diff --git a/experiments/final.py b/experiments/final.py
index f70c4e2..73c41f7 100644
--- a/experiments/final.py
+++ b/experiments/final.py
@@ -11,20 +11,59 @@
 filenames_valid = ['2016']
 features_train = ['air_temperature']
 features_predict = ['air_temperature']
+regularization = None
+"""
+Experiment I: Nodes per layer
+"""
+n_nodes = [0.2, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]
+act = 'relu'
+n_memory = 1
+n_dense_pre = 3
+n_dense_pos = 3
+memory_unit = 'lstm'
+for n_node in n_nodes:
+    model = create_model(batch_size=batch_size,
+                         t_train=t_train_h,
+                         t_pred=int(t_pred_d * 24 / t_pred_resolution_h),
+                         n_features_train=len(features_train),
+                         n_stations=n_stations,
+                         memory_unit=memory_unit,
+                         width=n_dense_pre,
+                         n_layers_memory=n_memory,
+                         n_layers_preprocessing=n_dense_pre,
+                         n_layers_postprocessing=n_dense_pos,
+                         n_features_pred=len(features_predict),
+                         activation=act,
+                         mask_value=MASK_VALUE,
+                         regularization=regularization)
+
+    # '{layer_pre}x{n_nodes}*{act}->[{memory}]{n_lstm}->{layer_pos}{n_nodes}*{act}'
+    log_dir = 'out/{}-{}-{}-{}{}-{}-{}-{}'.format(n_dense_pre, int(n_node * 10), act, memory_unit, n_memory,
+                                                  n_dense_pos, int(n_stations / n_dense_pre), act)
+
+    train(radius=radius,
+          batch_size=batch_size,
+          log_dir=log_dir,
+          t_train_h=t_train_h,
+          t_pred_d=t_pred_d,
+          t_pred_resolution_h=t_pred_resolution_h,
+          model_name=model,
+          filenames_train=filenames_train,
+          filenames_valid=filenames_valid,
+          features_train=features_train,
+          features_predict=features_predict,
+          )
 
 """
-Experiment I: Final
+Experiment II: Memory Depth
 """
-n_dense_pres = [1, 2, 3]
+n_lstms = [1, 2, 4, 8]
 n_node = 1.0
 act = 'relu'
-n_memory = 2
-n_dense_poss = [1, 2, 3]
+n_dense_pre = 3
+n_dense_pos = 3
 memory_unit = 'lstm'
-reg = None
-for i in range(3):
-    n_dense_pos = n_dense_poss[i]
-    n_dense_pre = n_dense_pres[i]
+for n_memory in n_lstms:
     model = create_model(batch_size=batch_size,
                          t_train=t_train_h,
                          t_pred=int(t_pred_d * 24 / t_pred_resolution_h),
@@ -38,7 +77,7 @@
                          n_features_pred=len(features_predict),
                          activation=act,
                          mask_value=MASK_VALUE,
-                         regularization=None)
+                         regularization=regularization)
 
     # '{layer_pre}x{n_nodes}*{act}->[{memory}]{n_lstm}->{layer_pos}{n_nodes}*{act}'
     log_dir = 'out/{}-{}-{}-{}{}-{}-{}-{}'.format(n_dense_pre, int(n_node * 10), act, memory_unit, n_memory,
@@ -56,3 +95,46 @@
           features_train=features_train,
           features_predict=features_predict,
           )
+
+"""
+Experiment III: Regularization
+"""
+n_nodes = 1.0
+act = 'relu'
+regs = ['Dropout50', 'Dropout25', 'l1', 'l2']
+n_memory = 2
+n_dense_pre = 3
+n_dense_pos = 3
+memory_unit = 'lstm'
+for reg in regs:
+    model = create_model(batch_size=batch_size,
+                         t_train=t_train_h,
+                         t_pred=int(t_pred_d * 24 / t_pred_resolution_h),
+                         n_features_train=len(features_train),
+                         n_stations=n_stations,
+                         memory_unit=memory_unit,
+                         width=n_dense_pre,
+                         n_layers_memory=n_memory,
+                         n_layers_preprocessing=n_dense_pre,
+                         n_layers_postprocessing=n_dense_pos,
+                         n_features_pred=len(features_predict),
+                         activation=act,
+                         mask_value=MASK_VALUE,
+                         regularization=reg)
+
+    # '{layer_pre}x{n_nodes}*{act}->[{memory}]{n_lstm}->{layer_pos}{n_nodes}*{act}'
+    log_dir = 'out/{}-{}-{}-{}{}-{}-{}-{}'.format(n_dense_pre, int(n_node * 10), act, memory_unit, n_memory,
+                                                  n_dense_pos, int(n_stations / n_dense_pre), act)
+
+    train(radius=radius,
+          batch_size=batch_size,
+          log_dir=log_dir,
+          t_train_h=t_train_h,
+          t_pred_d=t_pred_d,
+          t_pred_resolution_h=t_pred_resolution_h,
+          model_name=model,
+          filenames_train=filenames_train,
+          filenames_valid=filenames_valid,
+          features_train=features_train,
+          features_predict=features_predict,
+          )
\ No newline at end of file
diff --git a/plots/plot_architecture.py b/plots/plot_architecture.py
index 4af4780..4772e3b 100644
--- a/plots/plot_architecture.py
+++ b/plots/plot_architecture.py
@@ -3,30 +3,61 @@
 
