Model save path: ./New_Models/bn_True_dataset_CIFAR10_epochs_200_lr_0.01_model_type_vgg11_rand_seed_314159_test_samples_None_train_samples_None_weight_decay_0.02.pth.tar
Training Set:
Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01989092119038105
Inter Cos: 0.10477276146411896
Norm Quadratic Average: 27.59718132019043
Nearest Class Center Accuracy: 0.26968

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02000032551586628
Inter Cos: 0.07230734825134277
Norm Quadratic Average: 3.250821590423584
Nearest Class Center Accuracy: 0.40808

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.020505566149950027
Inter Cos: 0.054003722965717316
Norm Quadratic Average: 1.6066138744354248
Nearest Class Center Accuracy: 0.53482

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.017298024147748947
Inter Cos: 0.04592962935566902
Norm Quadratic Average: 1.1560012102127075
Nearest Class Center Accuracy: 0.62328

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.027779171243309975
Inter Cos: 0.04721260815858841
Norm Quadratic Average: 0.8123401403427124
Nearest Class Center Accuracy: 0.76754

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.054699722677469254
Inter Cos: 0.067746102809906
Norm Quadratic Average: 0.650607168674469
Nearest Class Center Accuracy: 0.88154

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.2747049331665039
Inter Cos: 0.20643015205860138
Norm Quadratic Average: 0.40925899147987366
Nearest Class Center Accuracy: 0.98558

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.8323101997375488
Inter Cos: 0.24707497656345367
Norm Quadratic Average: 0.6050108671188354
Nearest Class Center Accuracy: 1.0

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 2.0442566871643066
Linear Weight Rank: 10
Intra Cos: 0.9801322817802429
Inter Cos: 0.18913626670837402
Norm Quadratic Average: 22.7497501373291
Nearest Class Center Accuracy: 1.0

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 2.0467748641967773
Linear Weight Rank: 1459
Intra Cos: 0.9870058298110962
Inter Cos: 0.20590783655643463
Norm Quadratic Average: 15.299333572387695
Nearest Class Center Accuracy: 1.0

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.046468496322632
Linear Weight Rank: 9
Intra Cos: 0.9896532893180847
Inter Cos: 0.19746559858322144
Norm Quadratic Average: 10.534686088562012
Nearest Class Center Accuracy: 1.0

Output Layer:
Intra Cos: 0.99204421043396
Inter Cos: 0.1563846617937088
Norm Quadratic Average: 7.713838577270508
Nearest Class Center Accuracy: 1.0

Test Set:
Average Loss: 0.4365060848236084
Accuracy: 0.8627
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.12333197146654129, Weights: 0.00546084763482213
NC2 Equiangle: Features: 0.15566901101006403, Weights: 0.0979466332329644
NC3 Self-Duality: 0.05803583189845085
NC4 NCC Mismatch: 0.016000000000000014

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018526073545217514
Inter Cos: 0.1042705774307251
Norm Quadratic Average: 27.550134658813477
Nearest Class Center Accuracy: 0.284

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01880800910294056
Inter Cos: 0.07386759668588638
Norm Quadratic Average: 3.248845100402832
Nearest Class Center Accuracy: 0.4257

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.019462069496512413
Inter Cos: 0.055358339101076126
Norm Quadratic Average: 1.606869101524353
Nearest Class Center Accuracy: 0.5474

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01631704531610012
Inter Cos: 0.04680884629487991
Norm Quadratic Average: 1.157362461090088
Nearest Class Center Accuracy: 0.6289

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.023904867470264435
Inter Cos: 0.0478040911257267
Norm Quadratic Average: 0.8125680685043335
Nearest Class Center Accuracy: 0.73

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.042021822184324265
Inter Cos: 0.0709047019481659
Norm Quadratic Average: 0.6476890444755554
Nearest Class Center Accuracy: 0.7867

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1968865543603897
Inter Cos: 0.21397368609905243
Norm Quadratic Average: 0.4014042019844055
Nearest Class Center Accuracy: 0.8365

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.4707460105419159
Inter Cos: 0.29653415083885193
Norm Quadratic Average: 0.5609087944030762
Nearest Class Center Accuracy: 0.8621

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 2.0442566871643066
Linear Weight Rank: 10
Intra Cos: 0.5687439441680908
Inter Cos: 0.3140626549720764
Norm Quadratic Average: 20.333202362060547
Nearest Class Center Accuracy: 0.8617

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 2.0467748641967773
Linear Weight Rank: 1459
Intra Cos: 0.5821584463119507
Inter Cos: 0.3265012204647064
Norm Quadratic Average: 13.679447174072266
Nearest Class Center Accuracy: 0.8607

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.046468496322632
Linear Weight Rank: 9
Intra Cos: 0.5906076431274414
Inter Cos: 0.3282530903816223
Norm Quadratic Average: 9.415056228637695
Nearest Class Center Accuracy: 0.8601

Output Layer:
Intra Cos: 0.6150122284889221
Inter Cos: 0.31595665216445923
Norm Quadratic Average: 6.905761241912842
Nearest Class Center Accuracy: 0.8609

