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cleaned up for submission

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Tinsae
2026-07-10 20:13:13 +02:00
parent 47fbb19761
commit 7eff030e89
38 changed files with 39 additions and 4175 deletions
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40
Note.md
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@@ -1,40 +0,0 @@
Not at all! You are still completely faithful to Guo et al. Your current implementation does **not** break their framework.
In fact, your setup matches the exact methodology of the paper. There is a common misconception about what Guo et al. mean when they caution against calculating the Hessian inverse, and understanding how your feature extraction relates to their theory explains why your code remains completely valid.
---
## 1. What Guo et al. *Actually* Said
In the original paper (*"Certified Removal from Linear Models"*), Guo et al. state that explicitly calculating and inverting the Hessian matrix becomes prohibitively expensive when the parameter count $d$ scales up.
$$\text{Time Complexity to Invert } H = O(d^3)$$
However, the authors explicitly implemented and verified their approach on **linear classifiers** (like logistic regression) where the input feature dimension $d$ was small enough to handle directly.
When you strip out the heavy ResNet50 convolutional layers and turn the backbone into a static feature extractor, **you transform your deep network into a linear classifier.** ```
[Images] ──> [Frozen ResNet Backbone] ──> Extracted Feature Vector (d = 2048) ──> [Linear Head (model.fc)]
Because your feature vector is exactly 2,048 dimensions, your Hessian matrix is a modest $2048 \times 2048$.
Inverting a $2048 \times 2048$ matrix takes your CPU less than **0.5 seconds** ($2048^3$ operations is tiny for a modern processor). You are executing the exact mathematics Guo et al. prescribed for linear systems. You haven't broken their implementation; you have successfully reduced a non-convex deep learning problem into their exact convex linear domain.
## 2. Where Hessian-Free Approximations (Like LiSSA) Apply
The reason alternative methods like LiSSA or Conjugate Gradient exist is for scenarios where you *cannot* reduce the model to a small linear head.
If you decided to apply Certified Removal to the **entire ResNet50 network** (all 23.5 million parameters open), then you would be forced to abandon your exact matrix calculation. Inverting a $23.5\text{M} \times 23.5\text{M}$ matrix is impossible. In that specific scenario, you would have to use a Hessian-free approximation method to avoid breaking the budget.
---
## 3. The Core Alignment with the Paper
Your script implements the three pillars that define Guo et al.’s Certified Removal:
1. **The Optimization Target:** It uses an $L_2$-regularized objective function (`self.l2_reg`).
2. **The Newton Step:** It takes the exact second-order curvature correction ($H^{-1} \nabla$) to adjust the parameters.
3. **The Indistinguishability Guarantee:** It applies a privacy perturbation boundary check (`self.removal_bound`).
Your implementation is an elegant, academically sound adaptation of their linear model theory for a deep learning architecture. By handling the feature extraction step first, you made their exact algorithm run efficiently within a 4GB VRAM envelope.

67
Predict.py
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@@ -1,67 +0,0 @@
import torch
import numpy as np
@torch.inference_mode() # More memory-efficient than no_grad()
def get_loss_per_sample(model, data_loader, device):
"""
Returns a list of individual losses for every sample in the loader.
Useful for MIA to see how 'certain' the model is about specific images.
"""
model.eval()
criterion = torch.nn.CrossEntropyLoss(reduction='none') # Crucial: returns loss per image
all_losses = []
for inputs, labels in data_loader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
# Calculate loss for each image in the batch individually
loss = criterion(outputs, labels)
all_losses.extend(loss.cpu().numpy())
return all_losses
@torch.inference_mode()
def get_losses_by_class(model, data_loader, device):
"""
Returns a dictionary: { class_id: [list_of_losses_for_this_class] }
"""
model.eval()
criterion = torch.nn.CrossEntropyLoss(reduction='none')
class_losses = {}
for inputs, labels in data_loader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
# Get individual losses
losses = criterion(outputs, labels).cpu().numpy()
labels_np = labels.cpu().numpy()
for i, class_id in enumerate(labels_np):
if class_id not in class_losses:
class_losses[class_id] = []
class_losses[class_id].append(losses[i])
return class_losses
# evaluate MIA
def eval_MIA(forgotten_losses, never_seen_losses):
avg_f_loss = np.mean(forgotten_losses)
avg_ns_loss = np.mean(never_seen_losses)
print(f"Average Loss on Forgotten Identity: {avg_f_loss:.4f}")
print(f"Average Loss on Unknown Identities: {avg_ns_loss:.4f}")
if avg_f_loss < avg_ns_loss * 0.8:
print("MIA Warning: Model still shows high certainty on forgotten data.")
else:
print("MIA Success: Model treats forgotten data like unknown data.")

14
SetUp.py
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@@ -1,14 +0,0 @@
##
import torch
from torchvision import datasets, transforms, models
def get_device():
if torch.cuda.is_available():
# clear cach to boost memory
# for new round
torch.cuda.empty_cache()
return torch.device("cuda")
else:
return torch.device("cpu")

14
Tune.py
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@@ -5,7 +5,6 @@ from sklearn.metrics import classification_report
import copy
# Framework and Utility Imports
import SetUp
import Util
from sets.Data import *
from sets.IdentitySubset import IdentitySubset
@@ -43,6 +42,16 @@ RESOLUTION:int = 224
'''
ARCH = Architecture.RESNET34
def get_device():
if torch.cuda.is_available():
# clear cach to boost memory
# for new round
torch.cuda.empty_cache()
return torch.device("cuda")
else:
return torch.device("cpu")
# Data preparation and model setup
def prepare_data_and_model_environment():
@@ -50,7 +59,8 @@ def prepare_data_and_model_environment():
Handles environment discovery, downloads/loads datasets, generates
train-test class splits, and configures the architecture base.
"""
device = SetUp.get_device()
# get Cuda or CPU.
device = get_device()
dataset_name = Set_Name.CASIAFACES
if dataset_name == Set_Name.CASIAFACES:
SAMPLE_SIZE = 400

3
Util.py
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@@ -2,6 +2,7 @@
from pathlib import Path
import os
# used for logging metrics to files
def _log_to_csv(arch, mode, accuracy, report_dict, strategy):
"""Handles directory structures, file setups, and distinct CSV column formatting."""
#arch_name = model.__class__.__name__.lower()
@@ -33,7 +34,7 @@ def _log_to_csv(arch, mode, accuracy, report_dict, strategy):
print(f">> Direct CSV metrics appended to {csv_path}")
# creates a file for
def _initialize_log_file(log_file):
"""Creates a unique log file for this strategy with a header if it doesn't exist."""
log_file.parent.mkdir(parents=True, exist_ok=True)

4
architectures/EfficentNet.py
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@@ -11,8 +11,8 @@ class EfficientNet(Model):
m = models.efficientnet_b1(weights=models.EfficientNet_B1_Weights.DEFAULT)
# Unfreeze the last block for a lighter touch
for param in m.features[-1].parameters(): param.requires_grad = True
# Unfreeze the last block
# for param in m.features[-1].parameters(): param.requires_grad = True
# Standard classifier fix
m.classifier[1] = nn.Linear(m.classifier[1].in_features, self.size)

25
architectures/Model.py
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@@ -31,13 +31,13 @@ class Model(ABC):
optimizer = optim.Adam(filter(lambda p: p.requires_grad, self.model.parameters()), lr=rate, weight_decay=0.1)
scheduler = CosineAnnealingLR(optimizer, T_max=epochs, eta_min=1e-6)
# to save reports
# file_path = Path(f"{mode}/{self.__class__.__name__.lower()}/time_metrics.txt")
# Util._initialize_log_file(file_path)
# to save training time to reports
file_path = Path(f"{mode}/{self.__class__.__name__.lower()}/time_metrics.txt")
Util._initialize_log_file(file_path)
print(f"Starting training on {self.device}...")
# start_time = time.time()
start_time = time.time()
# training phase
self.model.train()
@@ -49,7 +49,7 @@ class Model(ABC):
optimizer.zero_grad()
# forward pass
outputs = self.model(inputs)
# comoutew loss
# comopute loss
loss = criterion(outputs, labels)
# backward pass
loss.backward()
@@ -58,9 +58,9 @@ class Model(ABC):
scheduler.step()
print(f"Epoch {epoch+1}/{epochs} | Loss: {total_loss / len(loader):.4f}")
#end_time = time.time()
#execution_time = end_time - start_time
#Util.log_metric(log_file=file_path, execution_time=execution_time)
end_time = time.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 complete.")
@@ -123,10 +123,10 @@ class Model(ABC):
print(f"Test Accuracy: {accuracy:.2f}%")
# 1. Print standard text report to terminal
print(classification_report(all_labels, all_preds, labels=classes, zero_division=0))
# Print standard text report to terminal
#print(classification_report(all_labels, all_preds, labels=classes, zero_division=0))
# 2. Extract structured dictionary metrics
# Structured dictionary metrics
report_dict = classification_report(
all_labels,
all_preds,
@@ -135,8 +135,7 @@ class Model(ABC):
zero_division=0
)
# 3. Delegate file tracking to isolated helper method
#self._log_to_csv(mode, accuracy,report_dict)
# report metrics to choriographer
return accuracy, report_dict

63
architectures/WFNet.py
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@@ -6,69 +6,6 @@ from torch.utils.data import DataLoader
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__()

3
eval/UnlearningAttack.py
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@@ -32,7 +32,8 @@ class UnlearningAttack:
if model1.__class__.__name__ == "WF_Module":
gate = torch.full((data.size(0),), target_class, device=device)
p1 = torch.softmax(model1(data, target_class_indices=gate), dim=1)
else:
else: # its a normal nn.Module
p1 = torch.softmax(model1(data), dim=1)
p2 = torch.softmax(model2(data), dim=1)
probs1.extend(p1.cpu().numpy()); probs2.extend(p2.cpu().numpy())

111
js_evaluator/JS_Evaluator.py
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@@ -1,111 +0,0 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torchvision.models as models
class ZeroRetrainForgettingEvaluator:
def __init__(self, unlearned_model: nn.Module, num_classes: int):
"""
Initializes the ZRF Evaluator.
Args:
unlearned_model (nn.Module): Your fine-tuned & unlearned ResNet-50.
num_classes (int): Number of classes used in your CelebA task.
"""
# select device
if torch.cuda.is_available():
self.device = torch.device("cuda")
elif hasattr(torch, "xpu") and torch.xpu.is_available():
self.device = torch.device("xpu") # For Intel GPUs using IPEX
else:
self.device = torch.device("cpu")
print(f"[INFO] Using device: {self.device}")
# prepare the unlearned model
self.unlearned_model = unlearned_model.to(self.device)
self.unlearned_model.eval()
# Instantiate a structurally matching, completely random model
print(f"[INFO] Initializing random baseline ResNet-50 with {num_classes} classes...")
self.random_model = self.get_random_model(num_classes)
self.random_model = self.random_model.to(self.device)
self.random_model.eval()
# gets randomly initialised model
# for comparison with unlearned model
def get_random_model(num_classes):
print(f"[INFO] Initializing random baseline ResNet-50 with {num_classes} classes...")
model = models.resnet50(weights=None)
model.fc = nn.Linear(model.fc.in_features, num_classes)
return model
# compute divergence
def _compute_js_divergence(self, p: torch.Tensor, q: torch.Tensor) -> float:
"""
Computes the Jensen-Shannon (JS) Divergence between two probability distributions.
Args:
p, q (Tensor): Tensors of shape (batch_size, num_classes) containing probabilities.
"""
# Avoid log(0) issues by adding a tiny epsilon
eps = 1e-12
p = torch.clamp(p, eps, 1.0)
q = torch.clamp(q, eps, 1.0)
# Calculate the midpoint distribution
m = 0.5 * (p + q)
# Compute KL Divergence natively: KL(P || M) and KL(Q || M)
kl_pm = torch.sum(p * (torch.log(p) - torch.log(m)), dim=1)
kl_qm = torch.sum(q * (torch.log(q) - torch.log(m)), dim=1)
# JS Divergence is the average of both KL divergences
js_div = 0.5 * (kl_pm + kl_qm)
# Return the mean divergence across the entire batch
return js_div.mean().item()
def evaluate_forget_class(self, dataset, batch_size: int = 32) -> float:
"""
Evaluates the unlearned model against the random model using images
from the forgotten class/identity.
Args:
dataset (Dataset): A PyTorch Dataset containing images of the forget set.
batch_size (int): Batch size for evaluation.
Returns:
float: The ZRF score (JS Divergence). A lower divergence means
the unlearned model is behaving exactly like a random model.
"""
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)
total_js_div = 0.0
total_samples = 0
# No gradients needed for evaluation
with torch.no_grad():
for images, _ in dataloader:
images = images.to(self.device)
batch_len = images.size(0)
# Get raw outputs (logits)
unlearned_logits = self.unlearned_model(images)
random_logits = self.random_model(images)
# Convert logits to probability distributions via Softmax
unlearned_probs = F.softmax(unlearned_logits, dim=1)
random_probs = F.softmax(random_logits, dim=1)
# Calculate JS divergence for this batch
batch_js = self._compute_js_divergence(unlearned_probs, random_probs)
# Weighted average based on batch size (handles final smaller batches perfectly)
total_js_div += batch_js * batch_len
total_samples += batch_len
final_zrf_score = total_js_div / total_samples
return final_zrf_score

29
reports/CertifiedUnlearning/RESNET34/forget.csv
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@@ -471,32 +471,3 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.1500,1.0000,0.1500,0.2609,1.0000,0.1500,0.2609
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0375,1.0000,0.0375,0.0723,1.0000,0.0375,0.0723
0.0250,1.0000,0.0250,0.0488,1.0000,0.0250,0.0488
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
471 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
472 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
473 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.1500 1.0000 0.1500 0.2609 1.0000 0.1500 0.2609
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0375 1.0000 0.0375 0.0723 1.0000 0.0375 0.0723
0.0250 1.0000 0.0250 0.0488 1.0000 0.0250 0.0488
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
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

29
reports/CertifiedUnlearning/RESNET34/forget_train.csv
View File

@@ -471,32 +471,3 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.1031,1.0000,0.1031,0.1870,1.0000,0.1031,0.1870
0.0375,1.0000,0.0375,0.0723,1.0000,0.0375,0.0723
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0656,1.0000,0.0656,0.1232,1.0000,0.0656,0.1232
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0094,1.0000,0.0094,0.0186,1.0000,0.0094,0.0186
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0875,1.0000,0.0875,0.1609,1.0000,0.0875,0.1609
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
471 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
472 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
473 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 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
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.1031 1.0000 0.1031 0.1870 1.0000 0.1031 0.1870
0.0375 1.0000 0.0375 0.0723 1.0000 0.0375 0.0723
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0656 1.0000 0.0656 0.1232 1.0000 0.0656 0.1232
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0094 1.0000 0.0094 0.0186 1.0000 0.0094 0.0186
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0875 1.0000 0.0875 0.1609 1.0000 0.0875 0.1609

