From f2cd3a2eb4a070a909d5c55e555c443c34196c43 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Mon, 6 May 2024 06:40:02 -0500 Subject: [PATCH] updating files --- doc/pub/week34/html/._week34-bs000.html | 2 +- doc/pub/week34/html/._week34-bs038.html | 15 +- doc/pub/week34/html/week34-bs.html | 2 +- doc/pub/week34/html/week34-reveal.html | 17 +- doc/pub/week34/html/week34-solarized.html | 17 +- doc/pub/week34/html/week34.html | 17 +- .../ipynb/Results/FigureFiles/Masses2016.png | Bin 17747 -> 23973 bytes doc/pub/week34/ipynb/ipynb-week34-src.tar.gz | Bin 103516 -> 103516 bytes doc/pub/week34/ipynb/week34.ipynb | 621 +++++++++--------- doc/src/week34/week34.do.txt | 16 +- 10 files changed, 368 insertions(+), 339 deletions(-) diff --git a/doc/pub/week34/html/._week34-bs000.html b/doc/pub/week34/html/._week34-bs000.html index 3ddc017a7..6c6ead9e7 100644 --- a/doc/pub/week34/html/._week34-bs000.html +++ b/doc/pub/week34/html/._week34-bs000.html @@ -416,7 +416,7 @@ MathJax.Hub.Config({ -->
- © 1999-2023, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license + © 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
diff --git a/doc/pub/week34/html/._week34-bs038.html b/doc/pub/week34/html/._week34-bs038.html index a23c118bb..5800ab148 100644 --- a/doc/pub/week34/html/._week34-bs038.html +++ b/doc/pub/week34/html/._week34-bs038.html @@ -1107,31 +1107,36 @@ functionality. 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) diff --git a/doc/pub/week34/html/week34-bs.html b/doc/pub/week34/html/week34-bs.html index 3ddc017a7..6c6ead9e7 100644 --- a/doc/pub/week34/html/week34-bs.html +++ b/doc/pub/week34/html/week34-bs.html @@ -416,7 +416,7 @@ MathJax.Hub.Config({ -->
- © 1999-2023, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license + © 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
diff --git a/doc/pub/week34/html/week34-reveal.html b/doc/pub/week34/html/week34-reveal.html index 329e9443d..fb54cbcbf 100644 --- a/doc/pub/week34/html/week34-reveal.html +++ b/doc/pub/week34/html/week34-reveal.html @@ -190,7 +190,7 @@ MathJax.Hub.Config({
- © 1999-2023, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license + © 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
@@ -2533,31 +2533,36 @@ functionality. 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) diff --git a/doc/pub/week34/html/week34-solarized.html b/doc/pub/week34/html/week34-solarized.html index d2c70dbae..77c2104e6 100644 --- a/doc/pub/week34/html/week34-solarized.html +++ b/doc/pub/week34/html/week34-solarized.html @@ -2515,31 +2515,36 @@ functionality. 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) @@ -3913,7 +3918,7 @@ Add now a model which allows you to make polynomials up to degree \( 15 \). Per
- © 1999-2023, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license + © 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
diff --git a/doc/pub/week34/html/week34.html b/doc/pub/week34/html/week34.html index 25c21c552..7d5f604f0 100644 --- a/doc/pub/week34/html/week34.html +++ b/doc/pub/week34/html/week34.html @@ -2592,31 +2592,36 @@ functionality. 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) @@ -3990,7 +3995,7 @@ Add now a model which allows you to make polynomials up to degree \( 15 \). Per
- © 1999-2023, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license + © 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
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