Model save path: ./New_Models/bn_False_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: 0.023969460278749466
Inter Cos: 0.09654621034860611
Norm Quadratic Average: 34.887046813964844
Nearest Class Center Accuracy: 0.300875

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.029614880681037903
Inter Cos: 0.10334988683462143
Norm Quadratic Average: 27.704496383666992
Nearest Class Center Accuracy: 0.361375

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03459552302956581
Inter Cos: 0.10220693051815033
Norm Quadratic Average: 33.23921585083008
Nearest Class Center Accuracy: 0.409375

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05075144022703171
Inter Cos: 0.12740807235240936
Norm Quadratic Average: 21.251211166381836
Nearest Class Center Accuracy: 0.433125

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0571175143122673
Inter Cos: 0.12723460793495178
Norm Quadratic Average: 19.25246238708496
Nearest Class Center Accuracy: 0.45925

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07260899990797043
Inter Cos: 0.13735787570476532
Norm Quadratic Average: 10.326635360717773
Nearest Class Center Accuracy: 0.511625

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09987429529428482
Inter Cos: 0.14810875058174133
Norm Quadratic Average: 7.5996413230896
Nearest Class Center Accuracy: 0.6775

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 98.05810546875
Linear Weight Rank: 4031
Intra Cos: 0.28869858384132385
Inter Cos: 0.2507086992263794
Norm Quadratic Average: 29.691730499267578
Nearest Class Center Accuracy: 0.970625

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 39.676395416259766
Linear Weight Rank: 3670
Intra Cos: 0.5730968713760376
Inter Cos: 0.38794344663619995
Norm Quadratic Average: 25.162317276000977
Nearest Class Center Accuracy: 0.99925

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.210232973098755
Linear Weight Rank: 10
Intra Cos: 0.7307292819023132
Inter Cos: 0.4942551851272583
Norm Quadratic Average: 28.96295928955078
Nearest Class Center Accuracy: 0.999625

Output Layer:
Intra Cos: 0.83868807554245
Inter Cos: 0.6582245826721191
Norm Quadratic Average: 34.596221923828125
Nearest Class Center Accuracy: 0.999625

Test Set:
Average Loss: 3.0272386093139647
Accuracy: 0.5955
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.23862938582897186, Weights: 0.045931439846754074
NC2 Equiangle: Features: 0.43378596835666233, Weights: 0.15282805760701498
NC3 Self-Duality: 0.45509836077690125
NC4 NCC Mismatch: 0.15149999999999997

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: 0.02494148537516594
Inter Cos: 0.07997334003448486
Norm Quadratic Average: 34.63673782348633
Nearest Class Center Accuracy: 0.3175

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03257632628083229
Inter Cos: 0.08907458931207657
Norm Quadratic Average: 27.55034065246582
Nearest Class Center Accuracy: 0.3755

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03582325205206871
Inter Cos: 0.09010564535856247
Norm Quadratic Average: 33.11260223388672
Nearest Class Center Accuracy: 0.434

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04916547238826752
Inter Cos: 0.11418676376342773
Norm Quadratic Average: 21.19183921813965
Nearest Class Center Accuracy: 0.4525

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05384879559278488
Inter Cos: 0.11439305543899536
Norm Quadratic Average: 19.23540496826172
Nearest Class Center Accuracy: 0.4675

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.060998644679784775
Inter Cos: 0.1266726851463318
Norm Quadratic Average: 10.306867599487305
Nearest Class Center Accuracy: 0.4875

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07098046690225601
Inter Cos: 0.13833142817020416
Norm Quadratic Average: 7.552595615386963
Nearest Class Center Accuracy: 0.517

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 98.05810546875
Linear Weight Rank: 4031
Intra Cos: 0.13709977269172668
Inter Cos: 0.23561890423297882
Norm Quadratic Average: 28.657691955566406
Nearest Class Center Accuracy: 0.5845

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 39.676395416259766
Linear Weight Rank: 3670
Intra Cos: 0.22465769946575165
Inter Cos: 0.3567468822002411
Norm Quadratic Average: 23.50124740600586
Nearest Class Center Accuracy: 0.589

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.210232973098755
Linear Weight Rank: 10
Intra Cos: 0.2649227976799011
Inter Cos: 0.4387168288230896
Norm Quadratic Average: 26.78593635559082
Nearest Class Center Accuracy: 0.5695

Output Layer:
Intra Cos: 0.3015911877155304
Inter Cos: 0.5465741157531738
Norm Quadratic Average: 31.846698760986328
Nearest Class Center Accuracy: 0.557

