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_0.005.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: 0.02586464211344719
Inter Cos: 0.02858048491179943
Norm Quadratic Average: 4.0359907150268555
Nearest Class Center Accuracy: 0.048

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.023841379210352898
Inter Cos: 0.023479491472244263
Norm Quadratic Average: 2.078720808029175
Nearest Class Center Accuracy: 0.0603

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.019133172929286957
Inter Cos: 0.017452748492360115
Norm Quadratic Average: 1.5042684078216553
Nearest Class Center Accuracy: 0.07022

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.024584202095866203
Inter Cos: 0.02050078473985195
Norm Quadratic Average: 1.0924128293991089
Nearest Class Center Accuracy: 0.08166

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03130556270480156
Inter Cos: 0.027545392513275146
Norm Quadratic Average: 0.9100381135940552
Nearest Class Center Accuracy: 0.09088

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.12125775963068008
Inter Cos: 0.0861906036734581
Norm Quadratic Average: 0.720187246799469
Nearest Class Center Accuracy: 0.09968

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.6764598488807678
Inter Cos: 0.21770302951335907
Norm Quadratic Average: 1.0823431015014648
Nearest Class Center Accuracy: 0.1

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 4.165572643280029
Linear Weight Rank: 424
Intra Cos: 0.9397732615470886
Inter Cos: 0.3170154392719269
Norm Quadratic Average: 40.267860412597656
Nearest Class Center Accuracy: 0.1

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 4.236489295959473
Linear Weight Rank: 1691
Intra Cos: 0.9626244306564331
Inter Cos: 0.346044659614563
Norm Quadratic Average: 31.97884750366211
Nearest Class Center Accuracy: 0.1

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 4.274652004241943
Linear Weight Rank: 96
Intra Cos: 0.9639706611633301
Inter Cos: 0.3429664671421051
Norm Quadratic Average: 29.18687629699707
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.9669443964958191
Inter Cos: 0.38073673844337463
Norm Quadratic Average: 28.685346603393555
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 1.6316663621902465
Accuracy: 0.6082
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.22081919014453888, Weights: 0.012177690863609314
NC2 Equiangle: Features: 0.2033733329387626, Weights: 0.16711955985637628
NC3 Self-Duality: 0.19161558151245117
NC4 NCC Mismatch: 0.14300000000000002

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: 0.012638802640140057
Inter Cos: 0.2472606599330902
Norm Quadratic Average: 4.064107894897461
Nearest Class Center Accuracy: 0.2591

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01610647514462471
Inter Cos: 0.20559994876384735
Norm Quadratic Average: 2.0937604904174805
Nearest Class Center Accuracy: 0.3929

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01419010479003191
Inter Cos: 0.1452012062072754
Norm Quadratic Average: 1.5102596282958984
Nearest Class Center Accuracy: 0.524

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.012277149595320225
Inter Cos: 0.1428762674331665
Norm Quadratic Average: 1.0945751667022705
Nearest Class Center Accuracy: 0.6356

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.012043391354382038
Inter Cos: 0.1422339528799057
Norm Quadratic Average: 0.9037532210350037
Nearest Class Center Accuracy: 0.6898

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.033913347870111465
Inter Cos: 0.283344566822052
Norm Quadratic Average: 0.6927985548973083
Nearest Class Center Accuracy: 0.6637

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.11974751204252243
Inter Cos: 0.5186683535575867
Norm Quadratic Average: 0.9027419686317444
Nearest Class Center Accuracy: 0.6214

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 4.165572643280029
Linear Weight Rank: 424
Intra Cos: 0.23224575817584991
Inter Cos: 0.5476281642913818
Norm Quadratic Average: 31.197254180908203
Nearest Class Center Accuracy: 0.6075

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 4.236489295959473
Linear Weight Rank: 1691
Intra Cos: 0.2591913938522339
Inter Cos: 0.5736027956008911
Norm Quadratic Average: 25.06852149963379
Nearest Class Center Accuracy: 0.6029

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 4.274652004241943
Linear Weight Rank: 96
Intra Cos: 0.2677958607673645
Inter Cos: 0.5850117802619934
Norm Quadratic Average: 23.166013717651367
Nearest Class Center Accuracy: 0.6007

Output Layer:
Intra Cos: 0.2725023329257965
Inter Cos: 0.61028653383255
Norm Quadratic Average: 22.636924743652344
Nearest Class Center Accuracy: 0.5986

