579219539673834
|
||||
0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413
|
||||
Polynomial degree: 2
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 2
|
||||
Error: 0.10398646080125035
|
||||
Bias^2: 0.1007711427354898
|
||||
Var: 0.0032153180657605116
|
||||
0.10398646080125035 >= 0.1007711427354898 + 0.0032153180657605116 = 0.10398646080125032
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 3
|
||||
Polynomial degree: 3
|
||||
Error: 0.06547790180152355
|
||||
Bias^2: 0.06208238634231949
|
||||
Var: 0.0033955154592040936
|
||||
@@ -1169,14 +1172,14 @@ Error: 0.06844519414009445
|
||||
Bias^2: 0.06453579006728324
|
||||
Var: 0.003909404072811226
|
||||
0.06844519414009445 >= 0.06453579006728324 + 0.003909404072811226 = 0.06844519414009446
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 5
|
||||
Polynomial degree: 5
|
||||
Error: 0.05227921801205686
|
||||
Bias^2: 0.0481872773043029
|
||||
Var: 0.004091940707753939
|
||||
0.05227921801205686 >= 0.0481872773043029 + 0.004091940707753939 = 0.052279218012056844
|
||||
Polynomial degree: 6
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 6
|
||||
Error: 0.037813671417389005
|
||||
Bias^2: 0.033657685071527665
|
||||
Var: 0.00415598634586135
|
||||
@@ -1186,14 +1189,14 @@ Error: 0.02760977349102253
|
||||
Bias^2: 0.022999498260366312
|
||||
Var: 0.004610275230656212
|
||||
0.02760977349102253 >= 0.022999498260366312 + 0.004610275230656212 = 0.027609773491022525
|
||||
Polynomial degree: 8
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 8
|
||||
Error: 0.017355848195593347
|
||||
Bias^2: 0.010331721306655127
|
||||
Var: 0.007024126888938232
|
||||
0.017355848195593347 >= 0.010331721306655127 + 0.007024126888938232 = 0.01735584819559336
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 9
|
||||
Polynomial degree: 9
|
||||
Error: 0.02660572763718093
|
||||
Bias^2: 0.010018312644137363
|
||||
Var: 0.016587414993043573
|
||||
@@ -1537,9 +1540,9 @@ 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_11529/626635268.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_58812/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
|
||||
plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11529/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
|
||||
plt.plot(polynomial, np.log10(testerror), label='Test Error')
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1773,7 +1776,7 @@ 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_11529/3817475779.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_58812/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
|
||||
plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -2662,7 +2665,7 @@ linear system as an equation would reduce this down to
|
||||
</div>
|
||||
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||||
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||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11529/4162706317.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_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.
|
||||
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|
||||
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|
||||
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|
||||
@@ -2806,7 +2809,7 @@ with the form utilized in linear regression, viz.</p>
|
||||
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|
||||
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|
||||
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|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11529/3777801602.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_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.
|
||||
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|
||||
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|
||||
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|
||||
@@ -2846,7 +2849,7 @@ cost function is given by</p>
|
||||
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|
||||
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|
||||
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|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11529/438060758.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_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.
|
||||
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
|
||||
</pre></div>
|
||||
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|
||||
@@ -2881,7 +2884,7 @@ cost function is given by</p>
|
||||
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|
||||
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|
||||
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|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11529/3544313922.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_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.
