Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_846264_weight_decay_0.003.pth.tar
Training Set:
Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.021450398489832878
Inter Cos: 0.11371058225631714
Norm Quadratic Average: 27.691619873046875
Nearest Class Center Accuracy: 0.279125

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02432899735867977
Inter Cos: 0.09564529359340668
Norm Quadratic Average: 32.83182144165039
Nearest Class Center Accuracy: 0.3015

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.030485890805721283
Inter Cos: 0.10009729117155075
Norm Quadratic Average: 25.666114807128906
Nearest Class Center Accuracy: 0.365375

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03522966057062149
Inter Cos: 0.10007845610380173
Norm Quadratic Average: 29.87298583984375
Nearest Class Center Accuracy: 0.410125

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05346803739666939
Inter Cos: 0.1262321174144745
Norm Quadratic Average: 18.285293579101562
Nearest Class Center Accuracy: 0.436125

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06336622685194016
Inter Cos: 0.12615978717803955
Norm Quadratic Average: 15.562949180603027
Nearest Class Center Accuracy: 0.459375

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08406630158424377
Inter Cos: 0.1405702382326126
Norm Quadratic Average: 8.004054069519043
Nearest Class Center Accuracy: 0.50925

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.11803846061229706
Inter Cos: 0.17091283202171326
Norm Quadratic Average: 5.5663161277771
Nearest Class Center Accuracy: 0.681125

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 87.82865905761719
Linear Weight Rank: 4031
Intra Cos: 0.3384966552257538
Inter Cos: 0.3237387537956238
Norm Quadratic Average: 22.3281307220459
Nearest Class Center Accuracy: 0.962

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 35.56303024291992
Linear Weight Rank: 3671
Intra Cos: 0.6217844486236572
Inter Cos: 0.4850447177886963
Norm Quadratic Average: 20.37984848022461
Nearest Class Center Accuracy: 0.997625

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.1394381523132324
Linear Weight Rank: 10
Intra Cos: 0.7414575815200806
Inter Cos: 0.5784976482391357
Norm Quadratic Average: 24.237926483154297
Nearest Class Center Accuracy: 0.999

Output Layer:
Intra Cos: 0.8345637917518616
Inter Cos: 0.7175050973892212
Norm Quadratic Average: 30.380508422851562
Nearest Class Center Accuracy: 0.99625

Test Set:
Average Loss: 2.692870246887207
Accuracy: 0.5845
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.24833394587039948, Weights: 0.049812089651823044
NC2 Equiangle: Features: 0.45877876281738283, Weights: 0.18075396219889323
NC3 Self-Duality: 0.4325946271419525
NC4 NCC Mismatch: 0.16149999999999998

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018480457365512848
Inter Cos: 0.09352367371320724
Norm Quadratic Average: 27.530664443969727
Nearest Class Center Accuracy: 0.2895

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02583128772675991
Inter Cos: 0.07825256139039993
Norm Quadratic Average: 32.5976448059082
Nearest Class Center Accuracy: 0.314

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03217697888612747
Inter Cos: 0.08998146653175354
Norm Quadratic Average: 25.53123664855957
Nearest Class Center Accuracy: 0.3755

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.035199303179979324
Inter Cos: 0.08865687996149063
Norm Quadratic Average: 29.788307189941406
Nearest Class Center Accuracy: 0.4285

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04908517748117447
Inter Cos: 0.11492370069026947
Norm Quadratic Average: 18.25265884399414
Nearest Class Center Accuracy: 0.447

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.057162921875715256
Inter Cos: 0.11290233582258224
Norm Quadratic Average: 15.554784774780273
Nearest Class Center Accuracy: 0.461

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06774595379829407
Inter Cos: 0.13433784246444702
Norm Quadratic Average: 7.988338470458984
Nearest Class Center Accuracy: 0.472

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07916931807994843
Inter Cos: 0.1591501086950302
Norm Quadratic Average: 5.522650718688965
Nearest Class Center Accuracy: 0.5115

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 87.82865905761719
Linear Weight Rank: 4031
Intra Cos: 0.1516885757446289
Inter Cos: 0.2799547612667084
Norm Quadratic Average: 21.44805335998535
Nearest Class Center Accuracy: 0.571

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 35.56303024291992
Linear Weight Rank: 3671
Intra Cos: 0.23626449704170227
Inter Cos: 0.40628984570503235
Norm Quadratic Average: 18.990318298339844
Nearest Class Center Accuracy: 0.571

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.1394381523132324
Linear Weight Rank: 10
Intra Cos: 0.2650296986103058
Inter Cos: 0.47971925139427185
Norm Quadratic Average: 22.439706802368164
Nearest Class Center Accuracy: 0.555

Output Layer:
Intra Cos: 0.2984734773635864
Inter Cos: 0.5755201578140259
Norm Quadratic Average: 27.991804122924805
Nearest Class Center Accuracy: 0.521

