New alphas for each model

general_plots_noisy.py

@@ -1,8 +1,8 @@
 import matplotlib.pyplot as plt
 import functions as aux
 
-model_type = 'LeNet'  # BCNN or LeNet
-dataset = 'CIFAR'  # MNIST or CIFAR
+model_type = 'BCNN'  # BCNN or LeNet
+dataset = 'MNIST'  # MNIST or CIFAR
 
 eff_df = aux.load_pickle("efficiency_data.pkl")
 
@@ -22,8 +22,8 @@ lenet_keys = ['conv1.weight', 'conv1.bias', 'conv2.weight', 'conv2.bias',
 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[dataset][model_type][size],
-             label='Efficiency')
+    #plt.plot(eff_df[dataset][model_type][size],
+    #         label='Efficiency')
     plt.plot(entropy_data[dataset][model_type][size],
              label='Entropy at noise 0.0')
 

get_entropy_noisy.py

@@ -63,7 +63,7 @@ bayes_keys = ['conv1.W_mu',
               'conv2.W_mu',
               'fc1.W_mu',
               'fc2.W_mu',
-              'fc3.W_mu',]
+              'fc3.W_mu']
 
 
 lenet_keys = ['conv1.weight', 'conv2.weight',