|
||||
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|
||||
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|
||||
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|
||||
@@ -2934,43 +2937,43 @@ constant as opposed to ridge and OLS. We get a sparse solution with
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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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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2nd degree coefficients:
|
||||
zero power: -2.438518460940532
|
||||
first power: 0.1509827118339884
|
||||
second power: -0.0006570036917825709
|
||||
zero power: -3.6801072677808095
|
||||
first power: 0.14054303349959596
|
||||
second power: -0.0002999281168222194
|
||||
</pre></div>
|
||||
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@@ -1483,13 +1486,11 @@ attributes at each step while growing the tree.</p>
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||||
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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>
|
||||
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|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test set accuracy with SVM: 0.63
|
||||
</pre></div>
|
||||
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|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test set accuracy with Decision Trees: 0.90
|
||||
<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
|
||||
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
|
||||
Test set accuracy with Decision Trees and scaled data: 0.89
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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>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
|
||||
|
||||
@@ -254,6 +254,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
@@ -614,10 +617,10 @@ 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>-0.10788894797253629
|
||||
3.6790874039223653
|
||||
[[ 1.18171035 3.52117449]
|
||||
[ 3.52117449 11.6382529 ]]
|
||||
<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]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -657,10 +660,10 @@ 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>0.08881838553924991
|
||||
1.6247013033699416
|
||||
[[1. 0.65701477]
|
||||
[0.65701477 1. ]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.09335279187105122
|
||||
2.2108106787032815
|
||||
[[1. 0.66771869]
|
||||
[0.66771869 1. ]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -689,30 +692,30 @@ 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>[[-0.05662878 1.37467921]
|
||||
[-1.56333824 -3.5136046 ]
|
||||
[ 0.15344015 1.34849765]
|
||||
[ 0.71518529 2.50939698]
|
||||
[-0.2748515 -2.1020484 ]
|
||||
[-0.10159408 -2.63005749]
|
||||
[-0.64480719 -1.2968224 ]
|
||||
[ 0.55460489 1.06146057]
|
||||
[ 1.03970435 4.18061699]
|
||||
[ 0.1782851 -0.93211852]]
|
||||
<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]]
|
||||
0 1
|
||||
0 -0.056629 1.374679
|
||||
1 -1.563338 -3.513605
|
||||
2 0.153440 1.348498
|
||||
3 0.715185 2.509397
|
||||
4 -0.274852 -2.102048
|
||||
5 -0.101594 -2.630057
|
||||
6 -0.644807 -1.296822
|
||||
7 0.554605 1.061461
|
||||
8 1.039704 4.180617
|
||||
9 0.178285 -0.932119
|
||||
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 1
|
||||
0 1.000000 0.845552
|
||||
1 0.845552 1.000000
|
||||
0 1.000000 0.946278
|
||||
1 0.946278 1.000000
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -769,37 +772,37 @@ 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 0.084442 0.087416 0.085903 0.084926 0.083769 0.078992 0.077203
|
||||
2 0.0 0.087416 0.092483 0.089654 0.089681 0.089361 0.082384 0.081118
|
||||
3 0.0 0.085903 0.089654 0.093779 0.092882 0.091642 0.089976 0.087925
|
||||
4 0.0 0.084926 0.089681 0.092882 0.092595 0.091896 0.089004 0.087346
|
||||
5 0.0 0.083769 0.089361 0.091642 0.091896 0.091694 0.087651 0.086360
|
||||
6 0.0 0.078992 0.082384 0.089976 0.089004 0.087651 0.088697 0.086573
|
||||
7 0.0 0.077203 0.081118 0.087925 0.087346 0.086360 0.086573 0.084743
|
||||
8 0.0 0.075459 0.079833 0.085858 0.085639 0.084999 0.084404 0.082852
|
||||
9 0.0 0.073790 0.078582 0.083819 0.083940 0.083630 0.082237 0.080953
|
||||
10 0.0 0.071660 0.074465 0.083896 0.082819 0.081372 0.084236 0.082113
