Model save path: ./New_Models/bn_True_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_338327_weight_decay_0.0005.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: 0.023909656330943108
Inter Cos: 0.10054656863212585
Norm Quadratic Average: 84.3665542602539
Nearest Class Center Accuracy: 0.334625

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.025860652327537537
Inter Cos: 0.09175340831279755
Norm Quadratic Average: 63.371280670166016
Nearest Class Center Accuracy: 0.3725

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022965768352150917
Inter Cos: 0.07039391994476318
Norm Quadratic Average: 67.73285675048828
Nearest Class Center Accuracy: 0.40175

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.031749527901411057
Inter Cos: 0.0794452428817749
Norm Quadratic Average: 43.26713180541992
Nearest Class Center Accuracy: 0.424

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03000243380665779
Inter Cos: 0.0673295184969902
Norm Quadratic Average: 44.14450454711914
Nearest Class Center Accuracy: 0.46275

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03988613933324814
Inter Cos: 0.0745028406381607
Norm Quadratic Average: 28.37469482421875
Nearest Class Center Accuracy: 0.55175

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06175775080919266
Inter Cos: 0.07646097987890244
Norm Quadratic Average: 20.045204162597656
Nearest Class Center Accuracy: 0.83825

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 98.98404693603516
Linear Weight Rank: 4031
Intra Cos: 0.18106132745742798
Inter Cos: 0.10674262791872025
Norm Quadratic Average: 106.34909057617188
Nearest Class Center Accuracy: 0.999875

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.00607681274414
Linear Weight Rank: 3670
Intra Cos: 0.4196912944316864
Inter Cos: 0.20414063334465027
Norm Quadratic Average: 55.51097869873047
Nearest Class Center Accuracy: 1.0

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.490142345428467
Linear Weight Rank: 10
Intra Cos: 0.6479445695877075
Inter Cos: 0.3209023177623749
Norm Quadratic Average: 39.00394058227539
Nearest Class Center Accuracy: 1.0

Output Layer:
Intra Cos: 0.8666988015174866
Inter Cos: 0.5287294387817383
Norm Quadratic Average: 27.024993896484375
Nearest Class Center Accuracy: 1.0

Test Set:
Average Loss: 2.7282750320434572
Accuracy: 0.591
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.22014620900154114, Weights: 0.013848986476659775
NC2 Equiangle: Features: 0.4468181610107422, Weights: 0.09006337059868706
NC3 Self-Duality: 0.6496181488037109
NC4 NCC Mismatch: 0.15700000000000003

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: 0.023206213489174843
Inter Cos: 0.08666937053203583
Norm Quadratic Average: 83.87256622314453
Nearest Class Center Accuracy: 0.352

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.026251310482621193
Inter Cos: 0.07962363958358765
Norm Quadratic Average: 63.03073501586914
Nearest Class Center Accuracy: 0.3985

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02335692197084427
Inter Cos: 0.06143052875995636
Norm Quadratic Average: 67.48983764648438
Nearest Class Center Accuracy: 0.4365

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.029822224751114845
Inter Cos: 0.07005664706230164
Norm Quadratic Average: 43.115238189697266
Nearest Class Center Accuracy: 0.45

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02744901366531849
Inter Cos: 0.058113448321819305
Norm Quadratic Average: 44.02309799194336
Nearest Class Center Accuracy: 0.4825

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02988131158053875
Inter Cos: 0.0726386085152626
Norm Quadratic Average: 28.26107406616211
Nearest Class Center Accuracy: 0.5

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03256114572286606
Inter Cos: 0.06429174542427063
Norm Quadratic Average: 19.885046005249023
Nearest Class Center Accuracy: 0.566

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 98.98404693603516
Linear Weight Rank: 4031
Intra Cos: 0.056451085954904556
Inter Cos: 0.1053454577922821
Norm Quadratic Average: 102.61481475830078
Nearest Class Center Accuracy: 0.611

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.00607681274414
Linear Weight Rank: 3670
Intra Cos: 0.11508768796920776
Inter Cos: 0.20805682241916656
Norm Quadratic Average: 51.30708694458008
Nearest Class Center Accuracy: 0.5885

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.490142345428467
Linear Weight Rank: 10
Intra Cos: 0.18306861817836761
Inter Cos: 0.3304959535598755
Norm Quadratic Average: 34.790218353271484
Nearest Class Center Accuracy: 0.58

Output Layer:
Intra Cos: 0.27148160338401794
Inter Cos: 0.5088575482368469
Norm Quadratic Average: 23.402374267578125
Nearest Class Center Accuracy: 0.561

