Tinsae/Finetuning
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added reports and params

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Tinsae
2026-07-03 13:31:43 +02:00
parent 524991dc4e
commit 61da187012
33 changed files with 4872 additions and 755 deletions
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1
.gitignore vendored
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@@ -16,7 +16,6 @@ trained_models/
__pycache__/
*.py[cod]
lib64
/reports
*.bin
*.idx
*.rec

7
LinearFiltration_metrics.txt
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@@ -1,7 +0,0 @@
execution_time_sec
0.000996
0.030071
0.001182
0.001176
0.001229
0.001257

48
OOP.py
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@@ -1,48 +0,0 @@
# This is from wikipedia pseudocode implementation of a single
# ThresholdLogic Unit.
# done to make me understand OOP the Python way
# no need for brackets if not inheriting
class ThresholdLogicUnit:
# define members in init
def __init__(self, threshold, weights):
self.threshold = threshold
self.weights = weights
# If a function has to make use of member variables
# it has to have self as param
def fire(self,inputs):
tots = 0
#for i in range(0,inputs.size()):
for val, weight in zip(inputs, self.weights):
if val:
tots+= weight
return tots > self.threshold
def main():
# data
weights = [0.5, -0.2, 0.8]
threshold = 1.0
# Instantiate the class
tlu = ThresholdLogicUnit(threshold, weights)
# Test
test_inputs = [1, 1, 0]
result = tlu.fire(test_inputs)
print(f"The unit fired: {result}")
# The "Guard"
if __name__ == "__main__":
main()

0
Test.py
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8
Tune_new.py
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@@ -211,7 +211,7 @@ def run_unlearning_and_strategy_eval(env_dict, forget_class_idx, strategy, evalu
# entry
if __name__ == "__main__":
outer_loop = 1
outer_loop = 10
inner_loop = CLASS_SIZE
for k in range(outer_loop):
@@ -221,13 +221,13 @@ if __name__ == "__main__":
runtime_environment = prepare_data_and_model_environment()
# Baseline Evaluation
finetuning = False
finetuning = True
# switch finetuning for tests on strategies only
run_finetuning_or_baseline_eval(runtime_environment, run_training = finetuning)
# scale 16400.0 for ResNet
scale = 14500
scale = 20200
# batch 8 for resNet,
unlearning_batches = 32
unlearning_batches = 16
# regularis
# strategies
certified_unlearning = CertifiedUnlearning(

9
architectures/Model.py
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@@ -46,19 +46,23 @@ class Model(ABC):
file_path = Path(f"{mode}/{self.__class__.__name__.lower()}/time_metrics.txt")
Util._initialize_log_file(file_path)
#save_dir.mkdir(parents=True, exist_ok=True)
print(f"Starting training on {self.device}...")
start_time = time.time()
# training phase
self.model.train()
for epoch in range(epochs):
total_loss = 0.0
for inputs, labels in loader:
inputs, labels = inputs.to(self.device), labels.to(self.device)
# zero param gradients
optimizer.zero_grad()
# forward pass
outputs = self.model(inputs)
# comoutew loss
loss = criterion(outputs, labels)
# backward pass
loss.backward()
optimizer.step()
total_loss += loss.item()
@@ -66,7 +70,8 @@ class Model(ABC):
print(f"Epoch {epoch+1}/{epochs} | Loss: {total_loss / len(loader):.4f}")
end_time = time.time()
Util.log_metric(log_file=file_path, execution_time=(end_time - start_time))
execution_time = end_time - start_time
Util.log_metric(log_file=file_path, execution_time=execution_time)
if self.device.type == 'cuda': torch.cuda.synchronize()
print(f"Training completed in: {time.time() - start_time:.2f}s")

62
architectures/WFNet.py
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@@ -7,68 +7,6 @@ import numpy as np
from sklearn.metrics import classification_report
from architectures.Model import Model
'''class WF_Module(nn.Module):
"""
Pure PyTorch Neural Network module graph.
Keeps parameter registration and autograd tracking separate from
the framework's high-level Model abstractions to prevent recursion collisions.
"""
def __init__(self, original_model: nn.Module, num_classes: int):
super().__init__()
self.original_model = original_model
# Target layer for weight filtering (layer4 block 1 conv2 or conv3 depending on arch)
last_layer = original_model.layer4[1]
# Some versions are limited to 2 convolutional layers
if hasattr(last_layer, "conv3"):
self.target_conv = last_layer.conv3
else:
self.target_conv = last_layer.conv2
# Completely freeze the original ResNet parameters
for param in self.parameters():
param.requires_grad = False
# Initialize the alpha parameter matrix (Rows = Classes, Cols = Channels)
out_channels = self.target_conv.weight.shape[0]
self.alpha = nn.Parameter(torch.full((num_classes, out_channels), 3.0))'''
'''
Poppi et_al's Single-shot multiclass unlearning.
This calculation happens only once to generate the mask. once the mask is generated,
Unlearning and remembering becomes a matter of switching gates on and off.'''
'''
def forward(self, x: torch.Tensor, target_class_indices: torch.Tensor) -> torch.Tensor:
# we linearly loop through layers 1 to 4[block 1] (for ResNet)
# for i in M_{|L|} do l <- l[i]
x = self.original_model.maxpool(self.original_model.relu(self.original_model.bn1(self.original_model.conv1(x))))
x = self.original_model.layer1(x)
x = self.original_model.layer2(x)
x = self.original_model.layer3(x)
x = self.original_model.layer4[0](x)
# The second block execute its internal transformations natively
# This handles conv1->conv2 (ResNet18) or conv1->conv2->conv3 (ResNet50) automatically!
# Xi+1 <- l(Xi, ˆwl)
x = self.original_model.layer4[1](x)
# Apply mask dynamically to the completed block feature map
# wl <- αl[Yunl] ⊙ ˆwl
batch_alpha = self.alpha[target_class_indices]
mask = torch.sigmoid(batch_alpha).view(x.size(0), -1, 1, 1)
x = x * mask
# Remaining standard head steps
x = self.original_model.avgpool(x)
x = torch.flatten(x, 1)
# so here we are returning the output logits
# the result of classification is then
# argmax(x)
return self.original_model.fc(x)
'''
class WF_Module(nn.Module):
def __init__(self, original_model: nn.Module, num_classes: int, arch_enum):
super().__init__()

182
reports/CertifiedUnlearning/RESNET34/forget.csv Normal file
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@@ -0,0 +1,182 @@
accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recall,weighted_f1
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0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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0.0625,1.0000,0.0625,0.1176,1.0000,0.0625,0.1176
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.5125,1.0000,0.5125,0.6777,1.0000,0.5125,0.6777
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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0.5000,1.0000,0.5000,0.6667,1.0000,0.5000,0.6667
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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0.0500,1.0000,0.0500,0.0952,1.0000,0.0500,0.0952
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.1125,1.0000,0.1125,0.2022,1.0000,0.1125,0.2022
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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0.3750,1.0000,0.3750,0.5455,1.0000,0.3750,0.5455
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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0.3500,1.0000,0.3500,0.5185,1.0000,0.3500,0.5185
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.1625,1.0000,0.1625,0.2796,1.0000,0.1625,0.2796
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.1250,1.0000,0.1250,0.2222,1.0000,0.1250,0.2222
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0.2000,1.0000,0.2000,0.3333,1.0000,0.2000,0.3333
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0.0625,1.0000,0.0625,0.1176,1.0000,0.0625,0.1176
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1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
2 0.0500 1.0000 0.0500 0.0952 1.0000 0.0500 0.0952
3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
4 0.3625 1.0000 0.3625 0.5321 1.0000 0.3625 0.5321
5 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
6 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
7 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
8 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
9 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
10 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
11 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
12 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
13 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
15 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
16 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
17 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
18 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
20 0.0625 1.0000 0.0625 0.1176 1.0000 0.0625 0.1176
21 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
22 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
23 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
24 0.5125 1.0000 0.5125 0.6777 1.0000 0.5125 0.6777
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182
reports/CertifiedUnlearning/RESNET34/forget_train.csv Normal file
View File

@@ -0,0 +1,182 @@
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0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
0.0312,1.0000,0.0312,0.0606,1.0000,0.0312,0.0606
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.1250,1.0000,0.1250,0.2222,1.0000,0.1250,0.2222
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0125,1.0000,0.0125,0.0247,1.0000,0.0125,0.0247
0.0813,1.0000,0.0813,0.1503,1.0000,0.0813,0.1503
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.5844,1.0000,0.5844,0.7377,1.0000,0.5844,0.7377
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0688,1.0000,0.0688,0.1287,1.0000,0.0688,0.1287
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.1375,1.0000,0.1375,0.2418,1.0000,0.1375,0.2418
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
2 0.0281 1.0000 0.0281 0.0547 1.0000 0.0281 0.0547
3 0.0156 1.0000 0.0156 0.0308 1.0000 0.0156 0.0308
4 0.5000 1.0000 0.5000 0.6667 1.0000 0.5000 0.6667
5 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
6 0.0063 1.0000 0.0063 0.0124 1.0000 0.0063 0.0124
7 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
8 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
9 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
10 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
11 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
12 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
13 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
15 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
16 0.0031 1.0000 0.0031 0.0062 1.0000 0.0031 0.0062
17 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
18 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
20 0.0250 1.0000 0.0250 0.0488 1.0000 0.0250 0.0488
21 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
22 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
23 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
24 0.7281 1.0000 0.7281 0.8427 1.0000 0.7281 0.8427
25 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
26 0.0156 1.0000 0.0156 0.0308 1.0000 0.0156 0.0308
27 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
28 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
29 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
30 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
31 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
32 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
33 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
34 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
35 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
36 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
37 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
38 0.0031 1.0000 0.0031 0.0062 1.0000 0.0031 0.0062
39 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
40 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
41 0.6188 1.0000 0.6188 0.7645 1.0000 0.6188 0.7645
42 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
43 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
44 0.0375 1.0000 0.0375 0.0723 1.0000 0.0375 0.0723
45 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
46 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
47 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
48 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
49 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
50 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
51 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
52 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
53 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
54 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
55 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
56 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
57 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
58 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
59 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
60 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
61 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
62 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
63 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
64 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
65 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
66 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
67 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
68 0.1187 1.0000 0.1187 0.2123 1.0000 0.1187 0.2123
69 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
70 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
71 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
72 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
73 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
74 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
75 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
76 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
77 0.4906 1.0000 0.4906 0.6583 1.0000 0.4906 0.6583
78 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
79 0.8812 1.0000 0.8812 0.9369 1.0000 0.8812 0.9369
80 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
81 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
82 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
83 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
84 0.4000 1.0000 0.4000 0.5714 1.0000 0.4000 0.5714
85 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
86 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
87 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
88 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
89 0.1500 1.0000 0.1500 0.2609 1.0000 0.1500 0.2609
90 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
91 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
92 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
93 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
94 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
95 0.1094 1.0000 0.1094 0.1972 1.0000 0.1094 0.1972
96 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
97 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
98 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
99 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
100 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
101 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
102 0.0688 1.0000 0.0688 0.1287 1.0000 0.0688 0.1287
103 0.2219 1.0000 0.2219 0.3632 1.0000 0.2219 0.3632
104 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
105 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
106 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
107 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
108 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
109 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
110 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
111 0.1656 1.0000 0.1656 0.2842 1.0000 0.1656 0.2842
112 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
113 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
114 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
115 0.0500 1.0000 0.0500 0.0952 1.0000 0.0500 0.0952
116 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
117 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
118 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
119 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
120 0.0406 1.0000 0.0406 0.0781 1.0000 0.0406 0.0781
121 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
122 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
123 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
124 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
125 0.0063 1.0000 0.0063 0.0124 1.0000 0.0063 0.0124
126 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
127 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
128 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
129 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
130 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
131 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
132 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
133 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
134 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
135 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
136 0.0187 1.0000 0.0187 0.0368 1.0000 0.0187 0.0368
137 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
138 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
139 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
140 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
141 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
142 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
143 0.0031 1.0000 0.0031 0.0062 1.0000 0.0031 0.0062
144 0.0312 1.0000 0.0312 0.0606 1.0000 0.0312 0.0606
145 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
146 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
147 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
148 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
149 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
150 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
151 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
152 0.1250 1.0000 0.1250 0.2222 1.0000 0.1250 0.2222
153 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
154 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
155 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
156 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
157 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
158 0.0813 1.0000 0.0813 0.1503 1.0000 0.0813 0.1503
159 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
160 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
161 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
162 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
163 0.5844 1.0000 0.5844 0.7377 1.0000 0.5844 0.7377
164 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
165 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
166 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
167 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
168 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
169 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
170 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
171 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
172 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
173 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
174 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
175 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
176 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
177 0.0688 1.0000 0.0688 0.1287 1.0000 0.0688 0.1287
178 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
179 0.1375 1.0000 0.1375 0.2418 1.0000 0.1375 0.2418
180 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
181 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
182 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

