Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_338327_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: 0.11371058225631714
Norm Quadratic Average: 27.691619873046875
Nearest Class Center Accuracy: 0.279125

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.023978356271982193
Inter Cos: 0.09650836884975433
Norm Quadratic Average: 34.81612014770508
Nearest Class Center Accuracy: 0.302

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.029689684510231018
Inter Cos: 0.10410625487565994
Norm Quadratic Average: 27.453889846801758
Nearest Class Center Accuracy: 0.363375

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03451639786362648
Inter Cos: 0.10518183559179306
Norm Quadratic Average: 33.162315368652344
Nearest Class Center Accuracy: 0.41025

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05060630664229393
Inter Cos: 0.13089238107204437
Norm Quadratic Average: 21.26038360595703
Nearest Class Center Accuracy: 0.435375

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05683046951889992
Inter Cos: 0.128784641623497
Norm Quadratic Average: 19.345949172973633
Nearest Class Center Accuracy: 0.46225

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07340597361326218
Inter Cos: 0.13710835576057434
Norm Quadratic Average: 10.382859230041504
Nearest Class Center Accuracy: 0.514375

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1012490913271904
Inter Cos: 0.14339949190616608
Norm Quadratic Average: 7.630155086517334
Nearest Class Center Accuracy: 0.687125

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 99.00297546386719
Linear Weight Rank: 4031
Intra Cos: 0.2889421582221985
Inter Cos: 0.25371241569519043
Norm Quadratic Average: 30.041967391967773
Nearest Class Center Accuracy: 0.971125

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.05790710449219
Linear Weight Rank: 3670
Intra Cos: 0.5690168738365173
Inter Cos: 0.39052093029022217
Norm Quadratic Average: 25.410173416137695
Nearest Class Center Accuracy: 0.99925

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.229599952697754
Linear Weight Rank: 10
Intra Cos: 0.7235928773880005
Inter Cos: 0.4959273934364319
Norm Quadratic Average: 29.225778579711914
Nearest Class Center Accuracy: 0.99975

Output Layer:
Intra Cos: 0.8284354209899902
Inter Cos: 0.661806583404541
Norm Quadratic Average: 34.88569259643555
Nearest Class Center Accuracy: 1.0

Test Set:
Average Loss: 3.1050342559814452
Accuracy: 0.59
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.24070855975151062, Weights: 0.04447104409337044
NC2 Equiangle: Features: 0.42936604817708335, Weights: 0.1502219623989529
NC3 Self-Duality: 0.45600271224975586
NC4 NCC Mismatch: 0.15400000000000003

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018480459228157997
Inter Cos: 0.09352367371320724
Norm Quadratic Average: 27.53066635131836
Nearest Class Center Accuracy: 0.2895

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.024872099980711937
Inter Cos: 0.08003228902816772
Norm Quadratic Average: 34.56925964355469
Nearest Class Center Accuracy: 0.3195

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03253195062279701
Inter Cos: 0.09005039930343628
Norm Quadratic Average: 27.302766799926758
Nearest Class Center Accuracy: 0.375

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03576549142599106
Inter Cos: 0.09297680109739304
Norm Quadratic Average: 33.03567886352539
Nearest Class Center Accuracy: 0.435

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.049089714884757996
Inter Cos: 0.11568202823400497
Norm Quadratic Average: 21.204303741455078
Nearest Class Center Accuracy: 0.456

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05369403958320618
Inter Cos: 0.11399299651384354
Norm Quadratic Average: 19.329946517944336
Nearest Class Center Accuracy: 0.4695

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06148393079638481
Inter Cos: 0.12782157957553864
Norm Quadratic Average: 10.365856170654297
Nearest Class Center Accuracy: 0.4895

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0709209069609642
Inter Cos: 0.13503991067409515
Norm Quadratic Average: 7.582582950592041
Nearest Class Center Accuracy: 0.523

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 99.00297546386719
Linear Weight Rank: 4031
Intra Cos: 0.13588407635688782
Inter Cos: 0.23425516486167908
Norm Quadratic Average: 28.97150993347168
Nearest Class Center Accuracy: 0.588

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.05790710449219
Linear Weight Rank: 3670
Intra Cos: 0.22028543055057526
Inter Cos: 0.3550918698310852
Norm Quadratic Average: 23.736148834228516
Nearest Class Center Accuracy: 0.584

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.229599952697754
Linear Weight Rank: 10
Intra Cos: 0.25946611166000366
Inter Cos: 0.4365709125995636
Norm Quadratic Average: 27.04116439819336
Nearest Class Center Accuracy: 0.5695

Output Layer:
Intra Cos: 0.29443520307540894
Inter Cos: 0.5468111634254456
Norm Quadratic Average: 32.1260871887207
Nearest Class Center Accuracy: 0.553

