Model save path: ./New_Models/bn_True_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_979323_weight_decay_0.0007.pth.tar
Training Set:
Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.021450400352478027
Inter Cos: 0.11371058970689774
Norm Quadratic Average: 27.691617965698242
Nearest Class Center Accuracy: 0.279125

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02309248223900795
Inter Cos: 0.10079304128885269
Norm Quadratic Average: 85.78313446044922
Nearest Class Center Accuracy: 0.350875

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.026155445724725723
Inter Cos: 0.09326521307229996
Norm Quadratic Average: 64.01260375976562
Nearest Class Center Accuracy: 0.37975

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02341340109705925
Inter Cos: 0.06693165749311447
Norm Quadratic Average: 68.02487182617188
Nearest Class Center Accuracy: 0.41

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03137108311057091
Inter Cos: 0.07831273972988129
Norm Quadratic Average: 42.722747802734375
Nearest Class Center Accuracy: 0.434625

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03138290345668793
Inter Cos: 0.07182879000902176
Norm Quadratic Average: 43.82992935180664
Nearest Class Center Accuracy: 0.4745

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.042342931032180786
Inter Cos: 0.07524781674146652
Norm Quadratic Average: 27.922164916992188
Nearest Class Center Accuracy: 0.56125

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.062497712671756744
Inter Cos: 0.07806245237588882
Norm Quadratic Average: 19.873029708862305
Nearest Class Center Accuracy: 0.84475

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 98.0356216430664
Linear Weight Rank: 4031
Intra Cos: 0.17657001316547394
Inter Cos: 0.09935668110847473
Norm Quadratic Average: 107.17855834960938
Nearest Class Center Accuracy: 1.0

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 39.63319396972656
Linear Weight Rank: 3670
Intra Cos: 0.4050060510635376
Inter Cos: 0.17717191576957703
Norm Quadratic Average: 55.14919662475586
Nearest Class Center Accuracy: 1.0

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.475038528442383
Linear Weight Rank: 10
Intra Cos: 0.6381205320358276
Inter Cos: 0.27444377541542053
Norm Quadratic Average: 38.06977844238281
Nearest Class Center Accuracy: 1.0

Output Layer:
Intra Cos: 0.8608394861221313
Inter Cos: 0.4913727641105652
Norm Quadratic Average: 26.10593032836914
Nearest Class Center Accuracy: 1.0

Test Set:
Average Loss: 2.5692989196777343
Accuracy: 0.5875
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.21189440786838531, Weights: 0.018538566306233406
NC2 Equiangle: Features: 0.43418439229329425, Weights: 0.09139595031738282
NC3 Self-Duality: 0.6328858733177185
NC4 NCC Mismatch: 0.14300000000000002

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018480457365512848
Inter Cos: 0.09352368116378784
Norm Quadratic Average: 27.530664443969727
Nearest Class Center Accuracy: 0.2895

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022901110351085663
Inter Cos: 0.08840139210224152
Norm Quadratic Average: 85.40289306640625
Nearest Class Center Accuracy: 0.3715

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0257667638361454
Inter Cos: 0.08211755752563477
Norm Quadratic Average: 63.680458068847656
Nearest Class Center Accuracy: 0.4045

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022091463208198547
Inter Cos: 0.05846002325415611
Norm Quadratic Average: 67.76313018798828
Nearest Class Center Accuracy: 0.4475

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.027452846989035606
Inter Cos: 0.06887291371822357
Norm Quadratic Average: 42.549232482910156
Nearest Class Center Accuracy: 0.4575

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.027065927162766457
Inter Cos: 0.06311435997486115
Norm Quadratic Average: 43.68207931518555
Nearest Class Center Accuracy: 0.496

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03103734366595745
Inter Cos: 0.07002099603414536
Norm Quadratic Average: 27.74494171142578
Nearest Class Center Accuracy: 0.5045

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03384559974074364
Inter Cos: 0.07441062480211258
Norm Quadratic Average: 19.674997329711914
Nearest Class Center Accuracy: 0.576

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 98.0356216430664
Linear Weight Rank: 4031
Intra Cos: 0.056455787271261215
Inter Cos: 0.10665754228830338
Norm Quadratic Average: 103.22701263427734
Nearest Class Center Accuracy: 0.615

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 39.63319396972656
Linear Weight Rank: 3670
Intra Cos: 0.11351444572210312
Inter Cos: 0.2024078667163849
Norm Quadratic Average: 50.803977966308594
Nearest Class Center Accuracy: 0.5855

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.475038528442383
Linear Weight Rank: 10
Intra Cos: 0.1793905794620514
Inter Cos: 0.3188222348690033
Norm Quadratic Average: 33.72418212890625
Nearest Class Center Accuracy: 0.577

Output Layer:
Intra Cos: 0.26938140392303467
Inter Cos: 0.4999677538871765
Norm Quadratic Average: 22.42148208618164
Nearest Class Center Accuracy: 0.565