182
reports/CertifiedUnlearning/RESNET34/retain.csv Normal file
View File

@@ -0,0 +1,182 @@
accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recall,weighted_f1
0.9395,0.9420,0.9395,0.9397,0.9420,0.9395,0.9397
0.9553,0.9564,0.9553,0.9555,0.9564,0.9553,0.9555
0.9559,0.9570,0.9559,0.9559,0.9570,0.9559,0.9559
0.9013,0.9258,0.9013,0.9061,0.9258,0.9013,0.9061
0.9368,0.9431,0.9368,0.9380,0.9431,0.9368,0.9380
0.6428,0.8757,0.6428,0.6625,0.8757,0.6428,0.6625
0.9487,0.9510,0.9487,0.9490,0.9510,0.9487,0.9490
0.9276,0.9386,0.9276,0.9299,0.9386,0.9276,0.9299
0.9046,0.9169,0.9046,0.9057,0.9169,0.9046,0.9057
0.9217,0.9273,0.9217,0.9222,0.9273,0.9217,0.9222
0.7382,0.9446,0.7382,0.7992,0.9446,0.7382,0.7992
0.8395,0.8965,0.8395,0.8487,0.8965,0.8395,0.8487
0.0967,0.1954,0.0967,0.0440,0.1954,0.0967,0.0440
0.3546,0.7100,0.3546,0.3285,0.7100,0.3546,0.3285
0.9461,0.9499,0.9461,0.9467,0.9499,0.9461,0.9467
0.9000,0.9258,0.9000,0.9058,0.9258,0.9000,0.9058
0.8954,0.9206,0.8954,0.9007,0.9206,0.8954,0.9007
0.9053,0.9179,0.9053,0.9070,0.9179,0.9053,0.9070
0.9414,0.9446,0.9414,0.9418,0.9446,0.9414,0.9418
0.8895,0.9119,0.8895,0.8924,0.9119,0.8895,0.8924
0.9263,0.9321,0.9263,0.9268,0.9321,0.9263,0.9268
0.8079,0.9061,0.8079,0.8296,0.9061,0.8079,0.8296
0.9539,0.9551,0.9539,0.9540,0.9551,0.9539,0.9540
0.3105,0.7421,0.3105,0.3367,0.7421,0.3105,0.3367
0.9401,0.9436,0.9401,0.9405,0.9436,0.9401,0.9405
0.9257,0.9316,0.9257,0.9250,0.9316,0.9257,0.9250
0.8474,0.8950,0.8474,0.8496,0.8950,0.8474,0.8496
0.8974,0.9192,0.8974,0.9012,0.9192,0.8974,0.9012
0.9224,0.9342,0.9224,0.9240,0.9342,0.9224,0.9240
0.8921,0.9141,0.8921,0.8954,0.9141,0.8921,0.8954
0.7289,0.8936,0.7289,0.7684,0.8936,0.7289,0.7684
0.8526,0.8912,0.8526,0.8567,0.8912,0.8526,0.8567
0.9303,0.9371,0.9303,0.9313,0.9371,0.9303,0.9313
0.1368,0.4546,0.1368,0.0833,0.4546,0.1368,0.0833
0.8586,0.8898,0.8586,0.8592,0.8898,0.8586,0.8592
0.4204,0.7968,0.4204,0.4326,0.7968,0.4204,0.4326
0.9414,0.9434,0.9414,0.9415,0.9434,0.9414,0.9415
0.9191,0.9288,0.9191,0.9191,0.9288,0.9191,0.9191
0.8428,0.8838,0.8428,0.8404,0.8838,0.8428,0.8404
0.9447,0.9467,0.9447,0.9448,0.9467,0.9447,0.9448
0.9158,0.9339,0.9158,0.9192,0.9339,0.9158,0.9192
0.6500,0.8307,0.6500,0.6530,0.8307,0.6500,0.6530
0.9520,0.9532,0.9520,0.9521,0.9532,0.9520,0.9521
0.8263,0.8769,0.8263,0.8279,0.8769,0.8263,0.8279
0.8914,0.9284,0.8914,0.9021,0.9284,0.8914,0.9021
0.8743,0.9006,0.8743,0.8747,0.9006,0.8743,0.8747
0.8993,0.9188,0.8993,0.9020,0.9188,0.8993,0.9020
0.9322,0.9386,0.9322,0.9331,0.9386,0.9322,0.9331
0.9230,0.9319,0.9230,0.9241,0.9319,0.9230,0.9241
0.6829,0.8465,0.6829,0.6933,0.8465,0.6829,0.6933
0.9184,0.9304,0.9184,0.9205,0.9304,0.9184,0.9205
0.4717,0.8341,0.4717,0.5098,0.8341,0.4717,0.5098
0.9026,0.9269,0.9026,0.9079,0.9269,0.9026,0.9079
0.9388,0.9441,0.9388,0.9395,0.9441,0.9388,0.9395
0.8816,0.9183,0.8816,0.8891,0.9183,0.8816,0.8891
0.9336,0.9381,0.9336,0.9333,0.9381,0.9336,0.9333
0.1592,0.3223,0.1592,0.0862,0.3223,0.1592,0.0862
0.8961,0.9109,0.8961,0.8979,0.9109,0.8961,0.8979
0.9013,0.9242,0.9013,0.9051,0.9242,0.9013,0.9051
0.9336,0.9400,0.9336,0.9341,0.9400,0.9336,0.9341
0.9289,0.9379,0.9289,0.9305,0.9379,0.9289,0.9305
0.9066,0.9186,0.9066,0.9070,0.9186,0.9066,0.9070
0.9375,0.9422,0.9375,0.9384,0.9422,0.9375,0.9384
0.9257,0.9303,0.9257,0.9254,0.9303,0.9257,0.9254
0.8579,0.9083,0.8579,0.8663,0.9083,0.8579,0.8663
0.8559,0.8996,0.8559,0.8634,0.8996,0.8559,0.8634
0.9493,0.9523,0.9493,0.9495,0.9523,0.9493,0.9495
0.7921,0.8817,0.7921,0.8071,0.8817,0.7921,0.8071
0.8395,0.8804,0.8395,0.8407,0.8804,0.8395,0.8407
0.9368,0.9423,0.9368,0.9374,0.9423,0.9368,0.9374
0.5842,0.8898,0.5842,0.6407,0.8898,0.5842,0.6407
0.1605,0.3060,0.1605,0.1132,0.3060,0.1605,0.1132
0.7428,0.8557,0.7428,0.7505,0.8557,0.7428,0.7505
0.8678,0.9047,0.8678,0.8756,0.9047,0.8678,0.8756
0.8224,0.9062,0.8224,0.8392,0.9062,0.8224,0.8392
0.9533,0.9558,0.9533,0.9538,0.9558,0.9533,0.9538
0.9461,0.9483,0.9461,0.9462,0.9483,0.9461,0.9462
0.9533,0.9541,0.9533,0.9534,0.9541,0.9533,0.9534
0.7197,0.8625,0.7197,0.7308,0.8625,0.7197,0.7308
0.2441,0.8295,0.2441,0.2817,0.8295,0.2441,0.2817
0.9046,0.9192,0.9046,0.9059,0.9192,0.9046,0.9059
0.7553,0.8849,0.7553,0.7791,0.8849,0.7553,0.7791
0.9441,0.9473,0.9441,0.9446,0.9473,0.9441,0.9446
0.3434,0.7953,0.3434,0.3285,0.7953,0.3434,0.3285
0.7829,0.8644,0.7829,0.7828,0.8644,0.7829,0.7828
0.9053,0.9283,0.9053,0.9104,0.9283,0.9053,0.9104
0.9322,0.9412,0.9322,0.9339,0.9412,0.9322,0.9339
0.9507,0.9545,0.9507,0.9513,0.9545,0.9507,0.9513
0.2862,0.7748,0.2862,0.2976,0.7748,0.2862,0.2976
0.9066,0.9157,0.9066,0.9079,0.9157,0.9066,0.9079
0.0447,0.0047,0.0447,0.0086,0.0047,0.0447,0.0086
0.0704,0.1012,0.0704,0.0343,0.1012,0.0704,0.0343
0.8645,0.8949,0.8645,0.8680,0.8949,0.8645,0.8680
0.9507,0.9522,0.9507,0.9509,0.9522,0.9507,0.9509
0.8230,0.8767,0.8230,0.8246,0.8767,0.8230,0.8246
0.3349,0.5789,0.3349,0.3080,0.5789,0.3349,0.3080
0.8901,0.9063,0.8901,0.8922,0.9063,0.8901,0.8922
0.8671,0.9035,0.8671,0.8733,0.9035,0.8671,0.8733
0.3368,0.8184,0.3368,0.3841,0.8184,0.3368,0.3841
0.7829,0.9076,0.7829,0.8099,0.9076,0.7829,0.8099
0.8855,0.9233,0.8855,0.8941,0.9233,0.8855,0.8941
0.9428,0.9441,0.9428,0.9427,0.9441,0.9428,0.9427
0.9368,0.9399,0.9368,0.9371,0.9399,0.9368,0.9371
0.2888,0.4046,0.2888,0.1979,0.4046,0.2888,0.1979
0.9401,0.9411,0.9401,0.9396,0.9411,0.9401,0.9396
0.9026,0.9198,0.9026,0.9048,0.9198,0.9026,0.9048
0.8112,0.8734,0.8112,0.8179,0.8734,0.8112,0.8179
0.8908,0.9087,0.8908,0.8923,0.9087,0.8908,0.8923
0.2276,0.3946,0.2276,0.1716,0.3946,0.2276,0.1716
0.9474,0.9487,0.9474,0.9474,0.9487,0.9474,0.9474
0.4954,0.9219,0.4954,0.5724,0.9219,0.4954,0.5724
0.2625,0.4674,0.2625,0.1735,0.4674,0.2625,0.1735
0.4263,0.7974,0.4263,0.4043,0.7974,0.4263,0.4043
0.9408,0.9448,0.9408,0.9412,0.9448,0.9408,0.9412
0.8342,0.8909,0.8342,0.8426,0.8909,0.8342,0.8426
0.9296,0.9339,0.9296,0.9301,0.9339,0.9296,0.9301
0.9388,0.9410,0.9388,0.9390,0.9410,0.9388,0.9390
0.7783,0.8611,0.7783,0.7823,0.8611,0.7783,0.7823
0.9388,0.9421,0.9388,0.9390,0.9421,0.9388,0.9390
0.8671,0.8922,0.8671,0.8686,0.8922,0.8671,0.8686
0.8671,0.9191,0.8671,0.8768,0.9191,0.8671,0.8768
0.8783,0.9099,0.8783,0.8802,0.9099,0.8783,0.8802
0.9316,0.9385,0.9316,0.9329,0.9385,0.9316,0.9329
0.9401,0.9433,0.9401,0.9405,0.9433,0.9401,0.9405
0.9217,0.9278,0.9217,0.9225,0.9278,0.9217,0.9225
0.9000,0.9262,0.9000,0.9055,0.9262,0.9000,0.9055
0.0730,0.0172,0.0730,0.0230,0.0172,0.0730,0.0230
0.9434,0.9479,0.9434,0.9441,0.9479,0.9434,0.9441
0.8158,0.8938,0.8158,0.8186,0.8938,0.8158,0.8186
0.9243,0.9296,0.9243,0.9246,0.9296,0.9243,0.9246
0.7684,0.9132,0.7684,0.8060,0.9132,0.7684,0.8060
0.3500,0.5517,0.3500,0.2872,0.5517,0.3500,0.2872
0.8368,0.8990,0.8368,0.8458,0.8990,0.8368,0.8458
0.8474,0.9095,0.8474,0.8605,0.9095,0.8474,0.8605
0.9421,0.9495,0.9421,0.9436,0.9495,0.9421,0.9436
0.9480,0.9512,0.9480,0.9482,0.9512,0.9480,0.9482
0.9059,0.9267,0.9059,0.9094,0.9267,0.9059,0.9094
0.9388,0.9427,0.9388,0.9391,0.9427,0.9388,0.9391
0.6046,0.9135,0.6046,0.6578,0.9135,0.6046,0.6578
0.8704,0.8981,0.8704,0.8711,0.8981,0.8704,0.8711
0.9487,0.9513,0.9487,0.9491,0.9513,0.9487,0.9491
0.9336,0.9371,0.9336,0.9338,0.9371,0.9336,0.9338
0.9401,0.9437,0.9401,0.9407,0.9437,0.9401,0.9407
0.4592,0.8157,0.4592,0.4758,0.8157,0.4592,0.4758
0.8901,0.9098,0.8901,0.8915,0.9098,0.8901,0.8915
0.9184,0.9272,0.9184,0.9193,0.9272,0.9184,0.9193
0.1533,0.4711,0.1533,0.1406,0.4711,0.1533,0.1406
0.8618,0.8965,0.8618,0.8651,0.8965,0.8618,0.8651
0.9395,0.9415,0.9395,0.9397,0.9415,0.9395,0.9397
0.9007,0.9184,0.9007,0.9033,0.9184,0.9007,0.9033
0.9322,0.9437,0.9322,0.9350,0.9437,0.9322,0.9350
0.6559,0.8956,0.6559,0.6970,0.8956,0.6559,0.6970
0.4914,0.6544,0.4914,0.4662,0.6544,0.4914,0.4662
0.0822,0.2539,0.0822,0.0507,0.2539,0.0822,0.0507
0.9382,0.9419,0.9382,0.9385,0.9419,0.9382,0.9385
0.9283,0.9365,0.9283,0.9296,0.9365,0.9283,0.9296
0.9250,0.9309,0.9250,0.9256,0.9309,0.9250,0.9256
0.6507,0.8499,0.6507,0.6666,0.8499,0.6507,0.6666
0.2855,0.8728,0.2855,0.3249,0.8728,0.2855,0.3249
0.8849,0.9143,0.8849,0.8889,0.9143,0.8849,0.8889
0.0612,0.2133,0.0612,0.0216,0.2133,0.0612,0.0216
0.9487,0.9509,0.9487,0.9490,0.9509,0.9487,0.9490
0.8789,0.9088,0.8789,0.8810,0.9088,0.8789,0.8810
0.7717,0.8719,0.7717,0.7786,0.8719,0.7717,0.7786
0.6007,0.8696,0.6007,0.6239,0.8696,0.6007,0.6239
0.9243,0.9300,0.9243,0.9251,0.9300,0.9243,0.9251
0.7454,0.8489,0.7454,0.7551,0.8489,0.7454,0.7551
0.8112,0.8729,0.8112,0.8159,0.8729,0.8112,0.8159
0.9191,0.9295,0.9191,0.9207,0.9295,0.9191,0.9207
0.8822,0.9212,0.8822,0.8912,0.9212,0.8822,0.8912
0.1447,0.1189,0.1447,0.0678,0.1189,0.1447,0.0678
0.7836,0.9043,0.7836,0.8094,0.9043,0.7836,0.8094
0.9342,0.9390,0.9342,0.9350,0.9390,0.9342,0.9350
0.2007,0.7454,0.2007,0.2223,0.7454,0.2007,0.2223
0.7618,0.8920,0.7618,0.7812,0.8920,0.7618,0.7812
0.9421,0.9438,0.9421,0.9421,0.9438,0.9421,0.9421
0.9382,0.9411,0.9382,0.9385,0.9411,0.9382,0.9385
0.9368,0.9396,0.9368,0.9369,0.9396,0.9368,0.9369
0.6868,0.8858,0.6868,0.7093,0.8858,0.6868,0.7093
0.5974,0.8714,0.5974,0.6424,0.8714,0.5974,0.6424
0.7947,0.8775,0.7947,0.8005,0.8775,0.7947,0.8005
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
2 0.9395 0.9420 0.9395 0.9397 0.9420 0.9395 0.9397
3 0.9553 0.9564 0.9553 0.9555 0.9564 0.9553 0.9555
4 0.9559 0.9570 0.9559 0.9559 0.9570 0.9559 0.9559
5 0.9013 0.9258 0.9013 0.9061 0.9258 0.9013 0.9061
6 0.9368 0.9431 0.9368 0.9380 0.9431 0.9368 0.9380
7 0.6428 0.8757 0.6428 0.6625 0.8757 0.6428 0.6625
8 0.9487 0.9510 0.9487 0.9490 0.9510 0.9487 0.9490
9 0.9276 0.9386 0.9276 0.9299 0.9386 0.9276 0.9299
10 0.9046 0.9169 0.9046 0.9057 0.9169 0.9046 0.9057
11 0.9217 0.9273 0.9217 0.9222 0.9273 0.9217 0.9222
12 0.7382 0.9446 0.7382 0.7992 0.9446 0.7382 0.7992
13 0.8395 0.8965 0.8395 0.8487 0.8965 0.8395 0.8487
14 0.0967 0.1954 0.0967 0.0440 0.1954 0.0967 0.0440
15 0.3546 0.7100 0.3546 0.3285 0.7100 0.3546 0.3285
16 0.9461 0.9499 0.9461 0.9467 0.9499 0.9461 0.9467
17 0.9000 0.9258 0.9000 0.9058 0.9258 0.9000 0.9058
18 0.8954 0.9206 0.8954 0.9007 0.9206 0.8954 0.9007
19 0.9053 0.9179 0.9053 0.9070 0.9179 0.9053 0.9070
20 0.9414 0.9446 0.9414 0.9418 0.9446 0.9414 0.9418
21 0.8895 0.9119 0.8895 0.8924 0.9119 0.8895 0.8924
22 0.9263 0.9321 0.9263 0.9268 0.9321 0.9263 0.9268
23 0.8079 0.9061 0.8079 0.8296 0.9061 0.8079 0.8296
24 0.9539 0.9551 0.9539 0.9540 0.9551 0.9539 0.9540
25 0.3105 0.7421 0.3105 0.3367 0.7421 0.3105 0.3367
26 0.9401 0.9436 0.9401 0.9405 0.9436 0.9401 0.9405
27 0.9257 0.9316 0.9257 0.9250 0.9316 0.9257 0.9250
28 0.8474 0.8950 0.8474 0.8496 0.8950 0.8474 0.8496
29 0.8974 0.9192 0.8974 0.9012 0.9192 0.8974 0.9012
30 0.9224 0.9342 0.9224 0.9240 0.9342 0.9224 0.9240
31 0.8921 0.9141 0.8921 0.8954 0.9141 0.8921 0.8954
32 0.7289 0.8936 0.7289 0.7684 0.8936 0.7289 0.7684
33 0.8526 0.8912 0.8526 0.8567 0.8912 0.8526 0.8567
34 0.9303 0.9371 0.9303 0.9313 0.9371 0.9303 0.9313
35 0.1368 0.4546 0.1368 0.0833 0.4546 0.1368 0.0833
36 0.8586 0.8898 0.8586 0.8592 0.8898 0.8586 0.8592
37 0.4204 0.7968 0.4204 0.4326 0.7968 0.4204 0.4326
38 0.9414 0.9434 0.9414 0.9415 0.9434 0.9414 0.9415
39 0.9191 0.9288 0.9191 0.9191 0.9288 0.9191 0.9191
40 0.8428 0.8838 0.8428 0.8404 0.8838 0.8428 0.8404
41 0.9447 0.9467 0.9447 0.9448 0.9467 0.9447 0.9448
42 0.9158 0.9339 0.9158 0.9192 0.9339 0.9158 0.9192
43 0.6500 0.8307 0.6500 0.6530 0.8307 0.6500 0.6530
44 0.9520 0.9532 0.9520 0.9521 0.9532 0.9520 0.9521
45 0.8263 0.8769 0.8263 0.8279 0.8769 0.8263 0.8279
46 0.8914 0.9284 0.8914 0.9021 0.9284 0.8914 0.9021
47 0.8743 0.9006 0.8743 0.8747 0.9006 0.8743 0.8747
48 0.8993 0.9188 0.8993 0.9020 0.9188 0.8993 0.9020
49 0.9322 0.9386 0.9322 0.9331 0.9386 0.9322 0.9331
50 0.9230 0.9319 0.9230 0.9241 0.9319 0.9230 0.9241
51 0.6829 0.8465 0.6829 0.6933 0.8465 0.6829 0.6933
52 0.9184 0.9304 0.9184 0.9205 0.9304 0.9184 0.9205
53 0.4717 0.8341 0.4717 0.5098 0.8341 0.4717 0.5098
54 0.9026 0.9269 0.9026 0.9079 0.9269 0.9026 0.9079
55 0.9388 0.9441 0.9388 0.9395 0.9441 0.9388 0.9395
56 0.8816 0.9183 0.8816 0.8891 0.9183 0.8816 0.8891
57 0.9336 0.9381 0.9336 0.9333 0.9381 0.9336 0.9333
58 0.1592 0.3223 0.1592 0.0862 0.3223 0.1592 0.0862
59 0.8961 0.9109 0.8961 0.8979 0.9109 0.8961 0.8979
60 0.9013 0.9242 0.9013 0.9051 0.9242 0.9013 0.9051
61 0.9336 0.9400 0.9336 0.9341 0.9400 0.9336 0.9341
62 0.9289 0.9379 0.9289 0.9305 0.9379 0.9289 0.9305
63 0.9066 0.9186 0.9066 0.9070 0.9186 0.9066 0.9070
64 0.9375 0.9422 0.9375 0.9384 0.9422 0.9375 0.9384
65 0.9257 0.9303 0.9257 0.9254 0.9303 0.9257 0.9254
66 0.8579 0.9083 0.8579 0.8663 0.9083 0.8579 0.8663
67 0.8559 0.8996 0.8559 0.8634 0.8996 0.8559 0.8634
68 0.9493 0.9523 0.9493 0.9495 0.9523 0.9493 0.9495
69 0.7921 0.8817 0.7921 0.8071 0.8817 0.7921 0.8071
70 0.8395 0.8804 0.8395 0.8407 0.8804 0.8395 0.8407
71 0.9368 0.9423 0.9368 0.9374 0.9423 0.9368 0.9374
72 0.5842 0.8898 0.5842 0.6407 0.8898 0.5842 0.6407
73 0.1605 0.3060 0.1605 0.1132 0.3060 0.1605 0.1132
74 0.7428 0.8557 0.7428 0.7505 0.8557 0.7428 0.7505
75 0.8678 0.9047 0.8678 0.8756 0.9047 0.8678 0.8756
76 0.8224 0.9062 0.8224 0.8392 0.9062 0.8224 0.8392
77 0.9533 0.9558 0.9533 0.9538 0.9558 0.9533 0.9538
78 0.9461 0.9483 0.9461 0.9462 0.9483 0.9461 0.9462
79 0.9533 0.9541 0.9533 0.9534 0.9541 0.9533 0.9534
80 0.7197 0.8625 0.7197 0.7308 0.8625 0.7197 0.7308
81 0.2441 0.8295 0.2441 0.2817 0.8295 0.2441 0.2817
82 0.9046 0.9192 0.9046 0.9059 0.9192 0.9046 0.9059
83 0.7553 0.8849 0.7553 0.7791 0.8849 0.7553 0.7791
84 0.9441 0.9473 0.9441 0.9446 0.9473 0.9441 0.9446
85 0.3434 0.7953 0.3434 0.3285 0.7953 0.3434 0.3285
86 0.7829 0.8644 0.7829 0.7828 0.8644 0.7829 0.7828
87 0.9053 0.9283 0.9053 0.9104 0.9283 0.9053 0.9104
88 0.9322 0.9412 0.9322 0.9339 0.9412 0.9322 0.9339
89 0.9507 0.9545 0.9507 0.9513 0.9545 0.9507 0.9513
90 0.2862 0.7748 0.2862 0.2976 0.7748 0.2862 0.2976
91 0.9066 0.9157 0.9066 0.9079 0.9157 0.9066 0.9079
92 0.0447 0.0047 0.0447 0.0086 0.0047 0.0447 0.0086
93 0.0704 0.1012 0.0704 0.0343 0.1012 0.0704 0.0343
94 0.8645 0.8949 0.8645 0.8680 0.8949 0.8645 0.8680
95 0.9507 0.9522 0.9507 0.9509 0.9522 0.9507 0.9509
96 0.8230 0.8767 0.8230 0.8246 0.8767 0.8230 0.8246
97 0.3349 0.5789 0.3349 0.3080 0.5789 0.3349 0.3080
98 0.8901 0.9063 0.8901 0.8922 0.9063 0.8901 0.8922
99 0.8671 0.9035 0.8671 0.8733 0.9035 0.8671 0.8733
100 0.3368 0.8184 0.3368 0.3841 0.8184 0.3368 0.3841
101 0.7829 0.9076 0.7829 0.8099 0.9076 0.7829 0.8099
102 0.8855 0.9233 0.8855 0.8941 0.9233 0.8855 0.8941
103 0.9428 0.9441 0.9428 0.9427 0.9441 0.9428 0.9427
104 0.9368 0.9399 0.9368 0.9371 0.9399 0.9368 0.9371
105 0.2888 0.4046 0.2888 0.1979 0.4046 0.2888 0.1979
106 0.9401 0.9411 0.9401 0.9396 0.9411 0.9401 0.9396
107 0.9026 0.9198 0.9026 0.9048 0.9198 0.9026 0.9048
108 0.8112 0.8734 0.8112 0.8179 0.8734 0.8112 0.8179
109 0.8908 0.9087 0.8908 0.8923 0.9087 0.8908 0.8923
110 0.2276 0.3946 0.2276 0.1716 0.3946 0.2276 0.1716
111 0.9474 0.9487 0.9474 0.9474 0.9487 0.9474 0.9474
112 0.4954 0.9219 0.4954 0.5724 0.9219 0.4954 0.5724
113 0.2625 0.4674 0.2625 0.1735 0.4674 0.2625 0.1735
114 0.4263 0.7974 0.4263 0.4043 0.7974 0.4263 0.4043
115 0.9408 0.9448 0.9408 0.9412 0.9448 0.9408 0.9412
116 0.8342 0.8909 0.8342 0.8426 0.8909 0.8342 0.8426
117 0.9296 0.9339 0.9296 0.9301 0.9339 0.9296 0.9301
118 0.9388 0.9410 0.9388 0.9390 0.9410 0.9388 0.9390
119 0.7783 0.8611 0.7783 0.7823 0.8611 0.7783 0.7823
120 0.9388 0.9421 0.9388 0.9390 0.9421 0.9388 0.9390
121 0.8671 0.8922 0.8671 0.8686 0.8922 0.8671 0.8686
122 0.8671 0.9191 0.8671 0.8768 0.9191 0.8671 0.8768
123 0.8783 0.9099 0.8783 0.8802 0.9099 0.8783 0.8802
124 0.9316 0.9385 0.9316 0.9329 0.9385 0.9316 0.9329
125 0.9401 0.9433 0.9401 0.9405 0.9433 0.9401 0.9405
126 0.9217 0.9278 0.9217 0.9225 0.9278 0.9217 0.9225
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5
reports/CertifiedUnlearning/params.txt Normal file
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Model, scale, l2_reg, s1, s2 BS gamma std
ResNet18 14500 0.00002 2 300 8
ResNet34 20200 0.000002 2 350 16 0.01 0.00001
GoogleNet

