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Morten Hjorth-Jensen
2024-05-06 06:40:02 -05:00
parent 6e806f21b5
commit f2cd3a2eb4
10 changed files with 368 additions and 339 deletions
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doc/pub/week34/html/._week34-bs000.html
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@@ -416,7 +416,7 @@ MathJax.Hub.Config({
</footer>
-->
<center style="font-size:80%">
<!-- copyright --> &copy; 1999-2023, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
<!-- copyright --> &copy; 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
</center>
</body>
</html>
doc/pub/week34/html/._week34-bs038.html
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@@ -1107,31 +1107,36 @@ functionality.
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.metrics</span> <span style="color: #008000; font-weight: bold">import</span> accuracy_score
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">seaborn</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">sns</span>
X_train <span style="color: #666666">=</span> X
Y_train <span style="color: #666666">=</span> Energies
n_hidden_neurons <span style="color: #666666">=</span> <span style="color: #666666">100</span>
n_hidden_neurons <span style="color: #666666">=</span> <span style="color: #666666">50</span>
epochs <span style="color: #666666">=</span> <span style="color: #666666">100</span>
<span style="color: #408080; font-style: italic"># store models for later use</span>
eta_vals <span style="color: #666666">=</span> np<span style="color: #666666">.</span>logspace(<span style="color: #666666">-5</span>, <span style="color: #666666">1</span>, <span style="color: #666666">7</span>)
lmbd_vals <span style="color: #666666">=</span> np<span style="color: #666666">.</span>logspace(<span style="color: #666666">-5</span>, <span style="color: #666666">1</span>, <span style="color: #666666">7</span>)
eta_vals <span style="color: #666666">=</span> np<span style="color: #666666">.</span>logspace(<span style="color: #666666">-3</span>, <span style="color: #666666">0</span>, <span style="color: #666666">4</span>)
lmbd_vals <span style="color: #666666">=</span> np<span style="color: #666666">.</span>logspace(<span style="color: #666666">-3</span>, <span style="color: #666666">0</span>, <span style="color: #666666">4</span>)
<span style="color: #408080; font-style: italic"># store the models for later use</span>
DNN_scikit <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros((<span style="color: #008000">len</span>(eta_vals), <span style="color: #008000">len</span>(lmbd_vals)), dtype<span style="color: #666666">=</span><span style="color: #008000">object</span>)
train_accuracy <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros((<span style="color: #008000">len</span>(eta_vals), <span style="color: #008000">len</span>(lmbd_vals)))
sns<span style="color: #666666">.</span>set()
<span style="color: #008000; font-weight: bold">for</span> i, eta <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">enumerate</span>(eta_vals):
<span style="color: #008000; font-weight: bold">for</span> j, lmbd <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">enumerate</span>(lmbd_vals):
dnn <span style="color: #666666">=</span> MLPRegressor(hidden_layer_sizes<span style="color: #666666">=</span>(n_hidden_neurons), activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;logistic&#39;</span>,
dnn <span style="color: #666666">=</span> MLPRegressor(hidden_layer_sizes<span style="color: #666666">=</span>(n_hidden_neurons), activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;relu&#39;</span>, solver<span style="color: #666666">=</span><span style="color: #BA2121">&#39;adam&#39;</span>,
alpha<span style="color: #666666">=</span>lmbd, learning_rate_init<span style="color: #666666">=</span>eta, max_iter<span style="color: #666666">=</span>epochs)
dnn<span style="color: #666666">.</span>fit(X_train, Y_train)
