Entropy_Data_Processing/general_plots_noisy.py

import matplotlib.pyplot as plt
import functions as aux

model_type = 'BCNN'  # BCNN or LeNet
dataset = 'MNIST'  # MNIST or CIFAR

eff_df = aux.load_pickle("efficiency_data.pkl")

entropy_data_noise = aux.load_pickle("entropy_data_noisy.pkl")
entropy_data = aux.load_pickle("entropy_data.pkl")

bayes_keys = ['conv1.W_mu', 'conv1.W_rho', 'conv1.bias_mu', 'conv1.bias_rho',
              'conv2.W_mu', 'conv2.W_rho', 'conv2.bias_mu', 'conv2.bias_rho',
              'fc1.W_mu', 'fc1.W_rho', 'fc1.bias_mu', 'fc1.bias_rho',
              'fc2.W_mu', 'fc2.W_rho', 'fc2.bias_mu', 'fc2.bias_rho',
              'fc3.W_mu', 'fc3.W_rho', 'fc3.bias_mu', 'fc3.bias_rho']

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, 'raleigh', 'erlang', 'exponential', 'uniform', 'impulse']

for size in range(1, 2):
    #plt.plot(eff_df[dataset][model_type][size],
    #         label='Efficiency')
    plt.plot(entropy_data[dataset][model_type][size],
             label='Entropy at noise 0.0')

for noise in all_noises:
    plt.plot(entropy_data_noise[dataset][model_type][noise],
             label='Entropy at noise {}'.format(noise))

plt.legend(loc='upper right')
# plt.legend(loc='lower right')
plt.show()