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Entropy_Data_Processing
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Finally entropy and efficiency are on the same scale

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Eduardo Cueto-Mendoza 2024-07-30 14:14:18 +01:00
parent 76b85a341f
commit 1a35d08f66
4 changed files with 33 additions and 18 deletions
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1
.gitignore vendored
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@ -1,2 +1,3 @@
__pycache__/ __pycache__/
*.pkl *.pkl
*.png

16
functions.py
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@ -46,9 +46,11 @@ def neumann_entropy(tensor):
if len(tensor_size) == 1: if len(tensor_size) == 1:
return 0 return 0
elif len(tensor_size) == 2: elif len(tensor_size) == 2:
e = alg.eigvals(tensor) e = alg.eigvals(tensor)#.real
# temp_abs = torch.abs(e) #se = sum(e)
temp_abs = e.real #e = e / se
temp_abs = torch.abs(e)
#temp_abs = e
temp = torch.log(temp_abs).real temp = torch.log(temp_abs).real
temp = torch.nan_to_num(temp, temp = torch.nan_to_num(temp,
nan=0.0, posinf=0.0, neginf=0.0) nan=0.0, posinf=0.0, neginf=0.0)
@ -56,9 +58,11 @@ def neumann_entropy(tensor):
elif len(tensor_size) > 2: elif len(tensor_size) > 2:
for i, x in enumerate(tensor): for i, x in enumerate(tensor):
for j, t in enumerate(x): for j, t in enumerate(x):
e = alg.eigvals(t) e = alg.eigvals(t)#.real
# temp_abs = torch.abs(e) #se = sum(e)
temp_abs = e.real #e = e / se
temp_abs = torch.abs(e)
# temp_abs = e
temp = torch.log(temp_abs).real temp = torch.log(temp_abs).real
temp = torch.nan_to_num(temp, temp = torch.nan_to_num(temp,
nan=0.0, posinf=0.0, neginf=0.0) nan=0.0, posinf=0.0, neginf=0.0)

14
general_plots.py
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@ -18,12 +18,12 @@ lenet_keys = ['conv1.weight', 'conv1.bias', 'conv2.weight', 'conv2.bias',
'fc3.bias'] 'fc3.bias']
for size in range(1, 8): for size in range(1, 8):
if size != 8: # if size != 8:
plt.plot(eff_df['CIFAR']['BCNN'][size], plt.plot(eff_df['MNIST']['BCNN'][size],
label='Efficiency size {}'.format(size)) label='Efficiency size {}'.format(size))
plt.plot(entropy_data['CIFAR']['BCNN'][size], plt.plot(entropy_data['MNIST']['BCNN'][size],
label='Entropy size {}'.format(size)) label='Entropy size {}'.format(size))
# plt.legend(loc='upper right') plt.legend(loc='upper right')
plt.legend(loc='lower right') # plt.legend(loc='lower right')
plt.show() plt.show()

20
get_entropy.py
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@ -1,6 +1,8 @@
import functions as aux import functions as aux
import statistics as st import statistics as st
alpha = 100000
models_bayes_cifar = aux.load_pickle("bayes_data_cifar.pkl") models_bayes_cifar = aux.load_pickle("bayes_data_cifar.pkl")
models_bayes_mnist = aux.load_pickle("bayes_data_mnist.pkl") models_bayes_mnist = aux.load_pickle("bayes_data_mnist.pkl")
models_lenet_cifar = aux.load_pickle("lenet_data_cifar.pkl") models_lenet_cifar = aux.load_pickle("lenet_data_cifar.pkl")
@ -34,7 +36,6 @@ bayes_keys = ['conv1.W_mu', 'conv1.W_rho', 'conv1.bias_mu', 'conv1.bias_rho',
lenet_keys = ['conv1.weight', 'conv1.bias', 'conv2.weight', 'conv2.bias', lenet_keys = ['conv1.weight', 'conv1.bias', 'conv2.weight', 'conv2.bias',
'fc1.weight', 'fc1.bias', 'fc2.weight', 'fc2.bias', 'fc3.weight', 'fc1.weight', 'fc1.bias', 'fc2.weight', 'fc2.bias', 'fc3.weight',
'fc3.bias'] 'fc3.bias']
"""
bayes_keys = ['conv1.W_mu', 'conv1.W_rho', bayes_keys = ['conv1.W_mu', 'conv1.W_rho',
'conv2.W_mu', 'conv2.W_rho', 'conv2.W_mu', 'conv2.W_rho',
@ -42,6 +43,15 @@ bayes_keys = ['conv1.W_mu', 'conv1.W_rho',
'fc2.W_mu', 'fc2.W_rho', 'fc2.W_mu', 'fc2.W_rho',
'fc3.W_mu', 'fc3.W_rho'] 'fc3.W_mu', 'fc3.W_rho']
"""
bayes_keys = ['conv1.W_mu',
'conv2.W_mu',
'fc1.W_mu',
'fc2.W_mu',
'fc3.W_mu',]
lenet_keys = ['conv1.weight', 'conv2.weight', lenet_keys = ['conv1.weight', 'conv2.weight',
'fc1.weight', 'fc2.weight', 'fc3.weight'] 'fc1.weight', 'fc2.weight', 'fc3.weight']
@ -67,7 +77,7 @@ for size in range(1, 8):
temp_epoch.append( temp_epoch.append(
temp_mean temp_mean
) )
entropy_data['CIFAR']['BCNN'][size] = temp_epoch entropy_data['CIFAR']['BCNN'][size] = [x / alpha for x in temp_epoch]# temp_epoch
# aux.save_pickle("bayes_data_cifar_ne.pkl", models_bayes_cifar) # aux.save_pickle("bayes_data_cifar_ne.pkl", models_bayes_cifar)
del models_bayes_cifar del models_bayes_cifar
@ -94,7 +104,7 @@ for size in range(1, 8):
temp_epoch.append( temp_epoch.append(
temp_mean temp_mean
) )
entropy_data['MNIST']['BCNN'][size] = temp_epoch entropy_data['MNIST']['BCNN'][size] = [x / alpha for x in temp_epoch]# temp_epoch
# aux.save_pickle("bayes_data_mnist_ne.pkl", models_bayes_mnist) # aux.save_pickle("bayes_data_mnist_ne.pkl", models_bayes_mnist)
del models_bayes_mnist del models_bayes_mnist
@ -121,7 +131,7 @@ for size in range(1, 8):
temp_epoch.append( temp_epoch.append(
temp_mean temp_mean
) )
entropy_data['CIFAR']['LeNet'][size] = temp_epoch entropy_data['CIFAR']['LeNet'][size] = [x / alpha for x in temp_epoch]# temp_epoch
# aux.save_pickle("lenet_data_cifar_ne.pkl", models_lenet_cifar) # aux.save_pickle("lenet_data_cifar_ne.pkl", models_lenet_cifar)
del models_lenet_cifar del models_lenet_cifar
@ -148,7 +158,7 @@ for size in range(1, 8):
temp_epoch.append( temp_epoch.append(
temp_mean temp_mean
) )
entropy_data['MNIST']['LeNet'][size] = temp_epoch entropy_data['MNIST']['LeNet'][size] = [x / alpha for x in temp_epoch]# temp_epoch
# aux.save_pickle("lenet_data_mnist_ne.pkl", models_lenet_mnist) # aux.save_pickle("lenet_data_mnist_ne.pkl", models_lenet_mnist)