Model save path: ./New_Models/bn_True_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_314159_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: 0.021450400352478027
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: 0.023080840706825256
Inter Cos: 0.10224051773548126
Norm Quadratic Average: 85.45760345458984
Nearest Class Center Accuracy: 0.32475

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.024963034316897392
Inter Cos: 0.08454453945159912
Norm Quadratic Average: 63.90233612060547
Nearest Class Center Accuracy: 0.357625

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.023612160235643387
Inter Cos: 0.06762291491031647
Norm Quadratic Average: 67.39158630371094
Nearest Class Center Accuracy: 0.39325

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.031707391142845154
Inter Cos: 0.07355283200740814
Norm Quadratic Average: 42.857025146484375
Nearest Class Center Accuracy: 0.41225

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03280603140592575
Inter Cos: 0.06329996883869171
Norm Quadratic Average: 43.89731216430664
Nearest Class Center Accuracy: 0.453125

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04596255347132683
Inter Cos: 0.0776706337928772
Norm Quadratic Average: 28.100671768188477
Nearest Class Center Accuracy: 0.532625

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06660868972539902
Inter Cos: 0.07606124132871628
Norm Quadratic Average: 19.955270767211914
Nearest Class Center Accuracy: 0.816

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 98.98152923583984
Linear Weight Rank: 4031
Intra Cos: 0.18795837461948395
Inter Cos: 0.10211322456598282
Norm Quadratic Average: 106.30974578857422
Nearest Class Center Accuracy: 1.0

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.01140594482422
Linear Weight Rank: 3670
Intra Cos: 0.4304767847061157
Inter Cos: 0.18289652466773987
Norm Quadratic Average: 55.614471435546875
Nearest Class Center Accuracy: 1.0

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.49800705909729
Linear Weight Rank: 10
Intra Cos: 0.6681689023971558
Inter Cos: 0.2812778949737549
Norm Quadratic Average: 38.90132522583008
Nearest Class Center Accuracy: 1.0

Output Layer:
Intra Cos: 0.8830908536911011
Inter Cos: 0.47776830196380615
Norm Quadratic Average: 27.01396942138672
Nearest Class Center Accuracy: 1.0

Test Set:
Average Loss: 2.7282735366821287
Accuracy: 0.576
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.20020513236522675, Weights: 0.019629525020718575
NC2 Equiangle: Features: 0.4350653754340278, Weights: 0.08952390882703994
NC3 Self-Duality: 0.6287647485733032
NC4 NCC Mismatch: 0.15549999999999997

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: 0.022241652011871338
Inter Cos: 0.08927546441555023
Norm Quadratic Average: 85.20318603515625
Nearest Class Center Accuracy: 0.3505

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02524011768400669
Inter Cos: 0.07691600173711777
Norm Quadratic Average: 63.68268585205078
Nearest Class Center Accuracy: 0.3815

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.024583589285612106
Inter Cos: 0.05931255593895912
Norm Quadratic Average: 67.2745361328125
Nearest Class Center Accuracy: 0.413

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.028699437156319618
Inter Cos: 0.06880488246679306
Norm Quadratic Average: 42.723182678222656
Nearest Class Center Accuracy: 0.435

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.028067274019122124
Inter Cos: 0.06017664447426796
Norm Quadratic Average: 43.767696380615234
Nearest Class Center Accuracy: 0.469

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.032942239195108414
Inter Cos: 0.07400647550821304
Norm Quadratic Average: 27.952857971191406
Nearest Class Center Accuracy: 0.4735

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0354209840297699
Inter Cos: 0.07055936753749847
Norm Quadratic Average: 19.767385482788086
Nearest Class Center Accuracy: 0.5395

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 98.98152923583984
Linear Weight Rank: 4031
Intra Cos: 0.05784768983721733
Inter Cos: 0.10116678476333618
Norm Quadratic Average: 102.61993408203125
Nearest Class Center Accuracy: 0.596

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.01140594482422
Linear Weight Rank: 3670
Intra Cos: 0.11769057810306549
Inter Cos: 0.1976645141839981
Norm Quadratic Average: 51.40412139892578
Nearest Class Center Accuracy: 0.5815

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.49800705909729
Linear Weight Rank: 10
Intra Cos: 0.18253612518310547
Inter Cos: 0.30934932827949524
Norm Quadratic Average: 34.624420166015625
Nearest Class Center Accuracy: 0.5635

Output Layer:
Intra Cos: 0.2583399713039398
Inter Cos: 0.48278898000717163
Norm Quadratic Average: 23.33317756652832
Nearest Class Center Accuracy: 0.5475

