Model save path: /content/drive/MyDrive/Neural Collapse/New_Models/bn_True_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: 0.09998954832553864
Inter Cos: 0.12351606786251068
Norm Quadratic Average: 81.64115905761719
Nearest Class Center Accuracy: 0.831

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.14000608026981354
Inter Cos: 0.13136500120162964
Norm Quadratic Average: 55.59880828857422
Nearest Class Center Accuracy: 0.848375

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.14248573780059814
Inter Cos: 0.12057354301214218
Norm Quadratic Average: 56.184959411621094
Nearest Class Center Accuracy: 0.8695

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1641211062669754
Inter Cos: 0.10934299230575562
Norm Quadratic Average: 34.14990997314453
Nearest Class Center Accuracy: 0.9005

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.16689249873161316
Inter Cos: 0.09076438099145889
Norm Quadratic Average: 34.89677810668945
Nearest Class Center Accuracy: 0.927875

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.20076830685138702
Inter Cos: 0.08909095823764801
Norm Quadratic Average: 23.983901977539062
Nearest Class Center Accuracy: 0.968875

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.28409186005592346
Inter Cos: 0.09795170277357101
Norm Quadratic Average: 18.437023162841797
Nearest Class Center Accuracy: 0.99525

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 98.96048736572266
Linear Weight Rank: 4031
Intra Cos: 0.47313717007637024
Inter Cos: 0.12442588806152344
Norm Quadratic Average: 116.47935485839844
Nearest Class Center Accuracy: 1.0

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.00766372680664
Linear Weight Rank: 3670
Intra Cos: 0.618665337562561
Inter Cos: 0.15051089227199554
Norm Quadratic Average: 62.43072509765625
Nearest Class Center Accuracy: 1.0

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.2626843452453613
Linear Weight Rank: 10
Intra Cos: 0.7436127066612244
Inter Cos: 0.18057125806808472
Norm Quadratic Average: 39.34907150268555
Nearest Class Center Accuracy: 1.0

Output Layer:
Intra Cos: 0.9015394449234009
Inter Cos: 0.2528614401817322
Norm Quadratic Average: 20.918197631835938
Nearest Class Center Accuracy: 1.0

Test Set:
Average Loss: 0.0967845066189766
Accuracy: 0.9755
NC1 Within Class Collapse: 1.7138808965682983
NC2 Equinorm: Features: 0.058844972401857376, Weights: 0.009077896364033222
NC2 Equiangle: Features: 0.18529593149820964, Weights: 0.08955667283799913
NC3 Self-Duality: 0.6293503642082214
NC4 NCC Mismatch: 0.009000000000000008

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: 0.12308482080698013
Inter Cos: 0.12876760959625244
Norm Quadratic Average: 80.1265869140625
Nearest Class Center Accuracy: 0.8245

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.14476555585861206
Inter Cos: 0.15238304436206818
Norm Quadratic Average: 54.858909606933594
Nearest Class Center Accuracy: 0.8455

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.14374692738056183
Inter Cos: 0.1409759819507599
Norm Quadratic Average: 55.51798629760742
Nearest Class Center Accuracy: 0.864

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.15947329998016357
Inter Cos: 0.13132862746715546
Norm Quadratic Average: 33.94940948486328
Nearest Class Center Accuracy: 0.8945

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1609581857919693
Inter Cos: 0.1089174672961235
Norm Quadratic Average: 34.74247741699219
Nearest Class Center Accuracy: 0.918

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.19033509492874146
Inter Cos: 0.10482273995876312
Norm Quadratic Average: 23.9198055267334
Nearest Class Center Accuracy: 0.9445

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.24971173703670502
Inter Cos: 0.10382117331027985
Norm Quadratic Average: 18.311771392822266
Nearest Class Center Accuracy: 0.97

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 98.96048736572266
Linear Weight Rank: 4031
Intra Cos: 0.3805493712425232
Inter Cos: 0.12827587127685547
Norm Quadratic Average: 114.23483276367188
Nearest Class Center Accuracy: 0.9765

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.00766372680664
Linear Weight Rank: 3670
Intra Cos: 0.5020963549613953
Inter Cos: 0.15439650416374207
Norm Quadratic Average: 60.8027229309082
Nearest Class Center Accuracy: 0.976

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.2626843452453613
Linear Weight Rank: 10
Intra Cos: 0.6172661781311035
Inter Cos: 0.1859188675880432
Norm Quadratic Average: 38.1652717590332
Nearest Class Center Accuracy: 0.976

Output Layer:
Intra Cos: 0.7740076184272766
Inter Cos: 0.2709752917289734
Norm Quadratic Average: 20.146739959716797
Nearest Class Center Accuracy: 0.974

