Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_338327_weight_decay_0.02.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: 0.024532020092010498
Inter Cos: 0.09681231528520584
Norm Quadratic Average: 23.323740005493164
Nearest Class Center Accuracy: 0.301

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03145059198141098
Inter Cos: 0.11359582096338272
Norm Quadratic Average: 15.966721534729004
Nearest Class Center Accuracy: 0.34225

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04229864478111267
Inter Cos: 0.1321951150894165
Norm Quadratic Average: 15.582441329956055
Nearest Class Center Accuracy: 0.39575

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07051706314086914
Inter Cos: 0.18265770375728607
Norm Quadratic Average: 8.99879264831543
Nearest Class Center Accuracy: 0.429125

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.11509476602077484
Inter Cos: 0.25200581550598145
Norm Quadratic Average: 6.385056972503662
Nearest Class Center Accuracy: 0.464125

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1656254380941391
Inter Cos: 0.3259091377258301
Norm Quadratic Average: 3.308145523071289
Nearest Class Center Accuracy: 0.496625

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.22553472220897675
Inter Cos: 0.41327965259552
Norm Quadratic Average: 2.2291481494903564
Nearest Class Center Accuracy: 0.54925

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 38.82895278930664
Linear Weight Rank: 4031
Intra Cos: 0.31462815403938293
Inter Cos: 0.4616420567035675
Norm Quadratic Average: 11.011741638183594
Nearest Class Center Accuracy: 0.60375

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 15.793462753295898
Linear Weight Rank: 3670
Intra Cos: 0.38221052289009094
Inter Cos: 0.5364465117454529
Norm Quadratic Average: 8.991451263427734
Nearest Class Center Accuracy: 0.6255

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.1731319427490234
Linear Weight Rank: 10
Intra Cos: 0.43644943833351135
Inter Cos: 0.6246420741081238
Norm Quadratic Average: 8.109395980834961
Nearest Class Center Accuracy: 0.621

Output Layer:
Intra Cos: 0.4664057195186615
Inter Cos: 0.7376670837402344
Norm Quadratic Average: 8.073838233947754
Nearest Class Center Accuracy: 0.601125

Test Set:
Average Loss: 1.1919022636413574
Accuracy: 0.5625
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.23761571943759918, Weights: 0.12953722476959229
NC2 Equiangle: Features: 0.5357057783338759, Weights: 0.24349437289767795
NC3 Self-Duality: 0.28718453645706177
NC4 NCC Mismatch: 0.15249999999999997

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: 0.025263745337724686
Inter Cos: 0.07981818169355392
Norm Quadratic Average: 23.168140411376953
Nearest Class Center Accuracy: 0.315

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03596273064613342
Inter Cos: 0.09519070386886597
Norm Quadratic Average: 15.852174758911133
Nearest Class Center Accuracy: 0.3535

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04649949073791504
Inter Cos: 0.11499565839767456
Norm Quadratic Average: 15.504281044006348
Nearest Class Center Accuracy: 0.4055

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07235682755708694
Inter Cos: 0.16115958988666534
Norm Quadratic Average: 8.971699714660645
Nearest Class Center Accuracy: 0.44

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.10038119554519653
Inter Cos: 0.22354036569595337
Norm Quadratic Average: 6.381404399871826
Nearest Class Center Accuracy: 0.464

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.12678778171539307
Inter Cos: 0.2860248386859894
Norm Quadratic Average: 3.3040103912353516
Nearest Class Center Accuracy: 0.4865

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1726047247648239
Inter Cos: 0.35837414860725403
Norm Quadratic Average: 2.2221179008483887
Nearest Class Center Accuracy: 0.5025

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 38.82895278930664
Linear Weight Rank: 4031
Intra Cos: 0.24836620688438416
Inter Cos: 0.42732688784599304
Norm Quadratic Average: 10.97528076171875
Nearest Class Center Accuracy: 0.552

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 15.793462753295898
Linear Weight Rank: 3670
Intra Cos: 0.31104859709739685
Inter Cos: 0.5178699493408203
Norm Quadratic Average: 8.987874031066895
Nearest Class Center Accuracy: 0.565

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.1731319427490234
Linear Weight Rank: 10
Intra Cos: 0.35451483726501465
Inter Cos: 0.5934151411056519
Norm Quadratic Average: 8.126514434814453
Nearest Class Center Accuracy: 0.5545

Output Layer:
Intra Cos: 0.36782848834991455
Inter Cos: 0.6942343711853027
Norm Quadratic Average: 8.11771297454834
Nearest Class Center Accuracy: 0.522

