Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_846264_weight_decay_0.01.pth.tar
Training Set:
Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.021450398489832878
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: 0.02468297630548477
Inter Cos: 0.09702218323945999
Norm Quadratic Average: 32.25407409667969
Nearest Class Center Accuracy: 0.29975

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03057822212576866
Inter Cos: 0.10209783166646957
Norm Quadratic Average: 24.868858337402344
Nearest Class Center Accuracy: 0.369375

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03795686364173889
Inter Cos: 0.11031851172447205
Norm Quadratic Average: 26.54292869567871
Nearest Class Center Accuracy: 0.416125

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05548533797264099
Inter Cos: 0.14123426377773285
Norm Quadratic Average: 14.96940803527832
Nearest Class Center Accuracy: 0.44

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07282960414886475
Inter Cos: 0.14968819916248322
Norm Quadratic Average: 10.667506217956543
Nearest Class Center Accuracy: 0.4695

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.10128889232873917
Inter Cos: 0.16552332043647766
Norm Quadratic Average: 4.715671539306641
Nearest Class Center Accuracy: 0.53375

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.16976012289524078
Inter Cos: 0.2131039798259735
Norm Quadratic Average: 2.84442138671875
Nearest Class Center Accuracy: 0.746125

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 62.78684616088867
Linear Weight Rank: 4031
Intra Cos: 0.4649060070514679
Inter Cos: 0.4374334514141083
Norm Quadratic Average: 13.963956832885742
Nearest Class Center Accuracy: 0.950375

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 25.486055374145508
Linear Weight Rank: 3670
Intra Cos: 0.6643341779708862
Inter Cos: 0.5924400091171265
Norm Quadratic Average: 15.07255744934082
Nearest Class Center Accuracy: 0.992375

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.979574680328369
Linear Weight Rank: 10
Intra Cos: 0.7381467223167419
Inter Cos: 0.6738400459289551
Norm Quadratic Average: 18.552106857299805
Nearest Class Center Accuracy: 0.994375

Output Layer:
Intra Cos: 0.8181371092796326
Inter Cos: 0.8064429759979248
Norm Quadratic Average: 24.85390281677246
Nearest Class Center Accuracy: 0.98325

Test Set:
Average Loss: 2.2174276733398437
Accuracy: 0.574
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.252693772315979, Weights: 0.07130567729473114
NC2 Equiangle: Features: 0.4748517778184679, Weights: 0.23535836537679036
NC3 Self-Duality: 0.3429368734359741
NC4 NCC Mismatch: 0.1915

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018480457365512848
Inter Cos: 0.09352367371320724
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: 0.026134135201573372
Inter Cos: 0.0794893428683281
Norm Quadratic Average: 32.01972198486328
Nearest Class Center Accuracy: 0.316

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03228882700204849
Inter Cos: 0.09195686131715775
Norm Quadratic Average: 24.743846893310547
Nearest Class Center Accuracy: 0.3765

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03781654313206673
Inter Cos: 0.09775011241436005
Norm Quadratic Average: 26.48745346069336
Nearest Class Center Accuracy: 0.4355

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.051309116184711456
Inter Cos: 0.12570244073867798
Norm Quadratic Average: 14.951164245605469
Nearest Class Center Accuracy: 0.4485

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06547139585018158
Inter Cos: 0.13056275248527527
Norm Quadratic Average: 10.670082092285156
Nearest Class Center Accuracy: 0.465

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08104506134986877
Inter Cos: 0.1492258906364441
Norm Quadratic Average: 4.709959030151367
Nearest Class Center Accuracy: 0.4965

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.10695604979991913
Inter Cos: 0.19589845836162567
Norm Quadratic Average: 2.8108909130096436
Nearest Class Center Accuracy: 0.527

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 62.78684616088867
Linear Weight Rank: 4031
Intra Cos: 0.20870454609394073
Inter Cos: 0.349051833152771
Norm Quadratic Average: 13.292099952697754
Nearest Class Center Accuracy: 0.5555

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 25.486055374145508
Linear Weight Rank: 3670
Intra Cos: 0.26175665855407715
Inter Cos: 0.4544726312160492
Norm Quadratic Average: 14.033220291137695
Nearest Class Center Accuracy: 0.5545

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.979574680328369
Linear Weight Rank: 10
Intra Cos: 0.2706141769886017
Inter Cos: 0.5143923759460449
Norm Quadratic Average: 17.21205711364746
Nearest Class Center Accuracy: 0.5505

Output Layer:
Intra Cos: 0.287936270236969
Inter Cos: 0.6075268387794495
Norm Quadratic Average: 22.866676330566406
Nearest Class Center Accuracy: 0.5165