|
||||
11 0.0 0.069730 0.072833 0.081583 0.080779 0.079598 0.081839 0.079946
|
||||
12 0.0 0.067898 0.071271 0.079347 0.078801 0.077874 0.079503 0.077829
|
||||
13 0.0 0.066167 0.069798 0.077201 0.076902 0.076222 0.077241 0.075780
|
||||
14 0.0 0.064543 0.068424 0.075155 0.075095 0.074656 0.075064 0.073809
|
||||
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
|
||||
|
||||
8 9 10 11 12 13 14
|
||||
0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
|
||||
1 0.075459 0.073790 0.071660 0.069730 0.067898 0.066167 0.064543
|
||||
2 0.079833 0.078582 0.074465 0.072833 0.071271 0.069798 0.068424
|
||||
3 0.085858 0.083819 0.083896 0.081583 0.079347 0.077201 0.075155
|
||||
4 0.085639 0.083940 0.082819 0.080779 0.078801 0.076902 0.075095
|
||||
5 0.084999 0.083630 0.081372 0.079598 0.077874 0.076222 0.074656
|
||||
6 0.084404 0.082237 0.084236 0.081839 0.079503 0.077241 0.075064
|
||||
7 0.082852 0.080953 0.082113 0.079946 0.077829 0.075780 0.073809
|
||||
8 0.081230 0.079591 0.079936 0.077991 0.076088 0.074246 0.072478
|
||||
9 0.079591 0.078208 0.077748 0.076019 0.074325 0.072688 0.071121
|
||||
10 0.079936 0.077748 0.081045 0.078681 0.076364 0.074111 0.071928
|
||||
11 0.077991 0.076019 0.078681 0.076508 0.074378 0.072303 0.070295
|
||||
12 0.076088 0.074325 0.076364 0.074378 0.072426 0.070527 0.068690
|
||||
13 0.074246 0.072688 0.074111 0.072303 0.070527 0.068798 0.067128
|
||||
14 0.072478 0.071121 0.071928 0.070295 0.068690 0.067128 0.065622
|
||||
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
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -988,10 +991,10 @@ 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 3.970238 1.999801
|
||||
1 1.999801 2.021273
|
||||
[[3.97023801 1.99980092]
|
||||
[1.99980092 2.02127327]]
|
||||
0 3.946263 1.971035
|
||||
1 1.971035 1.988524
|
||||
[[3.94626291 1.97103474]
|
||||
[1.97103474 1.98852413]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1018,11 +1021,11 @@ 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
|
||||
[[3.97023801 1.99980092]
|
||||
[1.99980092 2.02127327]]
|
||||
[[3.94626291 1.97103474]
|
||||
[1.97103474 1.98852413]]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/e5e6bf0c464c3e5a86a023adac1e329391745d4c31e520e53767d038a5bcf4ae.png" src="_images/e5e6bf0c464c3e5a86a023adac1e329391745d4c31e520e53767d038a5bcf4ae.png" />
|
||||
<img alt="_images/6ca0e6a8c6122c37cbb752b49917fd1e67a1de46298e8c2247a990d083fdb3c7.png" src="_images/6ca0e6a8c6122c37cbb752b49917fd1e67a1de46298e8c2247a990d083fdb3c7.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.
|
||||
@@ -1079,16 +1082,16 @@ 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
|
||||
5.220349900775413
|
||||
0.7711613838358012
|
||||
5.168112312789667
|
||||
0.7666747242371026
|
||||
First eigenvector
|
||||
[0.84795327 0.53007099]
|
||||
[0.84993979 0.52687982]
|
||||
Second eigenvector
|
||||
[-0.53007099 0.84795327]
|
||||
[-0.52687982 0.84993979]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Eigenvector of largest eigenvalue
|
||||
[-0.84795327 -0.53007099]
|
||||
[0.84993979 0.52687982]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
|
||||
|
||||
@@ -254,6 +254,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
|
||||
|
||||
@@ -254,6 +254,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
|
||||
|
||||
@@ -254,6 +254,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
@@ -584,37 +587,6 @@ Gaussian distribution.</p>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
|
||||
<span class="ne">NotFoundError</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">5</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">3</span> <span class="kn">import</span> <span class="nn">time</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">4</span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="ne">----> </span><span class="mi">5</span> <span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">6</span> <span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">image</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">7</span> <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
||||
|
||||
