Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_265358_weight_decay_0.005.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: 0.025573788210749626
Inter Cos: 0.10246038436889648
Norm Quadratic Average: 30.547578811645508
Nearest Class Center Accuracy: 0.307875

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.034852415323257446
Inter Cos: 0.11809787899255753
Norm Quadratic Average: 23.02665138244629
Nearest Class Center Accuracy: 0.360875

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04130009189248085
Inter Cos: 0.11739844083786011
Norm Quadratic Average: 25.12116813659668
Nearest Class Center Accuracy: 0.411875

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0576910525560379
Inter Cos: 0.1377897411584854
Norm Quadratic Average: 14.62589168548584
Nearest Class Center Accuracy: 0.4435

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0743611678481102
Inter Cos: 0.15101155638694763
Norm Quadratic Average: 11.983445167541504
Nearest Class Center Accuracy: 0.467875

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.10032881051301956
Inter Cos: 0.16868266463279724
Norm Quadratic Average: 6.050846099853516
Nearest Class Center Accuracy: 0.519125

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.15252803266048431
Inter Cos: 0.18679918348789215
Norm Quadratic Average: 4.053781032562256
Nearest Class Center Accuracy: 0.717375

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 79.7968978881836
Linear Weight Rank: 4031
Intra Cos: 0.4329935312271118
Inter Cos: 0.3607412874698639
Norm Quadratic Average: 17.50279426574707
Nearest Class Center Accuracy: 0.963125

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 32.33552932739258
Linear Weight Rank: 3671
Intra Cos: 0.6948352456092834
Inter Cos: 0.5184659361839294
Norm Quadratic Average: 17.213117599487305
Nearest Class Center Accuracy: 0.99875

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.126560926437378
Linear Weight Rank: 10
Intra Cos: 0.7689117193222046
Inter Cos: 0.6086069345474243
Norm Quadratic Average: 21.11922836303711
Nearest Class Center Accuracy: 0.9995

Output Layer:
Intra Cos: 0.8122621178627014
Inter Cos: 0.7580744028091431
Norm Quadratic Average: 27.348602294921875
Nearest Class Center Accuracy: 0.99825

Test Set:
Average Loss: 2.35835604095459
Accuracy: 0.5895
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.24065041542053223, Weights: 0.045475300401449203
NC2 Equiangle: Features: 0.43512403700086805, Weights: 0.20114059448242189
NC3 Self-Duality: 0.3908110558986664
NC4 NCC Mismatch: 0.14749999999999996

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: 0.02460976131260395
Inter Cos: 0.0966549664735794
Norm Quadratic Average: 30.434247970581055
Nearest Class Center Accuracy: 0.3275

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.036410510540008545
Inter Cos: 0.11072716116905212
Norm Quadratic Average: 22.9422607421875
Nearest Class Center Accuracy: 0.38

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04179699346423149
Inter Cos: 0.10769772529602051
Norm Quadratic Average: 25.055076599121094
Nearest Class Center Accuracy: 0.432

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0557301789522171
Inter Cos: 0.12274318933486938
Norm Quadratic Average: 14.571035385131836
Nearest Class Center Accuracy: 0.459

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07022424042224884
Inter Cos: 0.1358788013458252
Norm Quadratic Average: 11.944955825805664
Nearest Class Center Accuracy: 0.4715

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0852825939655304
Inter Cos: 0.15320369601249695
Norm Quadratic Average: 6.025848388671875
Nearest Class Center Accuracy: 0.48

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.10265505313873291
Inter Cos: 0.16713839769363403
Norm Quadratic Average: 4.011490821838379
Nearest Class Center Accuracy: 0.521

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 79.7968978881836
Linear Weight Rank: 4031
Intra Cos: 0.18646346032619476
Inter Cos: 0.3038194477558136
Norm Quadratic Average: 16.735387802124023
Nearest Class Center Accuracy: 0.59

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 32.33552932739258
Linear Weight Rank: 3671
Intra Cos: 0.25234007835388184
Inter Cos: 0.4238808751106262
Norm Quadratic Average: 16.038652420043945
Nearest Class Center Accuracy: 0.59

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.126560926437378
Linear Weight Rank: 10
Intra Cos: 0.2651558518409729
Inter Cos: 0.4977717101573944
Norm Quadratic Average: 19.5731201171875
Nearest Class Center Accuracy: 0.585

Output Layer:
Intra Cos: 0.2855038344860077
Inter Cos: 0.6152913570404053
Norm Quadratic Average: 25.185455322265625
Nearest Class Center Accuracy: 0.5575

