Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_200_lr_0.01_model_type_vgg11_rand_seed_979323_test_samples_None_train_samples_None_weight_decay_0.03.pth.tar
Training Set:
Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0198909230530262
Inter Cos: 0.10477276891469955
Norm Quadratic Average: 27.59718894958496
Nearest Class Center Accuracy: 0.26968

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02583959512412548
Inter Cos: 0.09916412085294724
Norm Quadratic Average: 12.665751457214355
Nearest Class Center Accuracy: 0.36582

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03931683674454689
Inter Cos: 0.10695323348045349
Norm Quadratic Average: 3.637268543243408
Nearest Class Center Accuracy: 0.46058

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08201063424348831
Inter Cos: 0.1717749536037445
Norm Quadratic Average: 1.1586965322494507
Nearest Class Center Accuracy: 0.55084

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1443907767534256
Inter Cos: 0.3967897593975067
Norm Quadratic Average: 0.5682014226913452
Nearest Class Center Accuracy: 0.5796

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.16575519740581512
Inter Cos: 0.5239229202270508
Norm Quadratic Average: 0.713748037815094
Nearest Class Center Accuracy: 0.60666

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.16789749264717102
Inter Cos: 0.5755969285964966
Norm Quadratic Average: 1.0883944034576416
Nearest Class Center Accuracy: 0.58548

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1907733529806137
Inter Cos: 0.6296868920326233
Norm Quadratic Average: 1.7588433027267456
Nearest Class Center Accuracy: 0.58956

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 1.7467045783996582
Linear Weight Rank: 3
Intra Cos: 0.20556148886680603
Inter Cos: 0.6440305709838867
Norm Quadratic Average: 12.237845420837402
Nearest Class Center Accuracy: 0.59638

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 1.7604364156723022
Linear Weight Rank: 2481
Intra Cos: 0.22246386110782623
Inter Cos: 0.6604263186454773
Norm Quadratic Average: 12.041389465332031
Nearest Class Center Accuracy: 0.60436

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 1.7811359167099
Linear Weight Rank: 9
Intra Cos: 0.30199894309043884
Inter Cos: 0.6646106243133545
Norm Quadratic Average: 9.977999687194824
Nearest Class Center Accuracy: 0.6221

Output Layer:
Intra Cos: 0.4076097309589386
Inter Cos: 0.7707796096801758
Norm Quadratic Average: 8.710433006286621
Nearest Class Center Accuracy: 0.61416

Test Set:
Average Loss: 1.0817477005004883
Accuracy: 0.6015
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.2718011736869812, Weights: 0.1724332869052887
NC2 Equiangle: Features: 0.5792426639133029, Weights: 0.3061103185017904
NC3 Self-Duality: 0.29207420349121094
NC4 NCC Mismatch: 0.22450000000000003

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018526077270507812
Inter Cos: 0.1042705774307251
Norm Quadratic Average: 27.550140380859375
Nearest Class Center Accuracy: 0.284

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02328561805188656
Inter Cos: 0.09982255101203918
Norm Quadratic Average: 12.663015365600586
Nearest Class Center Accuracy: 0.3777

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03833620250225067
Inter Cos: 0.10845942795276642
Norm Quadratic Average: 3.6384520530700684
Nearest Class Center Accuracy: 0.4649

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07905008643865585
Inter Cos: 0.17338939011096954
Norm Quadratic Average: 1.1593103408813477
Nearest Class Center Accuracy: 0.5542

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.14484815299510956
Inter Cos: 0.39418041706085205
Norm Quadratic Average: 0.568260133266449
Nearest Class Center Accuracy: 0.568

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.16824528574943542
Inter Cos: 0.5177196860313416
Norm Quadratic Average: 0.7134957909584045
Nearest Class Center Accuracy: 0.5833

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.17074726521968842
Inter Cos: 0.5525676608085632
Norm Quadratic Average: 1.0864442586898804
Nearest Class Center Accuracy: 0.5617

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.19409988820552826
Inter Cos: 0.6195119619369507
Norm Quadratic Average: 1.7545064687728882
Nearest Class Center Accuracy: 0.5668

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 1.7467045783996582
Linear Weight Rank: 3
Intra Cos: 0.2083400934934616
Inter Cos: 0.6324979066848755
Norm Quadratic Average: 12.211543083190918
Nearest Class Center Accuracy: 0.5751

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 1.7604364156723022
Linear Weight Rank: 2481
Intra Cos: 0.22407151758670807
Inter Cos: 0.6470752954483032
Norm Quadratic Average: 12.01913833618164
Nearest Class Center Accuracy: 0.5819

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 1.7811359167099
Linear Weight Rank: 9
Intra Cos: 0.2921474874019623
Inter Cos: 0.6484572291374207
Norm Quadratic Average: 9.971259117126465
Nearest Class Center Accuracy: 0.5982

Output Layer:
Intra Cos: 0.3987046778202057
Inter Cos: 0.7572188377380371
Norm Quadratic Average: 8.707796096801758
Nearest Class Center Accuracy: 0.5876

