Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_846264_weight_decay_0.0003.pth.tar
Training Set:
Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.021450398489832878
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: 0.024392398074269295
Inter Cos: 0.09514155238866806
Norm Quadratic Average: 32.838924407958984
Nearest Class Center Accuracy: 0.3035

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.030767470598220825
Inter Cos: 0.09941339492797852
Norm Quadratic Average: 25.612539291381836
Nearest Class Center Accuracy: 0.3705

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03498617559671402
Inter Cos: 0.09668105095624924
Norm Quadratic Average: 31.154094696044922
Nearest Class Center Accuracy: 0.413375

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05302394926548004
Inter Cos: 0.12237875163555145
Norm Quadratic Average: 20.07583999633789
Nearest Class Center Accuracy: 0.438125

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06290606409311295
Inter Cos: 0.11863144487142563
Norm Quadratic Average: 18.488285064697266
Nearest Class Center Accuracy: 0.465

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08214959502220154
Inter Cos: 0.1333601474761963
Norm Quadratic Average: 10.225285530090332
Nearest Class Center Accuracy: 0.513625

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.10838481038808823
Inter Cos: 0.1552102416753769
Norm Quadratic Average: 7.578014850616455
Nearest Class Center Accuracy: 0.6905

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 99.96395874023438
Linear Weight Rank: 4031
Intra Cos: 0.29667696356773376
Inter Cos: 0.27640628814697266
Norm Quadratic Average: 30.206771850585938
Nearest Class Center Accuracy: 0.96625

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.45443344116211
Linear Weight Rank: 3671
Intra Cos: 0.5785605311393738
Inter Cos: 0.4292049705982208
Norm Quadratic Average: 26.055599212646484
Nearest Class Center Accuracy: 0.998375

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.254638433456421
Linear Weight Rank: 10
Intra Cos: 0.7281612753868103
Inter Cos: 0.5367152094841003
Norm Quadratic Average: 30.343135833740234
Nearest Class Center Accuracy: 0.9995

Output Layer:
Intra Cos: 0.836306631565094
Inter Cos: 0.6959668397903442
Norm Quadratic Average: 37.24374008178711
Nearest Class Center Accuracy: 0.99825

Test Set:
Average Loss: 3.285243804931641
Accuracy: 0.5805
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.24475212395191193, Weights: 0.04379566013813019
NC2 Equiangle: Features: 0.45303243001302085, Weights: 0.16100806130303277
NC3 Self-Duality: 0.464966356754303
NC4 NCC Mismatch: 0.16800000000000004

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018480457365512848
Inter Cos: 0.09352367371320724
Norm Quadratic Average: 27.530664443969727
Nearest Class Center Accuracy: 0.2895

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.025703955441713333
Inter Cos: 0.0781281366944313
Norm Quadratic Average: 32.61669158935547
Nearest Class Center Accuracy: 0.32

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03197740390896797
Inter Cos: 0.08943678438663483
Norm Quadratic Average: 25.490896224975586
Nearest Class Center Accuracy: 0.384

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03469042107462883
Inter Cos: 0.0865059494972229
Norm Quadratic Average: 31.072954177856445
Nearest Class Center Accuracy: 0.436

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.048738010227680206
Inter Cos: 0.112486831843853
Norm Quadratic Average: 20.042064666748047
Nearest Class Center Accuracy: 0.451

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05712372437119484
Inter Cos: 0.10840625315904617
Norm Quadratic Average: 18.474021911621094
Nearest Class Center Accuracy: 0.4705

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06596539914608002
Inter Cos: 0.13351266086101532
Norm Quadratic Average: 10.201728820800781
Nearest Class Center Accuracy: 0.477

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0733955129981041
Inter Cos: 0.1460345983505249
Norm Quadratic Average: 7.524686336517334
Nearest Class Center Accuracy: 0.5235

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 99.96395874023438
Linear Weight Rank: 4031
Intra Cos: 0.1292046457529068
Inter Cos: 0.24823138117790222
Norm Quadratic Average: 29.103349685668945
Nearest Class Center Accuracy: 0.581

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.45443344116211
Linear Weight Rank: 3671
Intra Cos: 0.2095402628183365
Inter Cos: 0.37824204564094543
Norm Quadratic Average: 24.31177520751953
Nearest Class Center Accuracy: 0.5745

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.254638433456421
Linear Weight Rank: 10
Intra Cos: 0.24658143520355225
Inter Cos: 0.467212051153183
Norm Quadratic Average: 28.051067352294922
Nearest Class Center Accuracy: 0.5655

Output Layer:
Intra Cos: 0.28530922532081604
Inter Cos: 0.579033374786377
Norm Quadratic Average: 34.260555267333984
Nearest Class Center Accuracy: 0.54

