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({
 </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>
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.
 <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>
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({
 </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>
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({
 
 
 <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>
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.
 <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>
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.
 <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>
diff --git a/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016.png b/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016.png
index bbaa79e2f..fa96f6959 100644
Binary files a/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016.png and b/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016.png differ
diff --git a/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz b/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz
index a79017796..59a468d63 100644
Binary files a/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz and b/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz differ
diff --git a/doc/pub/week34/ipynb/week34.ipynb b/doc/pub/week34/ipynb/week34.ipynb
index 1441170cd..648837887 100644
--- a/doc/pub/week34/ipynb/week34.ipynb
+++ b/doc/pub/week34/ipynb/week34.ipynb
@@ -2,7 +2,7 @@
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@@ -954,7 +954,7 @@
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@@ -966,7 +966,7 @@
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@@ -977,7 +977,7 @@
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@@ -991,7 +991,7 @@
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@@ -1003,7 +1003,7 @@
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@@ -1017,7 +1017,7 @@
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@@ -1029,7 +1029,7 @@
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@@ -1060,7 +1060,7 @@
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@@ -1089,7 +1089,7 @@
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@@ -1103,7 +1103,7 @@
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@@ -1114,7 +1114,7 @@
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@@ -1128,7 +1128,7 @@
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@@ -1139,7 +1139,7 @@
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@@ -1153,7 +1153,7 @@
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@@ -1168,7 +1168,7 @@
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@@ -1182,7 +1182,7 @@
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@@ -1193,7 +1193,7 @@
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@@ -1208,7 +1208,7 @@
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@@ -1219,7 +1219,7 @@
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@@ -1234,7 +1234,7 @@
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@@ -1245,7 +1245,7 @@
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@@ -1261,7 +1261,7 @@
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@@ -1315,7 +1315,7 @@
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@@ -1327,7 +1327,7 @@
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@@ -1342,7 +1342,7 @@
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@@ -1364,7 +1364,7 @@
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@@ -1432,7 +1432,7 @@
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@@ -1851,7 +1851,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af64c684",
+   "id": "f0fa3206",
    "metadata": {
     "editable": true
    },
@@ -1864,7 +1864,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a305d9e7",
+   "id": "e7b223c5",
    "metadata": {
     "editable": true
    },
@@ -1895,7 +1895,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6346176",
+   "id": "e628fb6a",
    "metadata": {
     "editable": true
    },
@@ -1907,7 +1907,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d44d40a5",
+   "id": "8263fca7",
    "metadata": {
     "editable": true
    },
@@ -1925,7 +1925,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "3cfb45ba",
+   "id": "8b269629",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1952,7 +1952,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6c51480",
+   "id": "c68f4716",
    "metadata": {
     "editable": true
    },
@@ -1974,7 +1974,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "bcf2fd18",
+   "id": "b9536a0a",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2012,7 +2012,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "edb3061c",
+   "id": "545ce818",
    "metadata": {
     "editable": true
    },
@@ -2023,7 +2023,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fad1108",
+   "id": "90848339",
    "metadata": {
     "editable": true
    },
@@ -2036,7 +2036,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff182728",
+   "id": "c740c99d",
    "metadata": {
     "editable": true
    },
@@ -2057,7 +2057,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "48a36723",
+   "id": "deff6a8b",
    "metadata": {
     "editable": true
    },
@@ -2069,7 +2069,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4545533",
+   "id": "69d9fe36",
    "metadata": {
     "editable": true
    },
@@ -2079,7 +2079,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0405959d",
+   "id": "054d0dd1",
    "metadata": {
     "editable": true
    },
@@ -2091,7 +2091,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b66be271",
+   "id": "ed027b67",
    "metadata": {
     "editable": true
    },
@@ -2103,7 +2103,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c46555c6",
+   "id": "a741491c",
