Model save path: /content/drive/MyDrive/Neural Collapse/New_Models/bn_False_dataset_MNIST_epochs_100_lr_0.001_model_type_vgg11_weight_decay_0.0003.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: 23.532936096191406
Nearest Class Center Accuracy: 0.81175

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1113550066947937
Inter Cos: 0.13135114312171936
Norm Quadratic Average: 46.72207260131836
Nearest Class Center Accuracy: 0.823375

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.15167440474033356
Inter Cos: 0.16577385365962982
Norm Quadratic Average: 45.584842681884766
Nearest Class Center Accuracy: 0.810625

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.16994601488113403
Inter Cos: 0.17875131964683533
Norm Quadratic Average: 60.20793151855469
Nearest Class Center Accuracy: 0.823

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.19165067374706268
Inter Cos: 0.17429418861865997
Norm Quadratic Average: 40.8900032043457
Nearest Class Center Accuracy: 0.858375

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.21742628514766693
Inter Cos: 0.18886320292949677
Norm Quadratic Average: 40.013057708740234
Nearest Class Center Accuracy: 0.897625

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.2815755605697632
Inter Cos: 0.15696832537651062
Norm Quadratic Average: 23.772939682006836
Nearest Class Center Accuracy: 0.936875

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.4011910557746887
Inter Cos: 0.1759207397699356
Norm Quadratic Average: 18.778362274169922
Nearest Class Center Accuracy: 0.9755

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 99.92831420898438
Linear Weight Rank: 4031
Intra Cos: 0.6274899244308472
Inter Cos: 0.21040882170200348
Norm Quadratic Average: 81.80662536621094
Nearest Class Center Accuracy: 0.997625

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.39938735961914
Linear Weight Rank: 3671
Intra Cos: 0.7326773405075073
Inter Cos: 0.22095714509487152
Norm Quadratic Average: 52.163734436035156
Nearest Class Center Accuracy: 0.9995

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.4921534061431885
Linear Weight Rank: 10
Intra Cos: 0.7882065773010254
Inter Cos: 0.23496809601783752
Norm Quadratic Average: 40.14088439941406
Nearest Class Center Accuracy: 0.999875

Output Layer:
Intra Cos: 0.8505032062530518
Inter Cos: 0.34205853939056396
Norm Quadratic Average: 28.44580078125
Nearest Class Center Accuracy: 1.0

Test Set:
Average Loss: 0.08810179927945137
Accuracy: 0.9805
NC1 Within Class Collapse: 1.7194777727127075
NC2 Equinorm: Features: 0.09882959723472595, Weights: 0.017525438219308853
NC2 Equiangle: Features: 0.24316847059461805, Weights: 0.09108393987019857
NC3 Self-Duality: 0.55220627784729
NC4 NCC Mismatch: 0.015499999999999958

Layer 0: Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.10133806616067886
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: 0.1326708197593689
Inter Cos: 0.14780347049236298
Norm Quadratic Average: 45.54482650756836
Nearest Class Center Accuracy: 0.819

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.16805021464824677
Inter Cos: 0.19814404845237732
Norm Quadratic Average: 44.37800216674805
Nearest Class Center Accuracy: 0.8075

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.17565734684467316
Inter Cos: 0.21805983781814575
Norm Quadratic Average: 58.5609016418457
Nearest Class Center Accuracy: 0.8265

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.17116650938987732
Inter Cos: 0.20814667642116547
Norm Quadratic Average: 39.959327697753906
Nearest Class Center Accuracy: 0.857

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1908087283372879
Inter Cos: 0.2221316397190094
Norm Quadratic Average: 39.13776779174805
Nearest Class Center Accuracy: 0.8875

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.24787719547748566
Inter Cos: 0.1841099113225937
Norm Quadratic Average: 23.14458656311035
Nearest Class Center Accuracy: 0.933

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.35178351402282715
Inter Cos: 0.20815587043762207
Norm Quadratic Average: 18.165681838989258
Nearest Class Center Accuracy: 0.9585

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 99.92831420898438
Linear Weight Rank: 4031
Intra Cos: 0.5577571392059326
Inter Cos: 0.23068203032016754
Norm Quadratic Average: 78.5511474609375
Nearest Class Center Accuracy: 0.973

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.39938735961914
Linear Weight Rank: 3671
Intra Cos: 0.658381998538971
Inter Cos: 0.23881353437900543
Norm Quadratic Average: 50.0073356628418
Nearest Class Center Accuracy: 0.977

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.4921534061431885
Linear Weight Rank: 10
Intra Cos: 0.7087201476097107
Inter Cos: 0.24687109887599945
Norm Quadratic Average: 38.48715591430664
Nearest Class Center Accuracy: 0.976

Output Layer:
Intra Cos: 0.7631216049194336
Inter Cos: 0.3378061354160309
Norm Quadratic Average: 27.26206398010254
Nearest Class Center Accuracy: 0.9735