29
reports/CertifiedUnlearning/RESNET34/retain.csv
View File

@@ -471,32 +471,3 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.1026,0.1456,0.1026,0.0392,0.1456,0.1026,0.0392
0.6928,0.8887,0.6928,0.7218,0.8887,0.6928,0.7218
0.9191,0.9270,0.9191,0.9197,0.9270,0.9191,0.9197
0.4316,0.8326,0.4316,0.4030,0.8326,0.4316,0.4030
0.9289,0.9391,0.9289,0.9310,0.9391,0.9289,0.9310
0.9454,0.9476,0.9454,0.9456,0.9476,0.9454,0.9456
0.9046,0.9221,0.9046,0.9073,0.9221,0.9046,0.9073
0.9322,0.9393,0.9322,0.9332,0.9393,0.9322,0.9332
0.8842,0.9196,0.8842,0.8916,0.9196,0.8842,0.8916
0.9072,0.9258,0.9072,0.9111,0.9258,0.9072,0.9111
0.1322,0.3672,0.1322,0.0844,0.3672,0.1322,0.0844
0.9447,0.9469,0.9447,0.9451,0.9469,0.9447,0.9451
0.9336,0.9384,0.9336,0.9339,0.9384,0.9336,0.9339
0.7941,0.8921,0.7941,0.8103,0.8921,0.7941,0.8103
0.9447,0.9477,0.9447,0.9449,0.9477,0.9447,0.9449
0.1329,0.2409,0.1329,0.0850,0.2409,0.1329,0.0850
0.9513,0.9546,0.9513,0.9519,0.9546,0.9513,0.9519
0.9408,0.9464,0.9408,0.9416,0.9464,0.9408,0.9416
0.8987,0.9157,0.8987,0.8994,0.9157,0.8987,0.8994
0.8987,0.9205,0.8987,0.9035,0.9205,0.8987,0.9035
0.0750,0.0175,0.0750,0.0233,0.0175,0.0750,0.0233
0.4493,0.7334,0.4493,0.4059,0.7334,0.4493,0.4059
0.9322,0.9380,0.9322,0.9327,0.9380,0.9322,0.9327
0.7204,0.8401,0.7204,0.7178,0.8401,0.7204,0.7178
0.9428,0.9486,0.9428,0.9439,0.9486,0.9428,0.9439
0.9401,0.9450,0.9401,0.9408,0.9450,0.9401,0.9408
0.5875,0.8750,0.5875,0.6154,0.8750,0.5875,0.6154
0.9336,0.9402,0.9336,0.9342,0.9402,0.9336,0.9342
0.8257,0.8913,0.8257,0.8352,0.8913,0.8257,0.8352
0.8987,0.9353,0.8987,0.9086,0.9353,0.8987,0.9086
0.8882,0.9162,0.8882,0.8931,0.9162,0.8882,0.8931
0.9388,0.9419,0.9388,0.9392,0.9419,0.9388,0.9392
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
471 0.1026 0.1456 0.1026 0.0392 0.1456 0.1026 0.0392
472 0.6928 0.8887 0.6928 0.7218 0.8887 0.6928 0.7218
473 0.9191 0.9270 0.9191 0.9197 0.9270 0.9191 0.9197
0.4316 0.8326 0.4316 0.4030 0.8326 0.4316 0.4030
0.9289 0.9391 0.9289 0.9310 0.9391 0.9289 0.9310
0.9454 0.9476 0.9454 0.9456 0.9476 0.9454 0.9456
0.9046 0.9221 0.9046 0.9073 0.9221 0.9046 0.9073
0.9322 0.9393 0.9322 0.9332 0.9393 0.9322 0.9332
0.8842 0.9196 0.8842 0.8916 0.9196 0.8842 0.8916
0.9072 0.9258 0.9072 0.9111 0.9258 0.9072 0.9111
0.1322 0.3672 0.1322 0.0844 0.3672 0.1322 0.0844
0.9447 0.9469 0.9447 0.9451 0.9469 0.9447 0.9451
0.9336 0.9384 0.9336 0.9339 0.9384 0.9336 0.9339
0.7941 0.8921 0.7941 0.8103 0.8921 0.7941 0.8103
0.9447 0.9477 0.9447 0.9449 0.9477 0.9447 0.9449
0.1329 0.2409 0.1329 0.0850 0.2409 0.1329 0.0850
0.9513 0.9546 0.9513 0.9519 0.9546 0.9513 0.9519
0.9408 0.9464 0.9408 0.9416 0.9464 0.9408 0.9416
0.8987 0.9157 0.8987 0.8994 0.9157 0.8987 0.8994
0.8987 0.9205 0.8987 0.9035 0.9205 0.8987 0.9035
0.0750 0.0175 0.0750 0.0233 0.0175 0.0750 0.0233
0.4493 0.7334 0.4493 0.4059 0.7334 0.4493 0.4059
0.9322 0.9380 0.9322 0.9327 0.9380 0.9322 0.9327
0.7204 0.8401 0.7204 0.7178 0.8401 0.7204 0.7178
0.9428 0.9486 0.9428 0.9439 0.9486 0.9428 0.9439
0.9401 0.9450 0.9401 0.9408 0.9450 0.9401 0.9408
0.5875 0.8750 0.5875 0.6154 0.8750 0.5875 0.6154
0.9336 0.9402 0.9336 0.9342 0.9402 0.9336 0.9342
0.8257 0.8913 0.8257 0.8352 0.8913 0.8257 0.8352
0.8987 0.9353 0.8987 0.9086 0.9353 0.8987 0.9086
0.8882 0.9162 0.8882 0.8931 0.9162 0.8882 0.8931
0.9388 0.9419 0.9388 0.9392 0.9419 0.9388 0.9392

6
reports/CertifiedUnlearning/attack_values.csv
View File

@@ -19,10 +19,6 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
17,0.500000,0.989583, 0.062500, 0.503747
18,0.550000,1.000000, 0.166667, 0.495459
19,0.500000,0.916667, 0.052083, 0.551996
0,0.500000,0.937500, 0.020833, 0.532267
1,0.500000,0.937500, 0.093750, 0.503973
2,0.500000,0.989583, 0.104167, 0.570063
0,0.500000,0.402083, 0.031250, 0.511158
1,0.500000,0.154167, 0.312500, 0.496193
2,0.500000,0.602083, 0.145833, 0.530453
@@ -103,5 +99,3 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
17,0.500000,0.377083, 0.166667, 0.459667
18,0.600000,0.881250, 0.083333, 0.488006
19,0.500000,0.568750, 0.187500, 0.546058
0,0.500000,0.635417, 0.156250, 0.527750
1,0.500000,0.193750, 0.333333, 0.447474
1 target_class parameter_mia_accuracy lookalike_accuracy A-Dist JS-Dist
19 17 0.500000 0.989583 0.062500 0.503747
20 18 0.550000 1.000000 0.166667 0.495459
21 19 0.500000 0.916667 0.052083 0.551996
0 0.500000 0.937500 0.020833 0.532267
1 0.500000 0.937500 0.093750 0.503973
2 0.500000 0.989583 0.104167 0.570063
0 0.500000 0.402083 0.031250 0.511158
22 1 0 0.500000 0.154167 0.402083 0.312500 0.031250 0.496193 0.511158
23 2 1 0.500000 0.602083 0.154167 0.145833 0.312500 0.530453 0.496193
24 3 2 0.500000 0.852083 0.602083 0.135417 0.145833 0.491837 0.530453
99 18 17 0.600000 0.500000 0.881250 0.377083 0.083333 0.166667 0.488006 0.459667
100 19 18 0.500000 0.600000 0.568750 0.881250 0.187500 0.083333 0.546058 0.488006
101 0 19 0.500000 0.635417 0.568750 0.156250 0.187500 0.527750 0.546058
1 0.500000 0.193750 0.333333 0.447474

50
reports/LinearFiltration/RESNET34_old/full-set/forget.csv → reports/LinearFiltration/RESNET34/full-set/forget.csv
View File

@@ -479,53 +479,3 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
479 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
480 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
481 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 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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0.0000 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
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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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0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

50
reports/LinearFiltration/RESNET34_old/full-set/forget_train.csv → reports/LinearFiltration/RESNET34/full-set/forget_train.csv
View File

@@ -479,53 +479,3 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
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
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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
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
479 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
480 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
481 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 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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0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 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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0.0000 0.0000 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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0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 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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0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

52
reports/LinearFiltration/RESNET34_old/full-set/retain.csv → reports/LinearFiltration/RESNET34/full-set/retain.csv
View File

@@ -478,54 +478,4 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.9572,0.9584,0.9572,0.9574,0.9584,0.9572,0.9574
0.9546,0.9558,0.9546,0.9548,0.9558,0.9546,0.9548
0.9559,0.9568,0.9559,0.9560,0.9568,0.9559,0.9560
0.9579,0.9590,0.9579,0.9580,0.9590,0.9579,0.9580
0.9572,0.9582,0.9572,0.9574,0.9582,0.9572,0.9574
0.9553,0.9564,0.9553,0.9554,0.9564,0.9553,0.9554
0.9572,0.9583,0.9572,0.9574,0.9583,0.9572,0.9574
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
0.9520,0.9531,0.9520,0.9519,0.9531,0.9520,0.9519
0.9520,0.9530,0.9520,0.9519,0.9530,0.9520,0.9519
0.9526,0.9536,0.9526,0.9526,0.9536,0.9526,0.9526
0.9520,0.9531,0.9520,0.9519,0.9531,0.9520,0.9519
0.9520,0.9530,0.9520,0.9519,0.9530,0.9520,0.9519
0.9526,0.9536,0.9526,0.9526,0.9536,0.9526,0.9526
0.9520,0.9531,0.9520,0.9520,0.9531,0.9520,0.9520
0.9520,0.9529,0.9520,0.9518,0.9529,0.9520,0.9518
0.9579,0.9588,0.9579,0.9579,0.9588,0.9579,0.9579
0.9533,0.9542,0.9533,0.9532,0.9542,0.9533,0.9532
0.9507,0.9516,0.9507,0.9506,0.9516,0.9507,0.9506
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0.9579,0.9588,0.9579,0.9579,0.9588,0.9579,0.9579
0.9533,0.9542,0.9533,0.9532,0.9542,0.9533,0.9532
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0.9520,0.9529,0.9520,0.9519,0.9529,0.9520,0.9519
0.9579,0.9590,0.9579,0.9580,0.9590,0.9579,0.9580
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
478 0.9572 0.9584 0.9572 0.9574 0.9584 0.9572 0.9574
479 0.9546 0.9558 0.9546 0.9548 0.9558 0.9546 0.9548
480 0.9559 0.9568 0.9559 0.9560 0.9568 0.9559 0.9560
481 0.9579 0.9590 0.9579 0.9580 0.9590 0.9579 0.9580
0.9572 0.9582 0.9572 0.9574 0.9582 0.9572 0.9574
0.9553 0.9564 0.9553 0.9554 0.9564 0.9553 0.9554
0.9572 0.9583 0.9572 0.9574 0.9583 0.9572 0.9574
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
0.9520 0.9531 0.9520 0.9519 0.9531 0.9520 0.9519
0.9520 0.9530 0.9520 0.9519 0.9530 0.9520 0.9519
0.9526 0.9536 0.9526 0.9526 0.9536 0.9526 0.9526
0.9520 0.9531 0.9520 0.9519 0.9531 0.9520 0.9519
0.9520 0.9530 0.9520 0.9519 0.9530 0.9520 0.9519
0.9526 0.9536 0.9526 0.9526 0.9536 0.9526 0.9526
0.9520 0.9531 0.9520 0.9520 0.9531 0.9520 0.9520
0.9520 0.9529 0.9520 0.9518 0.9529 0.9520 0.9518
0.9579 0.9588 0.9579 0.9579 0.9588 0.9579 0.9579
0.9533 0.9542 0.9533 0.9532 0.9542 0.9533 0.9532
0.9507 0.9516 0.9507 0.9506 0.9516 0.9507 0.9506
0.9526 0.9536 0.9526 0.9525 0.9536 0.9526 0.9525
0.9520 0.9531 0.9520 0.9519 0.9531 0.9520 0.9519
0.9520 0.9530 0.9520 0.9519 0.9530 0.9520 0.9519
0.9526 0.9536 0.9526 0.9526 0.9536 0.9526 0.9526
0.9520 0.9531 0.9520 0.9520 0.9531 0.9520 0.9520
0.9520 0.9529 0.9520 0.9518 0.9529 0.9520 0.9518
0.9520 0.9531 0.9520 0.9519 0.9531 0.9520 0.9519
0.9520 0.9530 0.9520 0.9519 0.9530 0.9520 0.9519
0.9520 0.9531 0.9520 0.9519 0.9531 0.9520 0.9519
0.9520 0.9531 0.9520 0.9519 0.9531 0.9520 0.9519
0.9520 0.9530 0.9520 0.9519 0.9530 0.9520 0.9519
0.9526 0.9536 0.9526 0.9526 0.9536 0.9526 0.9526
0.9520 0.9531 0.9520 0.9520 0.9531 0.9520 0.9520
0.9520 0.9529 0.9520 0.9518 0.9529 0.9520 0.9518
0.9579 0.9588 0.9579 0.9579 0.9588 0.9579 0.9579
0.9533 0.9542 0.9533 0.9532 0.9542 0.9533 0.9532
0.9520 0.9531 0.9520 0.9519 0.9531 0.9520 0.9519
0.9520 0.9530 0.9520 0.9519 0.9530 0.9520 0.9519
0.9526 0.9536 0.9526 0.9526 0.9536 0.9526 0.9526
0.9520 0.9531 0.9520 0.9520 0.9531 0.9520 0.9520
0.9520 0.9529 0.9520 0.9518 0.9529 0.9520 0.9518
0.9579 0.9588 0.9579 0.9579 0.9588 0.9579 0.9579
0.9533 0.9542 0.9533 0.9532 0.9542 0.9533 0.9532
0.9507 0.9516 0.9507 0.9506 0.9516 0.9507 0.9506
0.9526 0.9536 0.9526 0.9525 0.9536 0.9526 0.9525
0.9520 0.9528 0.9520 0.9518 0.9528 0.9520 0.9518
0.9539 0.9551 0.9539 0.9539 0.9551 0.9539 0.9539
0.9513 0.9523 0.9513 0.9512 0.9523 0.9513 0.9512
0.9520 0.9529 0.9520 0.9519 0.9529 0.9520 0.9519

604
reports/LinearFiltration/RESNET34_old/forget.csv
View File

@@ -1,604 +0,0 @@
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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
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
0.9500,1.0000,0.9500,0.9744,1.0000,0.9500,0.9744
0.9500,1.0000,0.9500,0.9744,1.0000,0.9500,0.9744
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.9500,1.0000,0.9500,0.9744,1.0000,0.9500,0.9744
0.9500,1.0000,0.9500,0.9744,1.0000,0.9500,0.9744
0.9500,1.0000,0.9500,0.9744,1.0000,0.9500,0.9744
0.9500,1.0000,0.9500,0.9744,1.0000,0.9500,0.9744
0.9500,1.0000,0.9500,0.9744,1.0000,0.9500,0.9744
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.6750,1.0000,0.6750,0.8060,1.0000,0.6750,0.8060
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
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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
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0312,1.0000,0.0312,0.0606,1.0000,0.0312,0.0606
1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000
1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.9969,1.0000,0.9969,0.9984,1.0000,0.9969,0.9984
1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000
1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000
0.9969,1.0000,0.9969,0.9984,1.0000,0.9969,0.9984
1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.7781,1.0000,0.7781,0.8752,1.0000,0.7781,0.8752
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
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reports/LinearFiltration/RESNET34_old/retain.csv
View File

