Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_200_lr_0.01_model_type_vgg11_rand_seed_314159_test_samples_None_train_samples_None_weight_decay_0.03.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: 0.10477276146411896
Norm Quadratic Average: 27.59718132019043
Nearest Class Center Accuracy: 0.26968

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.025786496698856354
Inter Cos: 0.09782453626394272
Norm Quadratic Average: 12.85344409942627
Nearest Class Center Accuracy: 0.36248

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03616819903254509
Inter Cos: 0.09503267705440521
Norm Quadratic Average: 3.657968521118164
Nearest Class Center Accuracy: 0.45704

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08541929721832275
Inter Cos: 0.17904749512672424
Norm Quadratic Average: 1.1793954372406006
Nearest Class Center Accuracy: 0.54188

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.14842353761196136
Inter Cos: 0.425761342048645
Norm Quadratic Average: 0.5978290438652039
Nearest Class Center Accuracy: 0.55546

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.160297691822052
Inter Cos: 0.5555318593978882
Norm Quadratic Average: 0.7443230152130127
Nearest Class Center Accuracy: 0.58818

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1645602583885193
Inter Cos: 0.6094589233398438
Norm Quadratic Average: 1.1232088804244995
Nearest Class Center Accuracy: 0.56824

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1865418255329132
Inter Cos: 0.6623948812484741
Norm Quadratic Average: 1.7878056764602661
Nearest Class Center Accuracy: 0.57486

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 1.7256648540496826
Linear Weight Rank: 3
Intra Cos: 0.20050294697284698
Inter Cos: 0.6691872477531433
Norm Quadratic Average: 12.171399116516113
Nearest Class Center Accuracy: 0.58618

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 1.7393813133239746
Linear Weight Rank: 2485
Intra Cos: 0.2171320766210556
Inter Cos: 0.678155779838562
Norm Quadratic Average: 11.7858247756958
Nearest Class Center Accuracy: 0.59698

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 1.7594754695892334
Linear Weight Rank: 9
Intra Cos: 0.29369378089904785
Inter Cos: 0.66944420337677
Norm Quadratic Average: 9.669533729553223
Nearest Class Center Accuracy: 0.61436

Output Layer:
Intra Cos: 0.40955448150634766
Inter Cos: 0.7563996911048889
Norm Quadratic Average: 8.413694381713867
Nearest Class Center Accuracy: 0.60872

Test Set:
Average Loss: 1.1308582418441773
Accuracy: 0.5789
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.2707988917827606, Weights: 0.17599833011627197
NC2 Equiangle: Features: 0.5753016153971354, Weights: 0.3105157216389974
NC3 Self-Duality: 0.2849780321121216
NC4 NCC Mismatch: 0.26239999999999997

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018526073545217514
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: 0.023166848346590996
Inter Cos: 0.09839125722646713
Norm Quadratic Average: 12.851068496704102
Nearest Class Center Accuracy: 0.3756

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03480678051710129
Inter Cos: 0.09669120609760284
Norm Quadratic Average: 3.6599512100219727
Nearest Class Center Accuracy: 0.4622

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0831729993224144
Inter Cos: 0.1822526454925537
Norm Quadratic Average: 1.18062424659729
Nearest Class Center Accuracy: 0.5446

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.15318909287452698
Inter Cos: 0.4257843494415283
Norm Quadratic Average: 0.5986034274101257
Nearest Class Center Accuracy: 0.5437

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1642574965953827
Inter Cos: 0.550898015499115
Norm Quadratic Average: 0.7444305419921875
Nearest Class Center Accuracy: 0.5713

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.16726398468017578
Inter Cos: 0.6028008460998535
Norm Quadratic Average: 1.1218868494033813
Nearest Class Center Accuracy: 0.551

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.18915896117687225
Inter Cos: 0.6541343331336975
Norm Quadratic Average: 1.7849382162094116
Nearest Class Center Accuracy: 0.5591

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 1.7256648540496826
Linear Weight Rank: 3
Intra Cos: 0.2029135376214981
Inter Cos: 0.6599663496017456
Norm Quadratic Average: 12.158260345458984
Nearest Class Center Accuracy: 0.5696

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 1.7393813133239746
Linear Weight Rank: 2485
Intra Cos: 0.2196338027715683
Inter Cos: 0.6675915718078613
Norm Quadratic Average: 11.778576850891113
Nearest Class Center Accuracy: 0.5806

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 1.7594754695892334
Linear Weight Rank: 9
Intra Cos: 0.28869134187698364
Inter Cos: 0.6570312976837158
Norm Quadratic Average: 9.678449630737305
Nearest Class Center Accuracy: 0.5934

Output Layer:
Intra Cos: 0.40062302350997925
Inter Cos: 0.747618556022644
Norm Quadratic Average: 8.424718856811523
Nearest Class Center Accuracy: 0.5812

