Model save path: ./New_Models/bn_True_dataset_CIFAR100_epochs_200_lr_0.01_model_type_vgg11_rand_seed_265358_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: 29.326324462890625
Nearest Class Center Accuracy: 0.03258

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.025040140375494957
Inter Cos: 0.029248971492052078
Norm Quadratic Average: 4.062544822692871
Nearest Class Center Accuracy: 0.0477

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02288004942238331
Inter Cos: 0.024310236796736717
Norm Quadratic Average: 2.0914418697357178
Nearest Class Center Accuracy: 0.0608

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.017655855044722557
Inter Cos: 0.01753067784011364
Norm Quadratic Average: 1.4868508577346802
Nearest Class Center Accuracy: 0.0702

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.024525880813598633
Inter Cos: 0.02018563076853752
Norm Quadratic Average: 1.0826021432876587
Nearest Class Center Accuracy: 0.0815

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.031406551599502563
Inter Cos: 0.025718916207551956
Norm Quadratic Average: 0.9011943936347961
Nearest Class Center Accuracy: 0.0909

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.13290837407112122
Inter Cos: 0.07840496301651001
Norm Quadratic Average: 0.7144644856452942
Nearest Class Center Accuracy: 0.09974

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.684697151184082
Inter Cos: 0.2260153889656067
Norm Quadratic Average: 1.0800869464874268
Nearest Class Center Accuracy: 0.1

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 4.156643867492676
Linear Weight Rank: 428
Intra Cos: 0.9399254322052002
Inter Cos: 0.34133580327033997
Norm Quadratic Average: 40.136260986328125
Nearest Class Center Accuracy: 0.1

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 4.228785514831543
Linear Weight Rank: 1791
Intra Cos: 0.9596000909805298
Inter Cos: 0.37646839022636414
Norm Quadratic Average: 31.95722770690918
Nearest Class Center Accuracy: 0.1

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 4.267027378082275
Linear Weight Rank: 96
Intra Cos: 0.9619739651679993
Inter Cos: 0.3621343970298767
Norm Quadratic Average: 29.208969116210938
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.9643567204475403
Inter Cos: 0.3875848948955536
Norm Quadratic Average: 28.6829776763916
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 1.63475443611145
Accuracy: 0.6074
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.21767398715019226, Weights: 0.01207390520721674
NC2 Equiangle: Features: 0.2031954308712121, Weights: 0.16821547999526515
NC3 Self-Duality: 0.18581433594226837
NC4 NCC Mismatch: 0.13660000000000005

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.006621266715228558
Inter Cos: 0.4067547917366028
Norm Quadratic Average: 29.42218589782715
Nearest Class Center Accuracy: 0.1136

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.011453707702457905
Inter Cos: 0.24994313716888428
Norm Quadratic Average: 4.090825080871582
Nearest Class Center Accuracy: 0.259

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.015624376945197582
Inter Cos: 0.2032989114522934
Norm Quadratic Average: 2.1063039302825928
Nearest Class Center Accuracy: 0.3922

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.013925590552389622
Inter Cos: 0.14116263389587402
Norm Quadratic Average: 1.4928452968597412
Nearest Class Center Accuracy: 0.5222

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.012978062964975834
Inter Cos: 0.14490574598312378
Norm Quadratic Average: 1.084337830543518
Nearest Class Center Accuracy: 0.6373

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.012247416190803051
Inter Cos: 0.15069758892059326
Norm Quadratic Average: 0.8944498300552368
Nearest Class Center Accuracy: 0.6912

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03558642417192459
Inter Cos: 0.28806284070014954
Norm Quadratic Average: 0.6873485445976257
Nearest Class Center Accuracy: 0.6666

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.12056439369916916
Inter Cos: 0.5306735634803772
Norm Quadratic Average: 0.9051125645637512
Nearest Class Center Accuracy: 0.6245

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 4.156643867492676
Linear Weight Rank: 428
Intra Cos: 0.24662919342517853
Inter Cos: 0.5390824675559998
Norm Quadratic Average: 31.227571487426758
Nearest Class Center Accuracy: 0.6142

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 4.228785514831543
Linear Weight Rank: 1791
Intra Cos: 0.2625353932380676
Inter Cos: 0.5451282858848572
Norm Quadratic Average: 25.14439582824707
Nearest Class Center Accuracy: 0.6118

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 4.267027378082275
Linear Weight Rank: 96
Intra Cos: 0.25019922852516174
Inter Cos: 0.5413104891777039
Norm Quadratic Average: 23.276145935058594
Nearest Class Center Accuracy: 0.6087

Output Layer:
Intra Cos: 0.24893216788768768
Inter Cos: 0.5537235736846924
Norm Quadratic Average: 22.745384216308594
Nearest Class Center Accuracy: 0.603

