Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_314159_weight_decay_0.02.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: 0.025423096492886543
Inter Cos: 0.10263348370790482
Norm Quadratic Average: 21.82209587097168
Nearest Class Center Accuracy: 0.29825

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03406699746847153
Inter Cos: 0.1272924393415451
Norm Quadratic Average: 13.993200302124023
Nearest Class Center Accuracy: 0.32925

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04385480657219887
Inter Cos: 0.12926025688648224
Norm Quadratic Average: 13.13017749786377
Nearest Class Center Accuracy: 0.382125

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07326968014240265
Inter Cos: 0.18773747980594635
Norm Quadratic Average: 7.581587791442871
Nearest Class Center Accuracy: 0.41675

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.13191142678260803
Inter Cos: 0.2730123698711395
Norm Quadratic Average: 5.861567497253418
Nearest Class Center Accuracy: 0.44775

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.17860208451747894
Inter Cos: 0.3741361200809479
Norm Quadratic Average: 3.4831840991973877
Nearest Class Center Accuracy: 0.47075

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.22512580454349518
Inter Cos: 0.4496713876724243
Norm Quadratic Average: 2.4280266761779785
Nearest Class Center Accuracy: 0.51225

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 38.82194900512695
Linear Weight Rank: 4031
Intra Cos: 0.2951744496822357
Inter Cos: 0.47403019666671753
Norm Quadratic Average: 11.744059562683105
Nearest Class Center Accuracy: 0.55475

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 15.780709266662598
Linear Weight Rank: 3669
Intra Cos: 0.3429486155509949
Inter Cos: 0.5249956846237183
Norm Quadratic Average: 9.037894248962402
Nearest Class Center Accuracy: 0.570625

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.06398868560791
Linear Weight Rank: 10
Intra Cos: 0.38142573833465576
Inter Cos: 0.5958123803138733
Norm Quadratic Average: 7.706182956695557
Nearest Class Center Accuracy: 0.568375

Output Layer:
Intra Cos: 0.44195395708084106
Inter Cos: 0.6905773878097534
Norm Quadratic Average: 7.266538143157959
Nearest Class Center Accuracy: 0.547875

Test Set:
Average Loss: 1.2319483299255372
Accuracy: 0.549
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.22905783355236053, Weights: 0.11828107386827469
NC2 Equiangle: Features: 0.568589358859592, Weights: 0.2378069135877821
NC3 Self-Duality: 0.3182874023914337
NC4 NCC Mismatch: 0.16800000000000004

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: 0.02543289214372635
Inter Cos: 0.0962596908211708
Norm Quadratic Average: 21.720102310180664
Nearest Class Center Accuracy: 0.319

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.033870358020067215
Inter Cos: 0.11996905505657196
Norm Quadratic Average: 13.923895835876465
Nearest Class Center Accuracy: 0.3415

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0475984662771225
Inter Cos: 0.12324102967977524
Norm Quadratic Average: 13.069259643554688
Nearest Class Center Accuracy: 0.3915

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07426974177360535
Inter Cos: 0.1652047336101532
Norm Quadratic Average: 7.5503249168396
Nearest Class Center Accuracy: 0.424

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.10446526110172272
Inter Cos: 0.2418593317270279
Norm Quadratic Average: 5.848222732543945
Nearest Class Center Accuracy: 0.4405

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.12879645824432373
Inter Cos: 0.33215460181236267
Norm Quadratic Average: 3.475943088531494
Nearest Class Center Accuracy: 0.463

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.17061908543109894
Inter Cos: 0.3963265120983124
Norm Quadratic Average: 2.4220824241638184
Nearest Class Center Accuracy: 0.495

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 38.82194900512695
Linear Weight Rank: 4031
Intra Cos: 0.23915693163871765
Inter Cos: 0.4585505425930023
Norm Quadratic Average: 11.718792915344238
Nearest Class Center Accuracy: 0.5275

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 15.780709266662598
Linear Weight Rank: 3669
Intra Cos: 0.2952007055282593
Inter Cos: 0.5373709797859192
Norm Quadratic Average: 9.041731834411621
Nearest Class Center Accuracy: 0.5355

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.06398868560791
Linear Weight Rank: 10
Intra Cos: 0.34075894951820374
Inter Cos: 0.6052783727645874
Norm Quadratic Average: 7.725369930267334
Nearest Class Center Accuracy: 0.5285

Output Layer:
Intra Cos: 0.3940255343914032
Inter Cos: 0.6979196071624756
Norm Quadratic Average: 7.310047149658203
Nearest Class Center Accuracy: 0.5045

