Model save path: /content/drive/MyDrive/Neural Collapse/New_Models/bn_False_dataset_MNIST_epochs_50_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: 0.08946063369512558
Inter Cos: 0.11311884224414825
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: 0.11233128607273102
Inter Cos: 0.14136846363544464
Norm Quadratic Average: 43.2480354309082
Nearest Class Center Accuracy: 0.81625

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1508956253528595
Inter Cos: 0.17533473670482635
Norm Quadratic Average: 42.82858657836914
Nearest Class Center Accuracy: 0.79425

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.16905850172042847
Inter Cos: 0.19316783547401428
Norm Quadratic Average: 53.67287826538086
Nearest Class Center Accuracy: 0.80375

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.20129816234111786
Inter Cos: 0.1943349987268448
Norm Quadratic Average: 33.5804557800293
Nearest Class Center Accuracy: 0.83975

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.23406676948070526
Inter Cos: 0.20754759013652802
Norm Quadratic Average: 28.22188377380371
Nearest Class Center Accuracy: 0.888

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.32012149691581726
Inter Cos: 0.22048886120319366
Norm Quadratic Average: 15.222122192382812
Nearest Class Center Accuracy: 0.935125

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.45285719633102417
Inter Cos: 0.2543550729751587
Norm Quadratic Average: 10.582869529724121
Nearest Class Center Accuracy: 0.973875

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 74.40991973876953
Linear Weight Rank: 4031
Intra Cos: 0.6522619128227234
Inter Cos: 0.2858286201953888
Norm Quadratic Average: 45.73259353637695
Nearest Class Center Accuracy: 0.992

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 30.099992752075195
Linear Weight Rank: 3670
Intra Cos: 0.7387584447860718
Inter Cos: 0.293803334236145
Norm Quadratic Average: 29.24268341064453
Nearest Class Center Accuracy: 0.995125

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.1697099208831787
Linear Weight Rank: 10
Intra Cos: 0.7698784470558167
Inter Cos: 0.2708489000797272
Norm Quadratic Average: 22.174285888671875
Nearest Class Center Accuracy: 0.995625

Output Layer:
Intra Cos: 0.8000608682632446
Inter Cos: 0.3508857488632202
Norm Quadratic Average: 15.859231948852539
Nearest Class Center Accuracy: 0.9935

Test Set:
Average Loss: 0.07654666841030121
Accuracy: 0.9755
NC1 Within Class Collapse: 2.1799888610839844
NC2 Equinorm: Features: 0.1173994243144989, Weights: 0.020051296800374985
NC2 Equiangle: Features: 0.272103754679362, Weights: 0.10361957550048828
NC3 Self-Duality: 0.40818655490875244
NC4 NCC Mismatch: 0.013000000000000012

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: 0.13760827481746674
Inter Cos: 0.155544713139534
Norm Quadratic Average: 41.94833755493164
Nearest Class Center Accuracy: 0.8115

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1707238256931305
Inter Cos: 0.2075834572315216
Norm Quadratic Average: 41.5853157043457
Nearest Class Center Accuracy: 0.795

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.18403427302837372
Inter Cos: 0.23051396012306213
Norm Quadratic Average: 52.09003448486328
Nearest Class Center Accuracy: 0.801

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1823148876428604
Inter Cos: 0.23274968564510345
Norm Quadratic Average: 32.7626838684082
Nearest Class Center Accuracy: 0.843

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.2129100263118744
Inter Cos: 0.24277569353580475
Norm Quadratic Average: 27.5621337890625
Nearest Class Center Accuracy: 0.8785

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.291998952627182
Inter Cos: 0.2067936807870865
Norm Quadratic Average: 14.82983112335205
Nearest Class Center Accuracy: 0.928

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.40788695216178894
Inter Cos: 0.24107125401496887
Norm Quadratic Average: 10.260522842407227
Nearest Class Center Accuracy: 0.951

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 74.40991973876953
Linear Weight Rank: 4031
Intra Cos: 0.5875170826911926
Inter Cos: 0.2757261097431183
Norm Quadratic Average: 44.14785385131836
Nearest Class Center Accuracy: 0.9675

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 30.099992752075195
Linear Weight Rank: 3670
Intra Cos: 0.6661608219146729
Inter Cos: 0.2809697389602661
Norm Quadratic Average: 28.19121742248535
Nearest Class Center Accuracy: 0.9735

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.1697099208831787
Linear Weight Rank: 10
Intra Cos: 0.6927873492240906
Inter Cos: 0.2952800393104553
Norm Quadratic Average: 21.40828514099121
Nearest Class Center Accuracy: 0.9725

Output Layer:
Intra Cos: 0.7113717794418335
Inter Cos: 0.38090088963508606
Norm Quadratic Average: 15.298755645751953
Nearest Class Center Accuracy: 0.968

