Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_314159_weight_decay_0.0007.pth.tar
Training Set:
Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.021450400352478027
Inter Cos: 0.11371058970689774
Norm Quadratic Average: 27.691621780395508
Nearest Class Center Accuracy: 0.279125

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.025080397725105286
Inter Cos: 0.09501966834068298
Norm Quadratic Average: 33.95989990234375
Nearest Class Center Accuracy: 0.300875

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03249495103955269
Inter Cos: 0.11000906676054001
Norm Quadratic Average: 26.966999053955078
Nearest Class Center Accuracy: 0.352625

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.036506686359643936
Inter Cos: 0.1061975359916687
Norm Quadratic Average: 31.463796615600586
Nearest Class Center Accuracy: 0.40875

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05344725400209427
Inter Cos: 0.1351069062948227
Norm Quadratic Average: 19.83747673034668
Nearest Class Center Accuracy: 0.43625

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0660167783498764
Inter Cos: 0.1394588053226471
Norm Quadratic Average: 18.127363204956055
Nearest Class Center Accuracy: 0.4695

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08821051567792892
Inter Cos: 0.16535882651805878
Norm Quadratic Average: 9.929784774780273
Nearest Class Center Accuracy: 0.51375

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.11786847561597824
Inter Cos: 0.17997851967811584
Norm Quadratic Average: 7.239414215087891
Nearest Class Center Accuracy: 0.675125

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 98.05696105957031
Linear Weight Rank: 4031
Intra Cos: 0.31694573163986206
Inter Cos: 0.2700955271720886
Norm Quadratic Average: 28.46771812438965
Nearest Class Center Accuracy: 0.96225

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 39.68475341796875
Linear Weight Rank: 3670
Intra Cos: 0.6183381676673889
Inter Cos: 0.4325313866138458
Norm Quadratic Average: 24.68689727783203
Nearest Class Center Accuracy: 0.998375

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.2550673484802246
Linear Weight Rank: 10
Intra Cos: 0.7602677941322327
Inter Cos: 0.5431516170501709
Norm Quadratic Average: 29.093379974365234
Nearest Class Center Accuracy: 0.9995

Output Layer:
Intra Cos: 0.8600835204124451
Inter Cos: 0.7089259028434753
Norm Quadratic Average: 35.87016677856445
Nearest Class Center Accuracy: 0.9995

Test Set:
Average Loss: 3.2286564483642577
Accuracy: 0.588
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.25150424242019653, Weights: 0.0404852032661438
NC2 Equiangle: Features: 0.4122746361626519, Weights: 0.16735975477430556
NC3 Self-Duality: 0.4482496380805969
NC4 NCC Mismatch: 0.15900000000000003

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018480459228157997
Inter Cos: 0.09352368116378784
Norm Quadratic Average: 27.530664443969727
Nearest Class Center Accuracy: 0.2895

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.025309590622782707
Inter Cos: 0.08951502293348312
Norm Quadratic Average: 33.78139877319336
Nearest Class Center Accuracy: 0.318

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.034362103790044785
Inter Cos: 0.10634645074605942
Norm Quadratic Average: 26.84893798828125
Nearest Class Center Accuracy: 0.3735

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03733283653855324
Inter Cos: 0.09532362967729568
Norm Quadratic Average: 31.34086036682129
Nearest Class Center Accuracy: 0.4305

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05218382552266121
Inter Cos: 0.1223636195063591
Norm Quadratic Average: 19.78251075744629
Nearest Class Center Accuracy: 0.454

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.062107186764478683
Inter Cos: 0.12479811161756516
Norm Quadratic Average: 18.107282638549805
Nearest Class Center Accuracy: 0.471

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07396092265844345
Inter Cos: 0.1443880796432495
Norm Quadratic Average: 9.907916069030762
Nearest Class Center Accuracy: 0.48

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08317196369171143
Inter Cos: 0.1512809544801712
Norm Quadratic Average: 7.188929557800293
Nearest Class Center Accuracy: 0.516

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 98.05696105957031
Linear Weight Rank: 4031
Intra Cos: 0.13282349705696106
Inter Cos: 0.23328359425067902
Norm Quadratic Average: 27.46466827392578
Nearest Class Center Accuracy: 0.577

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 39.68475341796875
Linear Weight Rank: 3670
Intra Cos: 0.21305808424949646
Inter Cos: 0.35687893629074097
Norm Quadratic Average: 23.094032287597656
Nearest Class Center Accuracy: 0.5905

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.2550673484802246
Linear Weight Rank: 10
Intra Cos: 0.24882251024246216
Inter Cos: 0.44103363156318665
Norm Quadratic Average: 26.927711486816406
Nearest Class Center Accuracy: 0.5795

Output Layer:
Intra Cos: 0.2811107337474823
Inter Cos: 0.554983377456665
Norm Quadratic Average: 32.91384506225586
Nearest Class Center Accuracy: 0.558