@@ -1,604 +0,0 @@
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0.9520,0.9539,0.9520,0.9523,0.9539,0.9520,0.9523
0.9533,0.9547,0.9533,0.9535,0.9547,0.9533,0.9535
0.9546,0.9561,0.9546,0.9549,0.9561,0.9546,0.9549
0.9513,0.9531,0.9513,0.9516,0.9531,0.9513,0.9516
0.9533,0.9549,0.9533,0.9535,0.9549,0.9533,0.9535
0.9559,0.9570,0.9559,0.9561,0.9570,0.9559,0.9561
0.9513,0.9532,0.9513,0.9516,0.9532,0.9513,0.9516
0.9513,0.9531,0.9513,0.9516,0.9531,0.9513,0.9516
0.9520,0.9538,0.9520,0.9523,0.9538,0.9520,0.9523
0.9539,0.9554,0.9539,0.9542,0.9554,0.9539,0.9542
0.9526,0.9544,0.9526,0.9529,0.9544,0.9526,0.9529
0.9526,0.9541,0.9526,0.9529,0.9541,0.9526,0.9529
0.9533,0.9551,0.9533,0.9536,0.9551,0.9533,0.9536
0.9533,0.9545,0.9533,0.9535,0.9545,0.9533,0.9535
0.9546,0.9562,0.9546,0.9548,0.9562,0.9546,0.9548
0.9553,0.9570,0.9553,0.9555,0.9570,0.9553,0.9555
0.9566,0.9581,0.9566,0.9568,0.9581,0.9566,0.9568
0.9553,0.9568,0.9553,0.9555,0.9568,0.9553,0.9555
0.9513,0.9532,0.9513,0.9516,0.9532,0.9513,0.9516
0.9539,0.9556,0.9539,0.9542,0.9556,0.9539,0.9542
0.9520,0.9539,0.9520,0.9523,0.9539,0.9520,0.9523
0.9533,0.9547,0.9533,0.9535,0.9547,0.9533,0.9535
0.9546,0.9561,0.9546,0.9549,0.9561,0.9546,0.9549
0.9513,0.9531,0.9513,0.9516,0.9531,0.9513,0.9516
0.9533,0.9549,0.9533,0.9535,0.9549,0.9533,0.9535
0.9559,0.9570,0.9559,0.9561,0.9570,0.9559,0.9561
0.9513,0.9532,0.9513,0.9516,0.9532,0.9513,0.9516
0.9513,0.9531,0.9513,0.9516,0.9531,0.9513,0.9516
0.9520,0.9538,0.9520,0.9523,0.9538,0.9520,0.9523
0.9539,0.9554,0.9539,0.9542,0.9554,0.9539,0.9542
0.9526,0.9544,0.9526,0.9529,0.9544,0.9526,0.9529
0.9526,0.9541,0.9526,0.9529,0.9541,0.9526,0.9529
0.9533,0.9551,0.9533,0.9536,0.9551,0.9533,0.9536
0.9533,0.9545,0.9533,0.9535,0.9545,0.9533,0.9535
0.0526,0.0028,0.0526,0.0053,0.0028,0.0526,0.0053
0.0526,0.0028,0.0526,0.0053,0.0028,0.0526,0.0053
0.0526,0.0028,0.0526,0.0053,0.0028,0.0526,0.0053
0.0526,0.0028,0.0526,0.0053,0.0028,0.0526,0.0053
0.0526,0.0028,0.0526,0.0053,0.0028,0.0526,0.0053
0.0526,0.0028,0.0526,0.0053,0.0028,0.0526,0.0053
0.9526,0.9541,0.9526,0.9529,0.9541,0.9526,0.9529
0.9520,0.9541,0.9520,0.9525,0.9541,0.9520,0.9525
0.9520,0.9541,0.9520,0.9525,0.9541,0.9520,0.9525
0.9526,0.9541,0.9526,0.9529,0.9541,0.9526,0.9529
0.9533,0.9551,0.9533,0.9536,0.9551,0.9533,0.9536
0.9520,0.9541,0.9520,0.9525,0.9541,0.9520,0.9525
0.9520,0.9541,0.9520,0.9525,0.9541,0.9520,0.9525
0.9520,0.9541,0.9520,0.9525,0.9541,0.9520,0.9525
0.9520,0.9541,0.9520,0.9525,0.9541,0.9520,0.9525
0.9526,0.9541,0.9526,0.9529,0.9541,0.9526,0.9529
0.9526,0.9541,0.9526,0.9529,0.9541,0.9526,0.9529
0.9526,0.9541,0.9526,0.9529,0.9541,0.9526,0.9529
0.9533,0.9552,0.9533,0.9536,0.9552,0.9533,0.9536
0.9533,0.9551,0.9533,0.9538,0.9551,0.9533,0.9538
0.9526,0.9541,0.9526,0.9529,0.9541,0.9526,0.9529
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
2 0.9520 0.9531 0.9520 0.9519 0.9531 0.9520 0.9519
3 0.9520 0.9530 0.9520 0.9519 0.9530 0.9520 0.9519
4 0.9520 0.9530 0.9520 0.9519 0.9530 0.9520 0.9519
5 0.9513 0.9527 0.9513 0.9514 0.9527 0.9513 0.9514
6 0.9546 0.9553 0.9546 0.9545 0.9553 0.9546 0.9545
7 0.9507 0.9517 0.9507 0.9507 0.9517 0.9507 0.9507
8 0.9513 0.9527 0.9513 0.9513 0.9527 0.9513 0.9513
9 0.9559 0.9573 0.9559 0.9560 0.9573 0.9559 0.9560
10 0.9526 0.9538 0.9526 0.9527 0.9538 0.9526 0.9527
11 0.9493 0.9507 0.9493 0.9494 0.9507 0.9493 0.9494
12 0.9533 0.9544 0.9533 0.9533 0.9544 0.9533 0.9533
13 0.9513 0.9526 0.9513 0.9513 0.9526 0.9513 0.9513
14 0.9533 0.9546 0.9533 0.9534 0.9546 0.9533 0.9534
15 0.9500 0.9513 0.9500 0.9500 0.9513 0.9500 0.9500
16 0.9487 0.9498 0.9487 0.9487 0.9498 0.9487 0.9487
17 0.9507 0.9518 0.9507 0.9506 0.9518 0.9507 0.9506
18 0.9566 0.9579 0.9566 0.9566 0.9579 0.9566 0.9566
19 0.9507 0.9519 0.9507 0.9507 0.9519 0.9507 0.9507
20 0.9500 0.9515 0.9500 0.9501 0.9515 0.9500 0.9501
21 0.9520 0.9532 0.9520 0.9520 0.9532 0.9520 0.9520
22 0.9546 0.9558 0.9546 0.9547 0.9558 0.9546 0.9547
23 0.9586 0.9596 0.9586 0.9587 0.9596 0.9586 0.9587
24 0.9572 0.9579 0.9572 0.9572 0.9579 0.9572 0.9572
25 0.9559 0.9570 0.9559 0.9560 0.9570 0.9559 0.9560
26 0.9586 0.9594 0.9586 0.9586 0.9594 0.9586 0.9586
27 0.9592 0.9606 0.9592 0.9594 0.9606 0.9592 0.9594
28 0.9592 0.9601 0.9592 0.9593 0.9601 0.9592 0.9593
29 0.9553 0.9564 0.9553 0.9554 0.9564 0.9553 0.9554
30 0.9579 0.9587 0.9579 0.9579 0.9587 0.9579 0.9579
31 0.9559 0.9569 0.9559 0.9560 0.9569 0.9559 0.9560
32 0.9579 0.9590 0.9579 0.9580 0.9590 0.9579 0.9580
33 0.9579 0.9591 0.9579 0.9580 0.9591 0.9579 0.9580
34 0.9559 0.9569 0.9559 0.9560 0.9569 0.9559 0.9560
35 0.9572 0.9583 0.9572 0.9573 0.9583 0.9572 0.9573
36 0.9599 0.9609 0.9599 0.9600 0.9609 0.9599 0.9600
37 0.9566 0.9576 0.9566 0.9566 0.9576 0.9566 0.9566
38 0.9539 0.9551 0.9539 0.9540 0.9551 0.9539 0.9540
39 0.9579 0.9590 0.9579 0.9580 0.9590 0.9579 0.9580
40 0.9579 0.9588 0.9579 0.9579 0.9588 0.9579 0.9579
41 0.9539 0.9550 0.9539 0.9541 0.9550 0.9539 0.9541
42 0.9559 0.9563 0.9559 0.9558 0.9563 0.9559 0.9558
43 0.9579 0.9583 0.9579 0.9578 0.9583 0.9579 0.9578
44 0.9572 0.9575 0.9572 0.9571 0.9575 0.9572 0.9571
45 0.9546 0.9550 0.9546 0.9545 0.9550 0.9546 0.9545
46 0.9559 0.9564 0.9559 0.9558 0.9564 0.9559 0.9558
47 0.9579 0.9585 0.9579 0.9579 0.9585 0.9579 0.9579
48 0.9572 0.9579 0.9572 0.9572 0.9579 0.9572 0.9572
49 0.9539 0.9546 0.9539 0.9539 0.9546 0.9539 0.9539
50 0.9559 0.9564 0.9559 0.9558 0.9564 0.9559 0.9558
51 0.9566 0.9569 0.9566 0.9565 0.9569 0.9566 0.9565
52 0.9572 0.9578 0.9572 0.9572 0.9578 0.9572 0.9572
53 0.9546 0.9550 0.9546 0.9545 0.9550 0.9546 0.9545
54 0.9553 0.9558 0.9553 0.9552 0.9558 0.9553 0.9552
55 0.9539 0.9545 0.9539 0.9539 0.9545 0.9539 0.9539
56 0.9612 0.9616 0.9612 0.9611 0.9616 0.9612 0.9611
57 0.9553 0.9558 0.9553 0.9552 0.9558 0.9553 0.9552
58 0.9539 0.9544 0.9539 0.9539 0.9544 0.9539 0.9539
59 0.9553 0.9557 0.9553 0.9552 0.9557 0.9553 0.9552
60 0.9586 0.9590 0.9586 0.9584 0.9590 0.9586 0.9584
61 0.9546 0.9552 0.9546 0.9545 0.9552 0.9546 0.9545
62 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
63 0.9592 0.9601 0.9592 0.9593 0.9601 0.9592 0.9593
64 0.9599 0.9606 0.9599 0.9599 0.9606 0.9599 0.9599
65 0.9553 0.9565 0.9553 0.9554 0.9565 0.9553 0.9554
66 0.9586 0.9596 0.9586 0.9587 0.9596 0.9586 0.9587
67 0.9579 0.9591 0.9579 0.9580 0.9591 0.9579 0.9580
68 0.9592 0.9601 0.9592 0.9593 0.9601 0.9592 0.9593
69 0.9553 0.9563 0.9553 0.9553 0.9563 0.9553 0.9553
70 0.9586 0.9593 0.9586 0.9586 0.9593 0.9586 0.9586
71 0.9572 0.9581 0.9572 0.9573 0.9581 0.9572 0.9573
72 0.9566 0.9576 0.9566 0.9567 0.9576 0.9566 0.9567
73 0.9566 0.9575 0.9566 0.9566 0.9575 0.9566 0.9566
74 0.9579 0.9589 0.9579 0.9580 0.9589 0.9579 0.9580
75 0.9566 0.9577 0.9566 0.9567 0.9577 0.9566 0.9567
76 0.9599 0.9611 0.9599 0.9600 0.9611 0.9599 0.9600
77 0.9566 0.9578 0.9566 0.9567 0.9578 0.9566 0.9567
78 0.9539 0.9551 0.9539 0.9541 0.9551 0.9539 0.9541
79 0.9586 0.9595 0.9586 0.9586 0.9595 0.9586 0.9586
80 0.9579 0.9587 0.9579 0.9579 0.9587 0.9579 0.9579
81 0.9553 0.9564 0.9553 0.9554 0.9564 0.9553 0.9554
82 0.9539 0.9546 0.9539 0.9539 0.9546 0.9539 0.9539
83 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
84 0.9579 0.9582 0.9579 0.9578 0.9582 0.9579 0.9578
85 0.9546 0.9553 0.9546 0.9546 0.9553 0.9546 0.9546
86 0.9546 0.9553 0.9546 0.9546 0.9553 0.9546 0.9546
87 0.9599 0.9608 0.9599 0.9599 0.9608 0.9599 0.9599
88 0.9553 0.9558 0.9553 0.9553 0.9558 0.9553 0.9553
89 0.9539 0.9548 0.9539 0.9540 0.9548 0.9539 0.9540
90 0.9559 0.9567 0.9559 0.9559 0.9567 0.9559 0.9559
91 0.9566 0.9571 0.9566 0.9566 0.9571 0.9566 0.9566
92 0.9566 0.9573 0.9566 0.9566 0.9573 0.9566 0.9566
93 0.9539 0.9546 0.9539 0.9539 0.9546 0.9539 0.9539
94 0.9526 0.9533 0.9526 0.9526 0.9533 0.9526 0.9526
95 0.9546 0.9552 0.9546 0.9546 0.9552 0.9546 0.9546
96 0.9592 0.9599 0.9592 0.9593 0.9599 0.9592 0.9593
97 0.9546 0.9553 0.9546 0.9546 0.9553 0.9546 0.9546
98 0.9526 0.9534 0.9526 0.9527 0.9534 0.9526 0.9527
99 0.9553 0.9560 0.9553 0.9553 0.9560 0.9553 0.9553
100 0.9566 0.9572 0.9566 0.9566 0.9572 0.9566 0.9566
101 0.9533 0.9542 0.9533 0.9534 0.9542 0.9533 0.9534
102 0.9566 0.9573 0.9566 0.9566 0.9573 0.9566 0.9566
103 0.9579 0.9587 0.9579 0.9579 0.9587 0.9579 0.9579
104 0.9566 0.9571 0.9566 0.9566 0.9571 0.9566 0.9566
105 0.9559 0.9567 0.9559 0.9560 0.9567 0.9559 0.9560
106 0.9572 0.9580 0.9572 0.9573 0.9580 0.9572 0.9573
107 0.9605 0.9613 0.9605 0.9606 0.9613 0.9605 0.9606
108 0.9586 0.9592 0.9586 0.9586 0.9592 0.9586 0.9586
109 0.9572 0.9578 0.9572 0.9572 0.9578 0.9572 0.9572
110 0.9592 0.9597 0.9592 0.9592 0.9597 0.9592 0.9592
111 0.9566 0.9571 0.9566 0.9566 0.9571 0.9566 0.9566
112 0.9566 0.9574 0.9566 0.9566 0.9574 0.9566 0.9566
113 0.9566 0.9572 0.9566 0.9566 0.9572 0.9566 0.9566
114 0.9572 0.9578 0.9572 0.9572 0.9578 0.9572 0.9572
115 0.9572 0.9578 0.9572 0.9572 0.9578 0.9572 0.9572
116 0.9592 0.9599 0.9592 0.9592 0.9599 0.9592 0.9592
117 0.9579 0.9586 0.9579 0.9579 0.9586 0.9579 0.9579
118 0.9559 0.9567 0.9559 0.9560 0.9567 0.9559 0.9560
119 0.9559 0.9566 0.9559 0.9559 0.9566 0.9559 0.9559
120 0.9612 0.9618 0.9612 0.9612 0.9618 0.9612 0.9612
121 0.9566 0.9574 0.9566 0.9567 0.9574 0.9566 0.9567
122 0.9632 0.9636 0.9632 0.9632 0.9636 0.9632 0.9632
123 0.9612 0.9617 0.9612 0.9612 0.9617 0.9612 0.9612
124 0.9638 0.9642 0.9638 0.9638 0.9642 0.9638 0.9638
125 0.9612 0.9618 0.9612 0.9613 0.9618 0.9612 0.9613
126 0.9618 0.9623 0.9618 0.9619 0.9623 0.9618 0.9619
127 0.9632 0.9637 0.9632 0.9632 0.9637 0.9632 0.9632
128 0.9625 0.9632 0.9625 0.9626 0.9632 0.9625 0.9626
129 0.9599 0.9605 0.9599 0.9599 0.9605 0.9599 0.9599
130 0.9625 0.9631 0.9625 0.9625 0.9631 0.9625 0.9625
131 0.9632 0.9637 0.9632 0.9632 0.9637 0.9632 0.9632
132 0.9612 0.9619 0.9612 0.9613 0.9619 0.9612 0.9613
133 0.9618 0.9624 0.9618 0.9619 0.9624 0.9618 0.9619
134 0.9605 0.9611 0.9605 0.9606 0.9611 0.9605 0.9606
135 0.9625 0.9630 0.9625 0.9625 0.9630 0.9625 0.9625
136 0.9651 0.9657 0.9651 0.9652 0.9657 0.9651 0.9652
137 0.9612 0.9618 0.9612 0.9612 0.9618 0.9612 0.9612
138 0.9599 0.9605 0.9599 0.9600 0.9605 0.9599 0.9600
139 0.9612 0.9618 0.9612 0.9613 0.9618 0.9612 0.9613
140 0.9618 0.9624 0.9618 0.9619 0.9624 0.9618 0.9619
141 0.9605 0.9612 0.9605 0.9606 0.9612 0.9605 0.9606
142 0.9559 0.9571 0.9559 0.9560 0.9571 0.9559 0.9560
143 0.9559 0.9568 0.9559 0.9559 0.9568 0.9559 0.9559
144 0.9553 0.9559 0.9553 0.9553 0.9559 0.9553 0.9553
145 0.9546 0.9559 0.9546 0.9547 0.9559 0.9546 0.9547
146 0.9559 0.9568 0.9559 0.9560 0.9568 0.9559 0.9560
147 0.9599 0.9609 0.9599 0.9600 0.9609 0.9599 0.9600
148 0.9559 0.9569 0.9559 0.9560 0.9569 0.9559 0.9560
149 0.9546 0.9557 0.9546 0.9547 0.9557 0.9546 0.9547
150 0.9572 0.9583 0.9572 0.9573 0.9583 0.9572 0.9573
151 0.9586 0.9595 0.9586 0.9586 0.9595 0.9586 0.9586
152 0.9553 0.9563 0.9553 0.9553 0.9563 0.9553 0.9553
153 0.9572 0.9584 0.9572 0.9573 0.9584 0.9572 0.9573
154 0.9526 0.9538 0.9526 0.9528 0.9538 0.9526 0.9528
155 0.9553 0.9564 0.9553 0.9554 0.9564 0.9553 0.9554
156 0.9566 0.9575 0.9566 0.9566 0.9575 0.9566 0.9566
157 0.9546 0.9558 0.9546 0.9547 0.9558 0.9546 0.9547
158 0.9546 0.9559 0.9546 0.9548 0.9559 0.9546 0.9548
159 0.9553 0.9566 0.9553 0.9554 0.9566 0.9553 0.9554
160 0.9553 0.9562 0.9553 0.9553 0.9562 0.9553 0.9553
