Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_314159_weight_decay_0.005.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: 0.024689896032214165
Inter Cos: 0.09504595398902893
Norm Quadratic Average: 33.85164260864258
Nearest Class Center Accuracy: 0.29825

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03189628943800926
Inter Cos: 0.11180999130010605
Norm Quadratic Average: 27.305538177490234
Nearest Class Center Accuracy: 0.348125

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03685988858342171
Inter Cos: 0.10766038298606873
Norm Quadratic Average: 29.81981086730957
Nearest Class Center Accuracy: 0.404125

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0539691187441349
Inter Cos: 0.13839533925056458
Norm Quadratic Average: 17.63422203063965
Nearest Class Center Accuracy: 0.432875

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06985517591238022
Inter Cos: 0.15127292275428772
Norm Quadratic Average: 14.31612777709961
Nearest Class Center Accuracy: 0.4675

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09148706495761871
Inter Cos: 0.17473065853118896
Norm Quadratic Average: 6.976328372955322
Nearest Class Center Accuracy: 0.515125

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.13544082641601562
Inter Cos: 0.1987147331237793
Norm Quadratic Average: 4.5544586181640625
Nearest Class Center Accuracy: 0.688625

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 79.7913589477539
Linear Weight Rank: 4031
Intra Cos: 0.40216246247291565
Inter Cos: 0.33012911677360535
Norm Quadratic Average: 18.582605361938477
Nearest Class Center Accuracy: 0.955375

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 32.32630157470703
Linear Weight Rank: 3670
Intra Cos: 0.6858408451080322
Inter Cos: 0.4920668601989746
Norm Quadratic Average: 17.74683952331543
Nearest Class Center Accuracy: 0.997375

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.104957342147827
Linear Weight Rank: 10
Intra Cos: 0.7746415138244629
Inter Cos: 0.5819826126098633
Norm Quadratic Average: 21.626375198364258
Nearest Class Center Accuracy: 0.9995

Output Layer:
Intra Cos: 0.8514063358306885
Inter Cos: 0.7177863121032715
Norm Quadratic Average: 27.593353271484375
Nearest Class Center Accuracy: 0.99825

Test Set:
Average Loss: 2.4802060928344725
Accuracy: 0.5905
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.2410791963338852, Weights: 0.04517635703086853
NC2 Equiangle: Features: 0.42066480848524307, Weights: 0.20303124321831598
NC3 Self-Duality: 0.3842586278915405
NC4 NCC Mismatch: 0.15749999999999997

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: 0.025081418454647064
Inter Cos: 0.08885262906551361
Norm Quadratic Average: 33.662925720214844
Nearest Class Center Accuracy: 0.315

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03459273278713226
Inter Cos: 0.10722721368074417
Norm Quadratic Average: 27.17550277709961
Nearest Class Center Accuracy: 0.3705

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03799884766340256
Inter Cos: 0.09685803204774857
Norm Quadratic Average: 29.69911003112793
Nearest Class Center Accuracy: 0.427

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05278825759887695
Inter Cos: 0.12494026869535446
Norm Quadratic Average: 17.580318450927734
Nearest Class Center Accuracy: 0.45

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06598179042339325
Inter Cos: 0.13553324341773987
Norm Quadratic Average: 14.296143531799316
Nearest Class Center Accuracy: 0.4645

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0769135132431984
Inter Cos: 0.15322406589984894
Norm Quadratic Average: 6.961757659912109
Nearest Class Center Accuracy: 0.485

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0916384756565094
Inter Cos: 0.16812047362327576
Norm Quadratic Average: 4.519029140472412
Nearest Class Center Accuracy: 0.51

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 79.7913589477539
Linear Weight Rank: 4031
Intra Cos: 0.17110151052474976
Inter Cos: 0.28155362606048584
Norm Quadratic Average: 17.83428382873535
Nearest Class Center Accuracy: 0.5825

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 32.32630157470703
Linear Weight Rank: 3670
Intra Cos: 0.2522127032279968
Inter Cos: 0.404575377702713
Norm Quadratic Average: 16.563404083251953
Nearest Class Center Accuracy: 0.5865

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.104957342147827
Linear Weight Rank: 10
Intra Cos: 0.2694098949432373
Inter Cos: 0.475565105676651
Norm Quadratic Average: 20.057050704956055
Nearest Class Center Accuracy: 0.575

Output Layer:
Intra Cos: 0.29043763875961304
Inter Cos: 0.576036810874939
Norm Quadratic Average: 25.42220115661621
Nearest Class Center Accuracy: 0.5545

