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Entropy_Data_Processing
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Modified to accept different noise types epochs, and alphas

This commit is contained in:
Eduardo Cueto-Mendoza 2024-09-25 11:34:47 +01:00
parent 03f03d59d9
commit 67fcdcdab2
3 changed files with 26 additions and 10 deletions
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6
efficiency_computations.py
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@ -62,11 +62,15 @@ for data in data_types:
eff_data = dict(gpu_ene_data)
for data in data_types:
for model in model_types:
if model == 'fre':
alpha = 100
elif model == 'bay':
alpha = 1000
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
spl_ene_data[data][model][size][i]) * alpha
for data, o_data in zip(data_types, o_data_types):

8
general_plots_noisy.py
View File

@ -1,8 +1,8 @@
import matplotlib.pyplot as plt
import functions as aux
model_type = 'BCNN' # BCNN or LeNet
dataset = 'MNIST' # MNIST or CIFAR
model_type = 'LeNet' # BCNN or LeNet
dataset = 'CIFAR' # MNIST or CIFAR
eff_df = aux.load_pickle("efficiency_data.pkl")
@ -19,10 +19,10 @@ 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]
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['MNIST']['LeNet'][size],
plt.plot(eff_df[dataset][model_type][size],
label='Efficiency')
plt.plot(entropy_data[dataset][model_type][size],
label='Entropy at noise 0.0')

22
get_entropy_noisy.py
View File

@ -11,18 +11,30 @@ 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},
0.5: None, 0.75: None, 0.99: None,
'raleigh': None, 'erlang': None,
'exponential': None, 'uniform': None,
'impulse': None},
'LeNet':
{0.1: None, 0.25: None,
0.5: None, 0.75: None, 0.99: None},
0.5: None, 0.75: None, 0.99: None,
'raleigh': None, 'erlang': None,
'exponential': None, 'uniform': None,
'impulse': None},
},
'MNIST':
{'BCNN':
{0.1: None, 0.25: None,
0.5: None, 0.75: None, 0.99: None},
0.5: None, 0.75: None, 0.99: None,
'raleigh': None, 'erlang': None,
'exponential': None, 'uniform': None,
'impulse': None},
'LeNet':
{0.1: None, 0.25: None,
0.5: None, 0.75: None, 0.99: None},
0.5: None, 0.75: None, 0.99: None,
'raleigh': None, 'erlang': None,
'exponential': None, 'uniform': None,
'impulse': None},
},
}
@ -45,7 +57,7 @@ bayes_keys = ['conv1.W_mu', 'conv1.W_rho',
"""
noise_levels = [0.1, 0.25, 0.5, 0.75, 0.99]
noise_levels = [0.1, 0.25, 0.5, 0.75, 0.99, 'raleigh', 'erlang', 'exponential', 'uniform', 'impulse']
bayes_keys = ['conv1.W_mu',
'conv2.W_mu',