Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_338327_weight_decay_0.0001.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: 0.11371058225631714
Norm Quadratic Average: 27.691619873046875
Nearest Class Center Accuracy: 0.279125

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.023936225101351738
Inter Cos: 0.09584037959575653
Norm Quadratic Average: 35.07849884033203
Nearest Class Center Accuracy: 0.3025

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.029660388827323914
Inter Cos: 0.10217394679784775
Norm Quadratic Average: 27.360395431518555
Nearest Class Center Accuracy: 0.366125

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.033767495304346085
Inter Cos: 0.10086143016815186
Norm Quadratic Average: 32.846153259277344
Nearest Class Center Accuracy: 0.413125

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.049508314579725266
Inter Cos: 0.12500597536563873
Norm Quadratic Average: 20.9649658203125
Nearest Class Center Accuracy: 0.4385

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05476318672299385
Inter Cos: 0.12570568919181824
Norm Quadratic Average: 19.120906829833984
Nearest Class Center Accuracy: 0.468125

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07264426350593567
Inter Cos: 0.1339937299489975
Norm Quadratic Average: 10.329489707946777
Nearest Class Center Accuracy: 0.52375

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.10217436403036118
Inter Cos: 0.15237794816493988
Norm Quadratic Average: 7.685832977294922
Nearest Class Center Accuracy: 0.706625

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 100.91997528076172
Linear Weight Rank: 4031
Intra Cos: 0.2873397469520569
Inter Cos: 0.24517229199409485
Norm Quadratic Average: 30.28830337524414
Nearest Class Center Accuracy: 0.979

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.83172607421875
Linear Weight Rank: 3670
Intra Cos: 0.5654890537261963
Inter Cos: 0.3821195363998413
Norm Quadratic Average: 25.41415786743164
Nearest Class Center Accuracy: 0.999875

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.264744758605957
Linear Weight Rank: 10
Intra Cos: 0.7237926125526428
Inter Cos: 0.48956623673439026
Norm Quadratic Average: 29.026691436767578
Nearest Class Center Accuracy: 1.0

Output Layer:
Intra Cos: 0.8307187557220459
Inter Cos: 0.6637288331985474
Norm Quadratic Average: 34.7091178894043
Nearest Class Center Accuracy: 0.999875

Test Set:
Average Loss: 3.09309171295166
Accuracy: 0.5935
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.2365873008966446, Weights: 0.04513101279735565
NC2 Equiangle: Features: 0.42629203796386717, Weights: 0.14915119806925456
NC3 Self-Duality: 0.455731064081192
NC4 NCC Mismatch: 0.14100000000000001

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018480459228157997
Inter Cos: 0.09352367371320724
Norm Quadratic Average: 27.53066635131836
Nearest Class Center Accuracy: 0.2895

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02474028617143631
Inter Cos: 0.07953771203756332
Norm Quadratic Average: 34.83480453491211
Nearest Class Center Accuracy: 0.319

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.032361533492803574
Inter Cos: 0.08832751214504242
Norm Quadratic Average: 27.213979721069336
Nearest Class Center Accuracy: 0.377

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.034928400069475174
Inter Cos: 0.08893710374832153
Norm Quadratic Average: 32.71601104736328
Nearest Class Center Accuracy: 0.4385

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04784749075770378
Inter Cos: 0.10995658487081528
Norm Quadratic Average: 20.904237747192383
Nearest Class Center Accuracy: 0.4555

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05183667317032814
Inter Cos: 0.10965882986783981
Norm Quadratic Average: 19.096765518188477
Nearest Class Center Accuracy: 0.476

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06032835319638252
Inter Cos: 0.11984239518642426
Norm Quadratic Average: 10.305129051208496
Nearest Class Center Accuracy: 0.4945

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07107148319482803
Inter Cos: 0.13096825778484344
Norm Quadratic Average: 7.632699966430664
Nearest Class Center Accuracy: 0.5315

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 100.91997528076172
Linear Weight Rank: 4031
Intra Cos: 0.13388299942016602
Inter Cos: 0.22608910501003265
Norm Quadratic Average: 29.17702865600586
Nearest Class Center Accuracy: 0.595

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.83172607421875
Linear Weight Rank: 3670
Intra Cos: 0.21935506165027618
Inter Cos: 0.34653592109680176
Norm Quadratic Average: 23.687910079956055
Nearest Class Center Accuracy: 0.5955

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.264744758605957
Linear Weight Rank: 10
Intra Cos: 0.26187703013420105
Inter Cos: 0.429935485124588
Norm Quadratic Average: 26.770187377929688
Nearest Class Center Accuracy: 0.586

Output Layer:
Intra Cos: 0.29674649238586426
Inter Cos: 0.5430617928504944
Norm Quadratic Average: 31.84152603149414
Nearest Class Center Accuracy: 0.57

