Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_979323_weight_decay_0.01.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: 0.025158703327178955
Inter Cos: 0.10892167687416077
Norm Quadratic Average: 28.60609245300293
Nearest Class Center Accuracy: 0.312

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.027972737327218056
Inter Cos: 0.11135414242744446
Norm Quadratic Average: 22.736860275268555
Nearest Class Center Accuracy: 0.378375

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03632500022649765
Inter Cos: 0.1194954663515091
Norm Quadratic Average: 24.912750244140625
Nearest Class Center Accuracy: 0.425

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.057674139738082886
Inter Cos: 0.15293535590171814
Norm Quadratic Average: 14.464851379394531
Nearest Class Center Accuracy: 0.447

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07762502133846283
Inter Cos: 0.17660990357398987
Norm Quadratic Average: 10.557467460632324
Nearest Class Center Accuracy: 0.47775

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1027492955327034
Inter Cos: 0.19321946799755096
Norm Quadratic Average: 4.6692070960998535
Nearest Class Center Accuracy: 0.528625

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.17033633589744568
Inter Cos: 0.22429490089416504
Norm Quadratic Average: 2.7825160026550293
Nearest Class Center Accuracy: 0.728

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 62.780330657958984
Linear Weight Rank: 4031
Intra Cos: 0.47058796882629395
Inter Cos: 0.38290107250213623
Norm Quadratic Average: 12.760862350463867
Nearest Class Center Accuracy: 0.954125

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 25.48529624938965
Linear Weight Rank: 3670
Intra Cos: 0.6812542080879211
Inter Cos: 0.541474461555481
Norm Quadratic Average: 13.383691787719727
Nearest Class Center Accuracy: 0.99125

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.986644744873047
Linear Weight Rank: 10
Intra Cos: 0.7253863215446472
Inter Cos: 0.6292383074760437
Norm Quadratic Average: 16.46065330505371
Nearest Class Center Accuracy: 0.9935

Output Layer:
Intra Cos: 0.7741788625717163
Inter Cos: 0.7473902702331543
Norm Quadratic Average: 21.63357162475586
Nearest Class Center Accuracy: 0.98075

Test Set:
Average Loss: 1.8721229476928711
Accuracy: 0.593
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.26719117164611816, Weights: 0.06459353864192963
NC2 Equiangle: Features: 0.41994010077582467, Weights: 0.23099706437852646
NC3 Self-Duality: 0.3217773139476776
NC4 NCC Mismatch: 0.15749999999999997

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: 0.025220094248652458
Inter Cos: 0.09195179492235184
Norm Quadratic Average: 28.406330108642578
Nearest Class Center Accuracy: 0.331

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02930360846221447
Inter Cos: 0.09718000143766403
Norm Quadratic Average: 22.586421966552734
Nearest Class Center Accuracy: 0.391

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.035838861018419266
Inter Cos: 0.10613424330949783
Norm Quadratic Average: 24.796260833740234
Nearest Class Center Accuracy: 0.4495

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.053709983825683594
Inter Cos: 0.13545121252536774
Norm Quadratic Average: 14.401290893554688
Nearest Class Center Accuracy: 0.462

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06814222037792206
Inter Cos: 0.15513093769550323
Norm Quadratic Average: 10.525327682495117
Nearest Class Center Accuracy: 0.476

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07980610430240631
Inter Cos: 0.16589060425758362
Norm Quadratic Average: 4.64616060256958
Nearest Class Center Accuracy: 0.4875

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.10205347836017609
Inter Cos: 0.18980228900909424
Norm Quadratic Average: 2.7503554821014404
Nearest Class Center Accuracy: 0.5395

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 62.780330657958984
Linear Weight Rank: 4031
Intra Cos: 0.19036529958248138
Inter Cos: 0.3206205666065216
Norm Quadratic Average: 12.240753173828125
Nearest Class Center Accuracy: 0.588

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 25.48529624938965
Linear Weight Rank: 3670
Intra Cos: 0.23667606711387634
Inter Cos: 0.43778055906295776
Norm Quadratic Average: 12.58816146850586
Nearest Class Center Accuracy: 0.595

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.986644744873047
Linear Weight Rank: 10
Intra Cos: 0.23818761110305786
Inter Cos: 0.5110237002372742
Norm Quadratic Average: 15.40119743347168
Nearest Class Center Accuracy: 0.5795

Output Layer:
Intra Cos: 0.24785995483398438
Inter Cos: 0.6089295148849487
Norm Quadratic Average: 20.101099014282227
Nearest Class Center Accuracy: 0.5575