491
reports/LinearFiltration/RESNET34/forget.csv Normal file
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0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
4 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
5 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
6 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
7 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
8 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
9 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
10 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
11 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
12 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
13 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
15 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
16 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
17 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
18 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
20 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
21 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
22 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
23 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
24 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
25 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
26 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
27 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
28 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
29 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
30 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
31 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
32 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
33 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
34 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
35 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
36 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
37 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
38 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
39 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
40 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
41 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
42 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
43 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
44 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
45 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
46 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
47 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
48 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
49 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
50 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
51 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
52 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
53 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
54 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
55 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
56 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
57 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
58 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
59 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
60 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
61 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
62 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
63 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
64 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
65 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
66 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
67 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
68 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
69 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
70 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
71 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
72 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
73 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
74 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
75 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
76 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
77 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
78 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
79 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
80 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
81 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
82 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
83 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
84 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
85 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
86 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
87 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
88 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
89 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
90 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
91 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
92 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
93 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
94 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
95 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
96 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
97 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
98 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
99 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
100 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
101 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
102 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
103 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
104 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
105 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
106 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
107 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
108 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
109 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
110 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
111 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
112 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
113 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
114 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
115 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
116 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
117 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
118 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
119 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
120 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
121 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
122 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
123 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
124 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
125 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
126 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
127 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
128 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
129 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
130 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
131 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
132 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
133 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
134 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
135 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
136 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
137 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
138 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
139 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
140 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
141 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
142 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
143 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
144 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
145 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
146 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
147 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
148 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
149 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
150 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
151 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
152 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
153 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
154 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
155 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
156 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
157 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
158 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
159 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
160 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
161 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
162 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
163 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
164 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
165 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
166 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
167 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
168 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
169 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
170 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
171 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
172 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
173 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
174 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
175 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
176 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
177 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
178 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
179 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
180 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
181 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
182 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
183 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
184 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
185 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
186 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
187 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
188 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
189 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
190 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
191 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
192 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
193 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
194 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
195 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
196 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
197 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
198 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
199 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
200 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
201 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
202 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
203 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
204 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
205 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
206 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
207 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
208 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
209 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
210 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
211 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
212 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
213 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
214 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
215 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
216 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
217 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
218 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
219 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
220 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
221 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
222 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
223 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
224 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
225 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
226 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
227 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
228 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
229 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
230 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
231 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
232 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
233 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
234 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
235 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
236 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
237 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
238 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
239 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
240 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
241 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
242 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
243 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
244 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
245 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
246 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
247 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
248 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
249 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
250 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
251 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
252 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
253 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
254 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
255 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
256 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
257 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
258 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
259 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
260 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
261 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
262 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
263 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
264 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
265 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
266 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
267 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
268 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
269 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
270 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
271 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
272 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
273 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
274 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
275 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
276 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
277 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
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0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
4 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
5 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
6 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
7 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
8 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
9 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
10 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
11 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
12 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
13 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
15 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
16 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
17 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
18 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
20 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
21 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
22 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
23 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
24 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
25 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
26 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
27 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
28 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
29 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
30 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
31 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
32 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
33 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
34 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
35 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
36 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
37 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
38 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
39 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
40 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
41 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
42 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
43 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
44 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
45 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
46 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
47 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
48 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
49 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
50 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
51 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
52 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
53 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
54 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
55 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
56 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
57 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
58 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
59 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
60 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
61 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
62 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
63 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
64 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
65 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
66 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
67 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
68 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
69 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
70 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
71 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
72 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
73 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
74 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
75 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
76 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
77 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
78 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
79 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
80 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
81 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
82 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
83 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
84 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
85 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
86 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
87 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
88 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
89 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
90 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
91 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
92 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
93 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
94 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
95 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
96 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
97 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
98 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
99 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
100 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
101 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
102 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
103 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
104 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
105 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
106 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
107 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
108 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
109 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
110 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
111 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
112 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
113 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
114 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
115 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
116 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
117 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
118 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
119 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
120 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
121 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
122 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
123 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
124 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
125 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
126 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
127 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
128 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
129 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
130 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
131 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
132 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
133 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
134 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
135 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
136 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
137 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
138 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
139 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
140 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
141 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
142 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
143 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
144 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
145 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
146 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
147 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
148 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
149 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
150 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
151 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
152 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
153 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
154 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
155 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
156 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
157 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
158 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
159 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
160 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
161 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
162 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
163 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
164 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
165 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
166 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
167 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
168 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
169 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
170 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
171 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
172 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
173 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
174 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
175 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
176 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
177 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
178 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
179 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
180 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
181 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
182 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
183 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
184 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
185 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
186 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
187 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
188 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
189 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
190 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
191 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
192 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
193 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
194 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
195 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
196 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
197 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
198 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
199 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
200 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
201 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
202 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
203 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
204 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
205 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
206 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
207 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
208 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
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reports/LinearFiltration/RESNET34/retain.csv Normal file
View File

