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Using sum efficiency, and the per layer entropy mean

This commit is contained in:
Eduardo Cueto-Mendoza 2024-04-26 12:13:11 +01:00
parent cc92f4ce04
commit 76b85a341f
3 changed files with 109 additions and 23 deletions
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12
functions.py
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@ -49,9 +49,9 @@ def neumann_entropy(tensor):
e = alg.eigvals(tensor)
# temp_abs = torch.abs(e)
temp_abs = e.real
temp = torch.log(temp_abs)
temp[temp == float("Inf")] = 0
temp[temp == float("-Inf")] = 0
temp = torch.log(temp_abs).real
temp = torch.nan_to_num(temp,
nan=0.0, posinf=0.0, neginf=0.0)
return -1 * torch.sum(temp_abs * temp)
elif len(tensor_size) > 2:
for i, x in enumerate(tensor):
@ -59,9 +59,9 @@ def neumann_entropy(tensor):
e = alg.eigvals(t)
# temp_abs = torch.abs(e)
temp_abs = e.real
temp = torch.log(temp_abs)
temp[temp == float("Inf")] = 0
temp[temp == float("-Inf")] = 0
temp = torch.log(temp_abs).real
temp = torch.nan_to_num(temp,
nan=0.0, posinf=0.0, neginf=0.0)
return -1 * torch.sum(temp_abs * temp)

23
general_plots.py
View File

@ -3,8 +3,9 @@ import functions as aux
eff_df = aux.load_pickle("efficiency_data.pkl")
bayes_cifar_entropy = aux.load_pickle("bayes_data_cifar_ne.pkl")
bayes_mnist_entropy = aux.load_pickle("bayes_data_mnist_ne.pkl")
# bayes_cifar_entropy = aux.load_pickle("bayes_data_cifar_ne.pkl")
# lenet_mnist_entropy = aux.load_pickle("lenet_data_mnist_ne.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',
@ -16,17 +17,13 @@ lenet_keys = ['conv1.weight', 'conv1.bias', 'conv2.weight', 'conv2.bias',
'fc1.weight', 'fc1.bias', 'fc2.weight', 'fc2.bias', 'fc3.weight',
'fc3.bias']
"""
for size in range(1, 8):
if size != 3:
plt.plot(eff_df['CIFAR']['BCNN'][size], label='Size {}'.format(size))
if size != 8:
plt.plot(eff_df['CIFAR']['BCNN'][size],
label='Efficiency size {}'.format(size))
plt.plot(entropy_data['CIFAR']['BCNN'][size],
label='Entropy size {}'.format(size))
plt.legend(loc='upper right')
plt.show()
"""
temp = []
for epoch in range(0, 100):
temp.append(bayes_cifar_entropy[1][epoch]['conv2.W_mu'])
plt.plot(temp)
# plt.legend(loc='upper right')
plt.legend(loc='lower right')
plt.show()

97
get_entropy.py
View File

@ -1,10 +1,30 @@
import functions as aux
import statistics as st
models_bayes_cifar = aux.load_pickle("bayes_data_cifar.pkl")
models_bayes_mnist = aux.load_pickle("bayes_data_mnist.pkl")
models_lenet_cifar = aux.load_pickle("lenet_data_cifar.pkl")
models_lenet_mnist = aux.load_pickle("lenet_data_mnist.pkl")
entropy_data = {'CIFAR':
{'BCNN':
{1: None, 2: None, 3: None, 4: None,
5: None, 6: None, 7: None},
'LeNet':
{1: None, 2: None, 3: None, 4: None,
5: None, 6: None, 7: None}
},
'MNIST':
{'BCNN':
{1: None, 2: None, 3: None, 4: None,
5: None, 6: None, 7: None},
'LeNet':
{1: None, 2: None, 3: None, 4: None,
5: None, 6: None, 7: None}
},
}
"""
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',
@ -14,6 +34,16 @@ bayes_keys = ['conv1.W_mu', 'conv1.W_rho', 'conv1.bias_mu', 'conv1.bias_rho',
lenet_keys = ['conv1.weight', 'conv1.bias', 'conv2.weight', 'conv2.bias',
'fc1.weight', 'fc1.bias', 'fc2.weight', 'fc2.bias', 'fc3.weight',
'fc3.bias']
"""
bayes_keys = ['conv1.W_mu', 'conv1.W_rho',
'conv2.W_mu', 'conv2.W_rho',
'fc1.W_mu', 'fc1.W_rho',
'fc2.W_mu', 'fc2.W_rho',
'fc3.W_mu', 'fc3.W_rho']
lenet_keys = ['conv1.weight', 'conv2.weight',
'fc1.weight', 'fc2.weight', 'fc3.weight']
for model_size in range(1, 8):
for epoch in range(0, 100):
@ -25,7 +55,21 @@ for model_size in range(1, 8):
)
)
aux.save_pickle("bayes_data_cifar_ne.pkl", models_bayes_cifar)
for size in range(1, 8):
temp_epoch = []
for epoch in range(0, 100):
temp_mean = []
for layer in bayes_keys:
temp_mean.append(
models_bayes_cifar[size][epoch][layer].item()
)
temp_mean = st.mean(temp_mean)
temp_epoch.append(
temp_mean
)
entropy_data['CIFAR']['BCNN'][size] = temp_epoch
# aux.save_pickle("bayes_data_cifar_ne.pkl", models_bayes_cifar)
del models_bayes_cifar
for model_size in range(1, 8):
@ -38,7 +82,21 @@ for model_size in range(1, 8):
)
)
aux.save_pickle("bayes_data_mnist_ne.pkl", models_bayes_mnist)
for size in range(1, 8):
temp_epoch = []
for epoch in range(0, 100):
temp_mean = []
for layer in bayes_keys:
temp_mean.append(
models_bayes_mnist[size][epoch][layer].item()
)
temp_mean = st.mean(temp_mean)
temp_epoch.append(
temp_mean
)
entropy_data['MNIST']['BCNN'][size] = temp_epoch
# aux.save_pickle("bayes_data_mnist_ne.pkl", models_bayes_mnist)
del models_bayes_mnist
for model_size in range(1, 8):
@ -51,7 +109,21 @@ for model_size in range(1, 8):
)
)
aux.save_pickle("lenet_data_cifar_ne.pkl", models_lenet_cifar)
for size in range(1, 8):
temp_epoch = []
for epoch in range(0, 100):
temp_mean = []
for layer in lenet_keys:
temp_mean.append(
models_lenet_cifar[size][epoch][layer].item()
)
temp_mean = st.mean(temp_mean)
temp_epoch.append(
temp_mean
)
entropy_data['CIFAR']['LeNet'][size] = temp_epoch
# aux.save_pickle("lenet_data_cifar_ne.pkl", models_lenet_cifar)
del models_lenet_cifar
for model_size in range(1, 8):
@ -64,5 +136,22 @@ for model_size in range(1, 8):
)
)
aux.save_pickle("lenet_data_mnist_ne.pkl", models_lenet_mnist)
for size in range(1, 8):
temp_epoch = []
for epoch in range(0, 100):
temp_mean = []
for layer in lenet_keys:
temp_mean.append(
models_lenet_mnist[size][epoch][layer].item()
)
temp_mean = st.mean(temp_mean)
temp_epoch.append(
temp_mean
)
entropy_data['MNIST']['LeNet'][size] = temp_epoch
# aux.save_pickle("lenet_data_mnist_ne.pkl", models_lenet_mnist)
del models_lenet_mnist
aux.save_pickle("entropy_data.pkl", entropy_data)