37 lines
1.3 KiB
Python
37 lines
1.3 KiB
Python
import matplotlib.pyplot as plt
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import functions as aux
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model_type = 'BCNN' # BCNN or LeNet
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dataset = 'MNIST' # MNIST or CIFAR
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eff_df = aux.load_pickle("efficiency_data.pkl")
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entropy_data_noise = aux.load_pickle("entropy_data_noisy.pkl")
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entropy_data = aux.load_pickle("entropy_data.pkl")
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bayes_keys = ['conv1.W_mu', 'conv1.W_rho', 'conv1.bias_mu', 'conv1.bias_rho',
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'conv2.W_mu', 'conv2.W_rho', 'conv2.bias_mu', 'conv2.bias_rho',
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'fc1.W_mu', 'fc1.W_rho', 'fc1.bias_mu', 'fc1.bias_rho',
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'fc2.W_mu', 'fc2.W_rho', 'fc2.bias_mu', 'fc2.bias_rho',
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'fc3.W_mu', 'fc3.W_rho', 'fc3.bias_mu', 'fc3.bias_rho']
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lenet_keys = ['conv1.weight', 'conv1.bias', 'conv2.weight', 'conv2.bias',
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'fc1.weight', 'fc1.bias', 'fc2.weight', 'fc2.bias', 'fc3.weight',
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'fc3.bias']
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all_noises = [0.1, 0.25, 0.5, 0.75, 0.99, 'raleigh', 'erlang', 'exponential', 'uniform', 'impulse']
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for size in range(1, 2):
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#plt.plot(eff_df[dataset][model_type][size],
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# label='Efficiency')
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plt.plot(entropy_data[dataset][model_type][size],
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label='Entropy at noise 0.0')
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for noise in all_noises:
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plt.plot(entropy_data_noise[dataset][model_type][noise],
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label='Entropy at noise {}'.format(noise))
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plt.legend(loc='upper right')
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# plt.legend(loc='lower right')
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plt.show()
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