DNN_scikit[i][j] <span style="color: #666666">=</span> dnn
train_accuracy[i][j] <span style="color: #666666">=</span> dnn<span style="color: #666666">.</span>score(X_train, Y_train)
fity <span style="color: #666666">=</span> dnn<span style="color: #666666">.</span>predict(X_train)
MSE <span style="color: #666666">=</span> mean_squared_error(Y_train, fity)
<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Mean squared error: </span><span style="color: #BB6688; font-weight: bold">%.2f</span><span style="color: #BA2121">&quot;</span> <span style="color: #666666">%</span> mean_squared_error(Y_train, fity))
train_accuracy[i][j] <span style="color: #666666">=</span> MSE
fig, ax <span style="color: #666666">=</span> plt<span style="color: #666666">.</span>subplots(figsize <span style="color: #666666">=</span> (<span style="color: #666666">10</span>, <span style="color: #666666">10</span>))
sns<span style="color: #666666">.</span>heatmap(train_accuracy, annot<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>, ax<span style="color: #666666">=</span>ax, cmap<span style="color: #666666">=</span><span style="color: #BA2121">&quot;viridis&quot;</span>)
ax<span style="color: #666666">.</span>set_title(<span style="color: #BA2121">&quot;Training Accuracy&quot;</span>)
ax<span style="color: #666666">.</span>set_ylabel(<span style="color: #BA2121">&quot;$\eta$&quot;</span>)
ax<span style="color: #666666">.</span>set_xlabel(<span style="color: #BA2121">&quot;$\lambda$&quot;</span>)
plt<span style="color: #666666">.</span>show()
<span style="color: #008000">print</span>(train_accuracy)
</pre>
</div>
</div>
doc/pub/week34/html/week34-bs.html
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@@ -416,7 +416,7 @@ MathJax.Hub.Config({
</footer>
-->
<center style="font-size:80%">
<!-- copyright --> &copy; 1999-2023, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
<!-- copyright --> &copy; 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
</center>
</body>
</html>
doc/pub/week34/html/week34-reveal.html
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@@ -190,7 +190,7 @@ MathJax.Hub.Config({
<center style="font-size:80%">
<!-- copyright --> &copy; 1999-2023, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
<!-- copyright --> &copy; 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
</center>
</section>
@@ -2533,31 +2533,36 @@ functionality.
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> accuracy_score
<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">seaborn</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">sns</span>
X_train = X
Y_train = Energies
n_hidden_neurons = <span style="color: #B452CD">100</span>
n_hidden_neurons = <span style="color: #B452CD">50</span>
epochs = <span style="color: #B452CD">100</span>
<span style="color: #228B22"># store models for later use</span>
eta_vals = np.logspace(-<span style="color: #B452CD">5</span>, <span style="color: #B452CD">1</span>, <span style="color: #B452CD">7</span>)
lmbd_vals = np.logspace(-<span style="color: #B452CD">5</span>, <span style="color: #B452CD">1</span>, <span style="color: #B452CD">7</span>)
eta_vals = np.logspace(-<span style="color: #B452CD">3</span>, <span style="color: #B452CD">0</span>, <span style="color: #B452CD">4</span>)
lmbd_vals = np.logspace(-<span style="color: #B452CD">3</span>, <span style="color: #B452CD">0</span>, <span style="color: #B452CD">4</span>)
<span style="color: #228B22"># store the models for later use</span>
DNN_scikit = np.zeros((<span style="color: #658b00">len</span>(eta_vals), <span style="color: #658b00">len</span>(lmbd_vals)), dtype=<span style="color: #658b00">object</span>)
train_accuracy = np.zeros((<span style="color: #658b00">len</span>(eta_vals), <span style="color: #658b00">len</span>(lmbd_vals)))
sns.set()
<span style="color: #8B008B; font-weight: bold">for</span> i, eta <span style="color: #8B008B">in</span> <span style="color: #658b00">enumerate</span>(eta_vals):