    "metadata": {
     "editable": true
    },
@@ -2115,7 +2115,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "302671bd",
+   "id": "1528b10c",
    "metadata": {
     "editable": true
    },
@@ -2126,7 +2126,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2fc06159",
+   "id": "2324aa18",
    "metadata": {
     "editable": true
    },
@@ -2138,7 +2138,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6f5fa841",
+   "id": "5c819d3b",
    "metadata": {
     "editable": true
    },
@@ -2160,7 +2160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e5b5995",
+   "id": "2f692938",
    "metadata": {
     "editable": true
    },
@@ -2172,7 +2172,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebba0b41",
+   "id": "0118548c",
    "metadata": {
     "editable": true
    },
@@ -2185,7 +2185,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7de54a4f",
+   "id": "6a77f30f",
    "metadata": {
     "editable": true
    },
@@ -2202,7 +2202,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a68a4de9",
+   "id": "9e5e0f07",
    "metadata": {
     "editable": true
    },
@@ -2214,7 +2214,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50e333b5",
+   "id": "bbcb4130",
    "metadata": {
     "editable": true
    },
@@ -2224,7 +2224,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a76471b",
+   "id": "a8b137e1",
    "metadata": {
     "editable": true
    },
@@ -2236,7 +2236,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b2216b65",
+   "id": "f88c848c",
    "metadata": {
     "editable": true
    },
@@ -2246,7 +2246,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d11460a9",
+   "id": "6547c4ce",
    "metadata": {
     "editable": true
    },
@@ -2258,7 +2258,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5116a4f8",
+   "id": "5fa8bc43",
    "metadata": {
     "editable": true
    },
@@ -2268,7 +2268,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a93214bf",
+   "id": "3570dae8",
    "metadata": {
     "editable": true
    },
@@ -2280,7 +2280,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e70eb950",
+   "id": "2903e3c8",
    "metadata": {
     "editable": true
    },
@@ -2296,7 +2296,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4fa63019",
+   "id": "dce5fba5",
    "metadata": {
     "editable": true
    },
@@ -2308,7 +2308,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ed175b9",
+   "id": "7f49ea93",
    "metadata": {
     "editable": true
    },
@@ -2319,7 +2319,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f681ea0",
+   "id": "b229130c",
    "metadata": {
     "editable": true
    },
@@ -2331,7 +2331,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9f817bb",
+   "id": "00a8181b",
    "metadata": {
     "editable": true
    },
@@ -2345,7 +2345,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0e0bd41",
+   "id": "ed3e9886",
    "metadata": {
     "editable": true
    },
@@ -2357,7 +2357,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4825a6a",
+   "id": "9833ea6e",
    "metadata": {
     "editable": true
    },
@@ -2382,7 +2382,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "137dc3cc",
+   "id": "e586b4cd",
    "metadata": {
     "editable": true
    },
@@ -2399,7 +2399,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "cd6dfaa4",
+   "id": "5b86a2f2",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2443,7 +2443,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "63570821",
+   "id": "6d8999da",
    "metadata": {
     "editable": true
    },
@@ -2454,7 +2454,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "a6a38dec",
+   "id": "ca868b5c",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2476,7 +2476,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "90828be8",
+   "id": "265fc848",
    "metadata": {
     "editable": true
    },
@@ -2493,7 +2493,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "9deee8b0",
+   "id": "edef3728",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2514,7 +2514,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff2b0408",
+   "id": "dd0034af",
    "metadata": {
     "editable": true
    },
@@ -2528,7 +2528,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "0bee88ca",
+   "id": "0a2099c5",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2557,7 +2557,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c728bc6",
+   "id": "6d64a9c6",
    "metadata": {
     "editable": true
    },
@@ -2577,7 +2577,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "89042942",
+   "id": "c36ad284",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2594,7 +2594,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db2fb4d3",
+   "id": "0b18a2c0",
    "metadata": {
     "editable": true
    },
@@ -2606,7 +2606,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "34b7d0ec",
+   "id": "1ad1fff9",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2624,7 +2624,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1852d98",
+   "id": "8418a2aa",
    "metadata": {
     "editable": true
    },
@@ -2635,7 +2635,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "3bca0adf",
+   "id": "1ea1c99f",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2648,7 +2648,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be916886",
+   "id": "d184b5df",
    "metadata": {
     "editable": true
    },
@@ -2660,7 +2660,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "108c8400",
+   "id": "4bb96bf8",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2691,7 +2691,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d92084bc",
+   "id": "a6ca4c82",
    "metadata": {
     "editable": true
    },
@@ -2705,7 +2705,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "c2be0e0b",
+   "id": "c3072d47",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2716,36 +2716,41 @@
