Model save path: ./New_Models/bn_False_dataset_CIFAR100_epochs_200_lr_0.01_model_type_vgg11_rand_seed_979323_test_samples_None_train_samples_None_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: 0.022893190383911133
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: 0.042193129658699036
Inter Cos: 0.08035977184772491
Norm Quadratic Average: 40.30267333984375
Nearest Class Center Accuracy: 0.04162

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04750043526291847
Inter Cos: 0.08449842780828476
Norm Quadratic Average: 56.16843795776367
Nearest Class Center Accuracy: 0.04606

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04173208773136139
Inter Cos: 0.0435434989631176
Norm Quadratic Average: 103.7979965209961
Nearest Class Center Accuracy: 0.05248

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.042857784777879715
Inter Cos: 0.04590948671102524
Norm Quadratic Average: 75.0364990234375
Nearest Class Center Accuracy: 0.06224

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04336699843406677
Inter Cos: 0.03904254734516144
Norm Quadratic Average: 32.93239974975586
Nearest Class Center Accuracy: 0.06998

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09913794696331024
Inter Cos: 0.07346928119659424
Norm Quadratic Average: 7.611316680908203
Nearest Class Center Accuracy: 0.07706

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.4667971134185791
Inter Cos: 0.3083735406398773
Norm Quadratic Average: 2.8126132488250732
Nearest Class Center Accuracy: 0.09612

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 5.109145164489746
Linear Weight Rank: 1582
Intra Cos: 0.6842320561408997
Inter Cos: 0.38608965277671814
Norm Quadratic Average: 23.553760528564453
Nearest Class Center Accuracy: 0.09928

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 5.402129650115967
Linear Weight Rank: 3126
Intra Cos: 0.7454304099082947
Inter Cos: 0.4132334589958191
Norm Quadratic Average: 33.20204162597656
Nearest Class Center Accuracy: 0.09996

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 5.5621562004089355
Linear Weight Rank: 97
Intra Cos: 0.7479326128959656
Inter Cos: 0.40421468019485474
Norm Quadratic Average: 43.35734558105469
Nearest Class Center Accuracy: 0.09998

Output Layer:
Intra Cos: 0.7604395747184753
Inter Cos: 0.44659167528152466
Norm Quadratic Average: 61.78923416137695
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 3.462020011520386
Accuracy: 0.4322
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.2550956606864929, Weights: 0.044297318905591965
NC2 Equiangle: Features: 0.2680996981534091, Weights: 0.17733005099826388
NC3 Self-Duality: 0.3930708169937134
NC4 NCC Mismatch: 0.29969999999999997

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: 0.014996652491390705
Inter Cos: 0.31439077854156494
Norm Quadratic Average: 40.4801025390625
Nearest Class Center Accuracy: 0.1854

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.020797785371541977
Inter Cos: 0.36933037638664246
Norm Quadratic Average: 56.41863250732422
Nearest Class Center Accuracy: 0.2264

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01907048001885414
Inter Cos: 0.4275294244289398
Norm Quadratic Average: 104.38766479492188
Nearest Class Center Accuracy: 0.2583

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02445973828434944
Inter Cos: 0.3317618668079376
Norm Quadratic Average: 75.64877319335938
Nearest Class Center Accuracy: 0.3491

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02726716920733452
Inter Cos: 0.22694329917430878
Norm Quadratic Average: 33.167240142822266
Nearest Class Center Accuracy: 0.4438

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04387544095516205
Inter Cos: 0.37587970495224
Norm Quadratic Average: 7.633310317993164
Nearest Class Center Accuracy: 0.4536

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.13467928767204285
Inter Cos: 0.6349343657493591
Norm Quadratic Average: 2.75490403175354
Nearest Class Center Accuracy: 0.4108

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 5.109145164489746
Linear Weight Rank: 1582
Intra Cos: 0.16797970235347748
Inter Cos: 0.6683720946311951
Norm Quadratic Average: 22.685884475708008
Nearest Class Center Accuracy: 0.4188

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 5.402129650115967
Linear Weight Rank: 3126
Intra Cos: 0.1750696301460266
Inter Cos: 0.6812239289283752
Norm Quadratic Average: 31.785419464111328
Nearest Class Center Accuracy: 0.4291

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 5.5621562004089355
Linear Weight Rank: 97
Intra Cos: 0.1802234649658203
Inter Cos: 0.6639198660850525
Norm Quadratic Average: 41.66147232055664
Nearest Class Center Accuracy: 0.4293

Output Layer:
Intra Cos: 0.18260297179222107
Inter Cos: 0.6973814368247986
Norm Quadratic Average: 59.24605941772461
Nearest Class Center Accuracy: 0.4228