@@ -0,0 +1,491 @@
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0.9546,0.9560,0.9546,0.9548,0.9560,0.9546,0.9548
0.9579,0.9588,0.9579,0.9580,0.9588,0.9579,0.9580
0.9539,0.9554,0.9539,0.9542,0.9554,0.9539,0.9542
0.9539,0.9552,0.9539,0.9541,0.9552,0.9539,0.9541
0.9553,0.9565,0.9553,0.9554,0.9565,0.9553,0.9554
0.9559,0.9569,0.9559,0.9560,0.9569,0.9559,0.9560
0.9553,0.9566,0.9553,0.9555,0.9566,0.9553,0.9555
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
2 0.9526 0.9538 0.9526 0.9526 0.9538 0.9526 0.9526
3 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
4 0.9533 0.9546 0.9533 0.9533 0.9546 0.9533 0.9533
5 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
6 0.9533 0.9542 0.9533 0.9532 0.9542 0.9533 0.9532
7 0.9572 0.9581 0.9572 0.9572 0.9581 0.9572 0.9572
8 0.9559 0.9569 0.9559 0.9559 0.9569 0.9559 0.9559
9 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
10 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
11 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
12 0.9546 0.9558 0.9546 0.9546 0.9558 0.9546 0.9546
13 0.9539 0.9551 0.9539 0.9540 0.9551 0.9539 0.9540
14 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
15 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
16 0.9559 0.9571 0.9559 0.9559 0.9571 0.9559 0.9559
17 0.9520 0.9532 0.9520 0.9520 0.9532 0.9520 0.9520
18 0.9546 0.9556 0.9546 0.9547 0.9556 0.9546 0.9547
19 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
20 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
21 0.9533 0.9542 0.9533 0.9533 0.9542 0.9533 0.9533
22 0.9526 0.9538 0.9526 0.9526 0.9538 0.9526 0.9526
23 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
24 0.9533 0.9546 0.9533 0.9533 0.9546 0.9533 0.9533
25 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
26 0.9533 0.9542 0.9533 0.9532 0.9542 0.9533 0.9532
27 0.9572 0.9581 0.9572 0.9572 0.9581 0.9572 0.9572
28 0.9559 0.9569 0.9559 0.9559 0.9569 0.9559 0.9559
29 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
30 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
31 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
32 0.9546 0.9558 0.9546 0.9546 0.9558 0.9546 0.9546
33 0.9539 0.9551 0.9539 0.9540 0.9551 0.9539 0.9540
34 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
35 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
36 0.9559 0.9571 0.9559 0.9559 0.9571 0.9559 0.9559
37 0.9520 0.9532 0.9520 0.9520 0.9532 0.9520 0.9520
38 0.9546 0.9556 0.9546 0.9547 0.9556 0.9546 0.9547
39 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
40 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
41 0.9533 0.9542 0.9533 0.9533 0.9542 0.9533 0.9533
42 0.9526 0.9538 0.9526 0.9526 0.9538 0.9526 0.9526
43 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
44 0.9533 0.9546 0.9533 0.9533 0.9546 0.9533 0.9533
45 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
46 0.9533 0.9542 0.9533 0.9532 0.9542 0.9533 0.9532
47 0.9572 0.9581 0.9572 0.9572 0.9581 0.9572 0.9572
48 0.9559 0.9569 0.9559 0.9559 0.9569 0.9559 0.9559
49 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
50 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
51 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
52 0.9546 0.9558 0.9546 0.9546 0.9558 0.9546 0.9546
53 0.9539 0.9551 0.9539 0.9540 0.9551 0.9539 0.9540
54 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
55 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
56 0.9559 0.9571 0.9559 0.9559 0.9571 0.9559 0.9559
57 0.9520 0.9532 0.9520 0.9520 0.9532 0.9520 0.9520
58 0.9546 0.9556 0.9546 0.9547 0.9556 0.9546 0.9547
59 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
60 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
61 0.9533 0.9542 0.9533 0.9533 0.9542 0.9533 0.9533
62 0.9526 0.9538 0.9526 0.9526 0.9538 0.9526 0.9526
63 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
64 0.9533 0.9546 0.9533 0.9533 0.9546 0.9533 0.9533
65 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
66 0.9533 0.9542 0.9533 0.9532 0.9542 0.9533 0.9532
67 0.9572 0.9581 0.9572 0.9572 0.9581 0.9572 0.9572
68 0.9559 0.9569 0.9559 0.9559 0.9569 0.9559 0.9559
69 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
70 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
71 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
72 0.9546 0.9558 0.9546 0.9546 0.9558 0.9546 0.9546
73 0.9539 0.9551 0.9539 0.9540 0.9551 0.9539 0.9540
74 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
75 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
76 0.9559 0.9571 0.9559 0.9559 0.9571 0.9559 0.9559
77 0.9520 0.9532 0.9520 0.9520 0.9532 0.9520 0.9520
78 0.9546 0.9556 0.9546 0.9547 0.9556 0.9546 0.9547
79 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
80 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
81 0.9533 0.9542 0.9533 0.9533 0.9542 0.9533 0.9533
82 0.9526 0.9538 0.9526 0.9526 0.9538 0.9526 0.9526
83 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
84 0.9533 0.9546 0.9533 0.9533 0.9546 0.9533 0.9533
85 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
86 0.9533 0.9542 0.9533 0.9532 0.9542 0.9533 0.9532
87 0.9572 0.9581 0.9572 0.9572 0.9581 0.9572 0.9572
88 0.9559 0.9569 0.9559 0.9559 0.9569 0.9559 0.9559
89 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
90 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
91 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
92 0.9546 0.9558 0.9546 0.9546 0.9558 0.9546 0.9546
93 0.9539 0.9551 0.9539 0.9540 0.9551 0.9539 0.9540
94 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
95 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
96 0.9559 0.9571 0.9559 0.9559 0.9571 0.9559 0.9559
97 0.9520 0.9532 0.9520 0.9520 0.9532 0.9520 0.9520
98 0.9546 0.9556 0.9546 0.9547 0.9556 0.9546 0.9547
99 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
100 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
101 0.9533 0.9542 0.9533 0.9533 0.9542 0.9533 0.9533
102 0.9526 0.9538 0.9526 0.9526 0.9538 0.9526 0.9526
103 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
104 0.9533 0.9546 0.9533 0.9533 0.9546 0.9533 0.9533
105 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
106 0.9533 0.9542 0.9533 0.9532 0.9542 0.9533 0.9532
107 0.9572 0.9581 0.9572 0.9572 0.9581 0.9572 0.9572
108 0.9559 0.9569 0.9559 0.9559 0.9569 0.9559 0.9559
109 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
110 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
111 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
112 0.9546 0.9558 0.9546 0.9546 0.9558 0.9546 0.9546
113 0.9539 0.9551 0.9539 0.9540 0.9551 0.9539 0.9540
114 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
115 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
116 0.9559 0.9571 0.9559 0.9559 0.9571 0.9559 0.9559
117 0.9520 0.9532 0.9520 0.9520 0.9532 0.9520 0.9520
118 0.9546 0.9556 0.9546 0.9547 0.9556 0.9546 0.9547
119 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
120 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
121 0.9533 0.9542 0.9533 0.9533 0.9542 0.9533 0.9533
122 0.9526 0.9538 0.9526 0.9526 0.9538 0.9526 0.9526
123 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
124 0.9533 0.9546 0.9533 0.9533 0.9546 0.9533 0.9533
125 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
126 0.9533 0.9542 0.9533 0.9532 0.9542 0.9533 0.9532
127 0.9572 0.9581 0.9572 0.9572 0.9581 0.9572 0.9572
128 0.9559 0.9569 0.9559 0.9559 0.9569 0.9559 0.9559
129 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
130 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
131 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
132 0.9546 0.9558 0.9546 0.9546 0.9558 0.9546 0.9546
133 0.9539 0.9551 0.9539 0.9540 0.9551 0.9539 0.9540
134 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
135 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
136 0.9559 0.9571 0.9559 0.9559 0.9571 0.9559 0.9559
137 0.9520 0.9532 0.9520 0.9520 0.9532 0.9520 0.9520
138 0.9546 0.9556 0.9546 0.9547 0.9556 0.9546 0.9547
139 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
140 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
141 0.9533 0.9542 0.9533 0.9533 0.9542 0.9533 0.9533
142 0.9526 0.9538 0.9526 0.9526 0.9538 0.9526 0.9526
143 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
144 0.9533 0.9546 0.9533 0.9533 0.9546 0.9533 0.9533
145 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
146 0.9533 0.9542 0.9533 0.9532 0.9542 0.9533 0.9532
147 0.9572 0.9581 0.9572 0.9572 0.9581 0.9572 0.9572
148 0.9559 0.9569 0.9559 0.9559 0.9569 0.9559 0.9559
149 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
150 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
151 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
152 0.9546 0.9558 0.9546 0.9546 0.9558 0.9546 0.9546
153 0.9539 0.9551 0.9539 0.9540 0.9551 0.9539 0.9540
154 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
155 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
156 0.9559 0.9571 0.9559 0.9559 0.9571 0.9559 0.9559
157 0.9520 0.9532 0.9520 0.9520 0.9532 0.9520 0.9520
158 0.9546 0.9556 0.9546 0.9547 0.9556 0.9546 0.9547
159 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
160 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
161 0.9533 0.9542 0.9533 0.9533 0.9542 0.9533 0.9533
162 0.9526 0.9538 0.9526 0.9526 0.9538 0.9526 0.9526
163 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
164 0.9533 0.9546 0.9533 0.9533 0.9546 0.9533 0.9533
165 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
166 0.9533 0.9542 0.9533 0.9532 0.9542 0.9533 0.9532
167 0.9572 0.9581 0.9572 0.9572 0.9581 0.9572 0.9572
168 0.9559 0.9569 0.9559 0.9559 0.9569 0.9559 0.9559
169 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
170 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
171 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
172 0.9546 0.9558 0.9546 0.9546 0.9558 0.9546 0.9546
173 0.9539 0.9551 0.9539 0.9540 0.9551 0.9539 0.9540
174 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
175 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
176 0.9559 0.9571 0.9559 0.9559 0.9571 0.9559 0.9559
177 0.9520 0.9532 0.9520 0.9520 0.9532 0.9520 0.9520
178 0.9546 0.9556 0.9546 0.9547 0.9556 0.9546 0.9547
179 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
180 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
181 0.9533 0.9542 0.9533 0.9533 0.9542 0.9533 0.9533
182 0.9526 0.9538 0.9526 0.9526 0.9538 0.9526 0.9526
183 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
184 0.9533 0.9546 0.9533 0.9533 0.9546 0.9533 0.9533
185 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
186 0.9533 0.9542 0.9533 0.9532 0.9542 0.9533 0.9532
187 0.9572 0.9581 0.9572 0.9572 0.9581 0.9572 0.9572
188 0.9559 0.9569 0.9559 0.9559 0.9569 0.9559 0.9559
189 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
190 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
191 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
192 0.9546 0.9558 0.9546 0.9546 0.9558 0.9546 0.9546
193 0.9539 0.9551 0.9539 0.9540 0.9551 0.9539 0.9540
194 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
195 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
196 0.9559 0.9571 0.9559 0.9559 0.9571 0.9559 0.9559
197 0.9520 0.9532 0.9520 0.9520 0.9532 0.9520 0.9520
198 0.9546 0.9556 0.9546 0.9547 0.9556 0.9546 0.9547
199 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
200 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
201 0.9533 0.9542 0.9533 0.9533 0.9542 0.9533 0.9533
202 0.9526 0.9538 0.9526 0.9526 0.9538 0.9526 0.9526
203 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
204 0.9533 0.9546 0.9533 0.9533 0.9546 0.9533 0.9533
205 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
206 0.9533 0.9542 0.9533 0.9532 0.9542 0.9533 0.9532
207 0.9572 0.9581 0.9572 0.9572 0.9581 0.9572 0.9572
208 0.9559 0.9569 0.9559 0.9559 0.9569 0.9559 0.9559
209 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
210 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
211 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
212 0.9546 0.9558 0.9546 0.9546 0.9558 0.9546 0.9546
213 0.9539 0.9551 0.9539 0.9540 0.9551 0.9539 0.9540
214 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
215 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
216 0.9559 0.9571 0.9559 0.9559 0.9571 0.9559 0.9559
217 0.9520 0.9532 0.9520 0.9520 0.9532 0.9520 0.9520
218 0.9546 0.9556 0.9546 0.9547 0.9556 0.9546 0.9547
219 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
220 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
221 0.9533 0.9542 0.9533 0.9533 0.9542 0.9533 0.9533
222 0.9526 0.9538 0.9526 0.9526 0.9538 0.9526 0.9526
223 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
224 0.9533 0.9546 0.9533 0.9533 0.9546 0.9533 0.9533
225 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
226 0.9533 0.9542 0.9533 0.9532 0.9542 0.9533 0.9532
227 0.9572 0.9581 0.9572 0.9572 0.9581 0.9572 0.9572
228 0.9559 0.9569 0.9559 0.9559 0.9569 0.9559 0.9559
229 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
230 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
231 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
232 0.9546 0.9558 0.9546 0.9546 0.9558 0.9546 0.9546
233 0.9539 0.9551 0.9539 0.9540 0.9551 0.9539 0.9540
234 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
235 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
236 0.9559 0.9571 0.9559 0.9559 0.9571 0.9559 0.9559
237 0.9520 0.9532 0.9520 0.9520 0.9532 0.9520 0.9520
238 0.9546 0.9556 0.9546 0.9547 0.9556 0.9546 0.9547
239 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
240 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
241 0.9533 0.9542 0.9533 0.9533 0.9542 0.9533 0.9533
242 0.9526 0.9538 0.9526 0.9526 0.9538 0.9526 0.9526
243 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
244 0.9533 0.9546 0.9533 0.9533 0.9546 0.9533 0.9533
245 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
246 0.9533 0.9542 0.9533 0.9532 0.9542 0.9533 0.9532
247 0.9572 0.9581 0.9572 0.9572 0.9581 0.9572 0.9572
248 0.9559 0.9569 0.9559 0.9559 0.9569 0.9559 0.9559
249 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
250 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
251 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
252 0.9546 0.9558 0.9546 0.9546 0.9558 0.9546 0.9546
253 0.9539 0.9551 0.9539 0.9540 0.9551 0.9539 0.9540
254 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
255 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
256 0.9559 0.9571 0.9559 0.9559 0.9571 0.9559 0.9559
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491
reports/LinearFiltration/ResNet/time_metrics.txt Normal file
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@@ -0,0 +1,491 @@
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5
reports/WeightFiltration/Params.txt Normal file
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@@ -0,0 +1,5 @@
arch epochs lr gamma lambda
ResNet18 6 150 0.001 25
ResNet34 6 250 0.001 30
GoogleNet

