Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_314159_weight_decay_0.007.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: 0.024630194529891014
Inter Cos: 0.09468165040016174
Norm Quadratic Average: 33.49385452270508
Nearest Class Center Accuracy: 0.29925

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.031612709164619446
Inter Cos: 0.11012354493141174
Norm Quadratic Average: 26.858991622924805
Nearest Class Center Accuracy: 0.350375

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03658946231007576
Inter Cos: 0.10780629515647888
Norm Quadratic Average: 28.783811569213867
Nearest Class Center Accuracy: 0.409

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.053310684859752655
Inter Cos: 0.13965873420238495
Norm Quadratic Average: 16.65937042236328
Nearest Class Center Accuracy: 0.436625

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06942183524370193
Inter Cos: 0.15365611016750336
Norm Quadratic Average: 12.802657127380371
Nearest Class Center Accuracy: 0.47175

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09310568869113922
Inter Cos: 0.17795246839523315
Norm Quadratic Average: 5.946095943450928
Nearest Class Center Accuracy: 0.523875

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.14919698238372803
Inter Cos: 0.20947067439556122
Norm Quadratic Average: 3.718275308609009
Nearest Class Center Accuracy: 0.714625

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 72.49575805664062
Linear Weight Rank: 4031
Intra Cos: 0.4463299512863159
Inter Cos: 0.3742309510707855
Norm Quadratic Average: 16.185218811035156
Nearest Class Center Accuracy: 0.953375

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 29.391483306884766
Linear Weight Rank: 3669
Intra Cos: 0.7001442909240723
Inter Cos: 0.5305138230323792
Norm Quadratic Average: 16.348569869995117
Nearest Class Center Accuracy: 0.9955

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.0718908309936523
Linear Weight Rank: 10
Intra Cos: 0.7726848721504211
Inter Cos: 0.6153509616851807
Norm Quadratic Average: 20.141204833984375
Nearest Class Center Accuracy: 0.9985

Output Layer:
Intra Cos: 0.8479944467544556
Inter Cos: 0.7482430934906006
Norm Quadratic Average: 26.185611724853516
Nearest Class Center Accuracy: 0.99775

Test Set:
Average Loss: 2.381264495849609
Accuracy: 0.5895
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.24460755288600922, Weights: 0.05095221847295761
NC2 Equiangle: Features: 0.4200130886501736, Weights: 0.21569989522298177
NC3 Self-Duality: 0.36115115880966187
NC4 NCC Mismatch: 0.14900000000000002

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: 0.02500799484550953
Inter Cos: 0.08839275687932968
Norm Quadratic Average: 33.3086051940918
Nearest Class Center Accuracy: 0.3155

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03465629741549492
Inter Cos: 0.10532363504171371
Norm Quadratic Average: 26.731233596801758
Nearest Class Center Accuracy: 0.369

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03772209584712982
Inter Cos: 0.09666907042264938
Norm Quadratic Average: 28.66745376586914
Nearest Class Center Accuracy: 0.432

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05137290805578232
Inter Cos: 0.1257871836423874
Norm Quadratic Average: 16.608049392700195
Nearest Class Center Accuracy: 0.4525

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06452052295207977
Inter Cos: 0.13685521483421326
Norm Quadratic Average: 12.78084659576416
Nearest Class Center Accuracy: 0.4685

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07600026577711105
Inter Cos: 0.15468373894691467
Norm Quadratic Average: 5.929922580718994
Nearest Class Center Accuracy: 0.48

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09456714987754822
Inter Cos: 0.1745830476284027
Norm Quadratic Average: 3.681696891784668
Nearest Class Center Accuracy: 0.517

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 72.49575805664062
Linear Weight Rank: 4031
Intra Cos: 0.18547289073467255
Inter Cos: 0.3065285384654999
Norm Quadratic Average: 15.47747802734375
Nearest Class Center Accuracy: 0.59

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 29.391483306884766
Linear Weight Rank: 3669
Intra Cos: 0.2613039016723633
Inter Cos: 0.4162839651107788
Norm Quadratic Average: 15.251903533935547
Nearest Class Center Accuracy: 0.5905

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.0718908309936523
Linear Weight Rank: 10
Intra Cos: 0.2747529447078705
Inter Cos: 0.4798555374145508
Norm Quadratic Average: 18.685937881469727
Nearest Class Center Accuracy: 0.5775

Output Layer:
Intra Cos: 0.29252636432647705
Inter Cos: 0.5761373043060303
Norm Quadratic Average: 24.099702835083008
Nearest Class Center Accuracy: 0.5515

