Model save path: /content/drive/MyDrive/Neural Collapse/New_Models/bn_False_dataset_MNIST_epochs_100_lr_0.001_model_type_vgg11_weight_decay_0.003.pth.tar
Training Set:
Layer 0: Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08946064114570618
Inter Cos: 0.11311884969472885
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: 0.11241859942674637
Inter Cos: 0.13289521634578705
Norm Quadratic Average: 43.86383056640625
Nearest Class Center Accuracy: 0.82125

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1522773802280426
Inter Cos: 0.16921566426753998
Norm Quadratic Average: 42.945335388183594
Nearest Class Center Accuracy: 0.8045

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1688976287841797
Inter Cos: 0.18319375813007355
Norm Quadratic Average: 55.07512664794922
Nearest Class Center Accuracy: 0.811

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.19349415600299835
Inter Cos: 0.18364739418029785
Norm Quadratic Average: 36.03444290161133
Nearest Class Center Accuracy: 0.84475

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.22139425575733185
Inter Cos: 0.1980639100074768
Norm Quadratic Average: 32.48286819458008
Nearest Class Center Accuracy: 0.88675

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.2843427360057831
Inter Cos: 0.16556140780448914
Norm Quadratic Average: 18.264577865600586
Nearest Class Center Accuracy: 0.929125

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.41652724146842957
Inter Cos: 0.19507946074008942
Norm Quadratic Average: 13.468525886535645
Nearest Class Center Accuracy: 0.972

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 87.79183197021484
Linear Weight Rank: 4031
Intra Cos: 0.643176794052124
Inter Cos: 0.22956308722496033
Norm Quadratic Average: 58.19240188598633
Nearest Class Center Accuracy: 0.996875

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 35.500728607177734
Linear Weight Rank: 3671
Intra Cos: 0.7487934231758118
Inter Cos: 0.24129553139209747
Norm Quadratic Average: 37.61408996582031
Nearest Class Center Accuracy: 0.999375

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.33762526512146
Linear Weight Rank: 10
Intra Cos: 0.796761691570282
Inter Cos: 0.2623210847377777
Norm Quadratic Average: 29.25040626525879
Nearest Class Center Accuracy: 0.9995

Output Layer:
Intra Cos: 0.8462381958961487
Inter Cos: 0.36493319272994995
Norm Quadratic Average: 20.95787239074707
Nearest Class Center Accuracy: 0.999625

Test Set:
Average Loss: 0.0732354945242405
Accuracy: 0.9795
NC1 Within Class Collapse: 1.8851697444915771
NC2 Equinorm: Features: 0.09945913404226303, Weights: 0.02007382921874523
NC2 Equiangle: Features: 0.2573762469821506, Weights: 0.09559810426500108
NC3 Self-Duality: 0.47237148880958557
NC4 NCC Mismatch: 0.016000000000000014

Layer 0: Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.10133810341358185
Inter Cos: 0.11957792192697525
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: 0.1339341253042221
Inter Cos: 0.14995430409908295
Norm Quadratic Average: 42.71942138671875
Nearest Class Center Accuracy: 0.8175

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.16921979188919067
Inter Cos: 0.20214541256427765
Norm Quadratic Average: 41.7727165222168
Nearest Class Center Accuracy: 0.8025

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1798495054244995
Inter Cos: 0.22550009191036224
Norm Quadratic Average: 53.52021026611328
Nearest Class Center Accuracy: 0.8105

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.17340904474258423
Inter Cos: 0.21928457915782928
Norm Quadratic Average: 35.1849479675293
Nearest Class Center Accuracy: 0.844

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.19634538888931274
Inter Cos: 0.23384220898151398
Norm Quadratic Average: 31.76712417602539
Nearest Class Center Accuracy: 0.8795

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.25353536009788513
Inter Cos: 0.19895613193511963
Norm Quadratic Average: 17.771793365478516
Nearest Class Center Accuracy: 0.927

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.37122049927711487
Inter Cos: 0.22881899774074554
Norm Quadratic Average: 13.02241039276123
Nearest Class Center Accuracy: 0.955

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 87.79183197021484
Linear Weight Rank: 4031
Intra Cos: 0.5778700113296509
Inter Cos: 0.25069501996040344
Norm Quadratic Average: 55.9387092590332
Nearest Class Center Accuracy: 0.971

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 35.500728607177734
Linear Weight Rank: 3671
Intra Cos: 0.6775265336036682
Inter Cos: 0.2519248425960541
Norm Quadratic Average: 36.087921142578125
Nearest Class Center Accuracy: 0.9745

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.33762526512146
Linear Weight Rank: 10
Intra Cos: 0.7200156450271606
Inter Cos: 0.2652490735054016
Norm Quadratic Average: 28.079986572265625
Nearest Class Center Accuracy: 0.976

Output Layer:
Intra Cos: 0.7618741393089294
Inter Cos: 0.35810133814811707
Norm Quadratic Average: 20.10204315185547
Nearest Class Center Accuracy: 0.973

