Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_200_lr_0.01_model_type_vgg11_rand_seed_314159_test_samples_None_train_samples_None_weight_decay_0.02.pth.tar
Training Set:
Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01989092119038105
Inter Cos: 0.10477276146411896
Norm Quadratic Average: 27.59718132019043
Nearest Class Center Accuracy: 0.26968

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022208992391824722
Inter Cos: 0.10001654922962189
Norm Quadratic Average: 20.308012008666992
Nearest Class Center Accuracy: 0.36306

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.028082003816962242
Inter Cos: 0.09627658128738403
Norm Quadratic Average: 10.741147994995117
Nearest Class Center Accuracy: 0.45884

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.034025318920612335
Inter Cos: 0.0890205055475235
Norm Quadratic Average: 4.517167568206787
Nearest Class Center Accuracy: 0.56466

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08445822447538376
Inter Cos: 0.1648348569869995
Norm Quadratic Average: 0.8760983347892761
Nearest Class Center Accuracy: 0.60406

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.29802513122558594
Inter Cos: 0.4619499146938324
Norm Quadratic Average: 0.3506093919277191
Nearest Class Center Accuracy: 0.8064

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.5321689248085022
Inter Cos: 0.6126168966293335
Norm Quadratic Average: 0.4881080687046051
Nearest Class Center Accuracy: 0.91772

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.7333187460899353
Inter Cos: 0.6812817454338074
Norm Quadratic Average: 0.9688321948051453
Nearest Class Center Accuracy: 0.96396

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 2.641061544418335
Linear Weight Rank: 4
Intra Cos: 0.8204720616340637
Inter Cos: 0.6597874760627747
Norm Quadratic Average: 9.286264419555664
Nearest Class Center Accuracy: 0.98072

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 2.6418588161468506
Linear Weight Rank: 2638
Intra Cos: 0.836398184299469
Inter Cos: 0.6441918611526489
Norm Quadratic Average: 12.373324394226074
Nearest Class Center Accuracy: 0.98968

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.6406338214874268
Linear Weight Rank: 9
Intra Cos: 0.8479407429695129
Inter Cos: 0.6080000996589661
Norm Quadratic Average: 14.264883041381836
Nearest Class Center Accuracy: 0.99356

Output Layer:
Intra Cos: 0.8659895062446594
Inter Cos: 0.7073086500167847
Norm Quadratic Average: 18.277822494506836
Nearest Class Center Accuracy: 0.99642

Test Set:
Average Loss: 1.0195699861526488
Accuracy: 0.7255
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.2791215181350708, Weights: 0.08819010853767395
NC2 Equiangle: Features: 0.4308454725477431, Weights: 0.3119073232014974
NC3 Self-Duality: 0.23041334748268127
NC4 NCC Mismatch: 0.0796

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018526073545217514
Inter Cos: 0.1042705774307251
Norm Quadratic Average: 27.550134658813477
Nearest Class Center Accuracy: 0.284

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022503135725855827
Inter Cos: 0.100982166826725
Norm Quadratic Average: 20.300378799438477
Nearest Class Center Accuracy: 0.3788

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.027002494782209396
Inter Cos: 0.09801799803972244
Norm Quadratic Average: 10.753503799438477
Nearest Class Center Accuracy: 0.4673

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.032217495143413544
Inter Cos: 0.08962668478488922
Norm Quadratic Average: 4.527495861053467
Nearest Class Center Accuracy: 0.5667

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07831907272338867
Inter Cos: 0.16637010872364044
Norm Quadratic Average: 0.8777340054512024
Nearest Class Center Accuracy: 0.5893

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.24723659455776215
Inter Cos: 0.44657325744628906
Norm Quadratic Average: 0.34933996200561523
Nearest Class Center Accuracy: 0.6776

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.3723160922527313
Inter Cos: 0.5799650549888611
Norm Quadratic Average: 0.48217126727104187
Nearest Class Center Accuracy: 0.7052

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.46958956122398376
Inter Cos: 0.6419672966003418
Norm Quadratic Average: 0.9515184760093689
Nearest Class Center Accuracy: 0.7193

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 2.641061544418335
Linear Weight Rank: 4
Intra Cos: 0.49983447790145874
Inter Cos: 0.6275318264961243
Norm Quadratic Average: 9.07381534576416
Nearest Class Center Accuracy: 0.7218

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 2.6418588161468506
Linear Weight Rank: 2638
Intra Cos: 0.48311111330986023
Inter Cos: 0.5977053642272949
Norm Quadratic Average: 12.042739868164062
Nearest Class Center Accuracy: 0.7232

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.6406338214874268
Linear Weight Rank: 9
Intra Cos: 0.4717056453227997
Inter Cos: 0.5210745930671692
Norm Quadratic Average: 13.833392143249512
Nearest Class Center Accuracy: 0.7194

Output Layer:
Intra Cos: 0.44239863753318787
Inter Cos: 0.533469021320343
Norm Quadratic Average: 17.652511596679688
Nearest Class Center Accuracy: 0.715