492
reports/WeightFiltration/RESNET34/forget.csv Normal file
View File

@@ -0,0 +1,492 @@
accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recall,weighted_f1
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0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
2 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
4 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
5 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
6 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
7 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
8 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
9 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
10 0.0375 1.0000 0.0375 0.0723 1.0000 0.0375 0.0723
11 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
12 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
13 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
15 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
16 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
17 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
18 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
20 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
21 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
22 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
23 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
24 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
25 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
26 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
27 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
28 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
29 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
30 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
31 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
32 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
33 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
34 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
35 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
36 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
37 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
38 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
39 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
40 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
41 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
42 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
43 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
44 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
45 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
46 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
47 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
48 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
49 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
50 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
51 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
52 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
53 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
54 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
55 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
56 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
57 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
58 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
59 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
60 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
61 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
62 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
63 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
64 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
65 0.0500 1.0000 0.0500 0.0952 1.0000 0.0500 0.0952
66 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
67 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
68 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
69 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
70 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
71 0.0625 1.0000 0.0625 0.1176 1.0000 0.0625 0.1176
72 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
73 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
74 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
75 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
76 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
77 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
78 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
79 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
80 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
81 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
82 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
83 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
84 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
85 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
86 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
87 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
88 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
89 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
90 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
91 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
92 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
93 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
94 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
95 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
96 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
97 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
98 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
99 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
100 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
101 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
102 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
103 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
104 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
105 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
106 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
107 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
108 0.1375 1.0000 0.1375 0.2418 1.0000 0.1375 0.2418
109 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
110 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
111 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
112 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
113 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
114 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
115 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
116 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
117 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
118 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
119 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
120 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
121 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
122 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
123 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
124 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
125 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
126 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
127 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
128 0.2000 1.0000 0.2000 0.3333 1.0000 0.2000 0.3333
129 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
130 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
131 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
132 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
133 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
134 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
135 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
136 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
137 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
138 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
139 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
140 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
141 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
142 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
143 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
144 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
145 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
146 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
147 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
148 0.1875 1.0000 0.1875 0.3158 1.0000 0.1875 0.3158
149 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
150 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
151 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
152 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
153 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
154 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
155 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
156 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
157 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
158 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
159 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
160 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
161 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
162 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
163 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
164 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
165 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
166 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
167 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
168 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
169 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
170 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
171 0.0750 1.0000 0.0750 0.1395 1.0000 0.0750 0.1395
172 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
173 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
174 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
175 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
176 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
177 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
178 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
179 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
180 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
181 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
182 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
183 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
184 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
185 0.0250 1.0000 0.0250 0.0488 1.0000 0.0250 0.0488
186 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
187 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
188 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
189 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
190 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
191 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
192 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
193 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
194 0.1000 1.0000 0.1000 0.1818 1.0000 0.1000 0.1818
195 0.0250 1.0000 0.0250 0.0488 1.0000 0.0250 0.0488
196 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
197 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
198 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
199 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
200 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
201 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
202 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
203 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
204 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
205 0.0625 1.0000 0.0625 0.1176 1.0000 0.0625 0.1176
206 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
207 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
208 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
209 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
210 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
211 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
212 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
213 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
214 0.0375 1.0000 0.0375 0.0723 1.0000 0.0375 0.0723
215 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
216 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
217 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
218 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
219 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
220 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
221 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
222 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
223 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
224 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
225 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
226 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
227 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
228 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
229 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
230 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
231 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
232 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
233 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
234 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
235 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
236 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
237 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
238 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
239 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
240 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
241 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
242 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
243 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
244 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
245 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
246 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
247 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
248 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
249 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
250 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
251 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
252 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
253 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
254 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
255 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
256 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
257 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
258 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
259 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
260 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
261 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
262 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
263 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
264 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
265 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
266 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
267 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
268 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
269 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
270 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
271 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
272 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
273 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
274 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
275 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
276 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
277 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
278 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
279 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
280 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
281 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
282 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
283 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
284 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
285 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
286 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
287 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
288 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
289 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
290 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
291 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
292 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
293 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
294 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
295 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
296 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
297 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
298 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
299 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
300 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
301 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
302 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
303 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
304 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
305 0.0375 1.0000 0.0375 0.0723 1.0000 0.0375 0.0723
306 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
307 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
308 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
309 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
310 0.0375 1.0000 0.0375 0.0723 1.0000 0.0375 0.0723
311 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
312 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
313 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
314 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
315 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
316 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
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reports/WeightFiltration/RESNET34/forget_train.csv Normal file
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0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0063,1.0000,0.0063,0.0124,1.0000,0.0063,0.0124
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0063,1.0000,0.0063,0.0124,1.0000,0.0063,0.0124
0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0281,1.0000,0.0281,0.0547,1.0000,0.0281,0.0547
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0344,1.0000,0.0344,0.0665,1.0000,0.0344,0.0665
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
0.0156,1.0000,0.0156,0.0308,1.0000,0.0156,0.0308
0.0219,1.0000,0.0219,0.0428,1.0000,0.0219,0.0428
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0187,1.0000,0.0187,0.0368,1.0000,0.0187,0.0368
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0500,1.0000,0.0500,0.0952,1.0000,0.0500,0.0952
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
0.0719,1.0000,0.0719,0.1341,1.0000,0.0719,0.1341
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0250,1.0000,0.0250,0.0488,1.0000,0.0250,0.0488
0.1000,1.0000,0.1000,0.1818,1.0000,0.1000,0.1818
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0125,1.0000,0.0125,0.0247,1.0000,0.0125,0.0247
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
2 0.0656 1.0000 0.0656 0.1232 1.0000 0.0656 0.1232
3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
4 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
5 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
6 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
7 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
8 0.0094 1.0000 0.0094 0.0186 1.0000 0.0094 0.0186
9 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
10 0.0156 1.0000 0.0156 0.0308 1.0000 0.0156 0.0308
11 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
12 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
13 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
15 0.0031 1.0000 0.0031 0.0062 1.0000 0.0031 0.0062
16 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
17 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
18 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
19 0.0281 1.0000 0.0281 0.0547 1.0000 0.0281 0.0547
20 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
21 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
22 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
23 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
24 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
25 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
26 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
27 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
28 0.0063 1.0000 0.0063 0.0124 1.0000 0.0063 0.0124
29 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
30 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
31 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
32 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
33 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
34 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
35 0.0187 1.0000 0.0187 0.0368 1.0000 0.0187 0.0368
36 0.0031 1.0000 0.0031 0.0062 1.0000 0.0031 0.0062
37 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
38 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
39 0.0031 1.0000 0.0031 0.0062 1.0000 0.0031 0.0062
40 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
41 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
42 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
43 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
44 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
45 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
46 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
47 0.0031 1.0000 0.0031 0.0062 1.0000 0.0031 0.0062
48 0.0094 1.0000 0.0094 0.0186 1.0000 0.0094 0.0186
49 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
50 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
51 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
52 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
53 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
54 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
55 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
56 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
57 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
58 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
59 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
60 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
61 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
62 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
63 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
64 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
65 0.0469 1.0000 0.0469 0.0896 1.0000 0.0469 0.0896
66 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
67 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
68 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
69 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
70 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
71 0.1187 1.0000 0.1187 0.2123 1.0000 0.1187 0.2123
72 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
73 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
74 0.0031 1.0000 0.0031 0.0062 1.0000 0.0031 0.0062
75 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
76 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
77 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
78 0.0063 1.0000 0.0063 0.0124 1.0000 0.0063 0.0124
79 0.0063 1.0000 0.0063 0.0124 1.0000 0.0063 0.0124
80 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
81 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
82 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
83 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
84 0.0156 1.0000 0.0156 0.0308 1.0000 0.0156 0.0308
85 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
86 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
87 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
88 0.0031 1.0000 0.0031 0.0062 1.0000 0.0031 0.0062
89 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
90 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
91 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
92 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
93 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
94 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
95 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
96 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
97 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
98 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
99 0.0094 1.0000 0.0094 0.0186 1.0000 0.0094 0.0186
100 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
101 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
102 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
103 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
104 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
105 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
106 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
107 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
108 0.0844 1.0000 0.0844 0.1556 1.0000 0.0844 0.1556
109 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
110 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
111 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
112 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
113 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
114 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
115 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
116 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
117 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
118 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
119 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
120 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
121 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
122 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
123 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
124 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
125 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
126 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
127 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
128 0.1437 1.0000 0.1437 0.2514 1.0000 0.1437 0.2514
129 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
130 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
131 0.0031 1.0000 0.0031 0.0062 1.0000 0.0031 0.0062
132 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
133 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
134 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
135 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
136 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
137 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
138 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
139 0.0063 1.0000 0.0063 0.0124 1.0000 0.0063 0.0124
140 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
141 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
142 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
143 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
144 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
145 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
146 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
147 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
148 0.1187 1.0000 0.1187 0.2123 1.0000 0.1187 0.2123
149 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
150 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
151 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
152 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
153 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
154 0.0375 1.0000 0.0375 0.0723 1.0000 0.0375 0.0723
155 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
156 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
157 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
158 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
159 0.0031 1.0000 0.0031 0.0062 1.0000 0.0031 0.0062
160 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
161 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
162 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
163 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
164 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
165 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
166 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
167 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
168 0.0094 1.0000 0.0094 0.0186 1.0000 0.0094 0.0186
169 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
170 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
171 0.0719 1.0000 0.0719 0.1341 1.0000 0.0719 0.1341
172 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
173 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
174 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
175 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
176 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
177 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
178 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
179 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
180 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
181 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
182 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
183 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
184 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
185 0.0344 1.0000 0.0344 0.0665 1.0000 0.0344 0.0665
186 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
187 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
188 0.0156 1.0000 0.0156 0.0308 1.0000 0.0156 0.0308
189 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
190 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
191 0.0563 1.0000 0.0563 0.1065 1.0000 0.0563 0.1065
192 0.0031 1.0000 0.0031 0.0062 1.0000 0.0031 0.0062
193 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
194 0.1625 1.0000 0.1625 0.2796 1.0000 0.1625 0.2796
195 0.0500 1.0000 0.0500 0.0952 1.0000 0.0500 0.0952
196 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
197 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
198 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
199 0.0094 1.0000 0.0094 0.0186 1.0000 0.0094 0.0186
200 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
201 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
202 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
203 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
204 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
205 0.0531 1.0000 0.0531 0.1009 1.0000 0.0531 0.1009
206 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
207 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
208 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
209 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
210 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
211 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
212 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
213 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
214 0.0969 1.0000 0.0969 0.1766 1.0000 0.0969 0.1766
215 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
216 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
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reports/WeightFiltration/RESNET34/retain.csv Normal file
View File

