Model save path: ./New_Models/bn_True_dataset_CIFAR10_epochs_200_lr_0.01_model_type_vgg11_rand_seed_265358_test_samples_None_train_samples_None_weight_decay_0.03.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: 0.10477277636528015
Norm Quadratic Average: 27.59717559814453
Nearest Class Center Accuracy: 0.26968

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.020818311721086502
Inter Cos: 0.08763204514980316
Norm Quadratic Average: 2.6556150913238525
Nearest Class Center Accuracy: 0.39786

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02112019620835781
Inter Cos: 0.06177251785993576
Norm Quadratic Average: 1.3376531600952148
Nearest Class Center Accuracy: 0.5169

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.021511565893888474
Inter Cos: 0.05753662437200546
Norm Quadratic Average: 0.9756456613540649
Nearest Class Center Accuracy: 0.60994

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0325971394777298
Inter Cos: 0.06321223080158234
Norm Quadratic Average: 0.6720563173294067
Nearest Class Center Accuracy: 0.73724

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.055029675364494324
Inter Cos: 0.07013354450464249
Norm Quadratic Average: 0.6108196973800659
Nearest Class Center Accuracy: 0.84682

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.20292741060256958
Inter Cos: 0.2178240865468979
Norm Quadratic Average: 0.3895484507083893
Nearest Class Center Accuracy: 0.95826

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.8115490674972534
Inter Cos: 0.28180769085884094
Norm Quadratic Average: 0.4595741629600525
Nearest Class Center Accuracy: 0.99992

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 2.0008926391601562
Linear Weight Rank: 9
Intra Cos: 0.9780828356742859
Inter Cos: 0.21886874735355377
Norm Quadratic Average: 22.59996795654297
Nearest Class Center Accuracy: 0.99998

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 2.003405809402466
Linear Weight Rank: 1688
Intra Cos: 0.9848943948745728
Inter Cos: 0.22907641530036926
Norm Quadratic Average: 14.913368225097656
Nearest Class Center Accuracy: 0.99998

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.0033974647521973
Linear Weight Rank: 9
Intra Cos: 0.9876135587692261
Inter Cos: 0.21288859844207764
Norm Quadratic Average: 10.037629127502441
Nearest Class Center Accuracy: 0.99998

Output Layer:
Intra Cos: 0.9902895092964172
Inter Cos: 0.17940473556518555
Norm Quadratic Average: 7.205287456512451
Nearest Class Center Accuracy: 0.99998

Test Set:
Average Loss: 0.5089692793369294
Accuracy: 0.8375
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.11402641236782074, Weights: 0.008177267387509346
NC2 Equiangle: Features: 0.1555543475680881, Weights: 0.10394287109375
NC3 Self-Duality: 0.05258366838097572
NC4 NCC Mismatch: 0.015199999999999991

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018526077270507812
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: 0.01976286992430687
Inter Cos: 0.08922187983989716
Norm Quadratic Average: 2.6537423133850098
Nearest Class Center Accuracy: 0.4148

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02000538259744644
Inter Cos: 0.06324013322591782
Norm Quadratic Average: 1.3382889032363892
Nearest Class Center Accuracy: 0.5309

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02019760012626648
Inter Cos: 0.05846846476197243
Norm Quadratic Average: 0.976577877998352
Nearest Class Center Accuracy: 0.6145

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02877802401781082
Inter Cos: 0.06425592303276062
Norm Quadratic Average: 0.6723193526268005
Nearest Class Center Accuracy: 0.707

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04391804337501526
Inter Cos: 0.07283720374107361
Norm Quadratic Average: 0.6084092259407043
Nearest Class Center Accuracy: 0.7629

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.15359815955162048
Inter Cos: 0.22257161140441895
Norm Quadratic Average: 0.3846138119697571
Nearest Class Center Accuracy: 0.8079

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.4401922821998596
Inter Cos: 0.3216777741909027
Norm Quadratic Average: 0.42732515931129456
Nearest Class Center Accuracy: 0.8384

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 2.0008926391601562
Linear Weight Rank: 9
Intra Cos: 0.5431044101715088
Inter Cos: 0.3265647888183594
Norm Quadratic Average: 20.256956100463867
Nearest Class Center Accuracy: 0.8382

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 2.003405809402466
Linear Weight Rank: 1688
Intra Cos: 0.554591178894043
Inter Cos: 0.33926039934158325
Norm Quadratic Average: 13.37074089050293
Nearest Class Center Accuracy: 0.8384

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.0033974647521973
Linear Weight Rank: 9
Intra Cos: 0.562699019908905
Inter Cos: 0.3386220335960388
Norm Quadratic Average: 8.99152660369873
Nearest Class Center Accuracy: 0.8377

Output Layer:
Intra Cos: 0.5873928070068359
Inter Cos: 0.32889124751091003
Norm Quadratic Average: 6.46430778503418
Nearest Class Center Accuracy: 0.8379

