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New processing for noisy datasets

This commit is contained in:
Eduardo Cueto-Mendoza 2024-09-16 12:39:14 +01:00
parent 1a35d08f66
commit 03f03d59d9
6 changed files with 244 additions and 32 deletions
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27
efficiency_computations.py
View File

@ -2,6 +2,9 @@ import functions as aux
import statistics as st
# import pandas as pd
max_epoch = 30
max_size = 8
data_types = ['mni', 'cif']
model_types = ['fre', 'bay']
@ -15,41 +18,41 @@ gpu_exp_data = aux.load_pickle("result_gpu.pkl")
for data in data_types:
for model in model_types:
for size in range(1, 8):
for size in range(1, max_size):
gpu_ene_data[data][model][size] = \
list(
aux.split(gpu_ene_data[data][model][size], 100)
aux.split(gpu_ene_data[data][model][size], max_epoch)
)
for data in data_types:
for model in model_types:
for size in range(1, 8):
for size in range(1, max_size):
cpu_ene_data[data][model][size] = \
list(
aux.split(cpu_ene_data[data][model][size], 100)
aux.split(cpu_ene_data[data][model][size], max_epoch)
)
spl_ene_data = dict(gpu_ene_data)
for data in data_types:
for model in model_types:
for size in range(1, 8):
for i in range(0, 100):
for size in range(1, max_size):
for i in range(0, max_epoch):
spl_ene_data[data][model][size][i] = \
gpu_ene_data[data][model][size][i] +\
cpu_ene_data[data][model][size][i]
for data in data_types:
for model in model_types:
for size in range(1, 8):
for i in range(0, 100):
for size in range(1, max_size):
for i in range(0, max_epoch):
spl_ene_data[data][model][size][i] = \
sum(spl_ene_data[data][model][size][i])
for data in data_types:
for model in model_types:
for size in range(1, 8):
for size in range(1, max_size):
temp = []
for i in range(0, 100):
for i in range(0, max_epoch):
temp.append(
# st.mean(spl_ene_data[data][model][size][0:i+1])
sum(spl_ene_data[data][model][size][0:i+1])
@ -59,8 +62,8 @@ for data in data_types:
eff_data = dict(gpu_ene_data)
for data in data_types:
for model in model_types:
for size in range(1, 8):
for i in range(0, 100):
for size in range(1, max_size):
for i in range(0, max_epoch):
eff_data[data][model][size][i] = \
(gpu_exp_data[data][model][size]['acc'][i] /
spl_ene_data[data][model][size][i]) * 100

4
functions.py
View File

@ -5,6 +5,8 @@ import torch
def square_matrix(tensor):
tensor_size = tensor.size()
if len(tensor_size) == 0:
return tensor
if len(tensor_size) == 1:
temp = torch.zeros([tensor_size[0],
tensor_size[0]-1])
@ -43,6 +45,8 @@ def square_matrix(tensor):
def neumann_entropy(tensor):
tensor_size = tensor.size()
if len(tensor_size) == 0:
return tensor
if len(tensor_size) == 1:
return 0
elif len(tensor_size) == 2:

6
general_plots.py
View File

@ -18,10 +18,10 @@ lenet_keys = ['conv1.weight', 'conv1.bias', 'conv2.weight', 'conv2.bias',
'fc3.bias']
for size in range(1, 8):
# if size != 8:
plt.plot(eff_df['MNIST']['BCNN'][size],
# if size != 3:
plt.plot(eff_df['MNIST']['LeNet'][size],
label='Efficiency size {}'.format(size))
plt.plot(entropy_data['MNIST']['BCNN'][size],
plt.plot(entropy_data['MNIST']['LeNet'][size],
label='Entropy size {}'.format(size))
plt.legend(loc='upper right')

36
general_plots_noisy.py Normal file
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@ -0,0 +1,36 @@
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]
for size in range(1, 2):
plt.plot(eff_df['MNIST']['LeNet'][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()

