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Morten Hjorth-Jensen
2021-11-26 06:58:37 +01:00
parent 33eaaa3595
commit edc403ff1b
12 changed files with 1023 additions and 650 deletions
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doc/pub/week42/ipynb/week42.ipynb
+37 -4
View File
@@ -4564,10 +4564,31 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 1,
"id": "f5bc1a82",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"inputs = (n_inputs, pixel_width, pixel_height, depth) = (1797, 8, 8, 1)\n",
"labels = (n_inputs) = (1797,)\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x864 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# import necessary packages\n",
"import numpy as np\n",
@@ -4621,10 +4642,22 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 2,
"id": "35de7b26",
"metadata": {},
"outputs": [],
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'tensorflow'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44642/18380446.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mInput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSequential\u001b[0m \u001b[0;31m#This allows appending layers to existing models\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDense\u001b[0m \u001b[0;31m#This allows defining the characteristics of a particular layer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0moptimizers\u001b[0m \u001b[0;31m#This allows using whichever optimiser we want (sgd,adam,RMSprop)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
]
}
],
"source": [
"from tensorflow.keras import datasets, layers, models\n",
"from tensorflow.keras.layers import Input\n",
doc/pub/week43/ipynb/week43.ipynb
+15 -3
View File
@@ -154,7 +154,19 @@
"execution_count": 1,
"id": "d1e1f8b0",
"metadata": {},
"outputs": [],
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'tensorflow'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_45079/2517560119.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mInput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
@@ -3632,7 +3644,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -3646,7 +3658,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
"version": "3.9.7"
}
},
"nbformat": 4,
doc/pub/week47/html/._week47-bs000.html
+1 -1
View File
@@ -369,7 +369,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 25, 2021</h4>
<h4>Nov 26, 2021</h4>
</center> <!-- date -->
<br>
doc/pub/week47/html/._week47-bs037.html
+1 -1
View File
@@ -362,7 +362,7 @@ MathJax.Hub.Config({
<p>Huge amounts of data sets require automation, classical analysis tools often inadequate.
High energy physics hit this wall in the 90&#8217;s.
In 2009 single top quark production was determined via <a href="https://arxiv.org/pdf/0903.0850.pdf" target="_self">Boosted decision trees, Bayesian
Neural Networks, etc.</a>
Neural Networks, etc.</a>. Similarly, the search for Higgs was a statistical learning tour de force. See this link on <a href="https://www.kaggle.com/c/higgs-boson" target="_self">Kaggle.com</a>.
</p>
<p>
doc/pub/week47/html/._week47-bs041.html
+6 -1
View File
@@ -363,13 +363,18 @@ MathJax.Hub.Config({
<ul>
<li> Why?</li>
</ul>
<li> <b>Whitening</b></li>
<li> <a href="https://multivariatestatsjl.readthedocs.io/en/latest/whiten.html" target="_self">Whitening</a></li>
<ul>
<li> Decorrelates data</li>
<li> Can be hit or miss</li>
</ul>
<li> When to do train/test split?</li>
</ol>
<p>Whitening is a decorrelation transformation that transforms a set of
random variables into a set of new random variables with identity
covariance (uncorrelated with unit variances).
</p>
<p>
<!-- navigation buttons at the bottom of the page -->
<ul class="pagination">
doc/pub/week47/html/week47-bs.html
+1 -1
View File
@@ -369,7 +369,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 25, 2021</h4>
<h4>Nov 26, 2021</h4>
</center> <!-- date -->
<br>
doc/pub/week47/html/week47-reveal.html
+8 -3
View File
@@ -184,7 +184,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 25, 2021</h4>
<h4>Nov 26, 2021</h4>
</center> <!-- date -->
<br>
@@ -1732,7 +1732,7 @@ ethical conduct is emphasized throughout the course.
<p>Huge amounts of data sets require automation, classical analysis tools often inadequate.
High energy physics hit this wall in the 90&#8217;s.
In 2009 single top quark production was determined via <a href="https://arxiv.org/pdf/0903.0850.pdf" target="_blank">Boosted decision trees, Bayesian
Neural Networks, etc.</a>
Neural Networks, etc.</a>. Similarly, the search for Higgs was a statistical learning tour de force. See this link on <a href="https://www.kaggle.com/c/higgs-boson" target="_blank">Kaggle.com</a>.