161 0.9539 0.9551 0.9539 0.9541 0.9551 0.9539 0.9541
162 0.9572 0.9595 0.9572 0.9577 0.9595 0.9572 0.9577
163 0.9605 0.9629 0.9605 0.9610 0.9629 0.9605 0.9610
164 0.9586 0.9603 0.9586 0.9589 0.9603 0.9586 0.9589
165 0.9605 0.9626 0.9605 0.9610 0.9626 0.9605 0.9610
166 0.9592 0.9615 0.9592 0.9597 0.9615 0.9592 0.9597
167 0.9605 0.9623 0.9605 0.9608 0.9623 0.9605 0.9608
168 0.9586 0.9607 0.9586 0.9590 0.9607 0.9586 0.9590
169 0.9579 0.9605 0.9579 0.9584 0.9605 0.9579 0.9584
170 0.9599 0.9620 0.9599 0.9603 0.9620 0.9599 0.9603
171 0.9579 0.9600 0.9579 0.9583 0.9600 0.9579 0.9583
172 0.9579 0.9603 0.9579 0.9584 0.9603 0.9579 0.9584
173 0.9572 0.9597 0.9572 0.9577 0.9597 0.9572 0.9577
174 0.9566 0.9591 0.9566 0.9571 0.9591 0.9566 0.9571
175 0.9572 0.9595 0.9572 0.9577 0.9595 0.9572 0.9577
176 0.9586 0.9606 0.9586 0.9590 0.9606 0.9586 0.9590
177 0.9579 0.9604 0.9579 0.9584 0.9604 0.9579 0.9584
178 0.9572 0.9597 0.9572 0.9577 0.9597 0.9572 0.9577
179 0.9579 0.9600 0.9579 0.9583 0.9600 0.9579 0.9583
180 0.9586 0.9606 0.9586 0.9590 0.9606 0.9586 0.9590
181 0.9553 0.9582 0.9553 0.9559 0.9582 0.9553 0.9559
182 0.9599 0.9603 0.9599 0.9598 0.9603 0.9599 0.9598
183 0.9605 0.9612 0.9605 0.9606 0.9612 0.9605 0.9606
184 0.9592 0.9596 0.9592 0.9592 0.9596 0.9592 0.9592
185 0.9599 0.9604 0.9599 0.9599 0.9604 0.9599 0.9599
186 0.9605 0.9610 0.9605 0.9605 0.9610 0.9605 0.9605
187 0.9645 0.9650 0.9645 0.9645 0.9650 0.9645 0.9645
188 0.9632 0.9637 0.9632 0.9632 0.9637 0.9632 0.9632
189 0.9599 0.9604 0.9599 0.9599 0.9604 0.9599 0.9599
190 0.9612 0.9616 0.9612 0.9611 0.9616 0.9612 0.9611
191 0.9599 0.9605 0.9599 0.9599 0.9605 0.9599 0.9599
192 0.9599 0.9604 0.9599 0.9598 0.9604 0.9599 0.9598
193 0.9599 0.9603 0.9599 0.9598 0.9603 0.9599 0.9598
194 0.9592 0.9597 0.9592 0.9592 0.9597 0.9592 0.9592
195 0.9599 0.9605 0.9599 0.9599 0.9605 0.9599 0.9599
196 0.9632 0.9636 0.9632 0.9631 0.9636 0.9632 0.9631
197 0.9592 0.9598 0.9592 0.9592 0.9598 0.9592 0.9592
198 0.9599 0.9604 0.9599 0.9599 0.9604 0.9599 0.9599
199 0.9605 0.9610 0.9605 0.9605 0.9610 0.9605 0.9605
200 0.9625 0.9630 0.9625 0.9625 0.9630 0.9625 0.9625
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233 0.9559 0.9568 0.9559 0.9559 0.9568 0.9559 0.9559
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309 0.9553 0.9566 0.9553 0.9553 0.9566 0.9553 0.9553
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313 0.9546 0.9554 0.9546 0.9545 0.9554 0.9546 0.9545
314 0.9526 0.9538 0.9526 0.9527 0.9538 0.9526 0.9527
315 0.9553 0.9564 0.9553 0.9553 0.9564 0.9553 0.9553
316 0.9586 0.9596 0.9586 0.9586 0.9596 0.9586 0.9586
317 0.9553 0.9565 0.9553 0.9553 0.9565 0.9553 0.9553
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320 0.9546 0.9555 0.9546 0.9546 0.9555 0.9546 0.9546
321 0.9546 0.9554 0.9546 0.9546 0.9554 0.9546 0.9546
322 0.9500 0.9513 0.9500 0.9501 0.9513 0.9500 0.9501
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325 0.9507 0.9519 0.9507 0.9507 0.9519 0.9507 0.9507
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331 0.9507 0.9518 0.9507 0.9507 0.9518 0.9507 0.9507
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335 0.9513 0.9527 0.9513 0.9514 0.9527 0.9513 0.9514
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399 0.9572 0.9582 0.9572 0.9573 0.9582 0.9572 0.9573
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443 0.9546 0.9562 0.9546 0.9548 0.9562 0.9546 0.9548
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461 0.9546 0.9562 0.9546 0.9548 0.9562 0.9546 0.9548
462 0.9553 0.9570 0.9553 0.9555 0.9570 0.9553 0.9555
463 0.9566 0.9581 0.9566 0.9568 0.9581 0.9566 0.9568
464 0.9553 0.9568 0.9553 0.9555 0.9568 0.9553 0.9555
465 0.9513 0.9532 0.9513 0.9516 0.9532 0.9513 0.9516
466 0.9539 0.9556 0.9539 0.9542 0.9556 0.9539 0.9542
467 0.9520 0.9539 0.9520 0.9523 0.9539 0.9520 0.9523
468 0.9533 0.9547 0.9533 0.9535 0.9547 0.9533 0.9535
469 0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529
470 0.9533 0.9551 0.9533 0.9536 0.9551 0.9533 0.9536
471 0.9533 0.9545 0.9533 0.9535 0.9545 0.9533 0.9535
472 0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529
473 0.9533 0.9551 0.9533 0.9536 0.9551 0.9533 0.9536
474 0.9533 0.9545 0.9533 0.9535 0.9545 0.9533 0.9535
475 0.9546 0.9562 0.9546 0.9548 0.9562 0.9546 0.9548
476 0.9553 0.9570 0.9553 0.9555 0.9570 0.9553 0.9555
477 0.9566 0.9581 0.9566 0.9568 0.9581 0.9566 0.9568
478 0.9553 0.9568 0.9553 0.9555 0.9568 0.9553 0.9555
479 0.9513 0.9532 0.9513 0.9516 0.9532 0.9513 0.9516
480 0.9539 0.9556 0.9539 0.9542 0.9556 0.9539 0.9542
481 0.9520 0.9539 0.9520 0.9523 0.9539 0.9520 0.9523
482 0.9533 0.9547 0.9533 0.9535 0.9547 0.9533 0.9535
483 0.9546 0.9561 0.9546 0.9549 0.9561 0.9546 0.9549
484 0.9513 0.9531 0.9513 0.9516 0.9531 0.9513 0.9516
485 0.9533 0.9549 0.9533 0.9535 0.9549 0.9533 0.9535
486 0.9559 0.9570 0.9559 0.9561 0.9570 0.9559 0.9561
487 0.9513 0.9532 0.9513 0.9516 0.9532 0.9513 0.9516
488 0.9513 0.9531 0.9513 0.9516 0.9531 0.9513 0.9516
489 0.9520 0.9538 0.9520 0.9523 0.9538 0.9520 0.9523
490 0.9539 0.9554 0.9539 0.9542 0.9554 0.9539 0.9542
491 0.9526 0.9544 0.9526 0.9529 0.9544 0.9526 0.9529
492 0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529
493 0.9533 0.9551 0.9533 0.9536 0.9551 0.9533 0.9536
494 0.9533 0.9545 0.9533 0.9535 0.9545 0.9533 0.9535
495 0.9546 0.9562 0.9546 0.9548 0.9562 0.9546 0.9548
496 0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529
497 0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529
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499 0.9533 0.9545 0.9533 0.9535 0.9545 0.9533 0.9535
500 0.9546 0.9562 0.9546 0.9548 0.9562 0.9546 0.9548
501 0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529
502 0.9533 0.9551 0.9533 0.9536 0.9551 0.9533 0.9536
503 0.9533 0.9545 0.9533 0.9535 0.9545 0.9533 0.9535
504 0.9546 0.9562 0.9546 0.9548 0.9562 0.9546 0.9548
505 0.9553 0.9570 0.9553 0.9555 0.9570 0.9553 0.9555
506 0.9566 0.9581 0.9566 0.9568 0.9581 0.9566 0.9568
507 0.9553 0.9568 0.9553 0.9555 0.9568 0.9553 0.9555
508 0.9513 0.9532 0.9513 0.9516 0.9532 0.9513 0.9516
509 0.9539 0.9556 0.9539 0.9542 0.9556 0.9539 0.9542
510 0.9520 0.9539 0.9520 0.9523 0.9539 0.9520 0.9523
511 0.9533 0.9547 0.9533 0.9535 0.9547 0.9533 0.9535
512 0.9546 0.9561 0.9546 0.9549 0.9561 0.9546 0.9549
513 0.9513 0.9531 0.9513 0.9516 0.9531 0.9513 0.9516
514 0.9533 0.9549 0.9533 0.9535 0.9549 0.9533 0.9535
515 0.9559 0.9570 0.9559 0.9561 0.9570 0.9559 0.9561
516 0.9513 0.9532 0.9513 0.9516 0.9532 0.9513 0.9516
517 0.9513 0.9531 0.9513 0.9516 0.9531 0.9513 0.9516
518 0.9520 0.9538 0.9520 0.9523 0.9538 0.9520 0.9523
519 0.9539 0.9554 0.9539 0.9542 0.9554 0.9539 0.9542
520 0.9526 0.9544 0.9526 0.9529 0.9544 0.9526 0.9529
521 0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529
522 0.9533 0.9551 0.9533 0.9536 0.9551 0.9533 0.9536
523 0.9533 0.9545 0.9533 0.9535 0.9545 0.9533 0.9535
524 0.9546 0.9562 0.9546 0.9548 0.9562 0.9546 0.9548
525 0.9553 0.9570 0.9553 0.9555 0.9570 0.9553 0.9555
526 0.9566 0.9581 0.9566 0.9568 0.9581 0.9566 0.9568
527 0.9553 0.9568 0.9553 0.9555 0.9568 0.9553 0.9555
528 0.9513 0.9532 0.9513 0.9516 0.9532 0.9513 0.9516
529 0.9539 0.9556 0.9539 0.9542 0.9556 0.9539 0.9542
530 0.9520 0.9539 0.9520 0.9523 0.9539 0.9520 0.9523
531 0.9533 0.9547 0.9533 0.9535 0.9547 0.9533 0.9535
532 0.9546 0.9561 0.9546 0.9549 0.9561 0.9546 0.9549
533 0.9513 0.9531 0.9513 0.9516 0.9531 0.9513 0.9516
534 0.9533 0.9549 0.9533 0.9535 0.9549 0.9533 0.9535
535 0.9559 0.9570 0.9559 0.9561 0.9570 0.9559 0.9561
536 0.9513 0.9532 0.9513 0.9516 0.9532 0.9513 0.9516
537 0.9513 0.9531 0.9513 0.9516 0.9531 0.9513 0.9516
538 0.9520 0.9538 0.9520 0.9523 0.9538 0.9520 0.9523
539 0.9539 0.9554 0.9539 0.9542 0.9554 0.9539 0.9542
540 0.9526 0.9544 0.9526 0.9529 0.9544 0.9526 0.9529
541 0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529
542 0.9533 0.9551 0.9533 0.9536 0.9551 0.9533 0.9536
543 0.9533 0.9545 0.9533 0.9535 0.9545 0.9533 0.9535
544 0.9546 0.9562 0.9546 0.9548 0.9562 0.9546 0.9548
545 0.9553 0.9570 0.9553 0.9555 0.9570 0.9553 0.9555
546 0.9566 0.9581 0.9566 0.9568 0.9581 0.9566 0.9568
547 0.9553 0.9568 0.9553 0.9555 0.9568 0.9553 0.9555
548 0.9513 0.9532 0.9513 0.9516 0.9532 0.9513 0.9516
549 0.9539 0.9556 0.9539 0.9542 0.9556 0.9539 0.9542
550 0.9520 0.9539 0.9520 0.9523 0.9539 0.9520 0.9523
551 0.9533 0.9547 0.9533 0.9535 0.9547 0.9533 0.9535
552 0.9546 0.9561 0.9546 0.9549 0.9561 0.9546 0.9549
553 0.9513 0.9531 0.9513 0.9516 0.9531 0.9513 0.9516
554 0.9533 0.9549 0.9533 0.9535 0.9549 0.9533 0.9535
555 0.9559 0.9570 0.9559 0.9561 0.9570 0.9559 0.9561
556 0.9513 0.9532 0.9513 0.9516 0.9532 0.9513 0.9516
557 0.9513 0.9531 0.9513 0.9516 0.9531 0.9513 0.9516
558 0.9520 0.9538 0.9520 0.9523 0.9538 0.9520 0.9523
559 0.9539 0.9554 0.9539 0.9542 0.9554 0.9539 0.9542
560 0.9526 0.9544 0.9526 0.9529 0.9544 0.9526 0.9529
561 0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529
562 0.9533 0.9551 0.9533 0.9536 0.9551 0.9533 0.9536
563 0.9533 0.9545 0.9533 0.9535 0.9545 0.9533 0.9535
564 0.9546 0.9562 0.9546 0.9548 0.9562 0.9546 0.9548
565 0.9553 0.9570 0.9553 0.9555 0.9570 0.9553 0.9555
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567 0.9553 0.9568 0.9553 0.9555 0.9568 0.9553 0.9555
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570 0.9520 0.9539 0.9520 0.9523 0.9539 0.9520 0.9523
571 0.9533 0.9547 0.9533 0.9535 0.9547 0.9533 0.9535
572 0.9546 0.9561 0.9546 0.9549 0.9561 0.9546 0.9549
573 0.9513 0.9531 0.9513 0.9516 0.9531 0.9513 0.9516
574 0.9533 0.9549 0.9533 0.9535 0.9549 0.9533 0.9535
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576 0.9513 0.9532 0.9513 0.9516 0.9532 0.9513 0.9516
577 0.9513 0.9531 0.9513 0.9516 0.9531 0.9513 0.9516
578 0.9520 0.9538 0.9520 0.9523 0.9538 0.9520 0.9523
579 0.9539 0.9554 0.9539 0.9542 0.9554 0.9539 0.9542
580 0.9526 0.9544 0.9526 0.9529 0.9544 0.9526 0.9529
581 0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529
582 0.9533 0.9551 0.9533 0.9536 0.9551 0.9533 0.9536
583 0.9533 0.9545 0.9533 0.9535 0.9545 0.9533 0.9535
584 0.0526 0.0028 0.0526 0.0053 0.0028 0.0526 0.0053
585 0.0526 0.0028 0.0526 0.0053 0.0028 0.0526 0.0053
586 0.0526 0.0028 0.0526 0.0053 0.0028 0.0526 0.0053
587 0.0526 0.0028 0.0526 0.0053 0.0028 0.0526 0.0053
588 0.0526 0.0028 0.0526 0.0053 0.0028 0.0526 0.0053
589 0.0526 0.0028 0.0526 0.0053 0.0028 0.0526 0.0053
590 0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529
591 0.9520 0.9541 0.9520 0.9525 0.9541 0.9520 0.9525
592 0.9520 0.9541 0.9520 0.9525 0.9541 0.9520 0.9525
593 0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529
594 0.9533 0.9551 0.9533 0.9536 0.9551 0.9533 0.9536
595 0.9520 0.9541 0.9520 0.9525 0.9541 0.9520 0.9525
596 0.9520 0.9541 0.9520 0.9525 0.9541 0.9520 0.9525
597 0.9520 0.9541 0.9520 0.9525 0.9541 0.9520 0.9525
598 0.9520 0.9541 0.9520 0.9525 0.9541 0.9520 0.9525
599 0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529
600 0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529
601 0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529
602 0.9533 0.9552 0.9533 0.9536 0.9552 0.9533 0.9536
603 0.9533 0.9551 0.9533 0.9538 0.9551 0.9533 0.9538
604 0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529

