Model save path: /content/drive/MyDrive/Neural Collapse/New_Models/bn_False_dataset_MNIST_epochs_100_lr_0.001_model_type_vgg11_weight_decay_0.0001.pth.tar
Training Set:
Layer 0: Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08946067094802856
Inter Cos: 0.11311887204647064
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: 0.10864623636007309
Inter Cos: 0.1321992576122284
Norm Quadratic Average: 48.2043342590332
Nearest Class Center Accuracy: 0.825375

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.15162619948387146
Inter Cos: 0.16897544264793396
Norm Quadratic Average: 47.94678497314453
Nearest Class Center Accuracy: 0.806125

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.17044547200202942
Inter Cos: 0.17746607959270477
Norm Quadratic Average: 63.775062561035156
Nearest Class Center Accuracy: 0.819375

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.18830329179763794
Inter Cos: 0.17042754590511322
Norm Quadratic Average: 41.85041809082031
Nearest Class Center Accuracy: 0.859125

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.21406470239162445
Inter Cos: 0.1870175451040268
Norm Quadratic Average: 41.948089599609375
Nearest Class Center Accuracy: 0.894375

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.27343082427978516
Inter Cos: 0.17551717162132263
Norm Quadratic Average: 25.145551681518555
Nearest Class Center Accuracy: 0.938875

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.38935551047325134
Inter Cos: 0.1957753598690033
Norm Quadratic Average: 19.774518966674805
Nearest Class Center Accuracy: 0.978375

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 100.8970718383789
Linear Weight Rank: 4031
Intra Cos: 0.617323637008667
Inter Cos: 0.25270313024520874
Norm Quadratic Average: 86.25323486328125
Nearest Class Center Accuracy: 0.997875

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.788570404052734
Linear Weight Rank: 3670
Intra Cos: 0.7262334227561951
Inter Cos: 0.26349353790283203
Norm Quadratic Average: 54.806724548339844
Nearest Class Center Accuracy: 0.9995

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.5005035400390625
Linear Weight Rank: 10
Intra Cos: 0.7751530408859253
Inter Cos: 0.25662851333618164
Norm Quadratic Average: 41.82445526123047
Nearest Class Center Accuracy: 0.999875

Output Layer:
Intra Cos: 0.8283212184906006
Inter Cos: 0.3266879618167877
Norm Quadratic Average: 29.537185668945312
Nearest Class Center Accuracy: 0.999875

Test Set:
Average Loss: 0.09155710668861866
Accuracy: 0.9815
NC1 Within Class Collapse: 1.7371437549591064
NC2 Equinorm: Features: 0.1006871834397316, Weights: 0.014654168859124184
NC2 Equiangle: Features: 0.23554602728949653, Weights: 0.09253762563069662
NC3 Self-Duality: 0.5550020933151245
NC4 NCC Mismatch: 0.01200000000000001

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: 0.1325795203447342
Inter Cos: 0.1441495418548584
Norm Quadratic Average: 47.06093215942383
Nearest Class Center Accuracy: 0.817

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.17281976342201233
Inter Cos: 0.19756264984607697
Norm Quadratic Average: 46.71956253051758
Nearest Class Center Accuracy: 0.802

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.17986387014389038
Inter Cos: 0.21656320989131927
Norm Quadratic Average: 62.05441665649414
Nearest Class Center Accuracy: 0.817

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.17262998223304749
Inter Cos: 0.2030385285615921
Norm Quadratic Average: 40.76655578613281
Nearest Class Center Accuracy: 0.849

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1934821754693985
Inter Cos: 0.21898648142814636
Norm Quadratic Average: 40.95133972167969
Nearest Class Center Accuracy: 0.888

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.25048208236694336
Inter Cos: 0.20510223507881165
Norm Quadratic Average: 24.5666446685791
Nearest Class Center Accuracy: 0.933

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.3531219959259033
Inter Cos: 0.23455139994621277
Norm Quadratic Average: 19.182708740234375
Nearest Class Center Accuracy: 0.9565

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 100.8970718383789
Linear Weight Rank: 4031
Intra Cos: 0.5597283840179443
Inter Cos: 0.27155768871307373
Norm Quadratic Average: 82.93860626220703
Nearest Class Center Accuracy: 0.974

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.788570404052734
Linear Weight Rank: 3670
Intra Cos: 0.6621284484863281
Inter Cos: 0.2582457661628723
Norm Quadratic Average: 52.51801300048828
Nearest Class Center Accuracy: 0.9765

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.5005035400390625
Linear Weight Rank: 10
Intra Cos: 0.7064623236656189
Inter Cos: 0.24386778473854065
Norm Quadratic Average: 40.08143997192383
Nearest Class Center Accuracy: 0.977

Output Layer:
Intra Cos: 0.751990020275116
Inter Cos: 0.3207930326461792
Norm Quadratic Average: 28.279525756835938
Nearest Class Center Accuracy: 0.975

