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correcting typos

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Morten Hjorth-Jensen
2024-10-28 05:48:19 +01:00
parent f64815fcd2
commit 64847229ca
10 changed files with 257 additions and 272 deletions
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doc/pub/week44/html/._week44-bs000.html
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@@ -357,14 +357,11 @@ MathJax.Hub.Config({
<!-- author(s): Morten Hjorth-Jensen -->
<center>
<b>Morten Hjorth-Jensen</b> [1, 2]
<b>Morten Hjorth-Jensen</b>
</center>
<!-- institution(s) -->
<!-- institution -->
<center>
[1] <b>Department of Physics, University of Oslo</b>
</center>
<center>
[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b>
<b>Department of Physics, University of Oslo, Norway</b>
</center>
<br>
<center>
doc/pub/week44/html/._week44-bs052.html
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@@ -370,7 +370,7 @@ MathJax.Hub.Config({
model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Dense(n_neurons_connected, activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;relu&#39;</span>, kernel_regularizer<span style="color: #666666">=</span>regularizers<span style="color: #666666">.</span>l2(lmbd)))
model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Dense(n_categories, activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;softmax&#39;</span>, kernel_regularizer<span style="color: #666666">=</span>regularizers<span style="color: #666666">.</span>l2(lmbd)))
sgd <span style="color: #666666">=</span> optimizers<span style="color: #666666">.</span>SGD(lr<span style="color: #666666">=</span>eta)
sgd <span style="color: #666666">=</span> optimizers<span style="color: #666666">.</span>SGD(learning_rate<span style="color: #666666">=</span>eta)
model<span style="color: #666666">.</span>compile(loss<span style="color: #666666">=</span><span style="color: #BA2121">&#39;categorical_crossentropy&#39;</span>, optimizer<span style="color: #666666">=</span>sgd, metrics<span style="color: #666666">=</span>[<span style="color: #BA2121">&#39;accuracy&#39;</span>])
<span style="color: #008000; font-weight: bold">return</span> model
doc/pub/week44/html/._week44-bs058.html
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@@ -371,7 +371,7 @@ layer with 10 outputs and a softmax activation.
<pre style="line-height: 125%;">model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Flatten())
model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Dense(<span style="color: #666666">64</span>, activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;relu&#39;</span>))
model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Dense(<span style="color: #666666">10</span>))
Here<span style="color: #BA2121">&#39;s the complete architecture of our model.</span>
<span style="color: #408080; font-style: italic"># Here&#39;s the complete architecture of our model</span>
model<span style="color: #666666">.</span>summary()
</pre>
doc/pub/week44/html/week44-bs.html
+3 -6
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@@ -357,14 +357,11 @@ MathJax.Hub.Config({
<!-- author(s): Morten Hjorth-Jensen -->
<center>
<b>Morten Hjorth-Jensen</b> [1, 2]
<b>Morten Hjorth-Jensen</b>
</center>
<!-- institution(s) -->
<!-- institution -->
<center>
[1] <b>Department of Physics, University of Oslo</b>
</center>
<center>
[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b>
<b>Department of Physics, University of Oslo, Norway</b>
</center>
<br>
<center>
doc/pub/week44/html/week44-reveal.html
+5 -8
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@@ -173,14 +173,11 @@ MathJax.Hub.Config({
<!-- author(s): Morten Hjorth-Jensen -->
<center>
<b>Morten Hjorth-Jensen</b> [1, 2]
<b>Morten Hjorth-Jensen</b>
</center>
<!-- institution(s) -->
<!-- institution -->
<center>
[1] <b>Department of Physics, University of Oslo</b>
</center>
<center>
[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b>
<b>Department of Physics, University of Oslo, Norway</b>
</center>
<br>
<center>
@@ -1525,7 +1522,7 @@ X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=t
model.add(layers.Dense(n_neurons_connected, activation=<span style="color: #CD5555">&#39;relu&#39;</span>, kernel_regularizer=regularizers.l2(lmbd)))
model.add(layers.Dense(n_categories, activation=<span style="color: #CD5555">&#39;softmax&#39;</span>, kernel_regularizer=regularizers.l2(lmbd)))
sgd = optimizers.SGD(lr=eta)
sgd = optimizers.SGD(learning_rate=eta)
model.compile(loss=<span style="color: #CD5555">&#39;categorical_crossentropy&#39;</span>, optimizer=sgd, metrics=[<span style="color: #CD5555">&#39;accuracy&#39;</span>])
<span style="color: #8B008B; font-weight: bold">return</span> model
@@ -1801,7 +1798,7 @@ layer with 10 outputs and a softmax activation.