11
reports/LinearFiltration/ResNet/time_metrics.txt
View File

@@ -39,17 +39,6 @@ execution_time_sec
0.001871
0.001864
0.001900
1.571003
0.001921
0.001930
0.001908
0.001897
0.001901
0.001898
0.001900
0.001895
0.001903
0.005801
1.588161
0.002564
0.001957

0
reports/LinearFiltration/ResNet_old/time_metrics_full sample.txt.txt → reports/LinearFiltration/ResNet/time_metrics_full sample.txt.txt
View File

3
reports/LinearFiltration/ResNet_old/Report sample.txt
View File

@@ -1,3 +0,0 @@
on full data:
- average first run = 16.495034

590
reports/LinearFiltration/ResNet_old/old.txt
View File

@@ -1,590 +0,0 @@
execution_time_sec
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108
reports/LinearFiltration/attack_values_old.csv
View File

@@ -1,108 +0,0 @@
target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
0,0.500000,1.000000, 0.062500, 0.485899
1,0.500000,1.000000, 0.083333, 0.484178
2,0.500000,0.989583, 0.010417, 0.525418
3,0.500000,0.989583, 0.333333, 0.487282
4,0.500000,1.000000, 0.020833, 0.505002
5,0.500000,1.000000, 0.093750, 0.491621
6,0.500000,1.000000, 0.052083, 0.482068
7,0.500000,0.979167, 0.010417, 0.519800
8,0.500000,0.989583, 0.020833, 0.485863
9,0.500000,0.989583, 0.208333, 0.483455
10,0.500000,1.000000, 0.145833, 0.454615
11,0.500000,1.000000, 0.333333, 0.495618
12,0.500000,1.000000, 0.072917, 0.458427
13,0.500000,1.000000, 0.135417, 0.477052
14,0.500000,1.000000, 0.020833, 0.472611
15,0.500000,1.000000, 0.239583, 0.479607
16,0.500000,1.000000, 0.135417, 0.468667
17,0.500000,1.000000, 0.208333, 0.469592
18,0.500000,1.000000, 0.197917, 0.454318
19,0.500000,0.989583, 0.041667, 0.499034
0,0.500000,1.000000, 0.072917, 0.483694
1,0.500000,1.000000, 0.239583, 0.482249
2,0.500000,0.989583, 0.062500, 0.528699
3,0.500000,1.000000, 0.177083, 0.488769
0,0.500000,1.000000, 0.208333, 0.479962
1,0.500000,1.000000, 0.177083, 0.480766
2,0.500000,1.000000, 0.156250, 0.530593
3,0.500000,1.000000, 0.031250, 0.490093
4,0.500000,1.000000, 0.062500, 0.514937
5,0.500000,1.000000, 0.041667, 0.483999
6,0.500000,1.000000, 0.041667, 0.480639
7,0.500000,1.000000, 0.114583, 0.516643
8,0.500000,1.000000, 0.322917, 0.482295
9,0.500000,1.000000, 0.156250, 0.482486
10,0.500000,1.000000, 0.208333, 0.454807
11,0.500000,1.000000, 0.197917, 0.496418
12,0.500000,1.000000, 0.083333, 0.449275
13,0.500000,1.000000, 0.177083, 0.478610
14,0.500000,1.000000, 0.177083, 0.465639
15,0.500000,1.000000, 0.031250, 0.481693
16,0.500000,1.000000, 0.062500, 0.471196
17,0.500000,1.000000, 0.218750, 0.467138
18,0.500000,1.000000, 0.156250, 0.450509
19,0.500000,1.000000, 0.177083, 0.494708
0,0.500000,1.000000, 0.062500, 0.485891
1,0.500000,1.000000, 0.218750, 0.478561
2,0.500000,1.000000, 0.135417, 0.532665
3,0.500000,1.000000, 0.072917, 0.489767
4,0.500000,1.000000, 0.125000, 0.507482
5,0.500000,1.000000, 0.031250, 0.494227
6,0.500000,1.000000, 0.114583, 0.482238
7,0.500000,1.000000, 0.041667, 0.515044
8,0.500000,1.000000, 0.250000, 0.487606
9,0.500000,1.000000, 0.083333, 0.481131
10,0.500000,1.000000, 0.229167, 0.452552
11,0.500000,1.000000, 0.291667, 0.507944
12,0.500000,1.000000, 0.177083, 0.454018
13,0.500000,1.000000, 0.250000, 0.478261
14,0.500000,1.000000, 0.145833, 0.474314
15,0.500000,1.000000, 0.135417, 0.480764
16,0.500000,1.000000, 0.020833, 0.468673
17,0.500000,1.000000, 0.281250, 0.465219
18,0.493750,1.000000, 0.177083, 0.460478
19,0.500000,1.000000, 0.322917, 0.491559
0,0.500000,1.000000, 0.072917, 0.481852
1,0.500000,1.000000, 0.197917, 0.479149
2,0.500000,1.000000, 0.218750, 0.525174
3,0.500000,1.000000, 0.156250, 0.486232
4,0.500000,1.000000, 0.052083, 0.512113
5,0.500000,1.000000, 0.010417, 0.486220
6,0.500000,1.000000, 0.208333, 0.487183
7,0.500000,1.000000, 0.083333, 0.514833
8,0.500000,1.000000, 0.052083, 0.491940
9,0.500000,1.000000, 0.052083, 0.489207
10,0.500000,1.000000, 0.104167, 0.454086
11,0.500000,1.000000, 0.333333, 0.503387
12,0.500000,1.000000, 0.208333, 0.455327
13,0.500000,1.000000, 0.020833, 0.486724
14,0.500000,1.000000, 0.010417, 0.471118
15,0.500000,1.000000, 0.010417, 0.477412
16,0.500000,1.000000, 0.260417, 0.467595
17,0.500000,1.000000, 0.208333, 0.467013
18,0.500000,1.000000, 0.270833, 0.453672
19,0.500000,1.000000, 0.208333, 0.496708
0,0.500000,1.000000, 0.177083, 0.478702
1,0.500000,1.000000, 0.145833, 0.479852
2,0.500000,1.000000, 0.156250, 0.524556
3,0.500000,1.000000, 0.020833, 0.484795
4,0.500000,1.000000, 0.000000, 0.510410
5,0.500000,1.000000, 0.072917, 0.494813
6,0.500000,1.000000, 0.239583, 0.479916
7,0.500000,1.000000, 0.062500, 0.516064
8,0.500000,1.000000, 0.114583, 0.486330
9,0.500000,1.000000, 0.093750, 0.483989
10,0.500000,1.000000, 0.260417, 0.458786
11,0.500000,1.000000, 0.218750, 0.500200
12,0.500000,1.000000, 0.166667, 0.460794
13,0.500000,1.000000, 0.135417, 0.487145
14,0.500000,1.000000, 0.041667, 0.470029
15,0.500000,1.000000, 0.020833, 0.479780
16,0.500000,1.000000, 0.208333, 0.468624
17,0.500000,1.000000, 0.291667, 0.465381
18,0.493750,1.000000, 0.145833, 0.449917
19,0.500000,1.000000, 0.250000, 0.493480
0,0.500000,1.000000, 0.145833, 0.477865
1,0.500000,1.000000, 0.072917, 0.476946
2,0.500000,1.000000, 0.187500, 0.530780
1 target_class parameter_mia_accuracy lookalike_accuracy A-Dist JS-Dist
2 0 0.500000 1.000000 0.062500 0.485899
3 1 0.500000 1.000000 0.083333 0.484178
4 2 0.500000 0.989583 0.010417 0.525418
5 3 0.500000 0.989583 0.333333 0.487282
6 4 0.500000 1.000000 0.020833 0.505002
7 5 0.500000 1.000000 0.093750 0.491621
8 6 0.500000 1.000000 0.052083 0.482068
9 7 0.500000 0.979167 0.010417 0.519800
10 8 0.500000 0.989583 0.020833 0.485863
11 9 0.500000 0.989583 0.208333 0.483455
12 10 0.500000 1.000000 0.145833 0.454615
13 11 0.500000 1.000000 0.333333 0.495618
14 12 0.500000 1.000000 0.072917 0.458427
15 13 0.500000 1.000000 0.135417 0.477052
16 14 0.500000 1.000000 0.020833 0.472611
17 15 0.500000 1.000000 0.239583 0.479607
18 16 0.500000 1.000000 0.135417 0.468667
19 17 0.500000 1.000000 0.208333 0.469592
20 18 0.500000 1.000000 0.197917 0.454318
21 19 0.500000 0.989583 0.041667 0.499034
22 0 0.500000 1.000000 0.072917 0.483694
23 1 0.500000 1.000000 0.239583 0.482249
24 2 0.500000 0.989583 0.062500 0.528699
25 3 0.500000 1.000000 0.177083 0.488769
26 0 0.500000 1.000000 0.208333 0.479962
27 1 0.500000 1.000000 0.177083 0.480766
28 2 0.500000 1.000000 0.156250 0.530593
29 3 0.500000 1.000000 0.031250 0.490093
30 4 0.500000 1.000000 0.062500 0.514937
31 5 0.500000 1.000000 0.041667 0.483999
32 6 0.500000 1.000000 0.041667 0.480639
33 7 0.500000 1.000000 0.114583 0.516643
34 8 0.500000 1.000000 0.322917 0.482295
35 9 0.500000 1.000000 0.156250 0.482486
36 10 0.500000 1.000000 0.208333 0.454807
37 11 0.500000 1.000000 0.197917 0.496418
38 12 0.500000 1.000000 0.083333 0.449275
39 13 0.500000 1.000000 0.177083 0.478610
40 14 0.500000 1.000000 0.177083 0.465639
41 15 0.500000 1.000000 0.031250 0.481693
42 16 0.500000 1.000000 0.062500 0.471196
43 17 0.500000 1.000000 0.218750 0.467138
44 18 0.500000 1.000000 0.156250 0.450509
45 19 0.500000 1.000000 0.177083 0.494708
46 0 0.500000 1.000000 0.062500 0.485891
47 1 0.500000 1.000000 0.218750 0.478561
48 2 0.500000 1.000000 0.135417 0.532665
49 3 0.500000 1.000000 0.072917 0.489767
50 4 0.500000 1.000000 0.125000 0.507482
51 5 0.500000 1.000000 0.031250 0.494227
52 6 0.500000 1.000000 0.114583 0.482238
53 7 0.500000 1.000000 0.041667 0.515044
54 8 0.500000 1.000000 0.250000 0.487606
55 9 0.500000 1.000000 0.083333 0.481131
56 10 0.500000 1.000000 0.229167 0.452552
57 11 0.500000 1.000000 0.291667 0.507944
58 12 0.500000 1.000000 0.177083 0.454018
59 13 0.500000 1.000000 0.250000 0.478261
60 14 0.500000 1.000000 0.145833 0.474314
61 15 0.500000 1.000000 0.135417 0.480764
62 16 0.500000 1.000000 0.020833 0.468673
63 17 0.500000 1.000000 0.281250 0.465219
64 18 0.493750 1.000000 0.177083 0.460478
65 19 0.500000 1.000000 0.322917 0.491559
66 0 0.500000 1.000000 0.072917 0.481852
67 1 0.500000 1.000000 0.197917 0.479149
68 2 0.500000 1.000000 0.218750 0.525174
69 3 0.500000 1.000000 0.156250 0.486232
70 4 0.500000 1.000000 0.052083 0.512113
71 5 0.500000 1.000000 0.010417 0.486220
72 6 0.500000 1.000000 0.208333 0.487183
73 7 0.500000 1.000000 0.083333 0.514833
74 8 0.500000 1.000000 0.052083 0.491940
75 9 0.500000 1.000000 0.052083 0.489207
76 10 0.500000 1.000000 0.104167 0.454086
77 11 0.500000 1.000000 0.333333 0.503387
78 12 0.500000 1.000000 0.208333 0.455327
79 13 0.500000 1.000000 0.020833 0.486724
80 14 0.500000 1.000000 0.010417 0.471118
81 15 0.500000 1.000000 0.010417 0.477412
82 16 0.500000 1.000000 0.260417 0.467595
83 17 0.500000 1.000000 0.208333 0.467013
84 18 0.500000 1.000000 0.270833 0.453672
85 19 0.500000 1.000000 0.208333 0.496708
86 0 0.500000 1.000000 0.177083 0.478702
87 1 0.500000 1.000000 0.145833 0.479852
88 2 0.500000 1.000000 0.156250 0.524556
89 3 0.500000 1.000000 0.020833 0.484795
90 4 0.500000 1.000000 0.000000 0.510410
91 5 0.500000 1.000000 0.072917 0.494813
92 6 0.500000 1.000000 0.239583 0.479916
93 7 0.500000 1.000000 0.062500 0.516064
94 8 0.500000 1.000000 0.114583 0.486330
95 9 0.500000 1.000000 0.093750 0.483989
96 10 0.500000 1.000000 0.260417 0.458786
97 11 0.500000 1.000000 0.218750 0.500200
98 12 0.500000 1.000000 0.166667 0.460794
99 13 0.500000 1.000000 0.135417 0.487145
100 14 0.500000 1.000000 0.041667 0.470029
101 15 0.500000 1.000000 0.020833 0.479780
102 16 0.500000 1.000000 0.208333 0.468624
103 17 0.500000 1.000000 0.291667 0.465381
104 18 0.493750 1.000000 0.145833 0.449917
105 19 0.500000 1.000000 0.250000 0.493480
106 0 0.500000 1.000000 0.145833 0.477865
107 1 0.500000 1.000000 0.072917 0.476946
108 2 0.500000 1.000000 0.187500 0.530780

12
reports/Retrain/attack_values.csv
View File

@@ -1,7 +1,4 @@
target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
0,0.500000,0.000000, 0.156250, 0.000000
1,0.500000,0.000000, 0.104167, 0.000000
2,0.500000,0.000000, 0.114583, 0.000000
0,0.500000,0.000000, 0.125000, 0.000000
1,0.500000,0.000000, 0.072917, 0.000000
2,0.500000,0.000000, 0.020833, 0.000000
@@ -22,10 +19,6 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
17,0.500000,0.000000, 0.093750, 0.000000
18,0.531250,0.000000, 0.010417, 0.000000
19,0.500000,0.000000, 0.031250, 0.000000
0,0.500000,0.000000, 0.062500, 0.000000
1,0.500000,0.000000, 0.072917, 0.000000
2,0.500000,0.000000, 0.031250, 0.000000
3,0.500000,0.000000, 0.083333, 0.000000
0,0.500000,0.000000, 0.010417, 0.000000
1,0.500000,0.000000, 0.062500, 0.000000
2,0.500000,0.000000, 0.104167, 0.000000
@@ -106,8 +99,3 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
17,0.500000,0.000000, 0.125000, 0.000000
18,0.500000,0.000000, 0.114583, 0.000000
19,0.500000,0.000000, 0.104167, 0.000000
0,0.500000,0.000000, 0.031250, 0.000000
1,0.500000,0.000000, 0.072917, 0.000000
2,0.500000,0.000000, 0.104167, 0.000000
0,0.500000,0.000000, 0.041667, 0.000000
0,0.500000,0.000000, 0.052083, 0.000000
1 target_class parameter_mia_accuracy lookalike_accuracy A-Dist JS-Dist
0 0.500000 0.000000 0.156250 0.000000
1 0.500000 0.000000 0.104167 0.000000
2 0.500000 0.000000 0.114583 0.000000
2 0 0.500000 0.000000 0.125000 0.000000
3 1 0.500000 0.000000 0.072917 0.000000
4 2 0.500000 0.000000 0.020833 0.000000
19 17 0.500000 0.000000 0.093750 0.000000
20 18 0.531250 0.000000 0.010417 0.000000
21 19 0.500000 0.000000 0.031250 0.000000
0 0.500000 0.000000 0.062500 0.000000
1 0.500000 0.000000 0.072917 0.000000
2 0.500000 0.000000 0.031250 0.000000
3 0.500000 0.000000 0.083333 0.000000
22 0 0.500000 0.000000 0.010417 0.000000
23 1 0.500000 0.000000 0.062500 0.000000
24 2 0.500000 0.000000 0.104167 0.000000
99 17 0.500000 0.000000 0.125000 0.000000
100 18 0.500000 0.000000 0.114583 0.000000
101 19 0.500000 0.000000 0.104167 0.000000
0 0.500000 0.000000 0.031250 0.000000
1 0.500000 0.000000 0.072917 0.000000
2 0.500000 0.000000 0.104167 0.000000
0 0.500000 0.000000 0.041667 0.000000
0 0.500000 0.000000 0.052083 0.000000

