Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_314159_weight_decay_0.001.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: 0.02493979223072529
Inter Cos: 0.09440701454877853
Norm Quadratic Average: 33.759342193603516
Nearest Class Center Accuracy: 0.300375

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03248320519924164
Inter Cos: 0.10904288291931152
Norm Quadratic Average: 26.70088005065918
Nearest Class Center Accuracy: 0.353375

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03699469193816185
Inter Cos: 0.10554968565702438
Norm Quadratic Average: 30.932661056518555
Nearest Class Center Accuracy: 0.407125

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.053660403937101364
Inter Cos: 0.1340555101633072
Norm Quadratic Average: 19.35604476928711
Nearest Class Center Accuracy: 0.4355

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06717517226934433
Inter Cos: 0.13920438289642334
Norm Quadratic Average: 17.638952255249023
Nearest Class Center Accuracy: 0.46775

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08835799247026443
Inter Cos: 0.1594177484512329
Norm Quadratic Average: 9.627087593078613
Nearest Class Center Accuracy: 0.511875

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1180281713604927
Inter Cos: 0.17167924344539642
Norm Quadratic Average: 6.986313819885254
Nearest Class Center Accuracy: 0.674125

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 96.6565933227539
Linear Weight Rank: 4031
Intra Cos: 0.3170355260372162
Inter Cos: 0.2775719463825226
Norm Quadratic Average: 27.618669509887695
Nearest Class Center Accuracy: 0.958375

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 39.119384765625
Linear Weight Rank: 3670
Intra Cos: 0.6049062609672546
Inter Cos: 0.44271335005760193
Norm Quadratic Average: 24.1442928314209
Nearest Class Center Accuracy: 0.99825

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.2292582988739014
Linear Weight Rank: 10
Intra Cos: 0.7412902116775513
Inter Cos: 0.5532776713371277
Norm Quadratic Average: 28.573095321655273
Nearest Class Center Accuracy: 0.999625

Output Layer:
Intra Cos: 0.8395793437957764
Inter Cos: 0.7127872705459595
Norm Quadratic Average: 35.302791595458984
Nearest Class Center Accuracy: 0.999

Test Set:
Average Loss: 3.1261133880615235
Accuracy: 0.588
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.2528272569179535, Weights: 0.04678475484251976
NC2 Equiangle: Features: 0.4248986985948351, Weights: 0.16940571467081705
NC3 Self-Duality: 0.44738346338272095
NC4 NCC Mismatch: 0.15349999999999997

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: 0.025231875479221344
Inter Cos: 0.08879425376653671
Norm Quadratic Average: 33.57673263549805
Nearest Class Center Accuracy: 0.3185

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.034714266657829285
Inter Cos: 0.10522336512804031
Norm Quadratic Average: 26.57628059387207
Nearest Class Center Accuracy: 0.375

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.037937335669994354
Inter Cos: 0.09478262811899185
Norm Quadratic Average: 30.80971336364746
Nearest Class Center Accuracy: 0.4325

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05240807682275772
Inter Cos: 0.12152796238660812
Norm Quadratic Average: 19.299509048461914
Nearest Class Center Accuracy: 0.456

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06386256217956543
Inter Cos: 0.12538468837738037
Norm Quadratic Average: 17.618724822998047
Nearest Class Center Accuracy: 0.47

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07455012202262878
Inter Cos: 0.14197975397109985
Norm Quadratic Average: 9.608410835266113
Nearest Class Center Accuracy: 0.473

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08336473256349564
Inter Cos: 0.14650171995162964
Norm Quadratic Average: 6.941972732543945
Nearest Class Center Accuracy: 0.5195

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 96.6565933227539
Linear Weight Rank: 4031
Intra Cos: 0.1363457888364792
Inter Cos: 0.23808784782886505
Norm Quadratic Average: 26.606678009033203
Nearest Class Center Accuracy: 0.577

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 39.119384765625
Linear Weight Rank: 3670
Intra Cos: 0.21632155776023865
Inter Cos: 0.36455225944519043
Norm Quadratic Average: 22.552412033081055
Nearest Class Center Accuracy: 0.587

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.2292582988739014
Linear Weight Rank: 10
Intra Cos: 0.24869295954704285
Inter Cos: 0.4501228928565979
Norm Quadratic Average: 26.434484481811523
Nearest Class Center Accuracy: 0.5785

Output Layer:
Intra Cos: 0.2768644094467163
Inter Cos: 0.5625417232513428
Norm Quadratic Average: 32.43731689453125
Nearest Class Center Accuracy: 0.55

