Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_338327_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: 0.11371058225631714
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: 0.02386595495045185
Inter Cos: 0.09585261344909668
Norm Quadratic Average: 34.53809356689453
Nearest Class Center Accuracy: 0.299375

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02945740707218647
Inter Cos: 0.10460786521434784
Norm Quadratic Average: 27.717241287231445
Nearest Class Center Accuracy: 0.358

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0355747826397419
Inter Cos: 0.10801389813423157
Norm Quadratic Average: 31.65338134765625
Nearest Class Center Accuracy: 0.403875

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05223951116204262
Inter Cos: 0.13486935198307037
Norm Quadratic Average: 19.007875442504883
Nearest Class Center Accuracy: 0.432125

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0625661239027977
Inter Cos: 0.1451597362756729
Norm Quadratic Average: 15.201042175292969
Nearest Class Center Accuracy: 0.460875

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08111374080181122
Inter Cos: 0.15402483940124512
Norm Quadratic Average: 7.205541610717773
Nearest Class Center Accuracy: 0.517375

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.11720012128353119
Inter Cos: 0.17161445319652557
Norm Quadratic Average: 4.721859455108643
Nearest Class Center Accuracy: 0.68925

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 79.79289245605469
Linear Weight Rank: 4031
Intra Cos: 0.36324822902679443
Inter Cos: 0.3161197602748871
Norm Quadratic Average: 18.791839599609375
Nearest Class Center Accuracy: 0.9655

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 32.320926666259766
Linear Weight Rank: 3670
Intra Cos: 0.6474443674087524
Inter Cos: 0.4516727328300476
Norm Quadratic Average: 17.577333450317383
Nearest Class Center Accuracy: 0.9985

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.0885567665100098
Linear Weight Rank: 10
Intra Cos: 0.7508614659309387
Inter Cos: 0.5352573394775391
Norm Quadratic Average: 21.089399337768555
Nearest Class Center Accuracy: 0.99925

Output Layer:
Intra Cos: 0.832502543926239
Inter Cos: 0.6681994795799255
Norm Quadratic Average: 26.382720947265625
Nearest Class Center Accuracy: 0.9985

Test Set:
Average Loss: 2.350748794555664
Accuracy: 0.594
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.23841609060764313, Weights: 0.05220729857683182
NC2 Equiangle: Features: 0.4401686774359809, Weights: 0.18810443878173827
NC3 Self-Duality: 0.390328586101532
NC4 NCC Mismatch: 0.15349999999999997

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: 0.024927856400609016
Inter Cos: 0.07902977615594864
Norm Quadratic Average: 34.286407470703125
Nearest Class Center Accuracy: 0.3115

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03251666575670242
Inter Cos: 0.08985469490289688
Norm Quadratic Average: 27.553422927856445
Nearest Class Center Accuracy: 0.37

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.037253182381391525
Inter Cos: 0.09534923732280731
Norm Quadratic Average: 31.53236961364746
Nearest Class Center Accuracy: 0.4255

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05098804831504822
Inter Cos: 0.11935722082853317
Norm Quadratic Average: 18.952728271484375
Nearest Class Center Accuracy: 0.4505

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.059695858508348465
Inter Cos: 0.12804800271987915
Norm Quadratic Average: 15.190910339355469
Nearest Class Center Accuracy: 0.4655

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06893900781869888
Inter Cos: 0.13450655341148376
Norm Quadratic Average: 7.194809913635254
Nearest Class Center Accuracy: 0.482

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08255301415920258
Inter Cos: 0.15683922171592712
Norm Quadratic Average: 4.689302921295166
Nearest Class Center Accuracy: 0.512

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 79.79289245605469
Linear Weight Rank: 4031
Intra Cos: 0.17199915647506714
Inter Cos: 0.2848321199417114
Norm Quadratic Average: 18.067493438720703
Nearest Class Center Accuracy: 0.574

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 32.320926666259766
Linear Weight Rank: 3670
Intra Cos: 0.2577712833881378
Inter Cos: 0.3931441009044647
Norm Quadratic Average: 16.447509765625
Nearest Class Center Accuracy: 0.585

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.0885567665100098
Linear Weight Rank: 10
Intra Cos: 0.2778223156929016
Inter Cos: 0.4509313404560089
Norm Quadratic Average: 19.625301361083984
Nearest Class Center Accuracy: 0.573

Output Layer:
Intra Cos: 0.30373525619506836
Inter Cos: 0.5339846014976501
Norm Quadratic Average: 24.425365447998047
Nearest Class Center Accuracy: 0.556