38
reports/Retrain/attack_values_int.csv
View File

@@ -1,38 +0,0 @@
target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy
0,0.500000,11.573492,0.000000
1,0.500000,12.793120,0.000000
2,0.500000,12.951434,0.000000
3,0.500000,10.942259,0.000000
4,0.500000,10.675704,0.000000
5,0.500000,13.046491,0.000000
6,0.500000,11.343709,0.000000
7,0.500000,10.020437,0.000000
8,0.500000,12.896643,0.000000
9,0.500000,12.220088,0.000000
10,0.500000,11.315407,0.000000
11,0.450000,10.556920,0.000000
12,0.500000,10.925443,0.000000
13,0.500000,12.007375,0.000000
14,0.500000,11.471087,0.000000
15,0.500000,12.301955,0.000000
16,0.500000,10.831913,0.000000
17,0.500000,10.942777,0.000000
18,0.500000,11.699934,0.000000
19,0.500000,12.526654,0.000000
0,0.500000,11.601441,0.000000
0,0.500000,11.639872,0.000000
1,0.500000,12.723348,0.000000
2,0.500000,13.151769,0.000000
3,0.500000,10.969514,0.000000
4,0.500000,10.674448,0.000000
5,0.500000,13.068334,0.000000
6,0.500000,11.328772,0.000000
7,0.500000,9.926997,0.000000
8,0.500000,12.859781,0.000000
9,0.500000,12.061253,0.000000
10,0.500000,11.358070,0.000000
11,0.500000,10.484346,0.000000
12,0.500000,10.926878,0.000000
13,0.500000,12.002632,0.000000
0,0.500000,11.557904,0.000000
1,0.500000,12.819191,0.000000
1 target_class parameter_mia_accuracy latent_distance_tell lookalike_accuracy
2 0 0.500000 11.573492 0.000000
3 1 0.500000 12.793120 0.000000
4 2 0.500000 12.951434 0.000000
5 3 0.500000 10.942259 0.000000
6 4 0.500000 10.675704 0.000000
7 5 0.500000 13.046491 0.000000
8 6 0.500000 11.343709 0.000000
9 7 0.500000 10.020437 0.000000
10 8 0.500000 12.896643 0.000000
11 9 0.500000 12.220088 0.000000
12 10 0.500000 11.315407 0.000000
13 11 0.450000 10.556920 0.000000
14 12 0.500000 10.925443 0.000000
15 13 0.500000 12.007375 0.000000
16 14 0.500000 11.471087 0.000000
17 15 0.500000 12.301955 0.000000
18 16 0.500000 10.831913 0.000000
19 17 0.500000 10.942777 0.000000
20 18 0.500000 11.699934 0.000000
21 19 0.500000 12.526654 0.000000
22 0 0.500000 11.601441 0.000000
23 0 0.500000 11.639872 0.000000
24 1 0.500000 12.723348 0.000000
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26 3 0.500000 10.969514 0.000000
27 4 0.500000 10.674448 0.000000
28 5 0.500000 13.068334 0.000000
29 6 0.500000 11.328772 0.000000
30 7 0.500000 9.926997 0.000000
31 8 0.500000 12.859781 0.000000
32 9 0.500000 12.061253 0.000000
33 10 0.500000 11.358070 0.000000
34 11 0.500000 10.484346 0.000000
35 12 0.500000 10.926878 0.000000
36 13 0.500000 12.002632 0.000000
37 0 0.500000 11.557904 0.000000
38 1 0.500000 12.819191 0.000000

199
reports/WeightFiltration/RESNET34/forget.csv
View 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
0.1250,1.0000,0.1250,0.2222,1.0000,0.1250,0.2222
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
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0375,1.0000,0.0375,0.0723,1.0000,0.0375,0.0723
0.1750,1.0000,0.1750,0.2979,1.0000,0.1750,0.2979
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0375,1.0000,0.0375,0.0723,1.0000,0.0375,0.0723
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.2250,1.0000,0.2250,0.3673,1.0000,0.2250,0.3673
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
0.0875,1.0000,0.0875,0.1609,1.0000,0.0875,0.1609
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
0.0875,1.0000,0.0875,0.1609,1.0000,0.0875,0.1609
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.1750,1.0000,0.1750,0.2979,1.0000,0.1750,0.2979
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
0.0750,1.0000,0.0750,0.1395,1.0000,0.0750,0.1395
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
0.0125,1.0000,0.0125,0.0247,1.0000,0.0125,0.0247
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
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
0.0125,1.0000,0.0125,0.0247,1.0000,0.0125,0.0247
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
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
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
0.0250,1.0000,0.0250,0.0488,1.0000,0.0250,0.0488
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.2250,1.0000,0.2250,0.3673,1.0000,0.2250,0.3673
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
471 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
472 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
473 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 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
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 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
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0375 1.0000 0.0375 0.0723 1.0000 0.0375 0.0723
0.1750 1.0000 0.1750 0.2979 1.0000 0.1750 0.2979
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0375 1.0000 0.0375 0.0723 1.0000 0.0375 0.0723
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 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
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.2250 1.0000 0.2250 0.3673 1.0000 0.2250 0.3673
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
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
0.0875 1.0000 0.0875 0.1609 1.0000 0.0875 0.1609
0.0000 0.0000 0.0000 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
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
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
0.0875 1.0000 0.0875 0.1609 1.0000 0.0875 0.1609
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 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
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.1750 1.0000 0.1750 0.2979 1.0000 0.1750 0.2979
0.0000 0.0000 0.0000 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
0.0750 1.0000 0.0750 0.1395 1.0000 0.0750 0.1395
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 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
0.0000 0.0000 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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199
reports/WeightFiltration/RESNET34/forget_train.csv
View File

@@ -471,202 +471,3 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
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199
reports/WeightFiltration/RESNET34/retain.csv
View File