     "from sklearn.metrics import accuracy_score\n",
     "import seaborn as sns\n",
     "\n",
+    "\n",
     "X_train = X\n",
     "Y_train = Energies\n",
-    "n_hidden_neurons = 100\n",
+    "n_hidden_neurons = 50\n",
     "epochs = 100\n",
     "# store models for later use\n",
-    "eta_vals = np.logspace(-5, 1, 7)\n",
-    "lmbd_vals = np.logspace(-5, 1, 7)\n",
+    "eta_vals = np.logspace(-3, 0, 4)\n",
+    "lmbd_vals = np.logspace(-3, 0, 4)\n",
     "# store the models for later use\n",
     "DNN_scikit = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
     "train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))\n",
     "sns.set()\n",
     "for i, eta in enumerate(eta_vals):\n",
     "    for j, lmbd in enumerate(lmbd_vals):\n",
-    "        dnn = MLPRegressor(hidden_layer_sizes=(n_hidden_neurons), activation='logistic',\n",
+    "        dnn = MLPRegressor(hidden_layer_sizes=(n_hidden_neurons), activation='relu', solver='adam',\n",
     "                            alpha=lmbd, learning_rate_init=eta, max_iter=epochs)\n",
     "        dnn.fit(X_train, Y_train)\n",
     "        DNN_scikit[i][j] = dnn\n",
     "        train_accuracy[i][j] = dnn.score(X_train, Y_train)\n",
-    "\n",
+    "        fity = dnn.predict(X_train)\n",
+    "        MSE = mean_squared_error(Y_train, fity)\n",
+    "        print(\"Mean squared error: %.2f\" % mean_squared_error(Y_train, fity))\n",
+    "        train_accuracy[i][j] = MSE\n",
     "fig, ax = plt.subplots(figsize = (10, 10))\n",
     "sns.heatmap(train_accuracy, annot=True, ax=ax, cmap=\"viridis\")\n",
     "ax.set_title(\"Training Accuracy\")\n",
     "ax.set_ylabel(\"$\\eta$\")\n",
     "ax.set_xlabel(\"$\\lambda$\")\n",
-    "plt.show()"
+    "plt.show()\n",
+    "print(train_accuracy)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "01ca5c14",
+   "id": "ded40485",
    "metadata": {
     "editable": true
    },
@@ -2765,7 +2770,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0662575c",
+   "id": "bf969507",
    "metadata": {
     "editable": true
    },
@@ -2797,7 +2802,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7a55b7c9",
+   "id": "b1b95ecd",
    "metadata": {
     "editable": true
    },
@@ -2819,7 +2824,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6af39df1",
+   "id": "86523480",
    "metadata": {
     "editable": true
    },
@@ -2847,7 +2852,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b45544ef",
+   "id": "13322e35",
    "metadata": {
     "editable": true
    },
@@ -2862,7 +2867,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd6afdff",
+   "id": "0078a071",
    "metadata": {
     "editable": true
    },
@@ -2874,7 +2879,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d2daf299",
+   "id": "79bdef1f",
    "metadata": {
     "editable": true
    },
@@ -2889,7 +2894,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "549b4b0c",
+   "id": "151d70ab",
    "metadata": {
     "editable": true
    },
@@ -2902,7 +2907,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8f3cdc66",
+   "id": "d60946c5",
    "metadata": {
     "editable": true
    },
@@ -2914,7 +2919,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50eed8e6",
+   "id": "b5921125",
    "metadata": {
     "editable": true
    },
@@ -2924,7 +2929,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c6baade",
+   "id": "01a6db48",
    "metadata": {
     "editable": true
    },
@@ -2935,7 +2940,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8cf39797",
+   "id": "910c9857",
    "metadata": {
     "editable": true
    },
@@ -2953,7 +2958,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a2d633df",
+   "id": "bb4926b2",
    "metadata": {
     "editable": true
    },
@@ -2964,7 +2969,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed16c047",
+   "id": "16d82720",
    "metadata": {
     "editable": true
    },
@@ -2976,7 +2981,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "faef6266",
+   "id": "1985845d",
    "metadata": {
     "editable": true
    },
@@ -2986,7 +2991,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "566e0c96",
+   "id": "034b0a2f",
    "metadata": {
     "editable": true
    },
@@ -2998,7 +3003,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7e3af60",
+   "id": "79acfd67",
    "metadata": {
     "editable": true
    },
@@ -3008,7 +3013,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b97234c",
+   "id": "6268dff7",
    "metadata": {
     "editable": true
    },
@@ -3020,7 +3025,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c0f8dfd6",
+   "id": "5dfd0735",
    "metadata": {
     "editable": true
    },
@@ -3030,7 +3035,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4bed4254",
+   "id": "6fffafff",
    "metadata": {
     "editable": true
    },
@@ -3049,7 +3054,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e7d976e7",
+   "id": "3fb49db2",
    "metadata": {
     "editable": true
    },
@@ -3059,7 +3064,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a94f97d1",
+   "id": "86bc3042",
    "metadata": {
     "editable": true
    },
@@ -3071,7 +3076,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de6c76f7",
+   "id": "7257a7db",
    "metadata": {
     "editable": true
    },
@@ -3081,7 +3086,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0eb7405a",
+   "id": "7ad41796",
    "metadata": {
     "editable": true
    },
@@ -3097,7 +3102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cee39e35",
+   "id": "1f969e57",
    "metadata": {
     "editable": true
    },
@@ -3117,7 +3122,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eaa1c481",
+   "id": "26a5823e",
    "metadata": {
     "editable": true
    },
@@ -3127,7 +3132,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e12da8ee",
+   "id": "ca8996bd",
    "metadata": {
     "editable": true
