Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_314159_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: 0.021450400352478027
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: 0.024596290662884712
Inter Cos: 0.09701835364103317
Norm Quadratic Average: 32.64905548095703
Nearest Class Center Accuracy: 0.2985

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03179389610886574
Inter Cos: 0.11488024145364761
Norm Quadratic Average: 26.320131301879883
Nearest Class Center Accuracy: 0.348125

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03678988292813301
Inter Cos: 0.1099361777305603
Norm Quadratic Average: 27.202617645263672
Nearest Class Center Accuracy: 0.409

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05320921167731285
Inter Cos: 0.14164415001869202
Norm Quadratic Average: 15.279415130615234
Nearest Class Center Accuracy: 0.437625

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07164479047060013
Inter Cos: 0.16260655224323273
Norm Quadratic Average: 11.017606735229492
Nearest Class Center Accuracy: 0.472375

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0977388471364975
Inter Cos: 0.18953396379947662
Norm Quadratic Average: 4.933618068695068
Nearest Class Center Accuracy: 0.526125

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.16583748161792755
Inter Cos: 0.2225751131772995
Norm Quadratic Average: 2.910975217819214
Nearest Class Center Accuracy: 0.715

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 62.78022766113281
Linear Weight Rank: 4031
Intra Cos: 0.46459800004959106
Inter Cos: 0.41949623823165894
Norm Quadratic Average: 13.73038387298584
Nearest Class Center Accuracy: 0.931375

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 25.481184005737305
Linear Weight Rank: 3669
Intra Cos: 0.6750409603118896
Inter Cos: 0.5827682018280029
Norm Quadratic Average: 14.497227668762207
Nearest Class Center Accuracy: 0.98825

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.970879316329956
Linear Weight Rank: 10
Intra Cos: 0.7461490035057068
Inter Cos: 0.6741858124732971
Norm Quadratic Average: 17.808242797851562
Nearest Class Center Accuracy: 0.993375

Output Layer:
Intra Cos: 0.8318718075752258
Inter Cos: 0.8082329034805298
Norm Quadratic Average: 23.607881546020508
Nearest Class Center Accuracy: 0.98375

Test Set:
Average Loss: 2.142703025817871
Accuracy: 0.583
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.2537146210670471, Weights: 0.06592235714197159
NC2 Equiangle: Features: 0.42974395751953126, Weights: 0.23433397081163193
NC3 Self-Duality: 0.32109490036964417
NC4 NCC Mismatch: 0.18799999999999994

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: 0.02479933202266693
Inter Cos: 0.09045591205358505
Norm Quadratic Average: 32.477046966552734
Nearest Class Center Accuracy: 0.314

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03474465757608414
Inter Cos: 0.10974325984716415
Norm Quadratic Average: 26.19774055480957
Nearest Class Center Accuracy: 0.368

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0379716232419014
Inter Cos: 0.09944228082895279
Norm Quadratic Average: 27.092838287353516
Nearest Class Center Accuracy: 0.433

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05155934393405914
Inter Cos: 0.12779900431632996
Norm Quadratic Average: 15.23410701751709
Nearest Class Center Accuracy: 0.447

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06715020537376404
Inter Cos: 0.1450245976448059
Norm Quadratic Average: 11.001622200012207
Nearest Class Center Accuracy: 0.466

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0800504982471466
Inter Cos: 0.1699458807706833
Norm Quadratic Average: 4.921909332275391
Nearest Class Center Accuracy: 0.4815

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.10336750745773315
Inter Cos: 0.1863587647676468
Norm Quadratic Average: 2.8818917274475098
Nearest Class Center Accuracy: 0.5275

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 62.78022766113281
Linear Weight Rank: 4031
Intra Cos: 0.19600841403007507
Inter Cos: 0.3216574192047119
Norm Quadratic Average: 13.150362968444824
Nearest Class Center Accuracy: 0.585

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 25.481184005737305
Linear Weight Rank: 3669
Intra Cos: 0.2501277029514313
Inter Cos: 0.42833849787712097
Norm Quadratic Average: 13.577607154846191
Nearest Class Center Accuracy: 0.5885

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.970879316329956
Linear Weight Rank: 10
Intra Cos: 0.2553161084651947
Inter Cos: 0.49606776237487793
Norm Quadratic Average: 16.57332420349121
Nearest Class Center Accuracy: 0.566

Output Layer:
Intra Cos: 0.262067049741745
Inter Cos: 0.5980707406997681
Norm Quadratic Average: 21.750696182250977
Nearest Class Center Accuracy: 0.534

