Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_979323_weight_decay_0.0001.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: 27.691617965698242
Nearest Class Center Accuracy: 0.279125

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02555783838033676
Inter Cos: 0.10873404890298843
Norm Quadratic Average: 29.63471031188965
Nearest Class Center Accuracy: 0.319

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02813238464295864
Inter Cos: 0.1127958744764328
Norm Quadratic Average: 23.585376739501953
Nearest Class Center Accuracy: 0.38

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.036232344806194305
Inter Cos: 0.11602405458688736
Norm Quadratic Average: 28.90509796142578
Nearest Class Center Accuracy: 0.422

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05541751906275749
Inter Cos: 0.14738833904266357
Norm Quadratic Average: 18.586030960083008
Nearest Class Center Accuracy: 0.4475

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06961467862129211
Inter Cos: 0.1555604785680771
Norm Quadratic Average: 17.537744522094727
Nearest Class Center Accuracy: 0.47725

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0903237983584404
Inter Cos: 0.1619597226381302
Norm Quadratic Average: 9.864230155944824
Nearest Class Center Accuracy: 0.524

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.11738920211791992
Inter Cos: 0.17291046679019928
Norm Quadratic Average: 7.387934684753418
Nearest Class Center Accuracy: 0.694125

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 100.91639709472656
Linear Weight Rank: 4031
Intra Cos: 0.3006165027618408
Inter Cos: 0.24915379285812378
Norm Quadratic Average: 29.3239803314209
Nearest Class Center Accuracy: 0.971875

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.8410530090332
Linear Weight Rank: 3670
Intra Cos: 0.5800393223762512
Inter Cos: 0.3994896411895752
Norm Quadratic Average: 25.368619918823242
Nearest Class Center Accuracy: 0.999

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.274785280227661
Linear Weight Rank: 10
Intra Cos: 0.7352228164672852
Inter Cos: 0.5136829018592834
Norm Quadratic Average: 29.823286056518555
Nearest Class Center Accuracy: 0.99975

Output Layer:
Intra Cos: 0.814380943775177
Inter Cos: 0.6701194047927856
Norm Quadratic Average: 36.46329879760742
Nearest Class Center Accuracy: 0.995875

Test Set:
Average Loss: 3.1447639770507814
Accuracy: 0.599
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.25460708141326904, Weights: 0.0435030460357666
NC2 Equiangle: Features: 0.42077416314019095, Weights: 0.15893349117702907
NC3 Self-Duality: 0.45379745960235596
NC4 NCC Mismatch: 0.14400000000000002

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018480457365512848
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: 0.02510746568441391
Inter Cos: 0.09261427074670792
Norm Quadratic Average: 29.453487396240234
Nearest Class Center Accuracy: 0.335

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.029287094250321388
Inter Cos: 0.09863138943910599
Norm Quadratic Average: 23.440631866455078
Nearest Class Center Accuracy: 0.3975

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03621162846684456
Inter Cos: 0.10235411673784256
Norm Quadratic Average: 28.785436630249023
Nearest Class Center Accuracy: 0.4495

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05175843462347984
Inter Cos: 0.1298559457063675
Norm Quadratic Average: 18.506351470947266
Nearest Class Center Accuracy: 0.4675

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0623684786260128
Inter Cos: 0.1354503184556961
Norm Quadratic Average: 17.484601974487305
Nearest Class Center Accuracy: 0.479

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07426443696022034
Inter Cos: 0.13917110860347748
Norm Quadratic Average: 9.823330879211426
Nearest Class Center Accuracy: 0.4885

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0840437263250351
Inter Cos: 0.14558258652687073
Norm Quadratic Average: 7.33005952835083
Nearest Class Center Accuracy: 0.5325

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 100.91639709472656
Linear Weight Rank: 4031
Intra Cos: 0.1342904418706894
Inter Cos: 0.2337522804737091
Norm Quadratic Average: 28.362878799438477
Nearest Class Center Accuracy: 0.6105

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.8410530090332
Linear Weight Rank: 3670
Intra Cos: 0.20999084413051605
Inter Cos: 0.3631427586078644
Norm Quadratic Average: 23.87166976928711
Nearest Class Center Accuracy: 0.614

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.274785280227661
Linear Weight Rank: 10
Intra Cos: 0.24602505564689636
Inter Cos: 0.457444965839386
Norm Quadratic Average: 27.79173469543457
Nearest Class Center Accuracy: 0.599

Output Layer:
Intra Cos: 0.28522351384162903
Inter Cos: 0.575272798538208
Norm Quadratic Average: 33.83919906616211
Nearest Class Center Accuracy: 0.5825