<span style="color: #8B008B; font-weight: bold">for</span> j, lmbd <span style="color: #8B008B">in</span> <span style="color: #658b00">enumerate</span>(lmbd_vals):
dnn = MLPRegressor(hidden_layer_sizes=(n_hidden_neurons), activation=<span style="color: #CD5555">&#39;logistic&#39;</span>,
dnn = MLPRegressor(hidden_layer_sizes=(n_hidden_neurons), activation=<span style="color: #CD5555">&#39;relu&#39;</span>, solver=<span style="color: #CD5555">&#39;adam&#39;</span>,
alpha=lmbd, learning_rate_init=eta, max_iter=epochs)
dnn.fit(X_train, Y_train)
DNN_scikit[i][j] = dnn
train_accuracy[i][j] = dnn.score(X_train, Y_train)
fity = dnn.predict(X_train)
MSE = mean_squared_error(Y_train, fity)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Mean squared error: %.2f&quot;</span> % mean_squared_error(Y_train, fity))
train_accuracy[i][j] = MSE
fig, ax = plt.subplots(figsize = (<span style="color: #B452CD">10</span>, <span style="color: #B452CD">10</span>))
sns.heatmap(train_accuracy, annot=<span style="color: #8B008B; font-weight: bold">True</span>, ax=ax, cmap=<span style="color: #CD5555">&quot;viridis&quot;</span>)
ax.set_title(<span style="color: #CD5555">&quot;Training Accuracy&quot;</span>)
ax.set_ylabel(<span style="color: #CD5555">&quot;$\eta$&quot;</span>)
ax.set_xlabel(<span style="color: #CD5555">&quot;$\lambda$&quot;</span>)
plt.show()
<span style="color: #658b00">print</span>(train_accuracy)
</pre>
</div>
</div>
doc/pub/week34/html/week34-solarized.html
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@@ -2515,31 +2515,36 @@ functionality.
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> accuracy_score
<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">seaborn</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">sns</span>
X_train = X
Y_train = Energies
n_hidden_neurons = <span style="color: #B452CD">100</span>
n_hidden_neurons = <span style="color: #B452CD">50</span>
epochs = <span style="color: #B452CD">100</span>
<span style="color: #228B22"># store models for later use</span>
eta_vals = np.logspace(-<span style="color: #B452CD">5</span>, <span style="color: #B452CD">1</span>, <span style="color: #B452CD">7</span>)
lmbd_vals = np.logspace(-<span style="color: #B452CD">5</span>, <span style="color: #B452CD">1</span>, <span style="color: #B452CD">7</span>)
eta_vals = np.logspace(-<span style="color: #B452CD">3</span>, <span style="color: #B452CD">0</span>, <span style="color: #B452CD">4</span>)
lmbd_vals = np.logspace(-<span style="color: #B452CD">3</span>, <span style="color: #B452CD">0</span>, <span style="color: #B452CD">4</span>)
<span style="color: #228B22"># store the models for later use</span>
DNN_scikit = np.zeros((<span style="color: #658b00">len</span>(eta_vals), <span style="color: #658b00">len</span>(lmbd_vals)), dtype=<span style="color: #658b00">object</span>)
train_accuracy = np.zeros((<span style="color: #658b00">len</span>(eta_vals), <span style="color: #658b00">len</span>(lmbd_vals)))
sns.set()
<span style="color: #8B008B; font-weight: bold">for</span> i, eta <span style="color: #8B008B">in</span> <span style="color: #658b00">enumerate</span>(eta_vals):
<span style="color: #8B008B; font-weight: bold">for</span> j, lmbd <span style="color: #8B008B">in</span> <span style="color: #658b00">enumerate</span>(lmbd_vals):
dnn = MLPRegressor(hidden_layer_sizes=(n_hidden_neurons), activation=<span style="color: #CD5555">&#39;logistic&#39;</span>,
dnn = MLPRegressor(hidden_layer_sizes=(n_hidden_neurons), activation=<span style="color: #CD5555">&#39;relu&#39;</span>, solver=<span style="color: #CD5555">&#39;adam&#39;</span>,
alpha=lmbd, learning_rate_init=eta, max_iter=epochs)
dnn.fit(X_train, Y_train)
DNN_scikit[i][j] = dnn
train_accuracy[i][j] = dnn.score(X_train, Y_train)
fity = dnn.predict(X_train)
MSE = mean_squared_error(Y_train, fity)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Mean squared error: %.2f&quot;</span> % mean_squared_error(Y_train, fity))
train_accuracy[i][j] = MSE