<span class="n">File</span> <span class="o">~/</span><span class="n">miniforge3</span><span class="o">/</span><span class="n">envs</span><span class="o">/</span><span class="n">myenv</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python3</span><span class="mf">.9</span><span class="o">/</span><span class="n">site</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">tensorflow</span><span class="o">/</span><span class="fm">__init__</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">440</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">438</span> <span class="n">_plugin_dir</span> <span class="o">=</span> <span class="n">_os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">_s</span><span class="p">,</span> <span class="s1">'tensorflow-plugins'</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">439</span> <span class="k">if</span> <span class="n">_os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">_plugin_dir</span><span class="p">):</span>
|
||||
<span class="ne">--> </span><span class="mi">440</span> <span class="n">_ll</span><span class="o">.</span><span class="n">load_library</span><span class="p">(</span><span class="n">_plugin_dir</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">441</span> <span class="c1"># Load Pluggable Device Library</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">442</span> <span class="n">_ll</span><span class="o">.</span><span class="n">load_pluggable_device_library</span><span class="p">(</span><span class="n">_plugin_dir</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/framework/load_library.py:151,</span> in <span class="ni">load_library</span><span class="nt">(library_location)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">148</span> <span class="n">kernel_libraries</span> <span class="o">=</span> <span class="p">[</span><span class="n">library_location</span><span class="p">]</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">150</span> <span class="k">for</span> <span class="n">lib</span> <span class="ow">in</span> <span class="n">kernel_libraries</span><span class="p">:</span>
|
||||
<span class="ne">--> </span><span class="mi">151</span> <span class="n">py_tf</span><span class="o">.</span><span class="n">TF_LoadLibrary</span><span class="p">(</span><span class="n">lib</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">153</span> <span class="k">else</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">154</span> <span class="k">raise</span> <span class="ne">OSError</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">155</span> <span class="n">errno</span><span class="o">.</span><span class="n">ENOENT</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">156</span> <span class="s1">'The file or folder to load kernel libraries from does not exist.'</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">157</span> <span class="n">library_location</span><span class="p">)</span>
|
||||
|
||||
<span class="ne">NotFoundError</span>: dlopen(/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): symbol not found in flat namespace '_TF_GetInputPropertiesList'
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<p>Next we define functions, for ease of use later, to generate Gaussians and to
|
||||
set up our toy data set.</p>
|
||||
@@ -672,6 +644,9 @@ set up our toy data set.</p>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/386392c9fed6728256cbe115938f9a087bb6a8f2a530f613d32298404dd706c4.png" src="_images/386392c9fed6728256cbe115938f9a087bb6a8f2a530f613d32298404dd706c4.png" />
|
||||
</div>
|
||||
</div>
|
||||
<p>With the above dataset we start
|
||||
implementing the <span class="math notranslate nohighlight">\(k\)</span>-means algorithm.</p>
|
||||
@@ -732,6 +707,9 @@ implementing the <span class="math notranslate nohighlight">\(k\)</span>-means a
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/08a3e33657ef89b511498459dfb46eb29d76afce76a3d347fcb1177b92a2ff9b.png" src="_images/08a3e33657ef89b511498459dfb46eb29d76afce76a3d347fcb1177b92a2ff9b.png" />
|
||||
</div>
|
||||
</div>
|
||||
<p>So what do we have so far? We have ‘picked’ <span class="math notranslate nohighlight">\(k\)</span> centroids at random from our
|
||||
data points. There are other ways of more intelligently choosing their
|
||||
@@ -792,6 +770,11 @@ or a maximum amount of iterations.</p>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Converged at iteration 5