36
get_entropy.py
View File

@ -1,7 +1,9 @@
import functions as aux
import statistics as st
alpha = 100000
alpha = 10000
max_epoch = 30
max_size = 8
models_bayes_cifar = aux.load_pickle("bayes_data_cifar.pkl")
models_bayes_mnist = aux.load_pickle("bayes_data_mnist.pkl")
@ -55,8 +57,8 @@ bayes_keys = ['conv1.W_mu',
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):
for model_size in range(1, max_size):
for epoch in range(0, max_epoch):
for k in bayes_keys:
models_bayes_cifar[model_size][epoch][k] = \
aux.neumann_entropy(
@ -65,9 +67,9 @@ for model_size in range(1, 8):
)
)
for size in range(1, 8):
for size in range(1, max_size):
temp_epoch = []
for epoch in range(0, 100):
for epoch in range(0, max_epoch):
temp_mean = []
for layer in bayes_keys:
temp_mean.append(
@ -82,8 +84,8 @@ for size in range(1, 8):
# aux.save_pickle("bayes_data_cifar_ne.pkl", models_bayes_cifar)
del models_bayes_cifar
for model_size in range(1, 8):
for epoch in range(0, 100):
for model_size in range(1, max_size):
for epoch in range(0, max_epoch):
for k in bayes_keys:
models_bayes_mnist[model_size][epoch][k] = \
aux.neumann_entropy(
@ -92,9 +94,9 @@ for model_size in range(1, 8):
)
)
for size in range(1, 8):
for size in range(1, max_size):
temp_epoch = []
for epoch in range(0, 100):
for epoch in range(0, max_epoch):
temp_mean = []
for layer in bayes_keys:
temp_mean.append(
@ -109,8 +111,8 @@ for size in range(1, 8):
# aux.save_pickle("bayes_data_mnist_ne.pkl", models_bayes_mnist)
del models_bayes_mnist
for model_size in range(1, 8):
for epoch in range(0, 100):
for model_size in range(1, max_size):
for epoch in range(0, max_epoch):
for k in lenet_keys:
models_lenet_cifar[model_size][epoch][k] = \
aux.neumann_entropy(
@ -119,9 +121,9 @@ for model_size in range(1, 8):
)
)
for size in range(1, 8):
for size in range(1, max_size):
temp_epoch = []
for epoch in range(0, 100):
for epoch in range(0, max_epoch):
temp_mean = []
for layer in lenet_keys:
temp_mean.append(
@ -136,8 +138,8 @@ for size in range(1, 8):
# aux.save_pickle("lenet_data_cifar_ne.pkl", models_lenet_cifar)
del models_lenet_cifar
for model_size in range(1, 8):
for epoch in range(0, 100):
for model_size in range(1, max_size):
for epoch in range(0, max_epoch):
for k in lenet_keys:
models_lenet_mnist[model_size][epoch][k] = \
aux.neumann_entropy(
@ -146,9 +148,9 @@ for model_size in range(1, 8):
)
)
for size in range(1, 8):
for size in range(1, max_size):
temp_epoch = []
for epoch in range(0, 100):
for epoch in range(0, max_epoch):
temp_mean = []
for layer in lenet_keys:
temp_mean.append(