</p>
</section>
@@ -1789,7 +1789,7 @@ Neural Networks, etc.</a>
<p><li> Why?</li>
</ul>
<p>
<p><li> <b>Whitening</b></li>
<p><li> <a href="https://multivariatestatsjl.readthedocs.io/en/latest/whiten.html" target="_blank">Whitening</a></li>
<ul>
<p><li> Decorrelates data</li>
@@ -1799,6 +1799,11 @@ Neural Networks, etc.</a>
<p>
<p><li> When to do train/test split?</li>
</ol>
<p>
<p>Whitening is a decorrelation transformation that transforms a set of
random variables into a set of new random variables with identity
covariance (uncorrelated with unit variances).
</p>
</section>
<section>
doc/pub/week47/html/week47-solarized.html
+8 -3
View File
@@ -293,7 +293,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 25, 2021</h4>
<h4>Nov 26, 2021</h4>
</center> <!-- date -->
<br>
@@ -1654,7 +1654,7 @@ ethical conduct is emphasized throughout the course.
<p>Huge amounts of data sets require automation, classical analysis tools often inadequate.
High energy physics hit this wall in the 90&#8217;s.
In 2009 single top quark production was determined via <a href="https://arxiv.org/pdf/0903.0850.pdf" target="_blank">Boosted decision trees, Bayesian
Neural Networks, etc.</a>
Neural Networks, etc.</a>. Similarly, the search for Higgs was a statistical learning tour de force. See this link on <a href="https://www.kaggle.com/c/higgs-boson" target="_blank">Kaggle.com</a>.
</p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -1700,13 +1700,18 @@ Neural Networks, etc.</a>
<ul>
<li> Why?</li>
</ul>
<li> <b>Whitening</b></li>
<li> <a href="https://multivariatestatsjl.readthedocs.io/en/latest/whiten.html" target="_blank">Whitening</a></li>
<ul>
<li> Decorrelates data</li>
<li> Can be hit or miss</li>
</ul>
<li> When to do train/test split?</li>
</ol>
<p>Whitening is a decorrelation transformation that transforms a set of
random variables into a set of new random variables with identity
covariance (uncorrelated with unit variances).
</p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="which-activation-and-weights-to-choose-in-neural-networks">Which Activation and Weights to Choose in Neural Networks </h2>
doc/pub/week47/html/week47.html
+8 -3
View File
@@ -370,7 +370,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 25, 2021</h4>
<h4>Nov 26, 2021</h4>
</center> <!-- date -->
<br>
@@ -1731,7 +1731,7 @@ ethical conduct is emphasized throughout the course.
<p>Huge amounts of data sets require automation, classical analysis tools often inadequate.
High energy physics hit this wall in the 90&#8217;s.
In 2009 single top quark production was determined via <a href="https://arxiv.org/pdf/0903.0850.pdf" target="_blank">Boosted decision trees, Bayesian
Neural Networks, etc.</a>
Neural Networks, etc.</a>. Similarly, the search for Higgs was a statistical learning tour de force. See this link on <a href="https://www.kaggle.com/c/higgs-boson" target="_blank">Kaggle.com</a>.
</p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -1777,13 +1777,18 @@ Neural Networks, etc.</a>
<ul>
<li> Why?</li>
</ul>
<li> <b>Whitening</b></li>
<li> <a href="https://multivariatestatsjl.readthedocs.io/en/latest/whiten.html" target="_blank">Whitening</a></li>
<ul>
<li> Decorrelates data</li>
<li> Can be hit or miss</li>
</ul>
<li> When to do train/test split?</li>
</ol>
<p>Whitening is a decorrelation transformation that transforms a set of
random variables into a set of new random variables with identity
covariance (uncorrelated with unit variances).
</p>
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<h2 id="which-activation-and-weights-to-choose-in-neural-networks">Which Activation and Weights to Choose in Neural Networks </h2>
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@@ -1285,7 +1285,7 @@ o A lot of “word-of-mouth” development methods
Huge amounts of data sets require automation, classical analysis tools often inadequate.
High energy physics hit this wall in the 90’s.
In 2009 single top quark production was determined via "Boosted decision trees, Bayesian
Neural Networks, etc.":"https://arxiv.org/pdf/0903.0850.pdf"
Neural Networks, etc.":"https://arxiv.org/pdf/0903.0850.pdf". Similarly, the search for Higgs was a statistical learning tour de force. See this link on "Kaggle.com":"https://www.kaggle.com/c/higgs-boson".
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@@ -1327,11 +1327,14 @@ o Mean center your data
* Why?
o Normalize the variance
* Why?
o _Whitening_
o "Whitening":"https://multivariatestatsjl.readthedocs.io/en/latest/whiten.html"
* Decorrelates data
* Can be hit or miss
o When to do train/test split?
Whitening is a decorrelation transformation that transforms a set of
random variables into a set of new random variables with identity
covariance (uncorrelated with unit variances).
!split
===== Which Activation and Weights to Choose in Neural Networks =====