Model save path: ./New_Models/bn_True_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_338327_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: 0.11371058225631714
Norm Quadratic Average: 27.691619873046875
Nearest Class Center Accuracy: 0.279125

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.023990703746676445
Inter Cos: 0.10226675868034363
Norm Quadratic Average: 83.41304016113281
Nearest Class Center Accuracy: 0.332125

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.026184948161244392
Inter Cos: 0.09352362155914307
Norm Quadratic Average: 62.680904388427734
Nearest Class Center Accuracy: 0.36775

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022851012647151947
Inter Cos: 0.07227549701929092
Norm Quadratic Average: 66.89058685302734
Nearest Class Center Accuracy: 0.402625

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03188546746969223
Inter Cos: 0.0803435891866684
Norm Quadratic Average: 42.90191650390625
Nearest Class Center Accuracy: 0.417375

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.030985381454229355
Inter Cos: 0.06856850534677505
Norm Quadratic Average: 43.706298828125
Nearest Class Center Accuracy: 0.456125

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04177825525403023
Inter Cos: 0.07482032477855682
Norm Quadratic Average: 27.960834503173828
Nearest Class Center Accuracy: 0.554

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0614062175154686
Inter Cos: 0.07527106255292892
Norm Quadratic Average: 19.7586612701416
Nearest Class Center Accuracy: 0.847625

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 98.03897857666016
Linear Weight Rank: 4031
Intra Cos: 0.18330472707748413
Inter Cos: 0.10255047678947449
Norm Quadratic Average: 105.38501739501953
Nearest Class Center Accuracy: 1.0

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 39.62424087524414
Linear Weight Rank: 3670
Intra Cos: 0.416989266872406
Inter Cos: 0.18924042582511902
Norm Quadratic Average: 54.81466293334961
Nearest Class Center Accuracy: 1.0

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.468701124191284
Linear Weight Rank: 10
Intra Cos: 0.6415186524391174
Inter Cos: 0.3028241991996765
Norm Quadratic Average: 38.35686111450195
Nearest Class Center Accuracy: 1.0

Output Layer:
Intra Cos: 0.8619081974029541
Inter Cos: 0.507835328578949
Norm Quadratic Average: 26.404943466186523
Nearest Class Center Accuracy: 1.0

Test Set:
Average Loss: 2.66181876373291
Accuracy: 0.5865
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.21617494523525238, Weights: 0.015173118561506271
NC2 Equiangle: Features: 0.42584864298502606, Weights: 0.08876290851169162
NC3 Self-Duality: 0.6308094263076782
NC4 NCC Mismatch: 0.14949999999999997

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018480459228157997
Inter Cos: 0.09352367371320724
Norm Quadratic Average: 27.53066635131836
Nearest Class Center Accuracy: 0.2895

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.023111389949917793
Inter Cos: 0.08817262947559357
Norm Quadratic Average: 82.942626953125
Nearest Class Center Accuracy: 0.3505

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02665666677057743
Inter Cos: 0.08119840174913406
Norm Quadratic Average: 62.34364700317383
Nearest Class Center Accuracy: 0.398

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.023085080087184906
Inter Cos: 0.06313849240541458
Norm Quadratic Average: 66.64014434814453
Nearest Class Center Accuracy: 0.438

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.029993204399943352
Inter Cos: 0.07195590436458588
Norm Quadratic Average: 42.73136901855469
Nearest Class Center Accuracy: 0.4435

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.028311440721154213
Inter Cos: 0.06053498387336731
Norm Quadratic Average: 43.59445571899414
Nearest Class Center Accuracy: 0.4715

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03194509074091911
Inter Cos: 0.07291541248559952
Norm Quadratic Average: 27.85309600830078
Nearest Class Center Accuracy: 0.5015

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0326915942132473
Inter Cos: 0.0634363666176796
Norm Quadratic Average: 19.590547561645508
Nearest Class Center Accuracy: 0.56

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 98.03897857666016
Linear Weight Rank: 4031
Intra Cos: 0.053056225180625916
Inter Cos: 0.09853287041187286
Norm Quadratic Average: 101.40524291992188
Nearest Class Center Accuracy: 0.616

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 39.62424087524414
Linear Weight Rank: 3670
Intra Cos: 0.10719699412584305
Inter Cos: 0.194849893450737
Norm Quadratic Average: 50.482181549072266
Nearest Class Center Accuracy: 0.5855

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.468701124191284
Linear Weight Rank: 10
Intra Cos: 0.16515515744686127
Inter Cos: 0.30827435851097107
Norm Quadratic Average: 34.07818603515625
Nearest Class Center Accuracy: 0.577

Output Layer:
Intra Cos: 0.2351670265197754
Inter Cos: 0.4814680814743042
Norm Quadratic Average: 22.769380569458008
Nearest Class Center Accuracy: 0.5575

