Model save path: ./New_Models/bn_True_dataset_CIFAR100_epochs_200_lr_0.01_model_type_vgg11_rand_seed_979323_test_samples_None_train_samples_None_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: 0.022893190383911133
Inter Cos: 0.07011394202709198
Norm Quadratic Average: 29.326318740844727
Nearest Class Center Accuracy: 0.03258

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.024215705692768097
Inter Cos: 0.026212815195322037
Norm Quadratic Average: 28.630306243896484
Nearest Class Center Accuracy: 0.04844

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022508995607495308
Inter Cos: 0.022365765646100044
Norm Quadratic Average: 14.586602210998535
Nearest Class Center Accuracy: 0.05982

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01832055300474167
Inter Cos: 0.018960265442728996
Norm Quadratic Average: 12.253823280334473
Nearest Class Center Accuracy: 0.06856

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02359207347035408
Inter Cos: 0.02117658406496048
Norm Quadratic Average: 7.798267364501953
Nearest Class Center Accuracy: 0.07896

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.027543338015675545
Inter Cos: 0.025390885770320892
Norm Quadratic Average: 6.539221286773682
Nearest Class Center Accuracy: 0.0844

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06803479790687561
Inter Cos: 0.0479259118437767
Norm Quadratic Average: 4.446320533752441
Nearest Class Center Accuracy: 0.09634

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.2753720283508301
Inter Cos: 0.1271827518939972
Norm Quadratic Average: 3.4680449962615967
Nearest Class Center Accuracy: 0.09992

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 21.717926025390625
Linear Weight Rank: 4029
Intra Cos: 0.6520840525627136
Inter Cos: 0.2313581258058548
Norm Quadratic Average: 36.66399002075195
Nearest Class Center Accuracy: 0.1

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 12.25576114654541
Linear Weight Rank: 3641
Intra Cos: 0.807039737701416
Inter Cos: 0.30819547176361084
Norm Quadratic Average: 30.47637367248535
Nearest Class Center Accuracy: 0.1

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 8.50370979309082
Linear Weight Rank: 98
Intra Cos: 0.849345862865448
Inter Cos: 0.3203459084033966
Norm Quadratic Average: 31.003124237060547
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.8914664387702942
Inter Cos: 0.40448060631752014
Norm Quadratic Average: 43.478973388671875
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 2.486926876449585
Accuracy: 0.5476
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.24726296961307526, Weights: 0.037712328135967255
NC2 Equiangle: Features: 0.1773623441445707, Weights: 0.09687416153724747
NC3 Self-Duality: 0.4156600534915924
NC4 NCC Mismatch: 0.15590000000000004

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.006621268577873707
Inter Cos: 0.4067547619342804
Norm Quadratic Average: 29.42218780517578
Nearest Class Center Accuracy: 0.1136

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.012898511253297329
Inter Cos: 0.25533735752105713
Norm Quadratic Average: 28.831933975219727
Nearest Class Center Accuracy: 0.2642

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01551301870495081
Inter Cos: 0.19726425409317017
Norm Quadratic Average: 14.697382926940918
Nearest Class Center Accuracy: 0.3887

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.013651595450937748
Inter Cos: 0.14110861718654633
Norm Quadratic Average: 12.31121826171875
Nearest Class Center Accuracy: 0.5052

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.013227575458586216
Inter Cos: 0.13669511675834656
Norm Quadratic Average: 7.81588077545166
Nearest Class Center Accuracy: 0.5998

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.011832292191684246
Inter Cos: 0.12487969547510147
Norm Quadratic Average: 6.522845268249512
Nearest Class Center Accuracy: 0.6343

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.021283119916915894
Inter Cos: 0.17266510426998138
Norm Quadratic Average: 4.380794525146484
Nearest Class Center Accuracy: 0.6346

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06076137349009514
Inter Cos: 0.3356224000453949
Norm Quadratic Average: 3.2162561416625977
Nearest Class Center Accuracy: 0.5869

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 21.717926025390625
Linear Weight Rank: 4029
Intra Cos: 0.18852739036083221
Inter Cos: 0.45120730996131897
Norm Quadratic Average: 30.74852752685547
Nearest Class Center Accuracy: 0.5416

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 12.25576114654541
Linear Weight Rank: 3641
Intra Cos: 0.20747223496437073
Inter Cos: 0.48468807339668274
Norm Quadratic Average: 24.633037567138672
Nearest Class Center Accuracy: 0.5459

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 8.50370979309082
Linear Weight Rank: 98
Intra Cos: 0.20531027019023895
Inter Cos: 0.5356195569038391
Norm Quadratic Average: 25.208789825439453
Nearest Class Center Accuracy: 0.5449

Output Layer:
Intra Cos: 0.22034434974193573
Inter Cos: 0.61957848072052
Norm Quadratic Average: 35.6014289855957
Nearest Class Center Accuracy: 0.5403