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<p>We now have a simple , un-optimized <span class="math notranslate nohighlight">\(k\)</span>-means
|
||||
clustering implementation. Lets plot the final result</p>
|
||||
@@ -813,6 +796,9 @@ clustering implementation. Lets plot the final result</p>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/cd302a02ed7196309f1e57024d9834c2540eab56bf7818d2267779cea5221451.png" src="_images/cd302a02ed7196309f1e57024d9834c2540eab56bf7818d2267779cea5221451.png" />
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
|
||||
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
|
||||
|
||||
@@ -254,6 +254,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
|
||||
|
||||
@@ -254,6 +254,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
|
||||
|
||||
@@ -254,6 +254,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
|
||||
|
||||
@@ -254,6 +254,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
|
||||
|
||||
@@ -254,6 +254,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
|
||||
|
||||
@@ -252,6 +252,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
@@ -34,7 +34,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
|
||||
|
||||
@@ -254,6 +254,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
@@ -596,18 +599,18 @@ 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
|
||||
[[3.86119759]
|
||||
[3.07853436]]
|
||||
Eigenvalues of Hessian Matrix:[0.35325267 4.24060666]
|
||||
[[4.0010275 ]
|
||||
[3.10085547]]
|
||||
Eigenvalues of Hessian Matrix:[0.32462943 4.22813513]
|
||||
theta from own gd
|
||||
[[3.86119759]
|
||||
[3.07853436]]
|
||||
[[4.0010275 ]
|
||||
[3.10085547]]
|
||||
theta from own sdg
|
||||
[[3.90968018]
|
||||
[3.12597955]]
|
||||
[[4.08051993]
|
||||
[3.13522576]]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/68228e7f9ae763e6c8bc6963f6ca9426dc126d0bd9ad624f57ca2253da199b56.png" src="_images/68228e7f9ae763e6c8bc6963f6ca9426dc126d0bd9ad624f57ca2253da199b56.png" />
|
||||
<img alt="_images/28af7ec857f6b0c73278974d8a0f02963d4300b7ede7129fc2e95b8d68a8492b.png" src="_images/28af7ec857f6b0c73278974d8a0f02963d4300b7ede7129fc2e95b8d68a8492b.png" />
|
||||
</div>
|
||||
</div>
|
||||
<p>In the above code, we have use replacement in setting up the
|
||||
@@ -726,15 +729,17 @@ 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
|
||||
[[3.63498731]
|
||||
[3.34342385]]
|
||||
Eigenvalues of Hessian Matrix:[0.32784744 4.2127597 ]
|
||||
theta from own gd
|
||||
[[3.63498731]
|
||||
[3.34342385]]
|
||||
[[3.8679219 ]
|
||||
[3.12497024]]
|
||||
Eigenvalues of Hessian Matrix:[0.31887646 4.68712364]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/eb9c5e4869c3dd55d868b79cbe690dadeee491e8a4c96295144f0d7820c9de02.png" src="_images/eb9c5e4869c3dd55d868b79cbe690dadeee491e8a4c96295144f0d7820c9de02.png" />
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>theta from own gd
|
||||
[[3.8679219 ]
|
||||
[3.12497024]]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/634d0baa356c54adef6974350a5d0f31fb981ef2c1199e2bf8abc3dbbd76d38b.png" src="_images/634d0baa356c54adef6974350a5d0f31fb981ef2c1199e2bf8abc3dbbd76d38b.png" />
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
@@ -802,73 +807,73 @@ theta from own gd
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
|
||||
[[4.]
|
||||
[3.]]
|
||||
Eigenvalues of Hessian Matrix:[0.32280554 4.06602883]
|
||||
0 [-16.21191032] [-17.88160136]
|
||||
1 [-0.05113442] [0.04607198]
|
||||
2 [-0.04707482] [0.04241429]
|
||||
3 [-0.04333751] [0.03904698]
|
||||
4 [-0.0398969] [0.03594701]
|
||||
5 [-0.03672945] [0.03309314]
|
||||
6 [-0.03381347] [0.03046585]
|
||||
7 [-0.03112899] [0.02804714]
|
||||
8 [-0.02865763] [0.02582045]
|
||||
9 [-0.02638248] [0.02377054]
|
||||
10 [-0.02428795] [0.02188338]
|
||||
11 [-0.02235971] [0.02014604]
|
||||
12 [-0.02058455] [0.01854663]
|
||||
13 [-0.01895033] [0.0170742]
|
||||
14 [-0.01744584] [0.01571866]
|
||||
15 [-0.0160608] [0.01447074]
|
||||
16 [-0.01478572] [0.0133219]
|
||||
17 [-0.01361187] [0.01226426]
|
||||
18 [-0.01253121] [0.01129059]
|
||||
19 [-0.01153635] [0.01039422]
|
||||
20 [-0.01062047] [0.00956901]
|
||||
21 [-0.0097773] [0.00880932]
|
||||
22 [-0.00900107] [0.00810994]
|
||||