<pre style="font-size: 80%; line-height: 125%;">model.add(layers.Flatten())
model.add(layers.Dense(<span style="color: #B452CD">64</span>, activation=<span style="color: #CD5555">&#39;relu&#39;</span>))
model.add(layers.Dense(<span style="color: #B452CD">10</span>))
Here<span style="color: #CD5555">&#39;s the complete architecture of our model.</span>
<span style="color: #228B22"># Here&#39;s the complete architecture of our model</span>
model.summary()
</pre>
doc/pub/week44/html/week44-solarized.html
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@@ -274,14 +274,11 @@ MathJax.Hub.Config({
<!-- author(s): Morten Hjorth-Jensen -->
<center>
<b>Morten Hjorth-Jensen</b> [1, 2]
<b>Morten Hjorth-Jensen</b>
</center>
<!-- institution(s) -->
<!-- institution -->
<center>
[1] <b>Department of Physics, University of Oslo</b>
</center>
<center>
[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b>
<b>Department of Physics, University of Oslo, Norway</b>
</center>
<br>
<center>
@@ -1494,7 +1491,7 @@ X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=t
model.add(layers.Dense(n_neurons_connected, activation=<span style="color: #CD5555">&#39;relu&#39;</span>, kernel_regularizer=regularizers.l2(lmbd)))
model.add(layers.Dense(n_categories, activation=<span style="color: #CD5555">&#39;softmax&#39;</span>, kernel_regularizer=regularizers.l2(lmbd)))
sgd = optimizers.SGD(lr=eta)
sgd = optimizers.SGD(learning_rate=eta)
model.compile(loss=<span style="color: #CD5555">&#39;categorical_crossentropy&#39;</span>, optimizer=sgd, metrics=[<span style="color: #CD5555">&#39;accuracy&#39;</span>])
<span style="color: #8B008B; font-weight: bold">return</span> model
@@ -1769,7 +1766,7 @@ layer with 10 outputs and a softmax activation.
<pre style="line-height: 125%;">model.add(layers.Flatten())
model.add(layers.Dense(<span style="color: #B452CD">64</span>, activation=<span style="color: #CD5555">&#39;relu&#39;</span>))
model.add(layers.Dense(<span style="color: #B452CD">10</span>))
Here<span style="color: #CD5555">&#39;s the complete architecture of our model.</span>
<span style="color: #228B22"># Here&#39;s the complete architecture of our model</span>
model.summary()
</pre>
doc/pub/week44/html/week44.html
+5 -8
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@@ -351,14 +351,11 @@ MathJax.Hub.Config({
<!-- author(s): Morten Hjorth-Jensen -->
<center>
<b>Morten Hjorth-Jensen</b> [1, 2]
<b>Morten Hjorth-Jensen</b>
</center>
<!-- institution(s) -->
<!-- institution -->
<center>
[1] <b>Department of Physics, University of Oslo</b>
</center>
<center>
[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b>
<b>Department of Physics, University of Oslo, Norway</b>
</center>
<br>
<center>
@@ -1571,7 +1568,7 @@ X_train, X_test, Y_train, Y_test <span style="color: #666666">=</span> train_tes
model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Dense(n_neurons_connected, activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;relu&#39;</span>, kernel_regularizer<span style="color: #666666">=</span>regularizers<span style="color: #666666">.</span>l2(lmbd)))
model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Dense(n_categories, activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;softmax&#39;</span>, kernel_regularizer<span style="color: #666666">=</span>regularizers<span style="color: #666666">.</span>l2(lmbd)))
sgd <span style="color: #666666">=</span> optimizers<span style="color: #666666">.</span>SGD(lr<span style="color: #666666">=</span>eta)
sgd <span style="color: #666666">=</span> optimizers<span style="color: #666666">.</span>SGD(learning_rate<span style="color: #666666">=</span>eta)
model<span style="color: #666666">.</span>compile(loss<span style="color: #666666">=</span><span style="color: #BA2121">&#39;categorical_crossentropy&#39;</span>, optimizer<span style="color: #666666">=</span>sgd, metrics<span style="color: #666666">=</span>[<span style="color: #BA2121">&#39;accuracy&#39;</span>])
<span style="color: #008000; font-weight: bold">return</span> model
@@ -1846,7 +1843,7 @@ layer with 10 outputs and a softmax activation.
<pre style="line-height: 125%;">model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Flatten())
model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Dense(<span style="color: #666666">64</span>, activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;relu&#39;</span>))
model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Dense(<span style="color: #666666">10</span>))
Here<span style="color: #BA2121">&#39;s the complete architecture of our model.</span>
<span style="color: #408080; font-style: italic"># Here&#39;s the complete architecture of our model</span>
model<span style="color: #666666">.</span>summary()
</pre>
doc/pub/week44/ipynb/ipynb-week44-src.tar.gz
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doc/pub/week44/ipynb/week44.ipynb
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doc/src/week44/week44.do.txt
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@@ -1,5 +1,5 @@
TITLE: Week 44, Convolutional Neural Networks (CNN)
AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo, Norway
DATE: October 28-November 1
@@ -1131,7 +1131,7 @@ def create_convolutional_neural_network_keras(input_shape, receptive_field,
model.add(layers.Dense(n_neurons_connected, activation='relu', kernel_regularizer=regularizers.l2(lmbd)))
model.add(layers.Dense(n_categories, activation='softmax', kernel_regularizer=regularizers.l2(lmbd)))
sgd = optimizers.SGD(lr=eta)
sgd = optimizers.SGD(learning_rate=eta)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
return model
@@ -1294,7 +1294,7 @@ layer with 10 outputs and a softmax activation.
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10))
Here's the complete architecture of our model.
# Here's the complete architecture of our model
model.summary()
!ec