Model save path: ./New_Models/bn_True_dataset_CIFAR100_epochs_200_lr_0.01_model_type_vgg11_rand_seed_314159_test_samples_None_train_samples_None_weight_decay_0.0001.pth.tar
Training Set:
Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022893192246556282
Inter Cos: 0.07011394202709198
Norm Quadratic Average: 29.326322555541992
Nearest Class Center Accuracy: 0.03258

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.027740972116589546
Inter Cos: 0.027960218489170074
Norm Quadratic Average: 71.62348175048828
Nearest Class Center Accuracy: 0.04848

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022875169292092323
Inter Cos: 0.02392490766942501
Norm Quadratic Average: 38.966739654541016
Nearest Class Center Accuracy: 0.06054

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01906559057533741
Inter Cos: 0.020614398643374443
Norm Quadratic Average: 37.77753448486328
Nearest Class Center Accuracy: 0.0693

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02670860104262829
Inter Cos: 0.024507736787199974
Norm Quadratic Average: 23.87154197692871
Nearest Class Center Accuracy: 0.07764

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.035620179027318954
Inter Cos: 0.03253157436847687
Norm Quadratic Average: 27.94980239868164
Nearest Class Center Accuracy: 0.08372

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08222303539514542
Inter Cos: 0.06349126994609833
Norm Quadratic Average: 21.391590118408203
Nearest Class Center Accuracy: 0.09402

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.24742825329303741
Inter Cos: 0.15713533759117126
Norm Quadratic Average: 17.963302612304688
Nearest Class Center Accuracy: 0.09858

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 74.56865692138672
Linear Weight Rank: 4031
Intra Cos: 0.5325065851211548
Inter Cos: 0.2603904604911804
Norm Quadratic Average: 53.14292526245117
Nearest Class Center Accuracy: 0.0999

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 33.78921890258789
Linear Weight Rank: 3661
Intra Cos: 0.714358389377594
Inter Cos: 0.25026482343673706
Norm Quadratic Average: 43.34543991088867
Nearest Class Center Accuracy: 0.1

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 14.02737808227539
Linear Weight Rank: 98
Intra Cos: 0.7983579039573669
Inter Cos: 0.3029181957244873
Norm Quadratic Average: 44.00288009643555
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.8964741230010986
Inter Cos: 0.5484559535980225
Norm Quadratic Average: 87.72274017333984
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 4.328327908325195
Accuracy: 0.5381
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.3140696883201599, Weights: 0.029621578752994537
NC2 Equiangle: Features: 0.15521874013573234, Weights: 0.10197533809777462
NC3 Self-Duality: 0.5807620286941528
NC4 NCC Mismatch: 0.17479999999999996

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.006621268577873707
Inter Cos: 0.4067547917366028
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: 0.013033554889261723
Inter Cos: 0.26160475611686707
Norm Quadratic Average: 72.14385223388672
Nearest Class Center Accuracy: 0.2674

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.015504627488553524
Inter Cos: 0.2075154036283493
Norm Quadratic Average: 39.25997543334961
Nearest Class Center Accuracy: 0.3829

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.014356003142893314
Inter Cos: 0.15391483902931213
Norm Quadratic Average: 37.961158752441406
Nearest Class Center Accuracy: 0.4939

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.015287772752344608
Inter Cos: 0.1551956683397293
Norm Quadratic Average: 23.948322296142578
Nearest Class Center Accuracy: 0.5708

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01672491431236267
Inter Cos: 0.14612025022506714
Norm Quadratic Average: 27.94474983215332
Nearest Class Center Accuracy: 0.5863

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.031033586710691452
Inter Cos: 0.20622582733631134
Norm Quadratic Average: 21.235193252563477
Nearest Class Center Accuracy: 0.5872

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06802482903003693
Inter Cos: 0.3457189202308655
Norm Quadratic Average: 17.276371002197266
Nearest Class Center Accuracy: 0.5576

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 74.56865692138672
Linear Weight Rank: 4031
Intra Cos: 0.1313277631998062
Inter Cos: 0.44589468836784363
Norm Quadratic Average: 47.34904479980469
Nearest Class Center Accuracy: 0.5473

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 33.78921890258789
Linear Weight Rank: 3661
Intra Cos: 0.16617806255817413
Inter Cos: 0.43808379769325256
Norm Quadratic Average: 35.333683013916016
Nearest Class Center Accuracy: 0.5348

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 14.02737808227539
Linear Weight Rank: 98
Intra Cos: 0.16424643993377686
Inter Cos: 0.5120747089385986
Norm Quadratic Average: 34.889984130859375
Nearest Class Center Accuracy: 0.5313

Output Layer:
Intra Cos: 0.19080743193626404
Inter Cos: 0.7089938521385193
Norm Quadratic Average: 69.47418212890625
Nearest Class Center Accuracy: 0.5225