@@ -0,0 +1,492 @@
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0.9586,0.9595,0.9586,0.9587,0.9595,0.9586,0.9587
0.9546,0.9566,0.9546,0.9549,0.9566,0.9546,0.9549
0.9553,0.9566,0.9553,0.9555,0.9566,0.9553,0.9555
0.9546,0.9561,0.9546,0.9548,0.9561,0.9546,0.9548
0.9572,0.9580,0.9572,0.9573,0.9580,0.9572,0.9573
0.9553,0.9567,0.9553,0.9554,0.9567,0.9553,0.9554
0.9493,0.9508,0.9493,0.9495,0.9508,0.9493,0.9495
0.9559,0.9572,0.9559,0.9560,0.9572,0.9559,0.9560
0.9546,0.9556,0.9546,0.9547,0.9556,0.9546,0.9547
0.9592,0.9606,0.9592,0.9594,0.9606,0.9592,0.9594
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
2 0.9520 0.9530 0.9520 0.9520 0.9530 0.9520 0.9520
3 0.9559 0.9573 0.9559 0.9560 0.9573 0.9559 0.9560
4 0.9526 0.9540 0.9526 0.9526 0.9540 0.9526 0.9526
5 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
6 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
7 0.9566 0.9578 0.9566 0.9566 0.9578 0.9566 0.9566
8 0.9539 0.9551 0.9539 0.9539 0.9551 0.9539 0.9539
9 0.9520 0.9531 0.9520 0.9520 0.9531 0.9520 0.9520
10 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
11 0.9533 0.9544 0.9533 0.9534 0.9544 0.9533 0.9534
12 0.9546 0.9554 0.9546 0.9546 0.9554 0.9546 0.9546
13 0.9539 0.9553 0.9539 0.9540 0.9553 0.9539 0.9540
14 0.9513 0.9524 0.9513 0.9513 0.9524 0.9513 0.9513
15 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
16 0.9572 0.9584 0.9572 0.9573 0.9584 0.9572 0.9573
17 0.9533 0.9547 0.9533 0.9534 0.9547 0.9533 0.9534
18 0.9559 0.9569 0.9559 0.9560 0.9569 0.9559 0.9560
19 0.9539 0.9550 0.9539 0.9539 0.9550 0.9539 0.9539
20 0.9566 0.9574 0.9566 0.9565 0.9574 0.9566 0.9565
21 0.9533 0.9543 0.9533 0.9533 0.9543 0.9533 0.9533
22 0.9513 0.9524 0.9513 0.9513 0.9524 0.9513 0.9513
23 0.9559 0.9572 0.9559 0.9560 0.9572 0.9559 0.9560
24 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
25 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
26 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
27 0.9579 0.9590 0.9579 0.9579 0.9590 0.9579 0.9579
28 0.9539 0.9551 0.9539 0.9539 0.9551 0.9539 0.9539
29 0.9526 0.9538 0.9526 0.9527 0.9538 0.9526 0.9527
30 0.9526 0.9536 0.9526 0.9526 0.9536 0.9526 0.9526
31 0.9533 0.9544 0.9533 0.9534 0.9544 0.9533 0.9534
32 0.9546 0.9554 0.9546 0.9546 0.9554 0.9546 0.9546
33 0.9533 0.9546 0.9533 0.9534 0.9546 0.9533 0.9534
34 0.9513 0.9523 0.9513 0.9513 0.9523 0.9513 0.9513
35 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
36 0.9572 0.9584 0.9572 0.9573 0.9584 0.9572 0.9573
37 0.9533 0.9547 0.9533 0.9534 0.9547 0.9533 0.9534
38 0.9553 0.9561 0.9553 0.9553 0.9561 0.9553 0.9553
39 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
40 0.9559 0.9568 0.9559 0.9559 0.9568 0.9559 0.9559
41 0.9533 0.9543 0.9533 0.9533 0.9543 0.9533 0.9533
42 0.9526 0.9537 0.9526 0.9526 0.9537 0.9526 0.9526
43 0.9559 0.9573 0.9559 0.9560 0.9573 0.9559 0.9560
44 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
45 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
46 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
47 0.9559 0.9571 0.9559 0.9559 0.9571 0.9559 0.9559
48 0.9539 0.9551 0.9539 0.9539 0.9551 0.9539 0.9539
49 0.9526 0.9537 0.9526 0.9526 0.9537 0.9526 0.9526
50 0.9526 0.9536 0.9526 0.9526 0.9536 0.9526 0.9526
51 0.9533 0.9544 0.9533 0.9534 0.9544 0.9533 0.9534
52 0.9546 0.9554 0.9546 0.9546 0.9554 0.9546 0.9546
53 0.9533 0.9546 0.9533 0.9534 0.9546 0.9533 0.9534
54 0.9520 0.9530 0.9520 0.9520 0.9530 0.9520 0.9520
55 0.9539 0.9548 0.9539 0.9539 0.9548 0.9539 0.9539
56 0.9579 0.9590 0.9579 0.9579 0.9590 0.9579 0.9579
57 0.9533 0.9547 0.9533 0.9534 0.9547 0.9533 0.9534
58 0.9559 0.9568 0.9559 0.9560 0.9568 0.9559 0.9560
59 0.9539 0.9550 0.9539 0.9539 0.9550 0.9539 0.9539
60 0.9572 0.9580 0.9572 0.9572 0.9580 0.9572 0.9572
61 0.9533 0.9543 0.9533 0.9533 0.9543 0.9533 0.9533
62 0.9513 0.9524 0.9513 0.9513 0.9524 0.9513 0.9513
63 0.9559 0.9572 0.9559 0.9560 0.9572 0.9559 0.9560
64 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
65 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
66 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
67 0.9579 0.9590 0.9579 0.9579 0.9590 0.9579 0.9579
68 0.9539 0.9551 0.9539 0.9539 0.9551 0.9539 0.9539
69 0.9520 0.9531 0.9520 0.9520 0.9531 0.9520 0.9520
70 0.9526 0.9536 0.9526 0.9526 0.9536 0.9526 0.9526
71 0.9546 0.9555 0.9546 0.9546 0.9555 0.9546 0.9546
72 0.9546 0.9554 0.9546 0.9546 0.9554 0.9546 0.9546
73 0.9533 0.9546 0.9533 0.9534 0.9546 0.9533 0.9534
74 0.9520 0.9530 0.9520 0.9520 0.9530 0.9520 0.9520
75 0.9546 0.9554 0.9546 0.9546 0.9554 0.9546 0.9546
76 0.9566 0.9579 0.9566 0.9566 0.9579 0.9566 0.9566
77 0.9533 0.9546 0.9533 0.9534 0.9546 0.9533 0.9534
78 0.9553 0.9561 0.9553 0.9553 0.9561 0.9553 0.9553
79 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
80 0.9559 0.9568 0.9559 0.9559 0.9568 0.9559 0.9559
81 0.9539 0.9550 0.9539 0.9539 0.9550 0.9539 0.9539
82 0.9526 0.9537 0.9526 0.9526 0.9537 0.9526 0.9526
83 0.9559 0.9573 0.9559 0.9560 0.9573 0.9559 0.9560
84 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
85 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
86 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
87 0.9559 0.9571 0.9559 0.9559 0.9571 0.9559 0.9559
88 0.9533 0.9546 0.9533 0.9533 0.9546 0.9533 0.9533
89 0.9526 0.9537 0.9526 0.9526 0.9537 0.9526 0.9526
90 0.9526 0.9536 0.9526 0.9526 0.9536 0.9526 0.9526
91 0.9533 0.9544 0.9533 0.9534 0.9544 0.9533 0.9534
92 0.9553 0.9560 0.9553 0.9552 0.9560 0.9553 0.9552
93 0.9533 0.9546 0.9533 0.9534 0.9546 0.9533 0.9534
94 0.9520 0.9530 0.9520 0.9520 0.9530 0.9520 0.9520
95 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
96 0.9566 0.9579 0.9566 0.9566 0.9579 0.9566 0.9566
97 0.9533 0.9547 0.9533 0.9534 0.9547 0.9533 0.9534
98 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
99 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
100 0.9572 0.9580 0.9572 0.9572 0.9580 0.9572 0.9572
101 0.9533 0.9543 0.9533 0.9533 0.9543 0.9533 0.9533
102 0.9513 0.9525 0.9513 0.9513 0.9525 0.9513 0.9513
103 0.9559 0.9572 0.9559 0.9560 0.9572 0.9559 0.9560
104 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
105 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
106 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
107 0.9566 0.9578 0.9566 0.9566 0.9578 0.9566 0.9566
108 0.9539 0.9551 0.9539 0.9539 0.9551 0.9539 0.9539
109 0.9520 0.9531 0.9520 0.9520 0.9531 0.9520 0.9520
110 0.9526 0.9536 0.9526 0.9526 0.9536 0.9526 0.9526
111 0.9533 0.9544 0.9533 0.9534 0.9544 0.9533 0.9534
112 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
113 0.9533 0.9546 0.9533 0.9534 0.9546 0.9533 0.9534
114 0.9513 0.9524 0.9513 0.9513 0.9524 0.9513 0.9513
115 0.9539 0.9548 0.9539 0.9539 0.9548 0.9539 0.9539
116 0.9566 0.9578 0.9566 0.9566 0.9578 0.9566 0.9566
117 0.9533 0.9546 0.9533 0.9534 0.9546 0.9533 0.9534
118 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
119 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
120 0.9579 0.9588 0.9579 0.9579 0.9588 0.9579 0.9579
121 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
122 0.9520 0.9530 0.9520 0.9519 0.9530 0.9520 0.9519
123 0.9559 0.9573 0.9559 0.9560 0.9573 0.9559 0.9560
124 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
125 0.9520 0.9530 0.9520 0.9520 0.9530 0.9520 0.9520
126 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
127 0.9566 0.9578 0.9566 0.9566 0.9578 0.9566 0.9566
128 0.9539 0.9551 0.9539 0.9539 0.9551 0.9539 0.9539
129 0.9526 0.9537 0.9526 0.9526 0.9537 0.9526 0.9526
130 0.9526 0.9536 0.9526 0.9526 0.9536 0.9526 0.9526
131 0.9533 0.9544 0.9533 0.9534 0.9544 0.9533 0.9534
132 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
133 0.9539 0.9553 0.9539 0.9540 0.9553 0.9539 0.9540
134 0.9520 0.9530 0.9520 0.9520 0.9530 0.9520 0.9520
135 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
136 0.9566 0.9578 0.9566 0.9566 0.9578 0.9566 0.9566
137 0.9533 0.9546 0.9533 0.9534 0.9546 0.9533 0.9534
138 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
139 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
140 0.9572 0.9581 0.9572 0.9572 0.9581 0.9572 0.9572
141 0.9539 0.9550 0.9539 0.9539 0.9550 0.9539 0.9539
142 0.9513 0.9524 0.9513 0.9513 0.9524 0.9513 0.9513
143 0.9559 0.9573 0.9559 0.9560 0.9573 0.9559 0.9560
144 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
145 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
146 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
147 0.9579 0.9590 0.9579 0.9579 0.9590 0.9579 0.9579
148 0.9539 0.9551 0.9539 0.9539 0.9551 0.9539 0.9539
149 0.9526 0.9538 0.9526 0.9527 0.9538 0.9526 0.9527
150 0.9533 0.9541 0.9533 0.9532 0.9541 0.9533 0.9532
151 0.9533 0.9544 0.9533 0.9534 0.9544 0.9533 0.9534
152 0.9546 0.9555 0.9546 0.9546 0.9555 0.9546 0.9546
153 0.9533 0.9546 0.9533 0.9534 0.9546 0.9533 0.9534
154 0.9520 0.9530 0.9520 0.9520 0.9530 0.9520 0.9520
155 0.9546 0.9557 0.9546 0.9546 0.9557 0.9546 0.9546
156 0.9572 0.9584 0.9572 0.9573 0.9584 0.9572 0.9573
157 0.9539 0.9553 0.9539 0.9540 0.9553 0.9539 0.9540
158 0.9559 0.9569 0.9559 0.9560 0.9569 0.9559 0.9560
159 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
160 0.9572 0.9580 0.9572 0.9572 0.9580 0.9572 0.9572
161 0.9533 0.9543 0.9533 0.9533 0.9543 0.9533 0.9533
162 0.9513 0.9525 0.9513 0.9513 0.9525 0.9513 0.9513
163 0.9559 0.9573 0.9559 0.9560 0.9573 0.9559 0.9560
164 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
165 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
166 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
167 0.9566 0.9578 0.9566 0.9566 0.9578 0.9566 0.9566
168 0.9539 0.9551 0.9539 0.9539 0.9551 0.9539 0.9539
169 0.9520 0.9531 0.9520 0.9520 0.9531 0.9520 0.9520
170 0.9526 0.9536 0.9526 0.9526 0.9536 0.9526 0.9526
171 0.9539 0.9549 0.9539 0.9540 0.9549 0.9539 0.9540
172 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
173 0.9533 0.9546 0.9533 0.9534 0.9546 0.9533 0.9534
174 0.9520 0.9530 0.9520 0.9520 0.9530 0.9520 0.9520
175 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
176 0.9566 0.9579 0.9566 0.9566 0.9579 0.9566 0.9566
177 0.9533 0.9547 0.9533 0.9534 0.9547 0.9533 0.9534
178 0.9553 0.9561 0.9553 0.9553 0.9561 0.9553 0.9553
179 0.9546 0.9557 0.9546 0.9546 0.9557 0.9546 0.9546
180 0.9572 0.9580 0.9572 0.9572 0.9580 0.9572 0.9572
181 0.9533 0.9543 0.9533 0.9533 0.9543 0.9533 0.9533
182 0.9513 0.9524 0.9513 0.9513 0.9524 0.9513 0.9513
183 0.9553 0.9566 0.9553 0.9553 0.9566 0.9553 0.9553
184 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
185 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
186 0.9539 0.9550 0.9539 0.9540 0.9550 0.9539 0.9540
187 0.9566 0.9578 0.9566 0.9566 0.9578 0.9566 0.9566
188 0.9539 0.9551 0.9539 0.9539 0.9551 0.9539 0.9539
189 0.9526 0.9537 0.9526 0.9526 0.9537 0.9526 0.9526
190 0.9526 0.9535 0.9526 0.9526 0.9535 0.9526 0.9526
191 0.9533 0.9544 0.9533 0.9534 0.9544 0.9533 0.9534
192 0.9553 0.9561 0.9553 0.9552 0.9561 0.9553 0.9552
193 0.9526 0.9541 0.9526 0.9528 0.9541 0.9526 0.9528
194 0.9520 0.9530 0.9520 0.9520 0.9530 0.9520 0.9520
195 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
196 0.9572 0.9585 0.9572 0.9573 0.9585 0.9572 0.9573
197 0.9533 0.9547 0.9533 0.9534 0.9547 0.9533 0.9534
198 0.9546 0.9556 0.9546 0.9547 0.9556 0.9546 0.9547
199 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
200 0.9572 0.9581 0.9572 0.9572 0.9581 0.9572 0.9572
201 0.9533 0.9543 0.9533 0.9533 0.9543 0.9533 0.9533
202 0.9526 0.9537 0.9526 0.9526 0.9537 0.9526 0.9526
203 0.9559 0.9573 0.9559 0.9560 0.9573 0.9559 0.9560
204 0.9526 0.9539 0.9526 0.9526 0.9539 0.9526 0.9526
205 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
206 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
207 0.9579 0.9590 0.9579 0.9579 0.9590 0.9579 0.9579
208 0.9539 0.9551 0.9539 0.9539 0.9551 0.9539 0.9539
209 0.9526 0.9537 0.9526 0.9526 0.9537 0.9526 0.9526
210 0.9533 0.9541 0.9533 0.9532 0.9541 0.9533 0.9532
211 0.9533 0.9544 0.9533 0.9534 0.9544 0.9533 0.9534
212 0.9533 0.9542 0.9533 0.9533 0.9542 0.9533 0.9533
213 0.9539 0.9553 0.9539 0.9540 0.9553 0.9539 0.9540
214 0.9520 0.9530 0.9520 0.9520 0.9530 0.9520 0.9520
215 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
216 0.9579 0.9590 0.9579 0.9579 0.9590 0.9579 0.9579
217 0.9533 0.9547 0.9533 0.9534 0.9547 0.9533 0.9534
218 0.9553 0.9561 0.9553 0.9553 0.9561 0.9553 0.9553
219 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
220 0.9579 0.9588 0.9579 0.9579 0.9588 0.9579 0.9579
221 0.9533 0.9543 0.9533 0.9533 0.9543 0.9533 0.9533
222 0.9513 0.9525 0.9513 0.9513 0.9525 0.9513 0.9513
223 0.9559 0.9572 0.9559 0.9560 0.9572 0.9559 0.9560
224 0.9533 0.9545 0.9533 0.9533 0.9545 0.9533 0.9533
225 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
226 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
227 0.9579 0.9590 0.9579 0.9579 0.9590 0.9579 0.9579
228 0.9539 0.9551 0.9539 0.9539 0.9551 0.9539 0.9539
229 0.9526 0.9537 0.9526 0.9526 0.9537 0.9526 0.9526
230 0.9526 0.9536 0.9526 0.9526 0.9536 0.9526 0.9526
231 0.9533 0.9544 0.9533 0.9534 0.9544 0.9533 0.9534
232 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
233 0.9533 0.9546 0.9533 0.9534 0.9546 0.9533 0.9534
234 0.9513 0.9523 0.9513 0.9513 0.9523 0.9513 0.9513
235 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
236 0.9566 0.9578 0.9566 0.9566 0.9578 0.9566 0.9566
237 0.9533 0.9547 0.9533 0.9534 0.9547 0.9533 0.9534
238 0.9559 0.9569 0.9559 0.9560 0.9569 0.9559 0.9560
239 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
240 0.9566 0.9574 0.9566 0.9565 0.9574 0.9566 0.9565
241 0.9533 0.9543 0.9533 0.9533 0.9543 0.9533 0.9533
242 0.9520 0.9530 0.9520 0.9519 0.9530 0.9520 0.9519
243 0.9559 0.9573 0.9559 0.9560 0.9573 0.9559 0.9560
244 0.9533 0.9546 0.9533 0.9533 0.9546 0.9533 0.9533
245 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
246 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
247 0.9579 0.9590 0.9579 0.9579 0.9590 0.9579 0.9579
248 0.9539 0.9551 0.9539 0.9539 0.9551 0.9539 0.9539
249 0.9526 0.9537 0.9526 0.9526 0.9537 0.9526 0.9526
250 0.9526 0.9536 0.9526 0.9526 0.9536 0.9526 0.9526
251 0.9533 0.9544 0.9533 0.9534 0.9544 0.9533 0.9534
252 0.9539 0.9549 0.9539 0.9539 0.9549 0.9539 0.9539
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492
reports/WeightFiltration/ResNet/time_metrics.txt Normal file
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@@ -0,0 +1,492 @@
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0.000387
0.000433
0.000424
0.000423
0.000427
0.000384
0.000437
0.000426
0.000441
0.000394
0.000409
0.000383
0.000441
0.000408
176.808452
0.000383
0.000423
0.000386
0.000428
0.000436
0.000435
0.000423
0.000402
0.000430
0.000379
0.000426
0.000440
0.000424
0.000431
0.000453
0.000421
0.000428
0.000393
0.000428
176.260571
0.000384
0.000425
0.000443
0.000381
0.000426
0.000380
0.000378
0.000435
0.000423
0.000420
0.000416
0.000419
0.000383
0.000385
0.000413
0.000428
0.000416
0.000423
0.000428
176.686002
0.000424
0.001643
0.000429
0.000432
0.000433
0.000435
0.000427
0.000398
0.000427
0.000438
0.000432
0.000446
0.000421
0.000429
0.000434
0.000420
0.000418
0.000393
0.000435
176.287669
0.000415
0.000418
0.000423
0.000418
0.000424
0.000410
0.000421
0.000422
0.000427
0.000430
0.000426
0.000393
0.000429
0.000427
0.000416
0.000423
0.000435
0.000446
0.000432
176.646291
0.000424
0.000424
0.000463
0.000467
0.000413
0.000431
0.000420
0.000431
0.000428
0.000458
0.000447
0.000428
0.000427
0.000423
0.000436
0.000434
0.000438
0.000432
0.000432
176.790419
0.000381
0.000425
0.001399
0.000422
0.000422
0.000435
0.000424
0.000437
0.000416
0.000379
0.000454
0.000420
0.000383
0.000428
0.000433
0.000419
0.000429
0.000426
0.000377
176.561802
0.000431
0.000430
0.000424
0.000425
0.000459
0.000385
0.000389
0.000451
0.000429
0.000424
0.000427
0.000427
0.000430
0.000417
0.000429
0.001460
0.000392
0.001356
0.000413
177.180296
0.000432
0.000379
0.000428
0.000427
0.000438
0.000436
0.000419
0.000385
0.000427
0.000424
0.000436
0.000424
0.000377
0.000427
0.000421
0.000386
0.000425
0.001482
0.000421
177.225111
0.000435
0.000420
0.000438
0.000425
0.000438
0.000393
0.000434
0.000397
0.000428
0.000432
0.000427
0.000404
0.000431
0.000388
0.000427
0.000436
0.000427
0.000398
0.000403
183.682534
0.000395
0.000388
0.000383
0.000515
0.000383
0.000385
0.000386
0.000450
0.000374
0.000908
177.630187
0.000413
0.000469
0.000420
0.000416
0.000414
0.000419
0.000413
0.000417
0.000415
0.000411
0.000410
0.000424
0.000415
0.000449
0.000415
0.000423
0.000414
0.000415
0.000438
176.586045
0.000405
0.000396
0.000418
0.000412
0.000432
0.000416
0.000421
0.000425
0.000407
0.000418
0.000413
0.000444
0.000416
0.000420
0.000412
0.000417
0.000418
0.000418
0.000411
176.810020
0.000408
0.000406
0.000418
0.000431
0.000416
0.000409
0.000467
0.000414
0.000410
0.000405
0.000412
0.000416
0.000434
0.000403
0.000410
0.000436
0.000417
0.000408
0.000405
177.272377
0.000405
0.000436
0.000415
0.000409
0.000406
0.000405
0.000402
0.000410
0.000411
0.000398
0.000400
0.000416
0.000406
0.000412
0.000407
0.000402
0.000405
0.000430
0.000398
176.314326
0.000396
0.000398
0.000443
0.000400
0.000398
0.000405
0.000404
0.000405
0.000406
0.000406
0.000397
0.000403
0.000400
0.000410
0.000397
0.000406
0.000410
0.000438
0.000403
176.780033
0.000413
0.000410
0.000413
0.000399
0.000407
0.000409
0.000403
0.000407
0.000412
0.000406
0.000403
0.000413
0.000412
0.000406
0.000404
0.000406
0.000418
0.000412
0.000405
176.256956
0.000407
0.000419
0.000404
0.000402
0.000401
0.000403
0.000418
0.000411
0.000403
0.000392
0.000417
0.000408
0.000415
0.000417
0.000413
0.000410
0.000431
0.000414
0.000410
176.474485
0.000407
0.000406
0.000407
0.000406
0.000407
0.000451
0.000399
0.000415
0.000411
0.000407
0.000428
0.000411
0.000408
0.000408
0.000402
0.000407
0.000411
0.000402
0.000411
176.875764
0.000397
0.000400
0.000404
0.000404
0.000397
0.000411
0.000397
0.000408
0.000409
0.000406
0.000426
0.000454
0.000416
0.000380
0.000419
0.000408
0.000396
0.000410
0.000435