23 [-0.00828647] [0.00746609]
|
||||
24 [-0.0076286] [0.00687335]
|
||||
25 [-0.00702296] [0.00632767]
|
||||
26 [-0.0064654] [0.00582531]
|
||||
27 [-0.00595211] [0.00536283]
|
||||
28 [-0.00547956] [0.00493707]
|
||||
29 [-0.00504454] [0.00454511]
|
||||
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]
|
||||
theta from own gd
|
||||
[[3.98561349]
|
||||
[3.01296221]]
|
||||
0 [-0.00464405] [0.00418427]
|
||||
1 [-0.00427535] [0.00385208]
|
||||
2 [-0.00382532] [0.0034466]
|
||||
3 [-0.00338661] [0.00305133]
|
||||
4 [-0.00298614] [0.0026905]
|
||||
5 [-0.00262892] [0.00236865]
|
||||
6 [-0.00231304] [0.00208405]
|
||||
7 [-0.00203465] [0.00183321]
|
||||
8 [-0.00178959] [0.00161242]
|
||||
9 [-0.001574] [0.00141817]
|
||||
10 [-0.00138436] [0.00124731]
|
||||
11 [-0.00121757] [0.00109702]
|
||||
12 [-0.00107086] [0.00096484]
|
||||
13 [-0.00094183] [0.00084859]
|
||||
14 [-0.00082835] [0.00074634]
|
||||
15 [-0.00072855] [0.00065642]
|
||||
16 [-0.00064076] [0.00057733]
|
||||
17 [-0.00056356] [0.00050776]
|
||||
18 [-0.00049565] [0.00044658]
|
||||
19 [-0.00043593] [0.00039277]
|
||||
20 [-0.00038341] [0.00034545]
|
||||
21 [-0.00033721] [0.00030383]
|
||||
22 [-0.00029658] [0.00026722]
|
||||
23 [-0.00026085] [0.00023502]
|
||||
24 [-0.00022942] [0.0002067]
|
||||
25 [-0.00020177] [0.0001818]
|
||||
26 [-0.00017746] [0.00015989]
|
||||
27 [-0.00015608] [0.00014063]
|
||||
28 [-0.00013727] [0.00012368]
|
||||
29 [-0.00012073] [0.00010878]
|
||||
[[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]
|
||||
theta from own gd wth momentum
|
||||
[[3.99967105]
|
||||
[3.00029638]]
|
||||
[[3.99053294]
|
||||
[3.00780757]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -921,17 +926,17 @@ 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
|
||||
[[3.8324348]
|
||||
[3.0346417]]
|
||||
Eigenvalues of Hessian Matrix:[0.36106642 4.2729462 ]
|
||||
0 [-12.98689762] [-16.40152131]
|
||||
1 [-1.54737334e-14] [-3.53431592e-14]
|
||||
2 [4.59701721e-16] [5.2369504e-16]
|
||||
3 [4.59701721e-16] [5.2369504e-16]
|
||||
4 [4.59701721e-16] [5.2369504e-16]
|
||||
[[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]
|
||||
beta from own Newton code
|
||||
[[3.8324348]
|
||||
[3.0346417]]
|
||||
[[4.1739262 ]
|
||||
[2.85239235]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1020,18 +1025,18 @@ 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
|
||||
[[3.9972423 ]
|
||||
[3.14884659]]
|
||||
Eigenvalues of Hessian Matrix:[0.33571966 4.32437259]
|
||||
[[4.50455587]
|
||||
[2.69978846]]
|
||||
Eigenvalues of Hessian Matrix:[0.35172802 4.35427615]
|
||||
theta from own gd
|
||||
[[3.9972423 ]
|
||||
[3.14884659]]
|
||||
[[4.50455587]
|
||||
[2.69978846]]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/8b5ea8661960c5f546af7ea2ae617ed40b5e260cc45bd4bbb888cad2aad39c8f.png" src="_images/8b5ea8661960c5f546af7ea2ae617ed40b5e260cc45bd4bbb888cad2aad39c8f.png" />
|
||||
<img alt="_images/c8d1a6036144492783b21bafb5df5462fba5688ed1014961e0a3ecd4909a9caf.png" src="_images/c8d1a6036144492783b21bafb5df5462fba5688ed1014961e0a3ecd4909a9caf.png" />
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>theta from own sdg
|
||||
[[3.97120525]
|
||||
[3.17838946]]
|
||||
[[4.51550231]
|
||||
[2.6728048 ]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1113,15 +1118,15 @@ 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
|
||||
[[4.25680728]
|
||||
[2.80142034]]
|
||||
Eigenvalues of Hessian Matrix:[0.28283539 4.95706971]
|
||||
[[3.8301107 ]
|
||||
[3.31610195]]
|
||||
Eigenvalues of Hessian Matrix:[0.31047897 4.32701817]
|
||||
theta from own gd
|
||||
[[4.25522704]
|
||||
[2.80262454]]
|
||||
[[3.82958153]
|
||||
[3.31655284]]
|
||||
theta from own sdg with momentum
|
||||
[[4.23872708]
|
||||
[2.82197736]]
|
||||
[[3.82977678]
|
||||
[3.25067525]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1196,9 +1201,9 @@ 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
|
||||
[[2.0000319 ]
|
||||
[2.99982304]
|
||||
[4.00017365]]
|
||||
[[1.99978312]
|
||||
[3.00126504]
|
||||
[3.99880408]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1280,9 +1285,9 @@ 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
|
||||
[[2.00123675]
|
||||
[2.99488443]
|
||||
[4.00536626]]
|
||||
[[1.99827523]
|
||||
