Model save path: ./New_Models/bn_True_dataset_CIFAR10_epochs_200_lr_0.01_model_type_vgg11_rand_seed_265358_test_samples_None_train_samples_None_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: 0.01989092119038105
Inter Cos: 0.10477277636528015
Norm Quadratic Average: 27.59717559814453
Nearest Class Center Accuracy: 0.26968

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.019166268408298492
Inter Cos: 0.07019423693418503
Norm Quadratic Average: 6.515473365783691
Nearest Class Center Accuracy: 0.40544

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.019381802529096603
Inter Cos: 0.05389419570565224
Norm Quadratic Average: 3.1476502418518066
Nearest Class Center Accuracy: 0.53806

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.014683355577290058
Inter Cos: 0.04260076582431793
Norm Quadratic Average: 2.2939441204071045
Nearest Class Center Accuracy: 0.61818

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.020306481048464775
Inter Cos: 0.036286670714616776
Norm Quadratic Average: 1.5339401960372925
Nearest Class Center Accuracy: 0.7626

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03870728239417076
Inter Cos: 0.04235150292515755
Norm Quadratic Average: 1.0183967351913452
Nearest Class Center Accuracy: 0.8866

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.2107812464237213
Inter Cos: 0.1486993432044983
Norm Quadratic Average: 0.7150065302848816
Nearest Class Center Accuracy: 0.98934

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.7691813707351685
Inter Cos: 0.08979754149913788
Norm Quadratic Average: 0.8467802405357361
Nearest Class Center Accuracy: 1.0

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 2.3059463500976562
Linear Weight Rank: 2868
Intra Cos: 0.9739396572113037
Inter Cos: 0.005896752700209618
Norm Quadratic Average: 23.73482322692871
Nearest Class Center Accuracy: 1.0

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 2.352668285369873
Linear Weight Rank: 955
Intra Cos: 0.985320508480072
Inter Cos: 0.038607630878686905
Norm Quadratic Average: 16.81871223449707
Nearest Class Center Accuracy: 1.0

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.295342206954956
Linear Weight Rank: 9
Intra Cos: 0.9875738620758057
Inter Cos: 0.08332669734954834
Norm Quadratic Average: 12.24203872680664
Nearest Class Center Accuracy: 1.0

Output Layer:
Intra Cos: 0.99040687084198
Inter Cos: 0.12496239691972733
Norm Quadratic Average: 9.333388328552246
Nearest Class Center Accuracy: 1.0

Test Set:
Average Loss: 0.45584588918685914
Accuracy: 0.8633
NC1 Within Class Collapse: 3.387463092803955
NC2 Equinorm: Features: 0.128496453166008, Weights: 0.006169700995087624
NC2 Equiangle: Features: 0.12326009538438586, Weights: 0.020275955730014377
NC3 Self-Duality: 0.05604691058397293
NC4 NCC Mismatch: 0.018399999999999972

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018526077270507812
Inter Cos: 0.1042705774307251
Norm Quadratic Average: 27.550134658813477
Nearest Class Center Accuracy: 0.284

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01797725260257721
Inter Cos: 0.0719151645898819
Norm Quadratic Average: 6.510666370391846
Nearest Class Center Accuracy: 0.423

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018274500966072083
Inter Cos: 0.055190540850162506
Norm Quadratic Average: 3.1484086513519287
Nearest Class Center Accuracy: 0.5458

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.013711743988096714
Inter Cos: 0.043715305626392365
Norm Quadratic Average: 2.2970123291015625
Nearest Class Center Accuracy: 0.6234

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.017443403601646423
Inter Cos: 0.03697708249092102
Norm Quadratic Average: 1.5335731506347656
Nearest Class Center Accuracy: 0.7266

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.028857793658971786
Inter Cos: 0.0447087362408638
Norm Quadratic Average: 1.0109864473342896
Nearest Class Center Accuracy: 0.7868

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1453721821308136
Inter Cos: 0.1589982658624649
Norm Quadratic Average: 0.694689154624939
Nearest Class Center Accuracy: 0.8272

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.4238157272338867
Inter Cos: 0.21406130492687225
Norm Quadratic Average: 0.7773396968841553
Nearest Class Center Accuracy: 0.8601

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 2.3059463500976562
Linear Weight Rank: 2868
Intra Cos: 0.5709471702575684
Inter Cos: 0.23962409794330597
Norm Quadratic Average: 20.845294952392578
Nearest Class Center Accuracy: 0.8618

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 2.352668285369873
Linear Weight Rank: 955
Intra Cos: 0.5833095908164978
Inter Cos: 0.24710282683372498
Norm Quadratic Average: 14.714598655700684
Nearest Class Center Accuracy: 0.8619

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.295342206954956
Linear Weight Rank: 9
Intra Cos: 0.587037205696106
Inter Cos: 0.25830012559890747
Norm Quadratic Average: 10.719123840332031
Nearest Class Center Accuracy: 0.8618

Output Layer:
Intra Cos: 0.6003850698471069
Inter Cos: 0.2796107232570648
Norm Quadratic Average: 8.178202629089355
Nearest Class Center Accuracy: 0.8623

