Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_314159_weight_decay_0.003.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: 0.024803616106510162
Inter Cos: 0.09411349147558212
Norm Quadratic Average: 33.672332763671875
Nearest Class Center Accuracy: 0.29975

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.031690653413534164
Inter Cos: 0.10892187803983688
Norm Quadratic Average: 26.754119873046875
Nearest Class Center Accuracy: 0.35225

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03632573410868645
Inter Cos: 0.10740381479263306
Norm Quadratic Average: 29.972026824951172
Nearest Class Center Accuracy: 0.408625

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05340231582522392
Inter Cos: 0.13671918213367462
Norm Quadratic Average: 18.26473617553711
Nearest Class Center Accuracy: 0.43625

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06805140525102615
Inter Cos: 0.14335668087005615
Norm Quadratic Average: 15.766904830932617
Nearest Class Center Accuracy: 0.466375

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09026888012886047
Inter Cos: 0.16773609817028046
Norm Quadratic Average: 8.10457706451416
Nearest Class Center Accuracy: 0.5105

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.12783019244670868
Inter Cos: 0.1855333298444748
Norm Quadratic Average: 5.543415069580078
Nearest Class Center Accuracy: 0.682375

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 87.82093811035156
Linear Weight Rank: 4031
Intra Cos: 0.36999279260635376
Inter Cos: 0.3021557629108429
Norm Quadratic Average: 21.96291160583496
Nearest Class Center Accuracy: 0.958375

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 35.55992889404297
Linear Weight Rank: 3670
Intra Cos: 0.6736786961555481
Inter Cos: 0.4595348536968231
Norm Quadratic Average: 19.99062728881836
Nearest Class Center Accuracy: 0.99875

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.165019989013672
Linear Weight Rank: 10
Intra Cos: 0.7828763723373413
Inter Cos: 0.5486171841621399
Norm Quadratic Average: 24.02736473083496
Nearest Class Center Accuracy: 0.999125

Output Layer:
Intra Cos: 0.8629599809646606
Inter Cos: 0.6836804151535034
Norm Quadratic Average: 30.080617904663086
Nearest Class Center Accuracy: 0.999125

Test Set:
Average Loss: 2.721416732788086
Accuracy: 0.5825
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.2442256212234497, Weights: 0.04111945629119873
NC2 Equiangle: Features: 0.4162679460313585, Weights: 0.18609415690104167
NC3 Self-Duality: 0.4068930447101593
NC4 NCC Mismatch: 0.14400000000000002

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: 0.025156453251838684
Inter Cos: 0.08827339857816696
Norm Quadratic Average: 33.48915481567383
Nearest Class Center Accuracy: 0.3165

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03455927595496178
Inter Cos: 0.10485843569040298
Norm Quadratic Average: 26.630130767822266
Nearest Class Center Accuracy: 0.3715

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.037298355251550674
Inter Cos: 0.09646119177341461
Norm Quadratic Average: 29.846311569213867
Nearest Class Center Accuracy: 0.4315

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.051752254366874695
Inter Cos: 0.12354893982410431
Norm Quadratic Average: 18.211984634399414
Nearest Class Center Accuracy: 0.454

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06386669725179672
Inter Cos: 0.1282256543636322
Norm Quadratic Average: 15.748517990112305
Nearest Class Center Accuracy: 0.4675

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07520874589681625
Inter Cos: 0.14733439683914185
Norm Quadratic Average: 8.084258079528809
Nearest Class Center Accuracy: 0.474

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08675989508628845
Inter Cos: 0.1574409306049347
Norm Quadratic Average: 5.500065326690674
Nearest Class Center Accuracy: 0.519

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 87.82093811035156
Linear Weight Rank: 4031
Intra Cos: 0.15054678916931152
Inter Cos: 0.25814002752304077
Norm Quadratic Average: 21.100046157836914
Nearest Class Center Accuracy: 0.582

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 35.55992889404297
Linear Weight Rank: 3670
Intra Cos: 0.2339276671409607
Inter Cos: 0.38087382912635803
Norm Quadratic Average: 18.632410049438477
Nearest Class Center Accuracy: 0.5895

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.165019989013672
Linear Weight Rank: 10
Intra Cos: 0.25970739126205444
Inter Cos: 0.452914834022522
Norm Quadratic Average: 22.22877311706543
Nearest Class Center Accuracy: 0.5785

Output Layer:
Intra Cos: 0.2869367301464081
Inter Cos: 0.5507773160934448
Norm Quadratic Average: 27.654794692993164
Nearest Class Center Accuracy: 0.5585