[2.99976195]
|
||||
[3.99585312]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1368,9 +1373,9 @@ 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
|
||||
[[2.00003737]
|
||||
[2.99982141]
|
||||
[4.00019066]]
|
||||
[[2.00000058]
|
||||
[2.99998203]
|
||||
[4.00001412]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1443,7 +1448,7 @@ 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 0x130bd2520>]
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<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span><matplotlib.collections.PathCollection at 0x130a7b520>
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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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Applied Data Analysis and Machine Learning
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Review of Statistics with Resampling Techniques and Linear Algebra</span></p>
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<li class="toctree-l1"><a class="reference internal" href="additionweek42.html">Exercises Week 42: Logistic Regression and Optimization, reminders from week 38 and week 40</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week43.html">Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek43.html">Exercises week 43</a></li>
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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="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
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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 current active"><a class="current reference internal" href="#">Exercise week 47</a></li>
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<h1>Exercise week 47</h1>
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<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Exercise week 47</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="#exercise-1-linear-and-logistic-regression-methods">Exercise 1: Linear and logistic regression methods</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-2-deep-learning">Exercise 2: Deep learning</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-3-decision-trees-and-ensemble-methods">Exercise 3: Decision trees and ensemble methods</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-4-optimization-part">Exercise 4: Optimization part</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-5-analysis-of-results">Exercise 5: Analysis of results</a></li>
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doconce format html exercisesweek47.do.txt -->
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<!-- dom:TITLE: Exercise week 47 --><section class="tex2jax_ignore mathjax_ignore" id="exercise-week-47">
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<h1>Exercise week 47<a class="headerlink" href="#exercise-week-47" title="Link to this heading">#</a></h1>
|
||||
<p><strong>November 18-22, 2024</strong></p>
|
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<p>Date: <strong>Deadline is Friday November 22 at midnight</strong></p>
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</section>
|
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<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 set this week is meant as a summary of many of the
|
||||
central elements in various machine learning algorithms, with a slight
|
||||
bias towards deep learning methods and their training. You don’t need to answer all questions.</p>
|
||||
<p>The last weekly exercise (week 48) is a general course survey.</p>
|
||||
<section id="exercise-1-linear-and-logistic-regression-methods">
|
||||
<h2>Exercise 1: Linear and logistic regression methods<a class="headerlink" href="#exercise-1-linear-and-logistic-regression-methods" title="Link to this heading">#</a></h2>
|
||||
<ol class="arabic simple">
|
||||
<li><p>What is the main difference between ordinary least squares and Ridge regression?</p></li>
|
||||
<li><p>Which kind of data set would you use logistic regression for?</p></li>
|
||||
<li><p>In linear regression you assume that your output is described by a continuous non-stochastic function <span class="math notranslate nohighlight">\(f(x)\)</span>. Which is the equivalent function in logistic regression?</p></li>
