Model save path: ./New_Models/bn_False_dataset_CIFAR100_epochs_200_lr_0.01_model_type_vgg11_rand_seed_979323_test_samples_None_train_samples_None_weight_decay_0.0005.pth.tar
Training Set:
Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022893190383911133
Inter Cos: 0.07011394202709198
Norm Quadratic Average: 29.326318740844727
Nearest Class Center Accuracy: 0.03258

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.035760171711444855
Inter Cos: 0.04833856225013733
Norm Quadratic Average: 38.38793182373047
Nearest Class Center Accuracy: 0.04504

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03901628777384758
Inter Cos: 0.035477686673402786
Norm Quadratic Average: 48.339935302734375
Nearest Class Center Accuracy: 0.05458

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.032787930220365524
Inter Cos: 0.03427891060709953
Norm Quadratic Average: 73.4664535522461
Nearest Class Center Accuracy: 0.06438

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.033201783895492554
Inter Cos: 0.028263062238693237
Norm Quadratic Average: 45.149879455566406
Nearest Class Center Accuracy: 0.07258

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03293357416987419
Inter Cos: 0.0284593366086483
Norm Quadratic Average: 28.449628829956055
Nearest Class Center Accuracy: 0.07614

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07589326798915863
Inter Cos: 0.048526644706726074
Norm Quadratic Average: 10.096660614013672
Nearest Class Center Accuracy: 0.08626

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.29932650923728943
Inter Cos: 0.1654331386089325
Norm Quadratic Average: 5.347692966461182
Nearest Class Center Accuracy: 0.09896

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 21.438209533691406
Linear Weight Rank: 4030
Intra Cos: 0.6545579433441162
Inter Cos: 0.3272891938686371
Norm Quadratic Average: 27.141374588012695
Nearest Class Center Accuracy: 0.1

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 11.853452682495117
Linear Weight Rank: 3651
Intra Cos: 0.7155175805091858
Inter Cos: 0.3470093905925751
Norm Quadratic Average: 31.472415924072266
Nearest Class Center Accuracy: 0.1

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 8.812583923339844
Linear Weight Rank: 98
Intra Cos: 0.7411588430404663
Inter Cos: 0.35667237639427185
Norm Quadratic Average: 39.233890533447266
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.7871567010879517
Inter Cos: 0.4426952004432678
Norm Quadratic Average: 64.80686950683594
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 3.7544601821899413
Accuracy: 0.4863
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.23501089215278625, Weights: 0.03662758320569992
NC2 Equiangle: Features: 0.2200640684185606, Weights: 0.11199284100773359
NC3 Self-Duality: 0.47736048698425293
NC4 NCC Mismatch: 0.22909999999999997

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.006621268577873707
Inter Cos: 0.4067547619342804
Norm Quadratic Average: 29.42218780517578
Nearest Class Center Accuracy: 0.1136

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.015476531349122524
Inter Cos: 0.28152206540107727
Norm Quadratic Average: 38.62052536010742
Nearest Class Center Accuracy: 0.2311

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.021504677832126617
Inter Cos: 0.3043314516544342
Norm Quadratic Average: 48.67184829711914
Nearest Class Center Accuracy: 0.2972

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022265970706939697
Inter Cos: 0.25029322504997253
Norm Quadratic Average: 74.0297622680664
Nearest Class Center Accuracy: 0.3839

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022899512201547623
Inter Cos: 0.22458402812480927
Norm Quadratic Average: 45.57654571533203
Nearest Class Center Accuracy: 0.4753

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.020227260887622833
Inter Cos: 0.16494670510292053
Norm Quadratic Average: 28.619441986083984
Nearest Class Center Accuracy: 0.5239

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.028129354119300842
Inter Cos: 0.2276023030281067
Norm Quadratic Average: 10.026252746582031
Nearest Class Center Accuracy: 0.5211

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07189411669969559
Inter Cos: 0.4513740837574005
Norm Quadratic Average: 5.144162178039551
Nearest Class Center Accuracy: 0.5151

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 21.438209533691406
Linear Weight Rank: 4030
Intra Cos: 0.1373157799243927
Inter Cos: 0.5729821920394897
Norm Quadratic Average: 24.781763076782227
Nearest Class Center Accuracy: 0.4971

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 11.853452682495117
Linear Weight Rank: 3651
Intra Cos: 0.14814399182796478
Inter Cos: 0.5939033031463623
Norm Quadratic Average: 28.608583450317383
Nearest Class Center Accuracy: 0.4909

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 8.812583923339844
Linear Weight Rank: 98
Intra Cos: 0.14462602138519287
Inter Cos: 0.599073052406311
Norm Quadratic Average: 35.838600158691406
Nearest Class Center Accuracy: 0.4845

Output Layer:
Intra Cos: 0.15129730105400085
Inter Cos: 0.6557352542877197
Norm Quadratic Average: 59.419677734375
Nearest Class Center Accuracy: 0.4707