 from downloadData.functions.file_utils import load_file
 
-model_names = ['1-10-relu-lstm1-3-41-relu', '2-10-relu-lstm1-3-20-relu', '3-2-relu-lstm1-3-13-relu',
+csfont = {'fontname': 'Century Gothic'}
+std = 10.67839587
+post = ['3-10-relu-lstm1-1-55-relu', '3-10-relu-lstm1-2-55-relu' ,'3-10-relu-lstm1-4-55-relu',
+        '3-10-relu-lstm1-8-55-relu']
+pre = ['1-10-relu-lstm1-1-165-relu', '2-10-relu-lstm1-2-82-relu', '4-10-relu-lstm1-3-41-relu',
+       '8-10-relu-lstm1-8-20-relu']
+memory = ['3-10-relu-lstm1-3-13-relu', '3-10-relu-lstm2-3-13-relu', '3-10-relu-lstm4-3-13-relu',
+          '3-10-relu-lstm8-3-13-relu']
+layers = np.array([1, 2, 4, 8])
+plt.subplot(2, 1, 1)
+performances = []
+for i, r in enumerate(post):
+    mat = np.array(load_file('../out/' + r + '/log.csv'))
+    valid_loss = mat[1:, 2].astype(np.float32)
+    y = np.min(valid_loss)*std**2
+    performances.append(y)
+plt.bar(layers, performances, width=0.2, color='b', align='center')
+performances = []
+for i, r in enumerate(pre):
+    mat = np.array(load_file('../out/' + r + '/log.csv'))
+    valid_loss = mat[1:, 2].astype(np.float32)
+    y = np.min(valid_loss)*std**2
+    performances.append(y)
+plt.bar(layers - 0.2, performances, width=0.2, color='g', align='center')
+layers = np.array([1, 2, 4, 8])
+performances = []
+for i, r in enumerate(memory):
+    mat = np.array(load_file('../out/' + r + '/log.csv'))
+    valid_loss = mat[1:, 2].astype(np.float32)
+    y = np.min(valid_loss)*std**2
+    performances.append(y)
+plt.bar(layers + 0.2, performances, width=0.2, color='r', align='center')
+plt.legend(['Postprocessing', 'Preprocessing', 'Memory'], fontsize=16, prop={'family': 'Century Gothic'})
+plt.xlabel('Layers',fontsize=16, **csfont)
+plt.ylabel('Mean Squared Error [$^\circ$C$^2$]', fontsize=16, **csfont)
+plt.grid('on')
+plt.title('Evaluation of Model Architecture in Terms of Validation Loss ', fontsize=20, **csfont)
+plt.subplot(2, 1, 2)
+
+width = [0.2, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]
+model_names = ['3-2-relu-lstm1-3-13-relu',
                '3-5-relu-lstm1-3-13-relu',
-               '3-10-relu-lstm1-1-13-relu', '3-10-relu-lstm1-2-13-relu', '3-10-relu-lstm1-4-13-relu',
-               '3-10-relu-lstm1-8-13-relu', '3-10-relu-lstm2-3-13-relu', '3-10-relu-lstm4-3-13-relu',
-               '3-10-relu-lstm8-3-13-relu', '3-15-relu-lstm1-3-13-relu', '3-20-relu-lstm1-3-13-relu',
-               '3-25-relu-lstm1-3-13-relu', '3-30-relu-lstm1-3-13-relu', '4-10-relu-lstm1-3-10-relu',
-               '8-10-relu-lstm1-3-5-relu']
-legend = ['pre1-mem1-post3', 'pre2-mem1-post3', 'pre2-mem1-post3','pre3-mem1-post3', 'pre3-mem1-post1', 'pre3-mem1-post2',
-          'pre3-mem1-post4',
-          'pre3-mem1-post8', 'pre3-mem2-post3', 'pre3-mem4-post3', 'pre3-mem8-post3', 'pre3-mem1-post3',
-          'pre3-mem1-post3',
-          'pre3-mem1-post3', 'pre3-mem1-post3', 'pre4-mem1-post3', 'pre8-mem1-post3']
-legend = model_names
-layers = [5, 6, 6,7,5, 6, 8, 12, 8, 10, 14, 7, 7, 7, 7, 8, 12]
+               '3-10-relu-lstm1-3-13-relu',
+               '3-15-relu-lstm1-3-13-relu',
+               '3-20-relu-lstm1-3-13-relu',
+               '3-25-relu-lstm1-3-13-relu', '3-30-relu-lstm1-3-13-relu']
+performances = []
 for i, r in enumerate(model_names):
     mat = np.array(load_file('../out/' + r + '/log.csv'))
     valid_loss = mat[1:, 2].astype(np.float32)
-    x = layers[i]
-    y = np.min(valid_loss)
-    plt.plot(x, y, '*')
-    plt.annotate(legend[i], xy=(x, y))
+    y = np.min(valid_loss)*std**2
+    performances.append(y)
+plt.plot(width, performances, '--*')
 
-plt.xlabel('Layers')
-plt.ylabel('Normalized Mean Squared Error [°C^2]')
+plt.xlabel('*N_Nodes', fontsize=16, **csfont)
+plt.ylabel('Mean Squared Error  [$^\circ$C$^2$]', fontsize=16, **csfont)
 plt.grid('on')
-plt.title('Evaluation of Model Architecture in Terms of Validation Loss')
 plt.show()