Model save path: ./New_Models/bn_True_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_265358_weight_decay_0.0003.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: 0.027034515514969826
Inter Cos: 0.10381120443344116
Norm Quadratic Average: 85.881591796875
Nearest Class Center Accuracy: 0.33975

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03188971430063248
Inter Cos: 0.09283868968486786
Norm Quadratic Average: 62.78289031982422
Nearest Class Center Accuracy: 0.375375

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.027653630822896957
Inter Cos: 0.0748543068766594
Norm Quadratic Average: 68.0788345336914
Nearest Class Center Accuracy: 0.402

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.035200390964746475
Inter Cos: 0.08060023188591003
Norm Quadratic Average: 43.46010971069336
Nearest Class Center Accuracy: 0.427375

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0335778184235096
Inter Cos: 0.06710036098957062
Norm Quadratic Average: 44.39067459106445
Nearest Class Center Accuracy: 0.465875

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04557225480675697
Inter Cos: 0.07924040406942368
Norm Quadratic Average: 28.808120727539062
Nearest Class Center Accuracy: 0.551625

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05863552913069725
Inter Cos: 0.0768374502658844
Norm Quadratic Average: 20.1632080078125
Nearest Class Center Accuracy: 0.838875

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 99.94154357910156
Linear Weight Rank: 4031
Intra Cos: 0.17448900640010834
Inter Cos: 0.10535424202680588
Norm Quadratic Average: 107.11029815673828
Nearest Class Center Accuracy: 0.999875

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.40642166137695
Linear Weight Rank: 3671
Intra Cos: 0.40790724754333496
Inter Cos: 0.18529073894023895
Norm Quadratic Average: 56.46461486816406
Nearest Class Center Accuracy: 1.0

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.528031349182129
Linear Weight Rank: 10
Intra Cos: 0.6461800336837769
Inter Cos: 0.26961207389831543
Norm Quadratic Average: 39.64292526245117
Nearest Class Center Accuracy: 1.0

Output Layer:
Intra Cos: 0.8734557032585144
Inter Cos: 0.486549437046051
Norm Quadratic Average: 27.921003341674805
Nearest Class Center Accuracy: 1.0

Test Set:
Average Loss: 2.7393417739868164
Accuracy: 0.5785
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.20962662994861603, Weights: 0.019685858860611916
NC2 Equiangle: Features: 0.4473300509982639, Weights: 0.09022117190890842
NC3 Self-Duality: 0.6307165622711182
NC4 NCC Mismatch: 0.13849999999999996

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: 0.024823807179927826
Inter Cos: 0.09123330563306808
Norm Quadratic Average: 85.86569213867188
Nearest Class Center Accuracy: 0.354

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.030198289081454277
Inter Cos: 0.08826212584972382
Norm Quadratic Average: 62.79544448852539
Nearest Class Center Accuracy: 0.3945

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02673063613474369
Inter Cos: 0.06994029134511948
Norm Quadratic Average: 68.14676666259766
Nearest Class Center Accuracy: 0.423

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.032605141401290894
Inter Cos: 0.0815734937787056
Norm Quadratic Average: 43.46539306640625
Nearest Class Center Accuracy: 0.4505

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03013029880821705
Inter Cos: 0.0673198476433754
Norm Quadratic Average: 44.348480224609375
Nearest Class Center Accuracy: 0.477

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.034670934081077576
Inter Cos: 0.07668512314558029
Norm Quadratic Average: 28.733476638793945
Nearest Class Center Accuracy: 0.4865

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03458235040307045
Inter Cos: 0.07150325924158096
Norm Quadratic Average: 19.992891311645508
Nearest Class Center Accuracy: 0.5665

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 99.94154357910156
Linear Weight Rank: 4031
Intra Cos: 0.06001954898238182
Inter Cos: 0.10481417179107666
Norm Quadratic Average: 103.10227966308594
Nearest Class Center Accuracy: 0.6075

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.40642166137695
Linear Weight Rank: 3671
Intra Cos: 0.1227990910410881
Inter Cos: 0.19158649444580078
Norm Quadratic Average: 51.9016227722168
Nearest Class Center Accuracy: 0.5805

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.528031349182129
Linear Weight Rank: 10
Intra Cos: 0.1906757354736328
Inter Cos: 0.2987441420555115
Norm Quadratic Average: 35.03963088989258
Nearest Class Center Accuracy: 0.573

Output Layer:
Intra Cos: 0.27972477674484253
Inter Cos: 0.4655356705188751
Norm Quadratic Average: 23.922883987426758
Nearest Class Center Accuracy: 0.5555