    },
@@ -3138,7 +3143,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a874b4e",
+   "id": "94a29694",
    "metadata": {
     "editable": true
    },
@@ -3157,7 +3162,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3563833e",
+   "id": "2b37bb44",
    "metadata": {
     "editable": true
    },
@@ -3167,7 +3172,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed8da82f",
+   "id": "c3fcf170",
    "metadata": {
     "editable": true
    },
@@ -3179,7 +3184,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5cb47efb",
+   "id": "31791045",
    "metadata": {
     "editable": true
    },
@@ -3189,7 +3194,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79b29025",
+   "id": "212f90a6",
    "metadata": {
     "editable": true
    },
@@ -3200,7 +3205,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aa0b8ede",
+   "id": "29850c49",
    "metadata": {
     "editable": true
    },
@@ -3220,7 +3225,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f291e919",
+   "id": "b1ef4be4",
    "metadata": {
     "editable": true
    },
@@ -3232,7 +3237,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6f54577",
+   "id": "cc56f624",
    "metadata": {
     "editable": true
    },
@@ -3247,7 +3252,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "ab9ca1ae",
+   "id": "379ac7d7",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3327,7 +3332,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "669ba9ca",
+   "id": "72c8386b",
    "metadata": {
     "editable": true
    },
@@ -3337,7 +3342,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c72875c",
+   "id": "8c1dab4d",
    "metadata": {
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@@ -3349,7 +3354,7 @@
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@@ -3359,7 +3364,7 @@
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@@ -3370,7 +3375,7 @@
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@@ -3382,7 +3387,7 @@
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@@ -3392,7 +3397,7 @@
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@@ -3404,7 +3409,7 @@
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@@ -3414,7 +3419,7 @@
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@@ -3426,7 +3431,7 @@
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@@ -3439,7 +3444,7 @@
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@@ -3451,7 +3456,7 @@
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@@ -3461,7 +3466,7 @@
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@@ -3473,7 +3478,7 @@
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@@ -3485,7 +3490,7 @@
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@@ -3496,7 +3501,7 @@
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@@ -3508,7 +3513,7 @@
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@@ -3527,7 +3532,7 @@
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@@ -3540,7 +3545,7 @@
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@@ -3550,7 +3555,7 @@
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@@ -3562,7 +3567,7 @@
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@@ -3572,7 +3577,7 @@
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@@ -3584,7 +3589,7 @@
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@@ -3594,7 +3599,7 @@
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@@ -3606,7 +3611,7 @@
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@@ -3617,7 +3622,7 @@
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@@ -3629,7 +3634,7 @@
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@@ -3639,7 +3644,7 @@
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@@ -3651,7 +3656,7 @@
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@@ -3661,7 +3666,7 @@
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@@ -3673,7 +3678,7 @@
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@@ -3694,7 +3699,7 @@
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@@ -3708,7 +3713,7 @@
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@@ -3719,7 +3724,7 @@
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@@ -3731,7 +3736,7 @@
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@@ -3741,7 +3746,7 @@
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@@ -3753,7 +3758,7 @@
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@@ -3763,7 +3768,7 @@
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@@ -3775,7 +3780,7 @@
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@@ -3787,7 +3792,7 @@
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@@ -3801,7 +3806,7 @@
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@@ -3816,7 +3821,7 @@
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@@ -3827,7 +3832,7 @@
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@@ -3840,7 +3845,7 @@
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@@ -3851,7 +3856,7 @@
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@@ -3874,7 +3879,7 @@
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@@ -3888,7 +3893,7 @@
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@@ -3901,7 +3906,7 @@
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@@ -3912,7 +3917,7 @@
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@@ -3924,7 +3929,7 @@
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@@ -3935,7 +3940,7 @@
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@@ -3951,7 +3956,7 @@
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@@ -3962,7 +3967,7 @@
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@@ -3976,7 +3981,7 @@
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@@ -3998,7 +4003,7 @@
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@@ -4010,7 +4015,7 @@
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@@ -4020,7 +4025,7 @@
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@@ -4032,7 +4037,7 @@
