Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_200_lr_0.01_model_type_vgg11_rand_seed_314159_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: 0.01989092119038105
Inter Cos: 0.10477276146411896
Norm Quadratic Average: 27.59718132019043
Nearest Class Center Accuracy: 0.26968

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.023972701281309128
Inter Cos: 0.08792798966169357
Norm Quadratic Average: 27.390151977539062
Nearest Class Center Accuracy: 0.3922

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.028526416048407555
Inter Cos: 0.08059912919998169
Norm Quadratic Average: 24.37966537475586
Nearest Class Center Accuracy: 0.49478

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02763320691883564
Inter Cos: 0.06322350353002548
Norm Quadratic Average: 26.241840362548828
Nearest Class Center Accuracy: 0.57796

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.029516007751226425
Inter Cos: 0.05020836740732193
Norm Quadratic Average: 12.12781047821045
Nearest Class Center Accuracy: 0.67944

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.045809805393218994
Inter Cos: 0.054316289722919464
Norm Quadratic Average: 6.976635932922363
Nearest Class Center Accuracy: 0.75488

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.14252960681915283
Inter Cos: 0.13240735232830048
Norm Quadratic Average: 2.706671953201294
Nearest Class Center Accuracy: 0.88396

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.5493248105049133
Inter Cos: 0.23955368995666504
Norm Quadratic Average: 1.63231360912323
Nearest Class Center Accuracy: 0.99596

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 20.435466766357422
Linear Weight Rank: 4031
Intra Cos: 0.8302333354949951
Inter Cos: 0.21513454616069794
Norm Quadratic Average: 11.269773483276367
Nearest Class Center Accuracy: 0.99908

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 9.007856369018555
Linear Weight Rank: 3665
Intra Cos: 0.890508234500885
Inter Cos: 0.15522757172584534
Norm Quadratic Average: 12.088873863220215
Nearest Class Center Accuracy: 0.99998

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.699476957321167
Linear Weight Rank: 10
Intra Cos: 0.9010353088378906
Inter Cos: 0.16420629620552063
Norm Quadratic Average: 13.569801330566406
Nearest Class Center Accuracy: 1.0

Output Layer:
Intra Cos: 0.922668993473053
Inter Cos: 0.3109513223171234
Norm Quadratic Average: 16.776853561401367
Nearest Class Center Accuracy: 1.0

Test Set:
Average Loss: 0.8856068201065064
Accuracy: 0.8216
NC1 Within Class Collapse: 5.647517204284668
NC2 Equinorm: Features: 0.20969520509243011, Weights: 0.03129389137029648
NC2 Equiangle: Features: 0.19696729448106554, Weights: 0.06165800624423557
NC3 Self-Duality: 0.15772859752178192
NC4 NCC Mismatch: 0.05069999999999997

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018526073545217514
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: 0.022200046107172966
Inter Cos: 0.0884709507226944
Norm Quadratic Average: 27.37717628479004
Nearest Class Center Accuracy: 0.4052

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02688581310212612
Inter Cos: 0.08173749595880508
Norm Quadratic Average: 24.390588760375977
Nearest Class Center Accuracy: 0.4989

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02539493329823017
Inter Cos: 0.0640166848897934
Norm Quadratic Average: 26.266260147094727
Nearest Class Center Accuracy: 0.5831

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02603207156062126
Inter Cos: 0.05121287703514099
Norm Quadratic Average: 12.142463684082031
Nearest Class Center Accuracy: 0.6608

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03931691497564316
Inter Cos: 0.05613182857632637
Norm Quadratic Average: 6.968997478485107
Nearest Class Center Accuracy: 0.7031

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.11148926615715027
Inter Cos: 0.13558238744735718
Norm Quadratic Average: 2.689718008041382
Nearest Class Center Accuracy: 0.754

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.330247700214386
Inter Cos: 0.28212079405784607
Norm Quadratic Average: 1.5838942527770996
Nearest Class Center Accuracy: 0.8035

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 20.435466766357422
Linear Weight Rank: 4031
Intra Cos: 0.48716139793395996
Inter Cos: 0.34003645181655884
Norm Quadratic Average: 10.721724510192871
Nearest Class Center Accuracy: 0.8023

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 9.007856369018555
Linear Weight Rank: 3665
Intra Cos: 0.5035014152526855
Inter Cos: 0.32275286316871643
Norm Quadratic Average: 11.410911560058594
Nearest Class Center Accuracy: 0.8068

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.699476957321167
Linear Weight Rank: 10
Intra Cos: 0.4949813485145569
Inter Cos: 0.31392017006874084
Norm Quadratic Average: 12.784841537475586
Nearest Class Center Accuracy: 0.8113

Output Layer:
Intra Cos: 0.5108271241188049
Inter Cos: 0.35196244716644287
Norm Quadratic Average: 15.762801170349121
Nearest Class Center Accuracy: 0.8141

