Model save path: ./New_Models/bn_False_dataset_CIFAR100_epochs_200_lr_0.01_model_type_vgg11_rand_seed_979323_test_samples_None_train_samples_None_weight_decay_0.0001.pth.tar
Training Set:
Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022893190383911133
Inter Cos: 0.07011394202709198
Norm Quadratic Average: 29.326318740844727
Nearest Class Center Accuracy: 0.03258

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03262680768966675
Inter Cos: 0.031182564795017242
Norm Quadratic Average: 42.886619567871094
Nearest Class Center Accuracy: 0.0463

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.029962308704853058
Inter Cos: 0.03138251602649689
Norm Quadratic Average: 55.35629653930664
Nearest Class Center Accuracy: 0.0574

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.025262339040637016
Inter Cos: 0.025113247334957123
Norm Quadratic Average: 91.13978576660156
Nearest Class Center Accuracy: 0.06524

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.029805786907672882
Inter Cos: 0.02384944073855877
Norm Quadratic Average: 69.93817901611328
Nearest Class Center Accuracy: 0.07356

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.030151233077049255
Inter Cos: 0.024021785706281662
Norm Quadratic Average: 66.58209991455078
Nearest Class Center Accuracy: 0.0769

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06344152987003326
Inter Cos: 0.038809049874544144
Norm Quadratic Average: 31.77536392211914
Nearest Class Center Accuracy: 0.08798

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.18123027682304382
Inter Cos: 0.09072211384773254
Norm Quadratic Average: 17.40674591064453
Nearest Class Center Accuracy: 0.0965

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 74.55785369873047
Linear Weight Rank: 4031
Intra Cos: 0.5290133953094482
Inter Cos: 0.15899880230426788
Norm Quadratic Average: 61.691932678222656
Nearest Class Center Accuracy: 0.0994

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 33.234825134277344
Linear Weight Rank: 3664
Intra Cos: 0.6919611692428589
Inter Cos: 0.15213459730148315
Norm Quadratic Average: 45.08156204223633
Nearest Class Center Accuracy: 0.09996

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 13.71611213684082
Linear Weight Rank: 98
Intra Cos: 0.7832987308502197
Inter Cos: 0.19873164594173431
Norm Quadratic Average: 39.33096694946289
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.8784388303756714
Inter Cos: 0.40485817193984985
Norm Quadratic Average: 70.85021209716797
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 4.076235408782959
Accuracy: 0.4767
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.37764227390289307, Weights: 0.028538472950458527
NC2 Equiangle: Features: 0.14916459517045455, Weights: 0.09738910713581124
NC3 Self-Duality: 0.6487864851951599
NC4 NCC Mismatch: 0.21140000000000003

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.006621268577873707
Inter Cos: 0.4067547619342804
Norm Quadratic Average: 29.42218780517578
Nearest Class Center Accuracy: 0.1136

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.016121741384267807
Inter Cos: 0.24925246834754944
Norm Quadratic Average: 43.174129486083984
Nearest Class Center Accuracy: 0.2368

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.019223369657993317
Inter Cos: 0.23522642254829407
Norm Quadratic Average: 55.77280807495117
Nearest Class Center Accuracy: 0.3348

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.017175421118736267
Inter Cos: 0.19781532883644104
Norm Quadratic Average: 91.83375549316406
Nearest Class Center Accuracy: 0.4168

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.019663821905851364
Inter Cos: 0.1952700912952423
Norm Quadratic Average: 70.50284576416016
Nearest Class Center Accuracy: 0.4984

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.016468357294797897
Inter Cos: 0.14448979496955872
Norm Quadratic Average: 66.8028335571289
Nearest Class Center Accuracy: 0.5363

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02026650682091713
Inter Cos: 0.1802292913198471
Norm Quadratic Average: 31.212827682495117
Nearest Class Center Accuracy: 0.5365

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.038463614881038666
Inter Cos: 0.27465349435806274
Norm Quadratic Average: 16.196514129638672
Nearest Class Center Accuracy: 0.5126

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 74.55785369873047
Linear Weight Rank: 4031
Intra Cos: 0.09068029373884201
Inter Cos: 0.39312994480133057
Norm Quadratic Average: 51.17854690551758
Nearest Class Center Accuracy: 0.4729

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 33.234825134277344
Linear Weight Rank: 3664
Intra Cos: 0.1149541512131691
Inter Cos: 0.3958098590373993
Norm Quadratic Average: 35.221656799316406
Nearest Class Center Accuracy: 0.4689

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 13.71611213684082
Linear Weight Rank: 98
Intra Cos: 0.12935161590576172
Inter Cos: 0.4695779085159302
Norm Quadratic Average: 30.002302169799805
Nearest Class Center Accuracy: 0.4643

Output Layer:
Intra Cos: 0.12934865057468414
Inter Cos: 0.6725723743438721
Norm Quadratic Average: 53.5902099609375
Nearest Class Center Accuracy: 0.4593

