Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_265358_weight_decay_0.0005.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: 0.0261550135910511
Inter Cos: 0.10463353246450424
Norm Quadratic Average: 30.917518615722656
Nearest Class Center Accuracy: 0.310875

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03541827201843262
Inter Cos: 0.11635763198137283
Norm Quadratic Average: 23.564908981323242
Nearest Class Center Accuracy: 0.35975

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.039698418229818344
Inter Cos: 0.1071825847029686
Norm Quadratic Average: 27.29230499267578
Nearest Class Center Accuracy: 0.407625

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.053517524152994156
Inter Cos: 0.12692655622959137
Norm Quadratic Average: 16.970199584960938
Nearest Class Center Accuracy: 0.442125

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06667542457580566
Inter Cos: 0.13234399259090424
Norm Quadratic Average: 15.792049407958984
Nearest Class Center Accuracy: 0.469625

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08872761577367783
Inter Cos: 0.14478085935115814
Norm Quadratic Average: 8.922237396240234
Nearest Class Center Accuracy: 0.514125

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.12125782668590546
Inter Cos: 0.15733282268047333
Norm Quadratic Average: 6.7126569747924805
Nearest Class Center Accuracy: 0.6905

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 99.00728607177734
Linear Weight Rank: 4031
Intra Cos: 0.3150906562805176
Inter Cos: 0.28525716066360474
Norm Quadratic Average: 27.39061164855957
Nearest Class Center Accuracy: 0.965875

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.0744514465332
Linear Weight Rank: 3671
Intra Cos: 0.5932502150535583
Inter Cos: 0.439941942691803
Norm Quadratic Average: 24.03577995300293
Nearest Class Center Accuracy: 0.99925

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.2647933959960938
Linear Weight Rank: 10
Intra Cos: 0.7355025410652161
Inter Cos: 0.550381064414978
Norm Quadratic Average: 28.408599853515625
Nearest Class Center Accuracy: 0.99975

Output Layer:
Intra Cos: 0.8217675685882568
Inter Cos: 0.7205749750137329
Norm Quadratic Average: 35.325443267822266
Nearest Class Center Accuracy: 0.999125

Test Set:
Average Loss: 3.0098324737548827
Accuracy: 0.5925
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.23491908609867096, Weights: 0.04217422381043434
NC2 Equiangle: Features: 0.43781475490993926, Weights: 0.16775499979654948
NC3 Self-Duality: 0.4547538161277771
NC4 NCC Mismatch: 0.15149999999999997

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: 0.024990808218717575
Inter Cos: 0.096888467669487
Norm Quadratic Average: 30.813081741333008
Nearest Class Center Accuracy: 0.3325

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.036797769367694855
Inter Cos: 0.11217048764228821
Norm Quadratic Average: 23.49094009399414
Nearest Class Center Accuracy: 0.3755

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.040436211973428726
Inter Cos: 0.10488665103912354
Norm Quadratic Average: 27.228626251220703
Nearest Class Center Accuracy: 0.4265

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05186014622449875
Inter Cos: 0.11615008860826492
Norm Quadratic Average: 16.912355422973633
Nearest Class Center Accuracy: 0.4595

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06331395357847214
Inter Cos: 0.12273982912302017
Norm Quadratic Average: 15.749855995178223
Nearest Class Center Accuracy: 0.475

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07658851146697998
Inter Cos: 0.13610957562923431
Norm Quadratic Average: 8.88943099975586
Nearest Class Center Accuracy: 0.475

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.088135726749897
Inter Cos: 0.13914616405963898
Norm Quadratic Average: 6.656989097595215
Nearest Class Center Accuracy: 0.5215

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 99.00728607177734
Linear Weight Rank: 4031
Intra Cos: 0.14517192542552948
Inter Cos: 0.23849506676197052
Norm Quadratic Average: 26.393220901489258
Nearest Class Center Accuracy: 0.581

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.0744514465332
Linear Weight Rank: 3671
Intra Cos: 0.2257348746061325
Inter Cos: 0.367611289024353
Norm Quadratic Average: 22.47740364074707
Nearest Class Center Accuracy: 0.589

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.2647933959960938
Linear Weight Rank: 10
Intra Cos: 0.2627624273300171
Inter Cos: 0.46143409609794617
Norm Quadratic Average: 26.334989547729492
Nearest Class Center Accuracy: 0.579

Output Layer:
Intra Cos: 0.29820969700813293
Inter Cos: 0.5932891964912415
Norm Quadratic Average: 32.60696029663086
Nearest Class Center Accuracy: 0.556

