Model save path: /content/drive/MyDrive/Neural Collapse/New_Models/bn_False_dataset_MNIST_epochs_100_lr_0.001_model_type_vgg11_weight_decay_0.01.pth.tar
Training Set:
Layer 0: Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08946064859628677
Inter Cos: 0.11311884224414825
Norm Quadratic Average: 23.532936096191406
Nearest Class Center Accuracy: 0.81175

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.11656802147626877
Inter Cos: 0.13770824670791626
Norm Quadratic Average: 41.4545783996582
Nearest Class Center Accuracy: 0.8165

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.156852588057518
Inter Cos: 0.17949117720127106
Norm Quadratic Average: 43.38880157470703
Nearest Class Center Accuracy: 0.797625

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1764109879732132
Inter Cos: 0.199202299118042
Norm Quadratic Average: 54.597679138183594
Nearest Class Center Accuracy: 0.7985

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.19397389888763428
Inter Cos: 0.2108139991760254
Norm Quadratic Average: 33.348140716552734
Nearest Class Center Accuracy: 0.834875

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.23154422640800476
Inter Cos: 0.22849594056606293
Norm Quadratic Average: 25.72895050048828
Nearest Class Center Accuracy: 0.879125

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.31939372420310974
Inter Cos: 0.2162541151046753
Norm Quadratic Average: 13.064798355102539
Nearest Class Center Accuracy: 0.931

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.4773261845111847
Inter Cos: 0.26359647512435913
Norm Quadratic Average: 8.675922393798828
Nearest Class Center Accuracy: 0.972

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 62.743980407714844
Linear Weight Rank: 4031
Intra Cos: 0.6575329899787903
Inter Cos: 0.27831506729125977
Norm Quadratic Average: 39.23447036743164
Nearest Class Center Accuracy: 0.992875

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 25.40492057800293
Linear Weight Rank: 3670
Intra Cos: 0.7351388931274414
Inter Cos: 0.2914995551109314
Norm Quadratic Average: 26.848920822143555
Nearest Class Center Accuracy: 0.996

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.146822452545166
Linear Weight Rank: 10
Intra Cos: 0.7562915086746216
Inter Cos: 0.2908044457435608
Norm Quadratic Average: 21.27326011657715
Nearest Class Center Accuracy: 0.9955

Output Layer:
Intra Cos: 0.7761306762695312
Inter Cos: 0.3110232353210449
Norm Quadratic Average: 16.232086181640625
Nearest Class Center Accuracy: 0.993625

Test Set:
Average Loss: 0.06915821623802185
Accuracy: 0.9775
NC1 Within Class Collapse: 2.5262508392333984
NC2 Equinorm: Features: 0.12886984646320343, Weights: 0.02169693633913994
NC2 Equiangle: Features: 0.2780534108479818, Weights: 0.12251018948025173
NC3 Self-Duality: 0.3213025629520416
NC4 NCC Mismatch: 0.013499999999999956

Layer 0: Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.10133810341358185
Inter Cos: 0.11957791447639465
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: 0.13834179937839508
Inter Cos: 0.1569552719593048
Norm Quadratic Average: 40.33776092529297
Nearest Class Center Accuracy: 0.8095

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.17107799649238586
Inter Cos: 0.21509471535682678
Norm Quadratic Average: 42.20904541015625
Nearest Class Center Accuracy: 0.802

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.18713068962097168
Inter Cos: 0.2416325956583023
Norm Quadratic Average: 53.017982482910156
Nearest Class Center Accuracy: 0.8005

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1773863434791565
Inter Cos: 0.2444698065519333
Norm Quadratic Average: 32.46965789794922
Nearest Class Center Accuracy: 0.8405

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.21032041311264038
Inter Cos: 0.26377034187316895
Norm Quadratic Average: 25.126169204711914
Nearest Class Center Accuracy: 0.872

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.28834593296051025
Inter Cos: 0.23033840954303741
Norm Quadratic Average: 12.730077743530273
Nearest Class Center Accuracy: 0.9245

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.4285016655921936
Inter Cos: 0.2729777693748474
Norm Quadratic Average: 8.417437553405762
Nearest Class Center Accuracy: 0.953

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 62.743980407714844
Linear Weight Rank: 4031
Intra Cos: 0.5932226777076721
Inter Cos: 0.3048512637615204
Norm Quadratic Average: 37.897560119628906
Nearest Class Center Accuracy: 0.9705

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 25.40492057800293
Linear Weight Rank: 3670
Intra Cos: 0.6632891297340393
Inter Cos: 0.2873276174068451
Norm Quadratic Average: 25.884733200073242
Nearest Class Center Accuracy: 0.9755

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.146822452545166
Linear Weight Rank: 10
Intra Cos: 0.6796743273735046
Inter Cos: 0.28369399905204773
Norm Quadratic Average: 20.517948150634766
Nearest Class Center Accuracy: 0.9765

Output Layer:
Intra Cos: 0.6902980208396912
Inter Cos: 0.3284308612346649
Norm Quadratic Average: 15.635140419006348
Nearest Class Center Accuracy: 0.972