@@ -471,202 +471,3 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
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0.9513,0.9536,0.9513,0.9517,0.9536,0.9513,0.9517
0.9520,0.9538,0.9520,0.9523,0.9538,0.9520,0.9523
0.9513,0.9523,0.9513,0.9513,0.9523,0.9513,0.9513
0.9533,0.9550,0.9533,0.9536,0.9550,0.9533,0.9536
0.9586,0.9605,0.9586,0.9589,0.9605,0.9586,0.9589
0.9566,0.9583,0.9566,0.9568,0.9583,0.9566,0.9568
0.9533,0.9551,0.9533,0.9536,0.9551,0.9533,0.9536
0.9500,0.9528,0.9500,0.9505,0.9528,0.9500,0.9505
0.9559,0.9570,0.9559,0.9560,0.9570,0.9559,0.9560
0.9526,0.9541,0.9526,0.9529,0.9541,0.9526,0.9529
0.9533,0.9549,0.9533,0.9535,0.9549,0.9533,0.9535
0.9539,0.9559,0.9539,0.9543,0.9559,0.9539,0.9543
0.9500,0.9518,0.9500,0.9503,0.9518,0.9500,0.9503
0.9533,0.9547,0.9533,0.9535,0.9547,0.9533,0.9535
0.9546,0.9558,0.9546,0.9547,0.9558,0.9546,0.9547
0.9520,0.9539,0.9520,0.9522,0.9539,0.9520,0.9522
0.9480,0.9506,0.9480,0.9485,0.9506,0.9480,0.9485
0.9520,0.9541,0.9520,0.9522,0.9541,0.9520,0.9522
0.9513,0.9526,0.9513,0.9514,0.9526,0.9513,0.9514
0.9533,0.9546,0.9533,0.9535,0.9546,0.9533,0.9535
0.9526,0.9548,0.9526,0.9530,0.9548,0.9526,0.9530
0.9520,0.9542,0.9520,0.9524,0.9542,0.9520,0.9524
0.9513,0.9523,0.9513,0.9514,0.9523,0.9513,0.9514
0.9533,0.9553,0.9533,0.9536,0.9553,0.9533,0.9536
0.9513,0.9536,0.9513,0.9517,0.9536,0.9513,0.9517
0.9520,0.9540,0.9520,0.9523,0.9540,0.9520,0.9523
0.9513,0.9523,0.9513,0.9513,0.9523,0.9513,0.9513
0.9533,0.9549,0.9533,0.9535,0.9549,0.9533,0.9535
0.9513,0.9536,0.9513,0.9517,0.9536,0.9513,0.9517
0.9520,0.9540,0.9520,0.9523,0.9540,0.9520,0.9523
0.9520,0.9530,0.9520,0.9520,0.9530,0.9520,0.9520
0.9539,0.9557,0.9539,0.9542,0.9557,0.9539,0.9542
0.9579,0.9597,0.9579,0.9582,0.9597,0.9579,0.9582
0.9566,0.9583,0.9566,0.9568,0.9583,0.9566,0.9568
0.9533,0.9551,0.9533,0.9536,0.9551,0.9533,0.9536
0.9487,0.9521,0.9487,0.9494,0.9521,0.9487,0.9494
0.9546,0.9558,0.9546,0.9547,0.9558,0.9546,0.9547
0.9526,0.9539,0.9526,0.9529,0.9539,0.9526,0.9529
0.9539,0.9556,0.9539,0.9542,0.9556,0.9539,0.9542
0.9539,0.9559,0.9539,0.9543,0.9559,0.9539,0.9543
0.9500,0.9518,0.9500,0.9503,0.9518,0.9500,0.9503
0.9533,0.9547,0.9533,0.9535,0.9547,0.9533,0.9535
0.9546,0.9558,0.9546,0.9547,0.9558,0.9546,0.9547
0.9520,0.9539,0.9520,0.9522,0.9539,0.9520,0.9522
0.9493,0.9515,0.9493,0.9497,0.9515,0.9493,0.9497
0.9520,0.9543,0.9520,0.9523,0.9543,0.9520,0.9523
0.9520,0.9531,0.9520,0.9521,0.9531,0.9520,0.9521
0.9533,0.9546,0.9533,0.9535,0.9546,0.9533,0.9535
0.9533,0.9554,0.9533,0.9537,0.9554,0.9533,0.9537
0.9520,0.9540,0.9520,0.9523,0.9540,0.9520,0.9523
0.9513,0.9523,0.9513,0.9513,0.9523,0.9513,0.9513
0.9533,0.9551,0.9533,0.9536,0.9551,0.9533,0.9536
0.9572,0.9590,0.9572,0.9576,0.9590,0.9572,0.9576
0.9566,0.9583,0.9566,0.9568,0.9583,0.9566,0.9568
0.9533,0.9548,0.9533,0.9536,0.9548,0.9533,0.9536
0.9487,0.9518,0.9487,0.9493,0.9518,0.9487,0.9493
0.9539,0.9551,0.9539,0.9541,0.9551,0.9539,0.9541
0.9520,0.9534,0.9520,0.9522,0.9534,0.9520,0.9522
0.9546,0.9561,0.9546,0.9548,0.9561,0.9546,0.9548
0.9539,0.9559,0.9539,0.9543,0.9559,0.9539,0.9543
0.9500,0.9518,0.9500,0.9503,0.9518,0.9500,0.9503
0.9533,0.9547,0.9533,0.9535,0.9547,0.9533,0.9535
0.9546,0.9558,0.9546,0.9547,0.9558,0.9546,0.9547
0.9520,0.9539,0.9520,0.9522,0.9539,0.9520,0.9522
0.9487,0.9509,0.9487,0.9490,0.9509,0.9487,0.9490
0.9513,0.9536,0.9513,0.9516,0.9536,0.9513,0.9516
0.9513,0.9526,0.9513,0.9514,0.9526,0.9513,0.9514
0.9533,0.9546,0.9533,0.9535,0.9546,0.9533,0.9535
0.9513,0.9536,0.9513,0.9517,0.9536,0.9513,0.9517
0.9520,0.9540,0.9520,0.9523,0.9540,0.9520,0.9523
0.9513,0.9522,0.9513,0.9514,0.9522,0.9513,0.9514
0.9533,0.9550,0.9533,0.9536,0.9550,0.9533,0.9536
0.9579,0.9597,0.9579,0.9582,0.9597,0.9579,0.9582
0.9566,0.9584,0.9566,0.9568,0.9584,0.9566,0.9568
0.9539,0.9555,0.9539,0.9542,0.9555,0.9539,0.9542
0.9500,0.9531,0.9500,0.9506,0.9531,0.9500,0.9506
0.9553,0.9565,0.9553,0.9554,0.9565,0.9553,0.9554
0.9520,0.9534,0.9520,0.9522,0.9534,0.9520,0.9522
0.9546,0.9561,0.9546,0.9548,0.9561,0.9546,0.9548
0.9539,0.9559,0.9539,0.9543,0.9559,0.9539,0.9543
0.9500,0.9518,0.9500,0.9503,0.9518,0.9500,0.9503
0.9533,0.9547,0.9533,0.9535,0.9547,0.9533,0.9535
0.9546,0.9558,0.9546,0.9547,0.9558,0.9546,0.9547
0.9520,0.9539,0.9520,0.9522,0.9539,0.9520,0.9522
0.9487,0.9508,0.9487,0.9490,0.9508,0.9487,0.9490
0.9520,0.9542,0.9520,0.9523,0.9542,0.9520,0.9523
0.9507,0.9519,0.9507,0.9507,0.9519,0.9507,0.9507
0.9533,0.9545,0.9533,0.9535,0.9545,0.9533,0.9535
0.9513,0.9536,0.9513,0.9517,0.9536,0.9513,0.9517
0.9520,0.9542,0.9520,0.9524,0.9542,0.9520,0.9524
0.9513,0.9523,0.9513,0.9514,0.9523,0.9513,0.9514
0.9539,0.9555,0.9539,0.9541,0.9555,0.9539,0.9541
0.9572,0.9590,0.9572,0.9576,0.9590,0.9572,0.9576
0.9566,0.9583,0.9566,0.9568,0.9583,0.9566,0.9568
0.9539,0.9555,0.9539,0.9542,0.9555,0.9539,0.9542
0.9500,0.9528,0.9500,0.9505,0.9528,0.9500,0.9505
0.9539,0.9552,0.9539,0.9541,0.9552,0.9539,0.9541
0.9526,0.9539,0.9526,0.9529,0.9539,0.9526,0.9529
0.9533,0.9549,0.9533,0.9536,0.9549,0.9533,0.9536
0.9539,0.9559,0.9539,0.9543,0.9559,0.9539,0.9543
0.9507,0.9524,0.9507,0.9509,0.9524,0.9507,0.9509
0.9533,0.9547,0.9533,0.9535,0.9547,0.9533,0.9535
0.9546,0.9559,0.9546,0.9547,0.9559,0.9546,0.9547
0.9513,0.9532,0.9513,0.9516,0.9532,0.9513,0.9516
0.9493,0.9515,0.9493,0.9496,0.9515,0.9493,0.9496
0.9513,0.9535,0.9513,0.9516,0.9535,0.9513,0.9516
0.9520,0.9532,0.9520,0.9520,0.9532,0.9520,0.9520
0.9533,0.9546,0.9533,0.9535,0.9546,0.9533,0.9535
0.9513,0.9536,0.9513,0.9517,0.9536,0.9513,0.9517
0.9520,0.9538,0.9520,0.9523,0.9538,0.9520,0.9523
0.9513,0.9523,0.9513,0.9514,0.9523,0.9513,0.9514
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
471 0.9559 0.9566 0.9559 0.9560 0.9566 0.9559 0.9560
472 0.9566 0.9574 0.9566 0.9567 0.9574 0.9566 0.9567
473 0.9559 0.9571 0.9559 0.9561 0.9571 0.9559 0.9561
0.9546 0.9557 0.9546 0.9547 0.9557 0.9546 0.9547
0.9553 0.9563 0.9553 0.9554 0.9563 0.9553 0.9554
0.9507 0.9524 0.9507 0.9510 0.9524 0.9507 0.9510
0.9553 0.9571 0.9553 0.9557 0.9571 0.9553 0.9557
0.9579 0.9590 0.9579 0.9581 0.9590 0.9579 0.9581
0.9566 0.9580 0.9566 0.9568 0.9580 0.9566 0.9568
0.9546 0.9558 0.9546 0.9548 0.9558 0.9546 0.9548
0.9520 0.9531 0.9520 0.9520 0.9531 0.9520 0.9520
0.9572 0.9590 0.9572 0.9575 0.9590 0.9572 0.9575
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
0.9526 0.9540 0.9526 0.9528 0.9540 0.9526 0.9528
0.9572 0.9581 0.9572 0.9573 0.9581 0.9572 0.9573
0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
0.9526 0.9537 0.9526 0.9527 0.9537 0.9526 0.9527
0.9539 0.9545 0.9539 0.9540 0.9545 0.9539 0.9540
0.9566 0.9574 0.9566 0.9566 0.9574 0.9566 0.9566
0.9533 0.9542 0.9533 0.9534 0.9542 0.9533 0.9534
0.9539 0.9554 0.9539 0.9541 0.9554 0.9539 0.9541
0.9533 0.9544 0.9533 0.9534 0.9544 0.9533 0.9534
0.9533 0.9541 0.9533 0.9534 0.9541 0.9533 0.9534
0.9520 0.9530 0.9520 0.9521 0.9530 0.9520 0.9521
0.9513 0.9525 0.9513 0.9514 0.9525 0.9513 0.9514
0.9493 0.9509 0.9493 0.9496 0.9509 0.9493 0.9496
0.9487 0.9500 0.9487 0.9487 0.9500 0.9487 0.9487
0.9559 0.9575 0.9559 0.9561 0.9575 0.9559 0.9561
0.9520 0.9531 0.9520 0.9521 0.9531 0.9520 0.9521
0.9526 0.9536 0.9526 0.9527 0.9536 0.9526 0.9527
0.9539 0.9557 0.9539 0.9541 0.9557 0.9539 0.9541
0.9553 0.9559 0.9553 0.9552 0.9559 0.9553 0.9552
0.9520 0.9530 0.9520 0.9520 0.9530 0.9520 0.9520
0.9526 0.9541 0.9526 0.9528 0.9541 0.9526 0.9528
0.9566 0.9575 0.9566 0.9566 0.9575 0.9566 0.9566
0.9526 0.9541 0.9526 0.9528 0.9541 0.9526 0.9528
0.9526 0.9541 0.9526 0.9528 0.9541 0.9526 0.9528
0.9559 0.9569 0.9559 0.9560 0.9569 0.9559 0.9560
0.9546 0.9555 0.9546 0.9546 0.9555 0.9546 0.9546
0.9520 0.9530 0.9520 0.9521 0.9530 0.9520 0.9521
0.9539 0.9545 0.9539 0.9540 0.9545 0.9539 0.9540
0.9566 0.9574 0.9566 0.9566 0.9574 0.9566 0.9566
0.9526 0.9541 0.9526 0.9528 0.9541 0.9526 0.9528
0.9533 0.9542 0.9533 0.9534 0.9542 0.9533 0.9534
0.9533 0.9547 0.9533 0.9535 0.9547 0.9533 0.9535
0.9566 0.9575 0.9566 0.9566 0.9575 0.9566 0.9566
0.9526 0.9540 0.9526 0.9528 0.9540 0.9526 0.9528
0.9546 0.9558 0.9546 0.9547 0.9558 0.9546 0.9547
0.9526 0.9540 0.9526 0.9528 0.9540 0.9526 0.9528
0.9526 0.9540 0.9526 0.9528 0.9540 0.9526 0.9528
0.9520 0.9541 0.9520 0.9524 0.9541 0.9520 0.9524
0.9520 0.9542 0.9520 0.9524 0.9542 0.9520 0.9524
0.9507 0.9515 0.9507 0.9507 0.9515 0.9507 0.9507
0.9533 0.9548 0.9533 0.9535 0.9548 0.9533 0.9535
0.9566 0.9583 0.9566 0.9569 0.9583 0.9566 0.9569
0.9572 0.9588 0.9572 0.9574 0.9588 0.9572 0.9574
0.9533 0.9551 0.9533 0.9536 0.9551 0.9533 0.9536
0.9487 0.9518 0.9487 0.9493 0.9518 0.9487 0.9493
0.9546 0.9559 0.9546 0.9547 0.9559 0.9546 0.9547
0.9520 0.9534 0.9520 0.9522 0.9534 0.9520 0.9522
0.9539 0.9554 0.9539 0.9542 0.9554 0.9539 0.9542
0.9539 0.9559 0.9539 0.9543 0.9559 0.9539 0.9543
0.9500 0.9518 0.9500 0.9503 0.9518 0.9500 0.9503
0.9520 0.9542 0.9520 0.9524 0.9542 0.9520 0.9524
0.9520 0.9541 0.9520 0.9523 0.9541 0.9520 0.9523
0.9520 0.9542 0.9520 0.9524 0.9542 0.9520 0.9524
0.9520 0.9540 0.9520 0.9523 0.9540 0.9520 0.9523
0.9507 0.9515 0.9507 0.9507 0.9515 0.9507 0.9507
0.9526 0.9548 0.9526 0.9530 0.9548 0.9526 0.9530
0.9520 0.9542 0.9520 0.9524 0.9542 0.9520 0.9524
0.9520 0.9528 0.9520 0.9520 0.9528 0.9520 0.9520
0.9539 0.9557 0.9539 0.9542 0.9557 0.9539 0.9542
0.9579 0.9597 0.9579 0.9582 0.9597 0.9579 0.9582
0.9566 0.9582 0.9566 0.9568 0.9582 0.9566 0.9568
0.9533 0.9551 0.9533 0.9536 0.9551 0.9533 0.9536
0.9500 0.9528 0.9500 0.9505 0.9528 0.9500 0.9505
0.9546 0.9558 0.9546 0.9547 0.9558 0.9546 0.9547
0.9520 0.9534 0.9520 0.9522 0.9534 0.9520 0.9522
0.9546 0.9562 0.9546 0.9549 0.9562 0.9546 0.9549
0.9520 0.9543 0.9520 0.9524 0.9543 0.9520 0.9524
0.9520 0.9540 0.9520 0.9523 0.9540 0.9520 0.9523
0.9520 0.9529 0.9520 0.9520 0.9529 0.9520 0.9520
0.9513 0.9536 0.9513 0.9517 0.9536 0.9513 0.9517
0.9520 0.9538 0.9520 0.9523 0.9538 0.9520 0.9523
0.9513 0.9523 0.9513 0.9513 0.9523 0.9513 0.9513
0.9533 0.9550 0.9533 0.9536 0.9550 0.9533 0.9536
0.9586 0.9605 0.9586 0.9589 0.9605 0.9586 0.9589
0.9566 0.9583 0.9566 0.9568 0.9583 0.9566 0.9568
0.9533 0.9551 0.9533 0.9536 0.9551 0.9533 0.9536
0.9500 0.9528 0.9500 0.9505 0.9528 0.9500 0.9505
0.9559 0.9570 0.9559 0.9560 0.9570 0.9559 0.9560
0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529
0.9533 0.9549 0.9533 0.9535 0.9549 0.9533 0.9535
0.9539 0.9559 0.9539 0.9543 0.9559 0.9539 0.9543
0.9500 0.9518 0.9500 0.9503 0.9518 0.9500 0.9503
0.9533 0.9547 0.9533 0.9535 0.9547 0.9533 0.9535
0.9546 0.9558 0.9546 0.9547 0.9558 0.9546 0.9547
0.9520 0.9539 0.9520 0.9522 0.9539 0.9520 0.9522
0.9480 0.9506 0.9480 0.9485 0.9506 0.9480 0.9485
0.9520 0.9541 0.9520 0.9522 0.9541 0.9520 0.9522
0.9513 0.9526 0.9513 0.9514 0.9526 0.9513 0.9514
0.9533 0.9546 0.9533 0.9535 0.9546 0.9533 0.9535
0.9526 0.9548 0.9526 0.9530 0.9548 0.9526 0.9530
0.9520 0.9542 0.9520 0.9524 0.9542 0.9520 0.9524
0.9513 0.9523 0.9513 0.9514 0.9523 0.9513 0.9514
0.9533 0.9553 0.9533 0.9536 0.9553 0.9533 0.9536
0.9513 0.9536 0.9513 0.9517 0.9536 0.9513 0.9517
0.9520 0.9540 0.9520 0.9523 0.9540 0.9520 0.9523
0.9513 0.9523 0.9513 0.9513 0.9523 0.9513 0.9513
0.9533 0.9549 0.9533 0.9535 0.9549 0.9533 0.9535
0.9513 0.9536 0.9513 0.9517 0.9536 0.9513 0.9517
0.9520 0.9540 0.9520 0.9523 0.9540 0.9520 0.9523
0.9520 0.9530 0.9520 0.9520 0.9530 0.9520 0.9520
0.9539 0.9557 0.9539 0.9542 0.9557 0.9539 0.9542
0.9579 0.9597 0.9579 0.9582 0.9597 0.9579 0.9582
0.9566 0.9583 0.9566 0.9568 0.9583 0.9566 0.9568
0.9533 0.9551 0.9533 0.9536 0.9551 0.9533 0.9536
0.9487 0.9521 0.9487 0.9494 0.9521 0.9487 0.9494
0.9546 0.9558 0.9546 0.9547 0.9558 0.9546 0.9547
0.9526 0.9539 0.9526 0.9529 0.9539 0.9526 0.9529
0.9539 0.9556 0.9539 0.9542 0.9556 0.9539 0.9542
0.9539 0.9559 0.9539 0.9543 0.9559 0.9539 0.9543
0.9500 0.9518 0.9500 0.9503 0.9518 0.9500 0.9503
0.9533 0.9547 0.9533 0.9535 0.9547 0.9533 0.9535
0.9546 0.9558 0.9546 0.9547 0.9558 0.9546 0.9547
0.9520 0.9539 0.9520 0.9522 0.9539 0.9520 0.9522
0.9493 0.9515 0.9493 0.9497 0.9515 0.9493 0.9497
0.9520 0.9543 0.9520 0.9523 0.9543 0.9520 0.9523
0.9520 0.9531 0.9520 0.9521 0.9531 0.9520 0.9521
0.9533 0.9546 0.9533 0.9535 0.9546 0.9533 0.9535
0.9533 0.9554 0.9533 0.9537 0.9554 0.9533 0.9537
0.9520 0.9540 0.9520 0.9523 0.9540 0.9520 0.9523
0.9513 0.9523 0.9513 0.9513 0.9523 0.9513 0.9513
0.9533 0.9551 0.9533 0.9536 0.9551 0.9533 0.9536
0.9572 0.9590 0.9572 0.9576 0.9590 0.9572 0.9576
0.9566 0.9583 0.9566 0.9568 0.9583 0.9566 0.9568
0.9533 0.9548 0.9533 0.9536 0.9548 0.9533 0.9536
0.9487 0.9518 0.9487 0.9493 0.9518 0.9487 0.9493
0.9539 0.9551 0.9539 0.9541 0.9551 0.9539 0.9541
0.9520 0.9534 0.9520 0.9522 0.9534 0.9520 0.9522
0.9546 0.9561 0.9546 0.9548 0.9561 0.9546 0.9548
0.9539 0.9559 0.9539 0.9543 0.9559 0.9539 0.9543
0.9500 0.9518 0.9500 0.9503 0.9518 0.9500 0.9503
0.9533 0.9547 0.9533 0.9535 0.9547 0.9533 0.9535
0.9546 0.9558 0.9546 0.9547 0.9558 0.9546 0.9547
0.9520 0.9539 0.9520 0.9522 0.9539 0.9520 0.9522
0.9487 0.9509 0.9487 0.9490 0.9509 0.9487 0.9490
0.9513 0.9536 0.9513 0.9516 0.9536 0.9513 0.9516
0.9513 0.9526 0.9513 0.9514 0.9526 0.9513 0.9514
0.9533 0.9546 0.9533 0.9535 0.9546 0.9533 0.9535
0.9513 0.9536 0.9513 0.9517 0.9536 0.9513 0.9517
0.9520 0.9540 0.9520 0.9523 0.9540 0.9520 0.9523
0.9513 0.9522 0.9513 0.9514 0.9522 0.9513 0.9514
0.9533 0.9550 0.9533 0.9536 0.9550 0.9533 0.9536
0.9579 0.9597 0.9579 0.9582 0.9597 0.9579 0.9582
0.9566 0.9584 0.9566 0.9568 0.9584 0.9566 0.9568
0.9539 0.9555 0.9539 0.9542 0.9555 0.9539 0.9542
0.9500 0.9531 0.9500 0.9506 0.9531 0.9500 0.9506
0.9553 0.9565 0.9553 0.9554 0.9565 0.9553 0.9554
0.9520 0.9534 0.9520 0.9522 0.9534 0.9520 0.9522
0.9546 0.9561 0.9546 0.9548 0.9561 0.9546 0.9548
0.9539 0.9559 0.9539 0.9543 0.9559 0.9539 0.9543
0.9500 0.9518 0.9500 0.9503 0.9518 0.9500 0.9503
0.9533 0.9547 0.9533 0.9535 0.9547 0.9533 0.9535
0.9546 0.9558 0.9546 0.9547 0.9558 0.9546 0.9547
0.9520 0.9539 0.9520 0.9522 0.9539 0.9520 0.9522
0.9487 0.9508 0.9487 0.9490 0.9508 0.9487 0.9490
0.9520 0.9542 0.9520 0.9523 0.9542 0.9520 0.9523
0.9507 0.9519 0.9507 0.9507 0.9519 0.9507 0.9507
0.9533 0.9545 0.9533 0.9535 0.9545 0.9533 0.9535
0.9513 0.9536 0.9513 0.9517 0.9536 0.9513 0.9517
0.9520 0.9542 0.9520 0.9524 0.9542 0.9520 0.9524
0.9513 0.9523 0.9513 0.9514 0.9523 0.9513 0.9514
0.9539 0.9555 0.9539 0.9541 0.9555 0.9539 0.9541
0.9572 0.9590 0.9572 0.9576 0.9590 0.9572 0.9576
0.9566 0.9583 0.9566 0.9568 0.9583 0.9566 0.9568
0.9539 0.9555 0.9539 0.9542 0.9555 0.9539 0.9542
0.9500 0.9528 0.9500 0.9505 0.9528 0.9500 0.9505
0.9539 0.9552 0.9539 0.9541 0.9552 0.9539 0.9541
0.9526 0.9539 0.9526 0.9529 0.9539 0.9526 0.9529
0.9533 0.9549 0.9533 0.9536 0.9549 0.9533 0.9536
0.9539 0.9559 0.9539 0.9543 0.9559 0.9539 0.9543
0.9507 0.9524 0.9507 0.9509 0.9524 0.9507 0.9509
0.9533 0.9547 0.9533 0.9535 0.9547 0.9533 0.9535
0.9546 0.9559 0.9546 0.9547 0.9559 0.9546 0.9547
0.9513 0.9532 0.9513 0.9516 0.9532 0.9513 0.9516
0.9493 0.9515 0.9493 0.9496 0.9515 0.9493 0.9496
0.9513 0.9535 0.9513 0.9516 0.9535 0.9513 0.9516
0.9520 0.9532 0.9520 0.9520 0.9532 0.9520 0.9520
0.9533 0.9546 0.9533 0.9535 0.9546 0.9533 0.9535
0.9513 0.9536 0.9513 0.9517 0.9536 0.9513 0.9517
0.9520 0.9538 0.9520 0.9523 0.9538 0.9520 0.9523
0.9513 0.9523 0.9513 0.9514 0.9523 0.9513 0.9514