|
||||
<li><p>Can you find an analytic solution to a logistic regression type of problem?</p></li>
|
||||
<li><p>What kind of cost function would you use in logistic regression?</p></li>
|
||||
</ol>
|
||||
</section>
|
||||
<section id="exercise-2-deep-learning">
|
||||
<h2>Exercise 2: Deep learning<a class="headerlink" href="#exercise-2-deep-learning" title="Link to this heading">#</a></h2>
|
||||
<ol class="arabic simple">
|
||||
<li><p>What is an activation function and discuss the use of an activation function? Explain three different types of activation functions?</p></li>
|
||||
<li><p>Describe the architecture of a typical feed forward Neural Network (NN).</p></li>
|
||||
<li><p>You are using a deep neural network for a prediction task. After training your model, you notice that it is strongly overfitting the training set and that the performance on the test isn’t good. What can you do to reduce overfitting?</p></li>
|
||||
<li><p>How would you know if your model is suffering from the problem of exploding Gradients?</p></li>
|
||||
<li><p>Can you name and explain a few hyperparameters used for training a neural network?</p></li>
|
||||
<li><p>Describe the architecture of a typical Convolutional Neural Network (CNN)</p></li>
|
||||
<li><p>What is the vanishing gradient problem in Neural Networks and how to fix it?</p></li>
|
||||
<li><p>When it comes to training an artificial neural network, what could the reason be for why the cost/loss doesn’t decrease in a few epochs?</p></li>
|
||||
<li><p>How does L1/L2 regularization affect a neural network?</p></li>
|
||||
<li><p>What is(are) the advantage(s) of deep learning over traditional methods like linear regression or logistic regression?</p></li>
|
||||
</ol>
|
||||
</section>
|
||||
<section id="exercise-3-decision-trees-and-ensemble-methods">
|
||||
<h2>Exercise 3: Decision trees and ensemble methods<a class="headerlink" href="#exercise-3-decision-trees-and-ensemble-methods" title="Link to this heading">#</a></h2>
|
||||
<ol class="arabic simple">
|
||||
<li><p>Mention some pros and cons when using decision trees</p></li>
|
||||
<li><p>How do we grow a tree? And which are the main parameters?</p></li>
|
||||
<li><p>Mention some of the benefits with using ensemble methods (like bagging, random forests and boosting methods)?</p></li>
|
||||
<li><p>Why would you prefer a random forest instead of using Bagging to grow a forest?</p></li>
|
||||
<li><p>What is the basic philosophy behind boosting methods?</p></li>
|
||||
</ol>
|
||||
</section>
|
||||
<section id="exercise-4-optimization-part">
|
||||
<h2>Exercise 4: Optimization part<a class="headerlink" href="#exercise-4-optimization-part" title="Link to this heading">#</a></h2>
|
||||
<ol class="arabic simple">
|
||||
<li><p>Which is the basic mathematical root-finding method behind essentially all gradient descent approaches(stochastic and non-stochastic)?</p></li>
|
||||
<li><p>And why don’t we use it? Or stated differently, why do we introduce the learning rate as a parameter?</p></li>
|
||||
<li><p>What might happen if you set the momentum hyperparameter too close to 1 (e.g., 0.9999) when using an optimizer for the learning rate?</p></li>
|
||||
<li><p>Why should we use stochastic gradient descent instead of plain gradient descent?</p></li>
|
||||
<li><p>Which parameters would you need to tune when use a stochastic gradient descent approach?</p></li>
|
||||
</ol>
|
||||
</section>
|
||||
<section id="exercise-5-analysis-of-results">
|
||||
<h2>Exercise 5: Analysis of results<a class="headerlink" href="#exercise-5-analysis-of-results" title="Link to this heading">#</a></h2>
|
||||
<ol class="arabic simple">
|
||||
<li><p>How do you assess overfitting and underfitting?</p></li>
|
||||
<li><p>Why do we divide the data in test and train and/or eventually validation sets?</p></li>
|
||||
<li><p>Why would you use resampling methods in the data analysis? Mention some widely popular resampling methods.</p></li>
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||||
</html>
|
||||
@@ -33,7 +33,7 @@
|
||||
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
|
||||
|
||||
@@ -251,6 +251,9 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
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