Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_200_lr_0.01_model_type_vgg11_rand_seed_265358_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: 0.10477277636528015
Norm Quadratic Average: 27.59717559814453
Nearest Class Center Accuracy: 0.26968

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02357453852891922
Inter Cos: 0.09409396350383759
Norm Quadratic Average: 19.233489990234375
Nearest Class Center Accuracy: 0.36316

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03168155997991562
Inter Cos: 0.10015932470560074
Norm Quadratic Average: 10.23727035522461
Nearest Class Center Accuracy: 0.45814

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.041428808122873306
Inter Cos: 0.10070859640836716
Norm Quadratic Average: 4.668788433074951
Nearest Class Center Accuracy: 0.57102

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08601536601781845
Inter Cos: 0.1426182985305786
Norm Quadratic Average: 1.0363633632659912
Nearest Class Center Accuracy: 0.63482

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.3148365616798401
Inter Cos: 0.5489704608917236
Norm Quadratic Average: 0.602742612361908
Nearest Class Center Accuracy: 0.68508

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.508404552936554
Inter Cos: 0.7421258091926575
Norm Quadratic Average: 0.8507606387138367
Nearest Class Center Accuracy: 0.74996

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.5543126463890076
Inter Cos: 0.6952852010726929
Norm Quadratic Average: 1.4497439861297607
Nearest Class Center Accuracy: 0.85196

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 2.4833872318267822
Linear Weight Rank: 4
Intra Cos: 0.6244714260101318
Inter Cos: 0.65735924243927
Norm Quadratic Average: 12.257142066955566
Nearest Class Center Accuracy: 0.94034

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 2.4896597862243652
Linear Weight Rank: 2671
Intra Cos: 0.6465905904769897
Inter Cos: 0.7491456866264343
Norm Quadratic Average: 15.587284088134766
Nearest Class Center Accuracy: 0.97106

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.4892826080322266
Linear Weight Rank: 9
Intra Cos: 0.7283737063407898
Inter Cos: 0.7640878558158875
Norm Quadratic Average: 18.409379959106445
Nearest Class Center Accuracy: 0.97454

Output Layer:
Intra Cos: 0.7196289300918579
Inter Cos: 0.8923280835151672
Norm Quadratic Average: 24.493160247802734
Nearest Class Center Accuracy: 0.9685

Test Set:
Average Loss: 1.4694752853393556
Accuracy: 0.6868
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.4277563989162445, Weights: 0.12750691175460815
NC2 Equiangle: Features: 0.44637035793728297, Weights: 0.30324406094021267
NC3 Self-Duality: 0.32576271891593933
NC4 NCC Mismatch: 0.1391

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018526077270507812
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: 0.02229352295398712
Inter Cos: 0.0949181318283081
Norm Quadratic Average: 19.22271728515625
Nearest Class Center Accuracy: 0.3794

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03036099672317505
Inter Cos: 0.10150178521871567
Norm Quadratic Average: 10.244239807128906
Nearest Class Center Accuracy: 0.4638

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03868188336491585
Inter Cos: 0.10177730023860931
Norm Quadratic Average: 4.676250457763672
Nearest Class Center Accuracy: 0.5651

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07910197228193283
Inter Cos: 0.1441524475812912
Norm Quadratic Average: 1.038230299949646
Nearest Class Center Accuracy: 0.6196

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.2855677008628845
Inter Cos: 0.5352967381477356
Norm Quadratic Average: 0.6044204831123352
Nearest Class Center Accuracy: 0.6372

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.43204060196876526
Inter Cos: 0.7221238613128662
Norm Quadratic Average: 0.849473774433136
Nearest Class Center Accuracy: 0.6468

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.4484981298446655
Inter Cos: 0.6898440718650818
Norm Quadratic Average: 1.4381688833236694
Nearest Class Center Accuracy: 0.6916

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 2.4833872318267822
Linear Weight Rank: 4
Intra Cos: 0.46510398387908936
Inter Cos: 0.6097010970115662
Norm Quadratic Average: 12.008946418762207
Nearest Class Center Accuracy: 0.708

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 2.4896597862243652
Linear Weight Rank: 2671
Intra Cos: 0.40277862548828125
Inter Cos: 0.5483001470565796
Norm Quadratic Average: 15.045132637023926
Nearest Class Center Accuracy: 0.6876

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.4892826080322266
Linear Weight Rank: 9
Intra Cos: 0.3760862946510315
Inter Cos: 0.5054035186767578
Norm Quadratic Average: 17.641984939575195
Nearest Class Center Accuracy: 0.6592

Output Layer:
Intra Cos: 0.29988527297973633
Inter Cos: 0.5912445783615112
Norm Quadratic Average: 23.218616485595703
Nearest Class Center Accuracy: 0.6129

