diff --git a/efficiency_computations.py b/efficiency_computations.py
index 61530ce..70a7af1 100644
--- a/efficiency_computations.py
+++ b/efficiency_computations.py
@@ -62,11 +62,15 @@ for data in data_types:
 eff_data = dict(gpu_ene_data)
 for data in data_types:
     for model in model_types:
+        if model == 'fre':
+            alpha = 100
+        elif model == 'bay':
+            alpha = 1000
         for size in range(1, max_size):
             for i in range(0, max_epoch):
                 eff_data[data][model][size][i] = \
                     (gpu_exp_data[data][model][size]['acc'][i] /
-                     spl_ene_data[data][model][size][i]) * 100
+                     spl_ene_data[data][model][size][i]) * alpha
 
 
 for data, o_data in zip(data_types, o_data_types):
diff --git a/general_plots_noisy.py b/general_plots_noisy.py
index ff17bd7..3cccbd8 100644
--- a/general_plots_noisy.py
+++ b/general_plots_noisy.py
@@ -1,8 +1,8 @@
 import matplotlib.pyplot as plt
 import functions as aux
 
-model_type = 'BCNN'  # BCNN or LeNet
-dataset = 'MNIST'  # MNIST or CIFAR
+model_type = 'LeNet'  # BCNN or LeNet
+dataset = 'CIFAR'  # MNIST or CIFAR
 
 eff_df = aux.load_pickle("efficiency_data.pkl")
 
@@ -19,10 +19,10 @@ lenet_keys = ['conv1.weight', 'conv1.bias', 'conv2.weight', 'conv2.bias',
               'fc1.weight', 'fc1.bias', 'fc2.weight', 'fc2.bias', 'fc3.weight',
               'fc3.bias']
 
-all_noises = [0.1, 0.25, 0.5, 0.75, 0.99]
+all_noises = [0.1, 0.25, 0.5, 0.75, 0.99, 'raleigh', 'erlang', 'exponential', 'uniform', 'impulse']
 
 for size in range(1, 2):
-    plt.plot(eff_df['MNIST']['LeNet'][size],
+    plt.plot(eff_df[dataset][model_type][size],
              label='Efficiency')
     plt.plot(entropy_data[dataset][model_type][size],
              label='Entropy at noise 0.0')
diff --git a/get_entropy_noisy.py b/get_entropy_noisy.py
index e60398c..a4980e3 100644
--- a/get_entropy_noisy.py
+++ b/get_entropy_noisy.py
@@ -11,18 +11,30 @@ models_lenet_mnist = aux.load_pickle("lenet_data_mnist_noisy.pkl")
 entropy_data = {'CIFAR':
                 {'BCNN':
                  {0.1: None, 0.25: None,
-                  0.5: None, 0.75: None, 0.99: None},
+                  0.5: None, 0.75: None, 0.99: None,
+                  'raleigh': None, 'erlang': None,
+                  'exponential': None, 'uniform': None,
+                  'impulse': None},
                  'LeNet':
                  {0.1: None, 0.25: None,
-                  0.5: None, 0.75: None, 0.99: None},
+                  0.5: None, 0.75: None, 0.99: None,
+                  'raleigh': None, 'erlang': None,
+                  'exponential': None, 'uniform': None,
+                  'impulse': None},
                  },
                 'MNIST':
                 {'BCNN':
                  {0.1: None, 0.25: None,
-                  0.5: None, 0.75: None, 0.99: None},
+                  0.5: None, 0.75: None, 0.99: None,
+                  'raleigh': None, 'erlang': None,
+                  'exponential': None, 'uniform': None,
+                  'impulse': None},
                  'LeNet':
                  {0.1: None, 0.25: None,
-                  0.5: None, 0.75: None, 0.99: None},
+                  0.5: None, 0.75: None, 0.99: None,
+                  'raleigh': None, 'erlang': None,
+                  'exponential': None, 'uniform': None,
+                  'impulse': None},
                  },
                 }
 
@@ -45,7 +57,7 @@ bayes_keys = ['conv1.W_mu', 'conv1.W_rho',
 
 """
 
-noise_levels = [0.1, 0.25, 0.5, 0.75, 0.99]
+noise_levels = [0.1, 0.25, 0.5, 0.75, 0.99, 'raleigh', 'erlang', 'exponential', 'uniform', 'impulse']
 
 bayes_keys = ['conv1.W_mu',
               'conv2.W_mu',