fig, ax = plt.subplots(figsize = (<span style="color: #B452CD">10</span>, <span style="color: #B452CD">10</span>))
sns.heatmap(train_accuracy, annot=<span style="color: #8B008B; font-weight: bold">True</span>, ax=ax, cmap=<span style="color: #CD5555">&quot;viridis&quot;</span>)
ax.set_title(<span style="color: #CD5555">&quot;Training Accuracy&quot;</span>)
ax.set_ylabel(<span style="color: #CD5555">&quot;$\eta$&quot;</span>)
ax.set_xlabel(<span style="color: #CD5555">&quot;$\lambda$&quot;</span>)
plt.show()
<span style="color: #658b00">print</span>(train_accuracy)
</pre>
</div>
</div>
@@ -3913,7 +3918,7 @@ Add now a model which allows you to make polynomials up to degree \( 15 \). Per
<!-- --- end exercise --- -->
<!-- ------------------- end of main content --------------- -->
<center style="font-size:80%">
<!-- copyright --> &copy; 1999-2023, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
<!-- copyright --> &copy; 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
</center>
</body>
</html>
doc/pub/week34/html/week34.html
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@@ -2592,31 +2592,36 @@ functionality.
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.metrics</span> <span style="color: #008000; font-weight: bold">import</span> accuracy_score
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">seaborn</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">sns</span>
X_train <span style="color: #666666">=</span> X
Y_train <span style="color: #666666">=</span> Energies
n_hidden_neurons <span style="color: #666666">=</span> <span style="color: #666666">100</span>
n_hidden_neurons <span style="color: #666666">=</span> <span style="color: #666666">50</span>
epochs <span style="color: #666666">=</span> <span style="color: #666666">100</span>
<span style="color: #408080; font-style: italic"># store models for later use</span>
eta_vals <span style="color: #666666">=</span> np<span style="color: #666666">.</span>logspace(<span style="color: #666666">-5</span>, <span style="color: #666666">1</span>, <span style="color: #666666">7</span>)
lmbd_vals <span style="color: #666666">=</span> np<span style="color: #666666">.</span>logspace(<span style="color: #666666">-5</span>, <span style="color: #666666">1</span>, <span style="color: #666666">7</span>)
eta_vals <span style="color: #666666">=</span> np<span style="color: #666666">.</span>logspace(<span style="color: #666666">-3</span>, <span style="color: #666666">0</span>, <span style="color: #666666">4</span>)
lmbd_vals <span style="color: #666666">=</span> np<span style="color: #666666">.</span>logspace(<span style="color: #666666">-3</span>, <span style="color: #666666">0</span>, <span style="color: #666666">4</span>)
<span style="color: #408080; font-style: italic"># store the models for later use</span>
DNN_scikit <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros((<span style="color: #008000">len</span>(eta_vals), <span style="color: #008000">len</span>(lmbd_vals)), dtype<span style="color: #666666">=</span><span style="color: #008000">object</span>)
train_accuracy <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros((<span style="color: #008000">len</span>(eta_vals), <span style="color: #008000">len</span>(lmbd_vals)))
sns<span style="color: #666666">.</span>set()
<span style="color: #008000; font-weight: bold">for</span> i, eta <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">enumerate</span>(eta_vals):
<span style="color: #008000; font-weight: bold">for</span> j, lmbd <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">enumerate</span>(lmbd_vals):
dnn <span style="color: #666666">=</span> MLPRegressor(hidden_layer_sizes<span style="color: #666666">=</span>(n_hidden_neurons), activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;logistic&#39;</span>,
dnn <span style="color: #666666">=</span> MLPRegressor(hidden_layer_sizes<span style="color: #666666">=</span>(n_hidden_neurons), activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;relu&#39;</span>, solver<span style="color: #666666">=</span><span style="color: #BA2121">&#39;adam&#39;</span>,