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@@ -4044,7 +4049,7 @@
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@@ -4054,7 +4059,7 @@
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@@ -4066,7 +4071,7 @@
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@@ -4076,7 +4081,7 @@
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@@ -4088,7 +4093,7 @@
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@@ -4098,7 +4103,7 @@
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@@ -4110,7 +4115,7 @@
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@@ -4122,7 +4127,7 @@
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@@ -4132,7 +4137,7 @@
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@@ -4144,7 +4149,7 @@
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@@ -4154,7 +4159,7 @@
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@@ -4166,7 +4171,7 @@
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@@ -4178,7 +4183,7 @@
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@@ -4190,7 +4195,7 @@
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@@ -4200,7 +4205,7 @@
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@@ -4212,7 +4217,7 @@
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@@ -4222,7 +4227,7 @@
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@@ -4234,7 +4239,7 @@
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@@ -4244,7 +4249,7 @@
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@@ -4256,7 +4261,7 @@
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@@ -4267,7 +4272,7 @@
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@@ -4279,7 +4284,7 @@
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@@ -4289,7 +4294,7 @@
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@@ -4301,7 +4306,7 @@
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@@ -4311,7 +4316,7 @@
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@@ -4323,7 +4328,7 @@
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@@ -4336,7 +4341,7 @@
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@@ -4348,7 +4353,7 @@
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@@ -4360,7 +4365,7 @@
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@@ -4372,7 +4377,7 @@
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@@ -4384,7 +4389,7 @@
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@@ -4396,7 +4401,7 @@
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@@ -4406,7 +4411,7 @@
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@@ -4418,7 +4423,7 @@
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@@ -4430,7 +4435,7 @@
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@@ -4443,7 +4448,7 @@
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@@ -4470,7 +4475,7 @@
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@@ -4481,7 +4486,7 @@
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@@ -4577,7 +4582,7 @@
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@@ -4592,7 +4597,7 @@
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@@ -4614,7 +4619,7 @@
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@@ -4689,7 +4694,7 @@
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@@ -4701,7 +4706,7 @@
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@@ -4770,7 +4775,7 @@
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@@ -4784,7 +4789,7 @@
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    "cell_type": "code",
    "execution_count": 46,
-   "id": "58720a0b",
+   "id": "74dd9421",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4797,7 +4802,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e2be1bad",
+   "id": "2d7955a0",
    "metadata": {
     "editable": true
    },
@@ -4811,7 +4816,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "671c256f",
+   "id": "c29304b4",
    "metadata": {
     "editable": true
    },
@@ -4824,7 +4829,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc0d0301",
+   "id": "06b0b5bf",
    "metadata": {
     "editable": true
    },
@@ -4835,7 +4840,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "098a631b",
+   "id": "d5819abe",
    "metadata": {
     "editable": true
    },
@@ -4847,7 +4852,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2204de69",
+   "id": "95b1af41",
    "metadata": {
     "editable": true
    },
@@ -4857,7 +4862,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba98268d",
+   "id": "caba64c0",
    "metadata": {
     "editable": true
    },
@@ -4869,7 +4874,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "198496df",
+   "id": "d20f29ea",
    "metadata": {
     "editable": true
    },
@@ -4880,7 +4885,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "51511cdd",
+   "id": "69be1267",
    "metadata": {
     "editable": true
    },
@@ -4899,7 +4904,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "823feb37",
+   "id": "52031830",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4915,7 +4920,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fbebf00d",
+   "id": "9ac507e5",
    "metadata": {
     "editable": true
    },
@@ -4925,7 +4930,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "723300f3",
+   "id": "31a568f4",
    "metadata": {
     "editable": true
    },
@@ -4936,7 +4941,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0816e9d",
+   "id": "1086d6ac",
    "metadata": {
     "editable": true
    },
@@ -4948,7 +4953,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "791e50c5",
+   "id": "7b15bc16",
    "metadata": {
     "editable": true
    },
diff --git a/doc/src/week34/week34.do.txt b/doc/src/week34/week34.do.txt
index 8cd1fde09..7bb22b719 100644
--- a/doc/src/week34/week34.do.txt
+++ b/doc/src/week34/week34.do.txt
@@ -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