Model save path: /content/drive/MyDrive/Neural Collapse/New_Models/bn_False_dataset_MNIST_epochs_100_lr_0.001_model_type_vgg11_weight_decay_0.0005.pth.tar
Training Set:
Layer 0: Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08946066349744797
Inter Cos: 0.11311887949705124
Norm Quadratic Average: 23.53293800354004
Nearest Class Center Accuracy: 0.81175

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1128210723400116
Inter Cos: 0.13891476392745972
Norm Quadratic Average: 50.417457580566406
Nearest Class Center Accuracy: 0.812875

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1483282744884491
Inter Cos: 0.16966752707958221
Norm Quadratic Average: 50.477195739746094
Nearest Class Center Accuracy: 0.7925

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.16214238107204437
Inter Cos: 0.19123074412345886
Norm Quadratic Average: 67.70314025878906
Nearest Class Center Accuracy: 0.800375

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1731671839952469
Inter Cos: 0.18380190432071686
Norm Quadratic Average: 45.18399429321289
Nearest Class Center Accuracy: 0.833625

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1961452066898346
Inter Cos: 0.20886270701885223
Norm Quadratic Average: 42.592071533203125
Nearest Class Center Accuracy: 0.882375

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.263510525226593
Inter Cos: 0.2012418806552887
Norm Quadratic Average: 24.775617599487305
Nearest Class Center Accuracy: 0.927

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.3854105770587921
Inter Cos: 0.2372799962759018
Norm Quadratic Average: 18.831607818603516
Nearest Class Center Accuracy: 0.974

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 98.96589660644531
Linear Weight Rank: 4031
Intra Cos: 0.6193234324455261
Inter Cos: 0.23963716626167297
Norm Quadratic Average: 81.95989990234375
Nearest Class Center Accuracy: 0.998625

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.02022171020508
Linear Weight Rank: 3670
Intra Cos: 0.7315312027931213
Inter Cos: 0.25476697087287903
Norm Quadratic Average: 52.48430633544922
Nearest Class Center Accuracy: 0.999625

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.4765894412994385
Linear Weight Rank: 10
Intra Cos: 0.7857893109321594
Inter Cos: 0.2415025532245636
Norm Quadratic Average: 40.19092559814453
Nearest Class Center Accuracy: 0.99975

Output Layer:
Intra Cos: 0.8381072282791138
Inter Cos: 0.37043675780296326
Norm Quadratic Average: 28.71042251586914
Nearest Class Center Accuracy: 0.999875

Test Set:
Average Loss: 0.09120174986124038
Accuracy: 0.9805
NC1 Within Class Collapse: 1.805631160736084
NC2 Equinorm: Features: 0.10117360949516296, Weights: 0.015268986113369465
NC2 Equiangle: Features: 0.23466512891981336, Weights: 0.0962179183959961
NC3 Self-Duality: 0.5401684045791626
NC4 NCC Mismatch: 0.01100000000000001

Layer 0: Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.10133806616067886
Inter Cos: 0.11957792937755585
Norm Quadratic Average: 22.82430076599121
Nearest Class Center Accuracy: 0.809

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.13557907938957214
Inter Cos: 0.15515094995498657
Norm Quadratic Average: 49.066650390625
Nearest Class Center Accuracy: 0.8085

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.16367821395397186
Inter Cos: 0.2090843915939331
Norm Quadratic Average: 49.125205993652344
Nearest Class Center Accuracy: 0.796

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1708129197359085
Inter Cos: 0.2302655279636383
Norm Quadratic Average: 65.68939208984375
Nearest Class Center Accuracy: 0.804

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.15317098796367645
Inter Cos: 0.21474716067314148
Norm Quadratic Average: 44.05035400390625
Nearest Class Center Accuracy: 0.8325

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.17336627840995789
Inter Cos: 0.23783789575099945
Norm Quadratic Average: 41.58694839477539
Nearest Class Center Accuracy: 0.8735

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.23588880896568298
Inter Cos: 0.22322297096252441
Norm Quadratic Average: 24.137331008911133
Nearest Class Center Accuracy: 0.919

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.3421487510204315
Inter Cos: 0.263793408870697
Norm Quadratic Average: 18.263015747070312
Nearest Class Center Accuracy: 0.9525

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 98.96589660644531
Linear Weight Rank: 4031
Intra Cos: 0.5528687238693237
Inter Cos: 0.2635606825351715
Norm Quadratic Average: 78.69713592529297
Nearest Class Center Accuracy: 0.971

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.02022171020508
Linear Weight Rank: 3670
Intra Cos: 0.6618576049804688
Inter Cos: 0.264335960149765
Norm Quadratic Average: 50.24216842651367
Nearest Class Center Accuracy: 0.9765

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.4765894412994385
Linear Weight Rank: 10
Intra Cos: 0.7129135727882385
Inter Cos: 0.25901684165000916
Norm Quadratic Average: 38.484046936035156
Nearest Class Center Accuracy: 0.9765

Output Layer:
Intra Cos: 0.7593170404434204
Inter Cos: 0.3933965563774109
Norm Quadratic Average: 27.424179077148438
Nearest Class Center Accuracy: 0.9725