167
get_entropy_noisy.py Normal file
View File

@ -0,0 +1,167 @@
import functions as aux
import statistics as st
alpha = 10000
models_bayes_cifar = aux.load_pickle("bayes_data_cifar_noisy.pkl")
models_bayes_mnist = aux.load_pickle("bayes_data_mnist_noisy.pkl")
models_lenet_cifar = aux.load_pickle("lenet_data_cifar_noisy.pkl")
models_lenet_mnist = aux.load_pickle("lenet_data_mnist_noisy.pkl")
entropy_data = {'CIFAR':
{'BCNN':
{0.1: None, 0.25: None,
0.5: None, 0.75: None, 0.99: None},
'LeNet':
{0.1: None, 0.25: None,
0.5: None, 0.75: None, 0.99: None},
},
'MNIST':
{'BCNN':
{0.1: None, 0.25: None,
0.5: None, 0.75: None, 0.99: None},
'LeNet':
{0.1: None, 0.25: None,
0.5: None, 0.75: None, 0.99: 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',
'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']
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']
"""
noise_levels = [0.1, 0.25, 0.5, 0.75, 0.99]
bayes_keys = ['conv1.W_mu',
'conv2.W_mu',
'fc1.W_mu',
'fc2.W_mu',
'fc3.W_mu',]
lenet_keys = ['conv1.weight', 'conv2.weight',
'fc1.weight', 'fc2.weight', 'fc3.weight']
for noise in noise_levels:
for epoch in range(0, 30):
for k in bayes_keys:
models_bayes_cifar[noise][epoch][k] = \
aux.neumann_entropy(
aux.square_matrix(
models_bayes_cifar[noise][epoch][k]
)
)
for noise in noise_levels:
temp_epoch = []
for epoch in range(0, 30):
temp_mean = []
for layer in bayes_keys:
temp_mean.append(
models_bayes_cifar[noise][epoch][layer].item()
)
temp_mean = st.mean(temp_mean)
temp_epoch.append(temp_mean)
entropy_data['CIFAR']['BCNN'][noise] = [x / alpha for x in temp_epoch]# temp_epoch
# aux.save_pickle("bayes_data_cifar_ne.pkl", models_bayes_cifar)
del models_bayes_cifar
for noise in noise_levels:
for epoch in range(0, 30):
for k in bayes_keys:
models_bayes_mnist[noise][epoch][k] = \
aux.neumann_entropy(
aux.square_matrix(
models_bayes_mnist[noise][epoch][k]
)
)
for noise in noise_levels:
temp_epoch = []
for epoch in range(0, 30):
temp_mean = []
for layer in bayes_keys:
temp_mean.append(
models_bayes_mnist[noise][epoch][layer].item()
)
temp_mean = st.mean(temp_mean)
temp_epoch.append(
temp_mean
)
entropy_data['MNIST']['BCNN'][noise] = [x / alpha for x in temp_epoch]# temp_epoch
# aux.save_pickle("bayes_data_mnist_ne.pkl", models_bayes_mnist)
del models_bayes_mnist
for noise in noise_levels:
for epoch in range(0, 30):
for k in lenet_keys:
models_lenet_cifar[noise][epoch][k] = \
aux.neumann_entropy(
aux.square_matrix(
models_lenet_cifar[noise][epoch][k]
)
)
for noise in noise_levels:
temp_epoch = []
for epoch in range(0, 30):
temp_mean = []
for layer in lenet_keys:
temp_mean.append(
models_lenet_cifar[noise][epoch][layer].item()
)
temp_mean = st.mean(temp_mean)
temp_epoch.append(
temp_mean
)
entropy_data['CIFAR']['LeNet'][noise] = [x / alpha for x in temp_epoch]# temp_epoch
# aux.save_pickle("lenet_data_cifar_ne.pkl", models_lenet_cifar)
del models_lenet_cifar
for noise in noise_levels:
for epoch in range(0, 30):
for k in lenet_keys:
models_lenet_mnist[noise][epoch][k] = \
aux.neumann_entropy(
aux.square_matrix(
models_lenet_mnist[noise][epoch][k]
)
)
for noise in noise_levels:
temp_epoch = []
for epoch in range(0, 30):
temp_mean = []
for layer in lenet_keys:
temp_mean.append(
models_lenet_mnist[noise][epoch][layer].item()
)
temp_mean = st.mean(temp_mean)
temp_epoch.append(
temp_mean
)
entropy_data['MNIST']['LeNet'][noise] = [x / alpha for x in temp_epoch]# temp_epoch
# aux.save_pickle("lenet_data_mnist_ne.pkl", models_lenet_mnist)
del models_lenet_mnist
aux.save_pickle("entropy_data_noisy.pkl", entropy_data)