20
reports/base/RESNET34/Finetuned.csv Normal file
View File

@@ -0,0 +1,20 @@
accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recall,weighted_f1
0.9531,0.9542,0.9531,0.9531,0.9542,0.9531,0.9531
0.9575,0.9582,0.9575,0.9575,0.9582,0.9575,0.9575
0.9569,0.9580,0.9569,0.9570,0.9580,0.9569,0.9570
0.9537,0.9548,0.9538,0.9540,0.9548,0.9537,0.9540
0.9537,0.9557,0.9538,0.9541,0.9557,0.9537,0.9541
0.9556,0.9565,0.9556,0.9557,0.9565,0.9556,0.9557
0.9587,0.9595,0.9587,0.9587,0.9595,0.9587,0.9587
0.9587,0.9598,0.9587,0.9589,0.9598,0.9587,0.9589
0.9556,0.9565,0.9556,0.9557,0.9565,0.9556,0.9557
0.9550,0.9562,0.9550,0.9552,0.9562,0.9550,0.9552
0.9537,0.9553,0.9538,0.9540,0.9553,0.9537,0.9540
0.9525,0.9541,0.9525,0.9527,0.9541,0.9525,0.9527
0.9563,0.9573,0.9563,0.9563,0.9573,0.9563,0.9563
0.9575,0.9583,0.9575,0.9575,0.9583,0.9575,0.9575
0.9537,0.9555,0.9537,0.9540,0.9555,0.9537,0.9540
0.9563,0.9566,0.9563,0.9562,0.9566,0.9563,0.9562
0.9575,0.9585,0.9575,0.9575,0.9585,0.9575,0.9575
0.9537,0.9545,0.9538,0.9538,0.9545,0.9537,0.9538
0.9519,0.9529,0.9519,0.9518,0.9529,0.9519,0.9518
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
2 0.9531 0.9542 0.9531 0.9531 0.9542 0.9531 0.9531
3 0.9575 0.9582 0.9575 0.9575 0.9582 0.9575 0.9575
4 0.9569 0.9580 0.9569 0.9570 0.9580 0.9569 0.9570
5 0.9537 0.9548 0.9538 0.9540 0.9548 0.9537 0.9540
6 0.9537 0.9557 0.9538 0.9541 0.9557 0.9537 0.9541
7 0.9556 0.9565 0.9556 0.9557 0.9565 0.9556 0.9557
8 0.9587 0.9595 0.9587 0.9587 0.9595 0.9587 0.9587
9 0.9587 0.9598 0.9587 0.9589 0.9598 0.9587 0.9589
10 0.9556 0.9565 0.9556 0.9557 0.9565 0.9556 0.9557
11 0.9550 0.9562 0.9550 0.9552 0.9562 0.9550 0.9552
12 0.9537 0.9553 0.9538 0.9540 0.9553 0.9537 0.9540
13 0.9525 0.9541 0.9525 0.9527 0.9541 0.9525 0.9527
14 0.9563 0.9573 0.9563 0.9563 0.9573 0.9563 0.9563
15 0.9575 0.9583 0.9575 0.9575 0.9583 0.9575 0.9575
16 0.9537 0.9555 0.9537 0.9540 0.9555 0.9537 0.9540
17 0.9563 0.9566 0.9563 0.9562 0.9566 0.9563 0.9562
18 0.9575 0.9585 0.9575 0.9575 0.9585 0.9575 0.9575
19 0.9537 0.9545 0.9538 0.9538 0.9545 0.9537 0.9538
20 0.9519 0.9529 0.9519 0.9518 0.9529 0.9519 0.9518

9
retrain/resnet34/time_metrics.txt Normal file
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@@ -0,0 +1,9 @@
execution_time_sec
991.073163
978.072894
982.032442
1081.642368
985.512192
979.091341
978.852345
978.963139

167
sets/Casia.py
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@@ -1,167 +0,0 @@
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader, Subset
import torch
import numpy as np
import os
# train set transform
def train_transform(res):
return transforms.Compose([
transforms.Resize((res, res)),
transforms.RandomHorizontalFlip(p=0.5),
transforms.ColorJitter(
brightness=0.2,
contrast=0.2,
saturation=0.1
),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
# test set transform
def test_transform(res):
return transforms.Compose([
transforms.Resize((res, res)),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
# Load data using ImageFolder for CASIA-WebFace
'''
def get_set():
# This will check local cache first, then download if missing
print("Checking for CASIA-WebFace dataset...")
path = kagglehub.dataset_download("debarghamitraroy/casia-webface")
# Kagglehub often downloads a nested structure (e.g., path/casia-webface/casia-webface)
# We need the folder that directly contains the identity subfolders
# We'll check if there's a 'casia-webface' subfolder inside the downloaded path
sub_path = os.path.join(path, "casia-webface")
final_path = sub_path if os.path.exists(sub_path) else path
print(f"Loading dataset from: {final_path}")
return datasets.ImageFolder(
root=final_path,
transform=None
)'''
# Load data using ImageFolder for your UNPACKED images
def get_set():
# This must point to the folder created by Extractor.py
# NOT the kagglehub cache path
final_path = os.path.abspath("./data/casia-set")
if not os.path.exists(final_path):
raise FileNotFoundError(
f"Unpacked dataset not found at {final_path}. "
"Please run Extractor.py first!"
)
print(f"Loading unpacked CASIA dataset from: {final_path}")
return datasets.ImageFolder(
root=final_path,
transform=None
)
def get_ids_and_counts(dataset):
# ImageFolder stores labels in .targets
targets = torch.tensor(dataset.targets)
return torch.unique(
input = targets,
return_counts=True
)
def select_ids(dataset, sample_size, class_size):
ids, counts = get_ids_and_counts(dataset=dataset)
eligible_mask = counts >= sample_size
eligible_ids = ids[eligible_mask].numpy()
if len(eligible_ids) < class_size:
raise ValueError(
f"Only found {len(eligible_ids)} identities with {sample_size}+ images."
)
return np.random.choice(eligible_ids, class_size, replace=False)
def select_balanced_ids(dataset, class_size):
ids, counts = get_ids_and_counts(dataset=dataset)
sorted_indices = torch.argsort(counts, descending=True)
top_ids = ids[sorted_indices][:class_size].numpy()
return np.array(top_ids, dtype=int)
def get_indices(dataset, identities, split_at):
train_indices = []
test_indices = []
# We convert to numpy for faster searching with np.where
all_targets = np.array(dataset.targets)
for person_id in identities:
# Get all indices for this specific person
indices = np.where(all_targets == person_id)[0]
# Shuffle the indices for this person
np.random.shuffle(indices)
# Split data based on your split_at value
train_indices.extend(indices[:split_at])
test_indices.extend(indices[split_at:])
return train_indices, test_indices
# optional function to get max amount of samples per class
def select_top_ids(dataset, class_size):
ids, counts = get_ids_and_counts(dataset=dataset)
# sort by number of images (descending)
sorted_indices = torch.argsort(counts, descending=True)
top_ids = ids[sorted_indices][:class_size].numpy()
return np.array(top_ids, dtype=int)
def get_forget_retain_loaders(dataset: Dataset, forget_class_idx: int, batch_size: int = 32) -> tuple[DataLoader, DataLoader]:
"""
Splits an IdentitySubset or standard Dataset into forget and retain sets
based on a remapped target class index.
"""
# 1. Safely extract targets whether it's a standard dataset or a Subset wrapper
if hasattr(dataset, 'targets'):
targets = dataset.targets
elif hasattr(dataset, 'identity'): # Raw CelebA support
targets = dataset.identity
else:
# If it's an IdentitySubset or standard Subset, extract mapped targets sequentially
# This guarantees we get the 0 -> (n-1) remapped labels
targets = [dataset[i][1] for i in range(len(dataset))]
if not isinstance(targets, torch.Tensor):
targets = torch.tensor(targets)
# 2. Generate mask indices local to this subset
forget_indices = torch.where(targets == forget_class_idx)[0].tolist()
retain_indices = torch.where(targets != forget_class_idx)[0].tolist()
# 3. Create PyTorch Subsets
forget_subset = Subset(dataset, forget_indices)
retain_subset = Subset(dataset, retain_indices)
# 4. Wrap into clean DataLoaders
forget_loader = DataLoader(forget_subset, batch_size=batch_size, shuffle=False)
retain_loader = DataLoader(retain_subset, batch_size=batch_size, shuffle=True)
print(f"[Data Split] Local Class {forget_class_idx}: {len(forget_subset)} samples | Remaining Classes: {len(retain_subset)} samples.")
return forget_loader, retain_loader

21
sets/CasiaFace.py
View File

@@ -1,21 +0,0 @@
import os
from torchvision import datasets
from torch.utils.data import Dataset
import torch
from .Data import Data
class CasiaSet(Data):
def __init__(self, resolution: int = 224, sample_size = 190):
super().__init__(resolution = resolution, sample_size = sample_size)
def get_set(self) -> Data:
path_str = "./datasets/casia-set"
path = os.path.abspath(path_str)
if not os.path.exists(path):
raise FileNotFoundError(f"Unpacked dataset missing at {self.final_path}. Run Extractor.py first!")
print(f"Loading unpacked CASIA dataset from: {self.final_path}")
set = datasets.ImageFolder(root=path, transform=None)
# we set the target here
self.target = torch.tensor(set.targets)
return set

20
sets/CelebA.py
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@@ -1,20 +0,0 @@
from torchvision import datasets
from torch.utils.data import Dataset
import torch
from .Data import Data
class CelebA(Data):
def __init__(self, resolution: int = 224, sample_size = 30):
super().__init__(resolution, sample_size = sample_size)
def get_set(self):
set = datasets.CelebA(
root = "../data",
split='all',
target_type='identity',
download=False,
transform=None
)
# set the target first
self.target = set.identity
return set

15
sets/Data.py
View File

@@ -40,10 +40,11 @@ def test_transform(res):
)
])
# Load data with 'identity' as target and transform it
# Load data
def get_set(set_name:Set_Name):
return fetch_celeb_a() if set_name == Set_Name.CELEBA else fetch_casia_faces()
# celebA
def fetch_celeb_a():
return datasets.CelebA(
root='./data',
@@ -53,6 +54,7 @@ def fetch_celeb_a():
transform=None
)
# CASIA-WebFaces
def fetch_casia_faces():
# location of the data (path relative to project root)
final_path = os.path.abspath("./data/casia-set")
@@ -191,12 +193,12 @@ def vertical_split(dataset, batch_size,num_classes):
50% of each class goes to the Retain Set, 50% goes to the Forget Set.
"""
# 1. Group dataset indices by their respective ground-truth classes
# Dataset indices by their respective ground-truth classes
class_to_indices = {c: [] for c in range(num_classes)}
print(" [Vertical Split] Tracking class indices across the combined dataset...")
for idx in range(len(dataset)):
# Extract the label cleanly from the underlying dataset structure
# Extract labels
_, label = dataset[idx]
if label in class_to_indices:
class_to_indices[label].append(idx)
@@ -204,14 +206,14 @@ def vertical_split(dataset, batch_size,num_classes):
retain_indices = []
forget_indices = []
# 2. Slice each class identity vertically (exactly 50/50)
# Slice each class identity vertically (exactly 50/50)
for c, indices in class_to_indices.items():
if len(indices) < 2:
print(f" Warning: Class {c} has fewer than 2 samples. Cannot split vertically.")
retain_indices.extend(indices)
continue
# Deterministic shuffle per class to ensure honest distribution before splitting
# Suffle to ensure honest distribution before splitting
np.random.shuffle(indices)
mid = len(indices) // 2
@@ -220,11 +222,10 @@ def vertical_split(dataset, batch_size,num_classes):
print(f" Vertical split complete: Retain Index Size = {len(retain_indices)} | Forget Index Size = {len(forget_indices)}")
# 3. Construct lightweight PyTorch Subsets using our sliced index maps
# Subsets using our sliced index maps
retain_subset = Subset(dataset, retain_indices)
forget_subset = Subset(dataset, forget_indices)
# 4. Return pristine, shuffled DataLoaders mirroring your environment's batch specifications
retain_loader = DataLoader(retain_subset, batch_size=batch_size, shuffle=True)
forget_loader = DataLoader(forget_subset, batch_size=batch_size, shuffle=True)