215
reports/WeightFiltration/ResNet/time_metrics.txt
View File

@@ -299,17 +299,6 @@ execution_time_sec
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
@@ -450,207 +439,3 @@ execution_time_sec
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
87.953725
89.953191
88.555181
87.086629
86.067240
0.001263
0.000383
0.000499
0.000494
0.000391
0.000393
0.000394
87.004814
88.485772
0.000770
0.001780
0.000391
0.000384
0.000495
0.000501
0.000405
0.001766
0.000508
87.975398
0.000514
0.000446
0.001589
0.000390
0.000487
0.000440
0.001184
0.000422
0.000380
0.000461
89.646614
0.000455
0.000399
0.000369
87.205821
0.000449
0.001879
86.592527
0.000481
0.000432
0.000447
0.000428
0.000425
0.000443
0.000428
0.000424
0.000427
0.000426
0.000428
0.000430
0.000431
0.000426
0.000432
0.000424
0.000434
0.000419
0.000424
86.237751
0.000439
0.000432
0.000443
0.000430
0.000439
0.000434
0.000435
0.000428
0.000446
0.000441
0.000439
0.000435
0.000436
0.000433
0.000429
0.000434
0.000426
0.000430
0.000432
86.227529
0.000449
0.000436
0.000427
0.000430
0.000425
0.000427
0.000423
0.000425
0.000437
0.000438
0.000430
0.000399
0.000545
0.000430
0.000434
0.000425
0.000429
0.000436
0.000444
86.158356
0.000438
0.000437
0.000426
0.000436
0.000438
0.000434
0.000423
0.000469
0.000436
0.000431
0.000441
0.000431
0.000429
0.000433
0.000436
0.000442
0.000426
0.000455
0.000446
86.279183
0.000437
0.000406
0.000428
0.000433
0.000432
0.000430
0.000424
0.000421
0.000435
0.000428
0.000398
0.000432
0.000427
0.000407
0.000425
0.000433
0.000430
0.000422
0.000418
89.527112
87.450551
0.002478
0.000423
0.000417
0.000433
0.000426
0.000440
0.000826
0.000426
0.000440
0.000437
0.000425
0.000425
0.000422
0.000450
0.000423
0.000422
0.000422
0.000426
0.000428
86.247649
0.000436

7
reports/WeightFiltration/attack_values.csv
View File

@@ -19,10 +19,6 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
17,0.500000,0.927083, 0.114583, 0.536984
18,0.500000,0.947917, 0.072917, 0.529271
19,0.500000,0.958333, 0.062500, 0.552560
0,0.500000,0.968750, 0.208333, 0.536183
1,0.500000,0.968750, 0.125000, 0.538881
2,0.500000,0.958333, 0.031250, 0.567163
3,0.500000,0.979167, 0.114583, 0.562516
0,0.500000,0.952083, 0.000000, 0.537566
1,0.500000,0.912500, 0.083333, 0.535581
2,0.500000,0.958333, 0.010417, 0.564744
@@ -103,6 +99,3 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
17,0.500000,0.937500, 0.093750, 0.532926
18,0.500000,0.937500, 0.031250, 0.532182
19,0.500000,0.962500, 0.041667, 0.552156
0,0.500000,0.960417, 0.031250, 0.534253
1,0.500000,0.910417, 0.000000, 0.534778
2,0.500000,0.922917, 0.000000, 0.560311
1 target_class parameter_mia_accuracy lookalike_accuracy A-Dist JS-Dist
19 17 0.500000 0.927083 0.114583 0.536984
20 18 0.500000 0.947917 0.072917 0.529271
21 19 0.500000 0.958333 0.062500 0.552560
0 0.500000 0.968750 0.208333 0.536183
1 0.500000 0.968750 0.125000 0.538881
2 0.500000 0.958333 0.031250 0.567163
3 0.500000 0.979167 0.114583 0.562516
22 0 0.500000 0.952083 0.000000 0.537566
23 1 0.500000 0.912500 0.083333 0.535581
24 2 0.500000 0.958333 0.010417 0.564744
99 17 0.500000 0.937500 0.093750 0.532926
100 18 0.500000 0.937500 0.031250 0.532182
101 19 0.500000 0.962500 0.041667 0.552156
0 0.500000 0.960417 0.031250 0.534253
1 0.500000 0.910417 0.000000 0.534778
2 0.500000 0.922917 0.000000 0.560311

101
reports/WeightFiltration/attack_values_old.csv
View File

@@ -1,101 +0,0 @@
target_class,attack_mia_accuracy,latent_distance_tell
0,0.500000,1.107324
1,0.500000,1.182681
2,0.500000,1.628934
3,0.500000,1.260251
4,0.500000,1.399319
5,0.500000,2.046023
6,0.500000,1.750646
7,0.500000,1.243093
8,0.500000,1.809917
9,0.500000,1.702536
10,0.500000,1.291788
11,0.500000,1.434109
12,0.500000,1.448272
13,0.500000,1.694034
14,0.500000,1.717611
15,0.500000,1.758781
16,0.500000,1.188805
17,0.500000,1.591978
18,0.500000,1.445776
19,0.500000,1.626087
0,0.500000,1.158666
1,0.500000,1.245154
2,0.500000,1.558492
3,0.500000,1.358110
4,0.500000,1.339859
5,0.500000,2.025961
6,0.500000,1.828531
7,0.500000,1.186695
8,0.500000,1.920701
9,0.500000,1.718948
10,0.500000,1.374345
11,0.500000,1.495150
12,0.500000,1.368306
13,0.500000,1.710072
14,0.500000,1.700057
15,0.500000,1.766020
16,0.500000,1.160263
17,0.500000,1.634570
18,0.500000,1.461136
19,0.500000,1.697268
0,0.500000,1.092890
1,0.500000,1.238615
2,0.500000,1.704648
3,0.500000,1.394107
4,0.500000,1.365094
5,0.500000,2.032884
6,0.500000,1.833764
7,0.500000,1.225851
8,0.500000,1.807006
9,0.500000,1.704291
10,0.500000,1.358446
11,0.500000,1.555449
12,0.500000,1.387334
13,0.500000,1.693131
14,0.500000,1.736060
15,0.500000,1.768330
16,0.500000,1.190044
17,0.500000,1.585899
18,0.500000,1.482916
19,0.500000,1.691146
0,0.500000,1.141869
1,0.500000,1.352442
2,0.500000,1.695588
3,0.500000,1.432673
4,0.500000,1.314509
5,0.500000,2.010463
6,0.500000,1.817650
7,0.500000,1.291032
8,0.500000,1.703021
9,0.500000,1.802832
10,0.500000,1.355631
11,0.500000,1.485411
12,0.500000,1.441830
13,0.500000,1.728542
14,0.500000,1.740982
15,0.500000,1.764840
16,0.500000,1.210430
17,0.500000,1.645152
18,0.500000,1.471922
19,0.500000,1.709163
0,0.500000,1.122816
1,0.500000,1.332376
2,0.500000,1.646908
3,0.500000,1.429030
4,0.500000,1.321270
5,0.500000,2.033827
6,0.500000,1.863828
7,0.500000,1.242626
8,0.500000,1.924087
9,0.500000,1.760985
10,0.500000,1.423025
11,0.500000,1.428449
12,0.500000,1.390632
13,0.500000,1.619642
14,0.500000,1.745749
15,0.500000,1.734899
16,0.500000,1.144821
17,0.500000,1.548540
18,0.500000,1.452088
19,0.500000,1.721123
1 target_class attack_mia_accuracy latent_distance_tell
2 0 0.500000 1.107324
3 1 0.500000 1.182681
4 2 0.500000 1.628934
5 3 0.500000 1.260251
6 4 0.500000 1.399319
7 5 0.500000 2.046023
8 6 0.500000 1.750646
9 7 0.500000 1.243093
10 8 0.500000 1.809917
11 9 0.500000 1.702536
12 10 0.500000 1.291788
13 11 0.500000 1.434109
14 12 0.500000 1.448272
15 13 0.500000 1.694034
16 14 0.500000 1.717611
17 15 0.500000 1.758781
18 16 0.500000 1.188805
19 17 0.500000 1.591978
20 18 0.500000 1.445776
21 19 0.500000 1.626087
22 0 0.500000 1.158666
23 1 0.500000 1.245154
24 2 0.500000 1.558492
25 3 0.500000 1.358110
26 4 0.500000 1.339859
27 5 0.500000 2.025961
28 6 0.500000 1.828531
29 7 0.500000 1.186695
30 8 0.500000 1.920701
31 9 0.500000 1.718948
32 10 0.500000 1.374345
33 11 0.500000 1.495150
34 12 0.500000 1.368306
35 13 0.500000 1.710072
36 14 0.500000 1.700057
37 15 0.500000 1.766020
38 16 0.500000 1.160263
39 17 0.500000 1.634570
40 18 0.500000 1.461136
41 19 0.500000 1.697268
42 0 0.500000 1.092890
43 1 0.500000 1.238615
44 2 0.500000 1.704648
45 3 0.500000 1.394107
46 4 0.500000 1.365094
47 5 0.500000 2.032884
48 6 0.500000 1.833764
49 7 0.500000 1.225851
50 8 0.500000 1.807006
51 9 0.500000 1.704291
52 10 0.500000 1.358446
53 11 0.500000 1.555449
54 12 0.500000 1.387334
55 13 0.500000 1.693131
56 14 0.500000 1.736060
57 15 0.500000 1.768330
58 16 0.500000 1.190044
59 17 0.500000 1.585899
60 18 0.500000 1.482916
61 19 0.500000 1.691146
62 0 0.500000 1.141869
63 1 0.500000 1.352442
64 2 0.500000 1.695588
65 3 0.500000 1.432673
66 4 0.500000 1.314509
67 5 0.500000 2.010463
68 6 0.500000 1.817650
69 7 0.500000 1.291032
70 8 0.500000 1.703021
71 9 0.500000 1.802832
72 10 0.500000 1.355631
73 11 0.500000 1.485411
74 12 0.500000 1.441830
75 13 0.500000 1.728542
76 14 0.500000 1.740982
77 15 0.500000 1.764840
78 16 0.500000 1.210430
79 17 0.500000 1.645152
80 18 0.500000 1.471922
81 19 0.500000 1.709163
82 0 0.500000 1.122816
83 1 0.500000 1.332376
84 2 0.500000 1.646908
85 3 0.500000 1.429030
86 4 0.500000 1.321270
87 5 0.500000 2.033827
88 6 0.500000 1.863828
89 7 0.500000 1.242626
90 8 0.500000 1.924087
91 9 0.500000 1.760985
92 10 0.500000 1.423025
93 11 0.500000 1.428449
94 12 0.500000 1.390632
95 13 0.500000 1.619642
96 14 0.500000 1.745749
97 15 0.500000 1.734899
98 16 0.500000 1.144821
99 17 0.500000 1.548540
100 18 0.500000 1.452088
101 19 0.500000 1.721123

9
retrain/resnet34/time_metrics.txt
View File

@@ -1,9 +0,0 @@
execution_time_sec
991.073163
978.072894
982.032442
1081.642368
985.512192
979.091341
978.852345
978.963139

8
unlearning/CertifiedUnlearning.py
View File

@@ -7,7 +7,6 @@ from unlearning.Strategy import Strategy
from sets.Data import *
# Single-Batch Certified Unlearning for DNNs
class CertifiedUnlearning(Strategy):
"""
Implements Certified Unlearning for non-convex DNNs (Zhang et al.).
@@ -47,8 +46,7 @@ class CertifiedUnlearning(Strategy):
params_list = []
for name, p in inner_model.named_parameters():
if p.requires_grad:
# Append as a tuple so it can be unpacked as (name, param)
# so it can be unpacked as (name, param)
params_list.append((name, p))
return params_list if named else [e[1] for e in params_list]
@@ -133,7 +131,7 @@ class CertifiedUnlearning(Strategy):
h_estimate[k].copy_(h_estimate[k] + g[k] - (h_s[k] / self.scale))
# feed back on status
# feed back because this took agaes and it was not clear if things were working
if global_step % step_interval == 0 and current_pct < 100:
current_pct += 1
print(f"\rProgress: {current_pct}% done", end="", flush=True)
@@ -167,7 +165,7 @@ class CertifiedUnlearning(Strategy):
# Keep early low-level vision filters entirely pristine
pass
# Move to the next calculated Hessian vector block only after a valid update step
# to the next calculated Hessian vector block
delta_idx += 1
return model

5
unlearning/LinearFiltration.py
View File

@@ -124,7 +124,6 @@ class LinearFiltration(Strategy):
if self.A is None:
self._compute_A(
model = model,
#num_classes = num_classes,
loader = forget_loader,
device = device
)
@@ -147,6 +146,9 @@ class LinearFiltration(Strategy):
A_inv = torch.linalg.pinv(A_t, rcond=1e-2)
# 11
W_temp = B_z @ A_inv @ W
# Linear (Normalisation)filtration is done here,
# next steps are added for safe automated evaluations
# with one class removed, we have a head W_temp for 19 classes.
# but we have loaders for 20 classes for evaluation. so ,
# map K-1 W back to K x K.
@@ -164,6 +166,7 @@ class LinearFiltration(Strategy):
# neutralise forget bias if exists
if clf.bias is not None:
n_bias = clf.bias.data.clone()
# push it down to negative
n_bias[forget_index] = -1e9
clf.bias.copy_(n_bias)

2
unlearning/Retrain.py
View File

@@ -42,7 +42,7 @@ class Retrain(Strategy):
print(f"No naive model found for class {self.target_class_index} retraining a new one")
print(f">> Triggering Exact Unlearning Baseline (Retraining {self.arch.name} from pristine state)...")
print(f"Retraining {self.arch.name} from pristine state)...")
inner_model = getattr(model, "model", model)
if hasattr(inner_model, "fc"):
total_classes = inner_model.fc.out_features

107
unlearning/WF.py
View File

@@ -1,107 +0,0 @@
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from unlearning.Strategy import Strategy
from .wf.WF_Net import WF_Net
class WeightF(Strategy):
"""
Verbatim implementation of Poppi et al.'s WF-Net framework modified
for static, single-class unlearning extraction.
"""
def __init__(self, target_class_index: int, epochs: int = 10, lr: float = 0.2, gamma: float = 10.0):
super().__init__(target_class_index=target_class_index)
self.epochs = epochs
self.lr = lr
self.gamma = gamma
def _optimise_filter(self, model: nn.Module, forget_loader: DataLoader, retain_loader: DataLoader, device):
num_classes = model.fc.out_features
wf_model = WF_Net(original_model=model, num_classes=num_classes).to(device)
# Optimize only the specific alpha masks
optimizer = optim.Adam([wf_model.alpha], lr=self.lr)
criterion = nn.CrossEntropyLoss() # Default reduction is 'mean'
for epoch in range(self.epochs):
forget_iter = iter(forget_loader)
t_loss_r, t_loss_f = 0.0, 0.0
steps = 0
for r_inputs, r_labels in retain_loader:
r_inputs, r_labels = r_inputs.to(device), r_labels.to(device)
try:
f_inputs, _ = next(forget_iter)
except StopIteration:
forget_iter = iter(forget_loader)
f_inputs, _ = next(forget_iter)
f_inputs = f_inputs.to(device)
optimizer.zero_grad()
# Forward Pass
outputs_r = wf_model(r_inputs, target_unlearn_class=self.target_class_index)
outputs_f = wf_model(f_inputs, target_unlearn_class=self.target_class_index)
# Retain Loss (Mean over batch)
loss_r = criterion(outputs_r, r_labels)
# Forget Loss (Corrected to Mean over batch)
temperature = 1.0
logits_f_scaled = outputs_f / temperature
# Compute uniform target entropy per-sample, then average over the batch
log_probs_f = torch.log_softmax(logits_f_scaled, dim=-1)
uniform_target = torch.ones_like(logits_f_scaled) / num_classes
loss_f = -torch.sum(uniform_target * log_probs_f, dim=-1).mean()
total_loss = loss_r + (self.gamma * loss_f)
total_loss.backward()
optimizer.step()
t_loss_r += loss_r.item()
t_loss_f += loss_f.item()
steps += 1
print(f" Epoch {epoch+1}/{self.epochs} | Retain Loss: {t_loss_r/steps:.4f} | Forget Loss: {t_loss_f/steps:.4f}")
return wf_model
def _run(self, model: nn.Module, forget_loader: DataLoader, retain_loader: DataLoader) -> nn.Module:
device = next(model.parameters()).device
model.eval()
if hasattr(model, 'layer4') and len(model.layer4) > 1:
target_conv = model.layer4[1].conv2
else:
raise AttributeError("Model architecture does not match expected ResNet-18 structure.")
original_weights = target_conv.weight.data.clone().detach()
out_channels = original_weights.shape[0]
# Freeze global network layers
for p in model.parameters():
p.requires_grad = False
wf_model = self._optimise_filter(
model,
forget_loader=forget_loader,
retain_loader=retain_loader,
device=device,
)
# --- PERMANENT BAKING STEP ---
with torch.no_grad():
# Grab the alpha mask vector for the forgotten class and cast to 4D tensor shape
final_mask = torch.sigmoid(wf_model.alpha[self.target_class_index]).view(-1, 1, 1, 1)
# Apply filter masking permanently back onto the base layer
target_conv.weight.copy_(original_weights * final_mask)
# Unfreeze architecture parameters for evaluations downstream
for p in model.parameters():
p.requires_grad = True
print(f">> Permanently altered {out_channels} convolutional filters in layer4 via WF-Net.")
return model