alpha<span style="color: #666666">=</span>lmbd, learning_rate_init<span style="color: #666666">=</span>eta, max_iter<span style="color: #666666">=</span>epochs)
dnn<span style="color: #666666">.</span>fit(X_train, Y_train)
DNN_scikit[i][j] <span style="color: #666666">=</span> dnn
train_accuracy[i][j] <span style="color: #666666">=</span> dnn<span style="color: #666666">.</span>score(X_train, Y_train)
fity <span style="color: #666666">=</span> dnn<span style="color: #666666">.</span>predict(X_train)
MSE <span style="color: #666666">=</span> mean_squared_error(Y_train, fity)
<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Mean squared error: </span><span style="color: #BB6688; font-weight: bold">%.2f</span><span style="color: #BA2121">&quot;</span> <span style="color: #666666">%</span> mean_squared_error(Y_train, fity))
train_accuracy[i][j] <span style="color: #666666">=</span> MSE
fig, ax <span style="color: #666666">=</span> plt<span style="color: #666666">.</span>subplots(figsize <span style="color: #666666">=</span> (<span style="color: #666666">10</span>, <span style="color: #666666">10</span>))
sns<span style="color: #666666">.</span>heatmap(train_accuracy, annot<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>, ax<span style="color: #666666">=</span>ax, cmap<span style="color: #666666">=</span><span style="color: #BA2121">&quot;viridis&quot;</span>)
ax<span style="color: #666666">.</span>set_title(<span style="color: #BA2121">&quot;Training Accuracy&quot;</span>)
ax<span style="color: #666666">.</span>set_ylabel(<span style="color: #BA2121">&quot;$\eta$&quot;</span>)
ax<span style="color: #666666">.</span>set_xlabel(<span style="color: #BA2121">&quot;$\lambda$&quot;</span>)
plt<span style="color: #666666">.</span>show()
<span style="color: #008000">print</span>(train_accuracy)
</pre>
</div>
</div>
@@ -3990,7 +3995,7 @@ Add now a model which allows you to make polynomials up to degree \( 15 \). Per
<!-- --- end exercise --- -->
<!-- ------------------- end of main content --------------- -->
<center style="font-size:80%">
<!-- copyright --> &copy; 1999-2023, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
<!-- copyright --> &copy; 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
</center>
</body>
</html>
doc/pub/week34/ipynb/Results/FigureFiles/Masses2016.png
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doc/pub/week34/ipynb/week34.ipynb
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doc/src/week34/week34.do.txt
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@@ -1499,32 +1499,36 @@ from sklearn.neural_network import MLPRegressor
from sklearn.metrics import accuracy_score
import seaborn as sns
X_train = X
Y_train = Energies
n_hidden_neurons = 100
n_hidden_neurons = 50
epochs = 100
# store models for later use
eta_vals = np.logspace(-5, 1, 7)
lmbd_vals = np.logspace(-5, 1, 7)
eta_vals = np.logspace(-3, 0, 4)
lmbd_vals = np.logspace(-3, 0, 4)
# store the models for later use
DNN_scikit = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
sns.set()
for i, eta in enumerate(eta_vals):
for j, lmbd in enumerate(lmbd_vals):
dnn = MLPRegressor(hidden_layer_sizes=(n_hidden_neurons), activation='logistic',
dnn = MLPRegressor(hidden_layer_sizes=(n_hidden_neurons), activation='relu', solver='adam',
alpha=lmbd, learning_rate_init=eta, max_iter=epochs)
dnn.fit(X_train, Y_train)
DNN_scikit[i][j] = dnn
train_accuracy[i][j] = dnn.score(X_train, Y_train)
fity = dnn.predict(X_train)
MSE = mean_squared_error(Y_train, fity)
print("Mean squared error: %.2f" % mean_squared_error(Y_train, fity))
train_accuracy[i][j] = MSE
fig, ax = plt.subplots(figsize = (10, 10))
sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
ax.set_title("Training Accuracy")
ax.set_ylabel("$\eta$")
ax.set_xlabel("$\lambda$")
plt.show()
print(train_accuracy)
!ec