0
DataAnalyser.py → sets/DataAnalyser.py
View File

174
sets/Data_OOP.py
View File

@@ -1,174 +0,0 @@
import torch
import numpy as np
from abc import ABC, abstractmethod
from torchvision import transforms, datasets
from torch.utils.data import Dataset, DataLoader, Subset
class Data(ABC):
"""
Handles image pipelines, identity filtering, indexing, and unlearning splits.
"""
def __init__(self, res: int = 224, sample_size = 30, class_size = 20):
self.res = res
self.sample_size = sample_size
self.class_size = class_size
self.target = None # will have to be set in get_set()
def train_transform(self):
return transforms.Compose([
# ResNet expects 224 x 224 res
# Inception expects 299 x 299
transforms.Resize((self.res, self.res)),
transforms.RandomHorizontalFlip(p=0.5),
transforms.ColorJitter(
brightness=0.2,
contrast=0.2,
saturation=0.1
),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
def test_transform(self):
return transforms.Compose([
transforms.Resize((self.res, self.res)),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
@abstractmethod
def get_set(self)-> datasets.TorchDataset:
"""Loads and returns the raw underlying PyTorch Dataset instance."""
pass
def get_targets(self) -> torch.Tensor:
return self.target
def get_ids_and_counts(self) -> tuple[torch.Tensor, torch.Tensor]:
if self.target is None:
raise ValueError ("This should be called after the 'target' variable has been set.")
return torch.unique(
self.target,
return_counts=True
)
def select_ids(self) -> np.ndarray:
ids, counts = self.get_ids_and_counts()
eligible_mask = counts >= self.sample_size
eligible_ids = ids[eligible_mask].numpy()
if len(eligible_ids) < self.class_size:
raise ValueError(
f"Only found {len(eligible_ids)} identities with {self.sample_size}+ images."
)
return np.random.choice(eligible_ids, self.class_size, replace=False)
# Function to get max amount of samples per class
def select_top_ids(self) -> np.ndarray:
ids, counts = self.get_ids_and_counts()
# sort by number of images (descending)
sorted_indices = torch.argsort(counts, descending=True)
top_ids = ids[sorted_indices][:self.class_size].numpy()
return np.array(top_ids, dtype=int)
def get_indices(self, identities: np.ndarray, split_at: int, max_size: int = None) -> tuple[list, list]:
'''train_indices = []
test_indices = []
max_size = self.sample_size if max_size is None else max_size
# Pull raw target tensor array using concrete implementation rules
all_targets = np.array(self.get_targets().cpu())
np.random.seed(42)
for person_id in identities:
indices = np.where(all_targets == person_id)[0]
np.random.shuffle(indices)
# Constrain total sample tracking size if requested (e.g. CelebA ceiling)
current_pool = indices[:max_size] if max_size else indices
if split_at >= len(current_pool):
raise ValueError(f"Split point ({split_at}) exceeds slice size ({len(current_pool)}) for class {person_id}.")
train_indices.extend(current_pool[:split_at])
test_indices.extend(current_pool[split_at:])
return train_indices, test_indices'''
if split_at >= self.sample_size: # debug safety
raise ValueError(f"Split point ({split_at}) must be less than total size ({self.sample_size}).")
train_indices = []
test_indices = []
#training_sample = int(sample_size * training_ratio)
np.random.seed(42)
target = self.get_targets()
for person_id in identities:
# Get all indices for this specific person
indices = torch.where(target == person_id)[0].numpy()
# Shuffle the indices for this person
np.random.shuffle(indices)
# split data to testing and training
train_indices.extend(indices[:split_at])
test_indices.extend(indices[split_at:self.sample_size])
return train_indices, test_indices
@staticmethod
def get_unlearn_loaders(
dataset: Dataset,
forget_class_idx: int,
batch_size: int = 32
) -> tuple[DataLoader, DataLoader]:
"""Splits an IdentitySubset into forget/retain parts based on local class index."""
if hasattr(dataset, 'targets'):
targets = dataset.targets
elif hasattr(dataset, 'identity'):
targets = dataset.identity
else:
targets = [dataset[i][1] for i in range(len(dataset))]
if not isinstance(targets, torch.Tensor):
targets = torch.tensor(targets)
forget_indices = torch.where(targets == forget_class_idx)[0].tolist()
retain_indices = torch.where(targets != forget_class_idx)[0].tolist()
forget_subset = Subset(dataset, forget_indices)
retain_subset = Subset(dataset, retain_indices)
forget_loader = DataLoader(forget_subset, batch_size=batch_size, shuffle=False)
retain_loader = DataLoader(retain_subset, batch_size=batch_size, shuffle=True)
print(f"[Data Split] Local Class {forget_class_idx}: {len(forget_subset)} samples | Remaining Classes: {len(retain_subset)} samples.")
return forget_loader, retain_loader
@staticmethod
def getDataSet(set:SetType, sample_size):
# some test
if set == SetType.CASIA:
from sets.CasiaFace import CasiaFace
return CasiaFace(sample_size = sample_size)
if set == SetType.CELEBA:
from sets.CelebA import CelebA
return CelebA(sample_size=sample_size)
from enum import Enum, auto
class SetType(Enum):
CASIA = auto()
CELEBA = auto()

163
unlearning/CertifiedUnlearning.py
View File

@@ -14,9 +14,18 @@ class CertifiedUnlearning(Strategy):
Uses a modified, stabilized stochastic Newton step using Taylor-expansion
HVP estimation across the entire parameter space, capped with calibrated noise.
"""
def __init__(self, target_class_index: int, l2_reg: float = 0.0005,
gamma: float = 0.01, scale: float = 50000.0,
s1: int = 2, s2: int = 350, std: float = 0.001, unlearn_bs: int = 2):
def __init__(
self,
target_class_index: int,
l2_reg: float = 0.0005,
gamma: float = 0.01,
scale: float = 50000.0,
s1: int = 2,
s2: int = 350,
std: float = 0.001,
unlearn_bs: int = 2
):
super().__init__(target_class_index)
self.l2_reg = l2_reg
self.gamma = gamma
@@ -49,151 +58,12 @@ class CertifiedUnlearning(Strategy):
return params_list if named else [e[1] for e in params_list]
'''
def _compute_loss_gradient(self, model, loader, device: torch.device):
model.eval()
criterion = nn.CrossEntropyLoss(reduction='sum')
params = self.get_params(model, False) # [p for name, p in model.named_parameters() if p.requires_grad and "AuxLogits" not in name]
grad_accumulator = [torch.zeros_like(p, device = device) for p in params]
total_samples = 0'''
# Accumulate true data cross-entropy gradients
'''
for data, targets in loader:
total_samples += targets.shape[0]
data, targets = data.to(device), targets.to(device)
outputs = model(data)
loss = criterion(outputs, targets)
mini_grads = list(grad(loss, params, retain_graph=False))
for i in range(len(grad_accumulator)):
grad_accumulator[i] += mini_grads[i].cpu().detach()
# Empirical data mean conversion
for i in range(len(grad_accumulator)):
grad_accumulator[i] /= total_samples
# L2 weight regularization
l2_reg_term = 0.0
for param in params:
if param.requires_grad:
l2_reg_term += torch.sum(param ** 2)
reg_grads = list(grad(self.l2_reg * l2_reg_term, params))
for i in range(len(grad_accumulator)):
grad_accumulator[i] += reg_grads[i].cpu().detach()
return [p.to(device) for p in grad_accumulator]
'''
'''
with torch.set_grad_enabled(True):
for data, targets in loader:
total_samples += targets.shape[0]
data, targets = data.to(device), targets.to(device)
outputs = model(data)
loss = criterion(outputs, targets)
mini_grads = grad(loss, params, retain_graph=False)
for i in range(len(grad_accumulator)):
grad_accumulator[i] += mini_grads[i]
# Empirical data mean conversion
for i in range(len(grad_accumulator)):
grad_accumulator[i] /= total_samples
# OPTIMIZATION 2: Analytical L2 Regularization Gradient instead of autograd
# d/dx (l2_reg * x^2) = 2 * l2_reg * x
for i, param in enumerate(params):
grad_accumulator[i] += 2 * self.l2_reg * param.detach()
return grad_accumulator
def _hvp(self, loss, params, v):
first_grads = grad(loss, params, retain_graph=True, create_graph=True)
elemwise_products = 0
'''
'''
for grad_elem, v_elem in zip(first_grads, v):
elemwise_products += torch.sum(grad_elem * v_elem)
elemwise_products = sum(torch.sum(g_elem * v_elem) for g_elem, v_elem in zip(first_grads, v))
return grad(elemwise_products, params, create_graph=False)'''
'''
def _stochastic_newton_update(self, g, dataset, model, device):
model.eval()
criterion = nn.CrossEntropyLoss()
params = self.get_params(model, False) # [p for p in model.parameters() if p.requires_grad]
h_res = [torch.zeros_like(p) for p in g]
# progress
total_steps = self.s1 * self.s2
step_interval = max(1, total_steps // 100)
global_step = 0
current_pct = 0
sampler = RandomSampler(dataset, replacement=True, num_samples=self.unlearn_bs * self.s2)
res_loader = DataLoader(dataset, batch_size=self.unlearn_bs, sampler=sampler)
res_iter = iter(res_loader)
for _ in range(self.s1):
h_estimate = [p.clone() for p in g]
sampler = RandomSampler(dataset, replacement=True, num_samples=self.unlearn_bs * self.s2)
res_loader = DataLoader(dataset, batch_size=self.unlearn_bs, sampler=sampler)
res_iter = iter(res_loader)
for _ in range(self.s2):
global_step += 1
if global_step % step_interval == 0 and current_pct < 100:
current_pct += 1
print(f"\rProgress: {current_pct}% done", end="", flush=True)
try:
data, target = next(res_iter)
except StopIteration:
res_iter = iter(res_loader)
data, target = next(res_iter)
data, target = data.to(device), target.to(device)
outputs = model(data)
loss = criterion(outputs, target)
l2_reg_term = sum(p.pow(2).sum() for p in params)
'for param in params:
#if param.requires_grad:
l2_reg_term += torch.sum(param ** 2)
loss += (self.l2_reg + self.gamma) * l2_reg_term
h_s = self._hvp(loss, params, h_estimate)
with torch.no_grad():
for k in range(len(params)):
h_estimate[k].copy_(h_estimate[k] + g[k] - (h_s[k] / self.scale))
#h_res[k] += h_estimate[k] / self.scale
#next_estimate = h_estimate[k].data + g[k].data - (h_s[k].data / self.scale)
#h_estimate[k] = next_estimate.clone()
del h_s, loss, outputs
#for k in range(len(params)):
# h_res[k] = h_res[k] + h_estimate[k] / self.scale
with torch.no_grad():
for k in range(len(params)):
h_res[k] += h_estimate[k] / self.scale
return [p / self.s1 for p in h_res]
'''
def _compute_loss_gradient(self, model, loader, device: torch.device):
model.eval()
criterion = nn.CrossEntropyLoss(reduction='sum')
params = self.get_params(model, False)
# OPTIMIZATION 1: Keep accumulator on GPU device directly
grad_accumulator = [torch.zeros_like(p, device=device) for p in params]
total_samples = 0
@@ -208,12 +78,11 @@ class CertifiedUnlearning(Strategy):
for i in range(len(grad_accumulator)):
grad_accumulator[i] += mini_grads[i]
# Empirical data mean conversion
# Data mean conversion
for i in range(len(grad_accumulator)):
grad_accumulator[i] /= total_samples
# OPTIMIZATION 2: Analytical L2 Regularization Gradient instead of autograd
# d/dx (l2_reg * x^2) = 2 * l2_reg * x
# regularisation gradient
for i, param in enumerate(params):
grad_accumulator[i] += 2 * self.l2_reg * param.detach()
@@ -243,7 +112,7 @@ class CertifiedUnlearning(Strategy):
for _ in range(self.s1):
h_estimate = [p.clone() for p in g]
# hesian estimation
for _ in range(self.s2):
global_step += 1
@@ -255,7 +124,7 @@ class CertifiedUnlearning(Strategy):
data, target = data.to(device), target.to(device)
# OPTIMIZATION 3: Clean up graph creation for loss & L2
# forward
outputs = model(data)
loss = criterion(outputs, target)

79
unlearning/Retrain.py Normal file
View File

@@ -0,0 +1,79 @@
import time
from pathlib import Path
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from unlearning.Strategy import Strategy
class Retrain(Strategy):
"""
Implements the Exact Unlearning Baseline by retraining the model architecture
completely from scratch using only the retained dataset partition.
"""
def __init__(self, target_class_index: int, lr: float = 0.01,
weight_decay: float = 0.0005, epochs: int = 5):
super().__init__(target_class_index)
self.lr = lr
self.weight_decay = weight_decay
self.epochs = epochs
def _reset_weights(self, model: nn.Module):
"""
Re-initializes all learnable parameters of the model to clear pre-trained memories.
"""
inner_model = getattr(model, "model", model)
for layer in inner_model.modules():
if hasattr(layer, 'reset_parameters'):
layer.reset_parameters()
def _run(self, model: nn.Module, forget_loader: DataLoader, retain_loader: DataLoader) -> nn.Module:
device = next(model.parameters()).device
print(">> Triggering Exact Unlearning Baseline (Retraining from scratch)...")
# 1. Clear the pre-trained state completely
self._reset_weights(model) # model should be loaded here or weights reset to ImageNet (pretrained default)
model.train()
# fresh optimizer for this clean environment
optimizer = optim.SGD(
model.parameters(),
lr=self.lr,
momentum=0.9,
weight_decay=self.weight_decay
)
criterion = nn.CrossEntropyLoss()
# 3. Standard training loop over the Retain set
for epoch in range(self.epochs):
running_loss = 0.0
total_samples = 0
for data, targets in retain_loader:
data, targets = data.to(device), targets.to(device)
optimizer.zero_grad()
outputs = model(data)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
running_loss += loss.item() * targets.size(0)
total_samples += targets.size(0)
epoch_loss = running_loss / total_samples
print(f" [Retrain] Epoch {epoch+1}/{self.epochs} completed. Loss: {epoch_loss:.4f}")
print(">> Retraining pipeline finished. Baseline weights established.")
return model
def _split_data(self, dataset):
# Dynamically pulls loaders from your Data.py script
from sets.Data import get_unlearning_loaders
return get_unlearning_loaders(
dataset=dataset,
forget_class_idx=self.target_class_index,
batch_size=32
)

17
unlearning/WeightFiltration.py
View File

@@ -11,15 +11,14 @@ from sets.Data import vertical_split
class WeightFiltration(Strategy):
def __init__(self,
target_class_index: int,
arch,
num_classes: int = 20,
epochs: int = 6,
lr: float = 100.0,
gamma: float = 0.01,
lambda_1 = 25
):
target_class_index: int,
arch,
num_classes: int = 20,
epochs: int = 6,
lr: float = 100.0,
gamma: float = 0.01,
lambda_1 = 25
):
super().__init__(target_class_index=target_class_index)
self.epochs = epochs
self.lr = lr

86
unlearning/wf/WF_Net.py
View File

@@ -1,86 +0,0 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
class WF_Net(nn.Module):
"""
Implements Poppi et al.'s WF Model structure.
Wraps a pre-trained ResNet-18 and dynamically applies
weight-space gating matrix multiplication during the forward step.
"""
def __init__(self, original_model: nn.Module, num_classes: int):
super().__init__()
# Extract the sequence of blocks/layers L from the original model
self.conv1 = original_model.conv1
self.bn1 = original_model.bn1
self.relu = original_model.relu
self.maxpool = original_model.maxpool
self.layer1 = original_model.layer1
self.layer2 = original_model.layer2
self.layer3 = original_model.layer3
self.layer4 = original_model.layer4
self.avgpool = original_model.avgpool
self.fc = original_model.fc
# Target layer for filtering: layer4 block 1 conv2
# We extract its static tensor data out of the autograd parameter pool
self.target_conv = self.layer4[1].conv2
self.original_w = nn.Parameter(self.target_conv.weight.data.clone().detach(), requires_grad=False)
# Require: Alpha gating matrix. Shape: (num_classes, out_channels)
# Initialized to 1.5 as per Poppi et al.'s verbatim specification
out_channels = self.original_w.shape[0]
#self.alpha = nn.Parameter(torch.ones(num_classes, out_channels) * 1.5)
self.alpha = nn.Parameter(torch.ones(num_classes, out_channels))
def forward(self, x: torch.Tensor, target_class_indices: torch.Tensor) -> torch.Tensor:
# 1. Run through early sequence of layers undisturbed
x = self.maxpool(self.relu(self.bn1(self.conv1(x))))
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
# Run layer4 block 0 normally
x = self.layer4[0](x)
# -------------------------------------------------------------
# HERE IT IS: Save the structural skip connection (identity)
# BEFORE modifying features via block 1's convolutions
# -------------------------------------------------------------
identity = x
# Now enter layer4 block 1
x = self.layer4[1].conv1(x)
x = self.layer4[1].bn1(x)
x = self.layer4[1].relu(x)
# [Your Step 1 Masking Math happens right here...]
batch_alpha = self.alpha[target_class_indices]
mask = torch.sigmoid(batch_alpha).view(x.size(0), -1, 1, 1)
# Run the functional convolution
x = F.conv2d(
x,
weight=self.original_w,
bias=self.target_conv.bias,
stride=self.target_conv.stride,
padding=self.target_conv.padding
)
# Apply your WF-Net channel mask
x = x * mask
x = self.layer4[1].bn2(x)
# -------------------------------------------------------------
# HERE IT IS USED: Add the pristine identity back to the gated output
# -------------------------------------------------------------
x = self.layer4[1].relu(x + identity)
# Final Classification Head Sequence
x = self.avgpool(x)
x = torch.flatten(x, 1)
y_out = self.fc(x)
return y_out