Model save path: ./New_Models/bn_True_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_314159_weight_decay_0.003.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: 0.022825157269835472
Inter Cos: 0.10199553519487381
Norm Quadratic Average: 75.66172790527344
Nearest Class Center Accuracy: 0.323375

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02524729259312153
Inter Cos: 0.08295723050832748
Norm Quadratic Average: 56.49007797241211
Nearest Class Center Accuracy: 0.359625

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.024214960634708405
Inter Cos: 0.06878875941038132
Norm Quadratic Average: 59.66291809082031
Nearest Class Center Accuracy: 0.39275

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.032240863889455795
Inter Cos: 0.07521732896566391
Norm Quadratic Average: 37.78761291503906
Nearest Class Center Accuracy: 0.41875

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03243636339902878
Inter Cos: 0.06481321156024933
Norm Quadratic Average: 38.675506591796875
Nearest Class Center Accuracy: 0.461

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.044791027903556824
Inter Cos: 0.07799696177244186
Norm Quadratic Average: 24.796491622924805
Nearest Class Center Accuracy: 0.552875

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06729437410831451
Inter Cos: 0.07497742772102356
Norm Quadratic Average: 17.639135360717773
Nearest Class Center Accuracy: 0.848125

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 87.79586791992188
Linear Weight Rank: 4031
Intra Cos: 0.20987839996814728
Inter Cos: 0.10604982823133469
Norm Quadratic Average: 95.80374145507812
Nearest Class Center Accuracy: 1.0

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 35.49331283569336
Linear Weight Rank: 3670
Intra Cos: 0.4919821321964264
Inter Cos: 0.20089659094810486
Norm Quadratic Average: 47.782081604003906
Nearest Class Center Accuracy: 1.0

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.2825698852539062
Linear Weight Rank: 10
Intra Cos: 0.7296419143676758
Inter Cos: 0.3054788112640381
Norm Quadratic Average: 32.203250885009766
Nearest Class Center Accuracy: 1.0

Output Layer:
Intra Cos: 0.8961799740791321
Inter Cos: 0.46685469150543213
Norm Quadratic Average: 21.503372192382812
Nearest Class Center Accuracy: 1.0

Test Set:
Average Loss: 2.168087287902832
Accuracy: 0.5875
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.2020758092403412, Weights: 0.019009534269571304
NC2 Equiangle: Features: 0.41836539374457465, Weights: 0.09424126942952474
NC3 Self-Duality: 0.5922167897224426
NC4 NCC Mismatch: 0.14649999999999996

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: 0.022129777818918228
Inter Cos: 0.08872240781784058
Norm Quadratic Average: 75.41547393798828
Nearest Class Center Accuracy: 0.348

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.025609062984585762
Inter Cos: 0.07888984680175781
Norm Quadratic Average: 56.2911376953125
Nearest Class Center Accuracy: 0.379

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.025130558758974075
Inter Cos: 0.0634821206331253
Norm Quadratic Average: 59.56209182739258
Nearest Class Center Accuracy: 0.4155

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02919071353971958
Inter Cos: 0.07136961817741394
Norm Quadratic Average: 37.698211669921875
Nearest Class Center Accuracy: 0.4445

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.028214236721396446
Inter Cos: 0.06276020407676697
Norm Quadratic Average: 38.59171676635742
Nearest Class Center Accuracy: 0.476

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0325549952685833
Inter Cos: 0.07496591657400131
Norm Quadratic Average: 24.690710067749023
Nearest Class Center Accuracy: 0.4955

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03464977443218231
Inter Cos: 0.06755124032497406
Norm Quadratic Average: 17.465553283691406
Nearest Class Center Accuracy: 0.5605

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 87.79586791992188
Linear Weight Rank: 4031
Intra Cos: 0.05954026058316231
Inter Cos: 0.106086865067482
Norm Quadratic Average: 91.89768981933594
Nearest Class Center Accuracy: 0.6105

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 35.49331283569336
Linear Weight Rank: 3670
Intra Cos: 0.1247481182217598
Inter Cos: 0.2120313048362732
Norm Quadratic Average: 43.58890151977539
Nearest Class Center Accuracy: 0.591

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.2825698852539062
Linear Weight Rank: 10
Intra Cos: 0.19047172367572784
Inter Cos: 0.3211796283721924
Norm Quadratic Average: 28.260910034179688
Nearest Class Center Accuracy: 0.579

Output Layer:
Intra Cos: 0.2550174593925476
Inter Cos: 0.46766287088394165
Norm Quadratic Average: 18.36716651916504
Nearest Class Center Accuracy: 0.569

