Model save path: ./New_Models/bn_True_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_338327_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: 0.021450400352478027
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: 0.023538226261734962
Inter Cos: 0.10022683441638947
Norm Quadratic Average: 85.9076156616211
Nearest Class Center Accuracy: 0.333625

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02542145550251007
Inter Cos: 0.09016530960798264
Norm Quadratic Average: 64.35374450683594
Nearest Class Center Accuracy: 0.36975

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02256353572010994
Inter Cos: 0.070212721824646
Norm Quadratic Average: 68.97235107421875
Nearest Class Center Accuracy: 0.40225

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.031503502279520035
Inter Cos: 0.07762320339679718
Norm Quadratic Average: 44.06809616088867
Nearest Class Center Accuracy: 0.41675

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03073025867342949
Inter Cos: 0.06625296175479889
Norm Quadratic Average: 45.07806396484375
Nearest Class Center Accuracy: 0.455875

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04084379971027374
Inter Cos: 0.07669133692979813
Norm Quadratic Average: 28.853328704833984
Nearest Class Center Accuracy: 0.544875

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06131772696971893
Inter Cos: 0.07537371665239334
Norm Quadratic Average: 20.311857223510742
Nearest Class Center Accuracy: 0.827625

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 100.90135192871094
Linear Weight Rank: 4031
Intra Cos: 0.17974135279655457
Inter Cos: 0.11135166883468628
Norm Quadratic Average: 108.18189239501953
Nearest Class Center Accuracy: 1.0

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.780662536621094
Linear Weight Rank: 3670
Intra Cos: 0.4179845452308655
Inter Cos: 0.20312723517417908
Norm Quadratic Average: 56.98183822631836
Nearest Class Center Accuracy: 1.0

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.532667636871338
Linear Weight Rank: 10
Intra Cos: 0.6512796878814697
Inter Cos: 0.31537291407585144
Norm Quadratic Average: 40.4008674621582
Nearest Class Center Accuracy: 1.0

Output Layer:
Intra Cos: 0.8732892274856567
Inter Cos: 0.5264356136322021
Norm Quadratic Average: 28.39460563659668
Nearest Class Center Accuracy: 1.0

Test Set:
Average Loss: 2.8008744430541994
Accuracy: 0.5785
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.2131606638431549, Weights: 0.012660187669098377
NC2 Equiangle: Features: 0.44978374905056423, Weights: 0.08964008755154079
NC3 Self-Duality: 0.6506667137145996
NC4 NCC Mismatch: 0.15700000000000003

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018480459228157997
Inter Cos: 0.09352367371320724
Norm Quadratic Average: 27.53066635131836
Nearest Class Center Accuracy: 0.2895

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022763432934880257
Inter Cos: 0.08643238246440887
Norm Quadratic Average: 85.41134643554688
Nearest Class Center Accuracy: 0.351

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.025849459692835808
Inter Cos: 0.07822630554437637
Norm Quadratic Average: 64.01229858398438
Nearest Class Center Accuracy: 0.402

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022957010194659233
Inter Cos: 0.061562199145555496
Norm Quadratic Average: 68.72532653808594
Nearest Class Center Accuracy: 0.439

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.029652999714016914
Inter Cos: 0.07009614259004593
Norm Quadratic Average: 43.8911018371582
Nearest Class Center Accuracy: 0.4455

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.028111794963479042
Inter Cos: 0.05998827517032623
Norm Quadratic Average: 44.93550109863281
Nearest Class Center Accuracy: 0.475

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.031091639772057533
Inter Cos: 0.07457973808050156
Norm Quadratic Average: 28.698883056640625
Nearest Class Center Accuracy: 0.49

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03304855898022652
Inter Cos: 0.06613200902938843
Norm Quadratic Average: 20.135696411132812
Nearest Class Center Accuracy: 0.5605

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 100.90135192871094
Linear Weight Rank: 4031
Intra Cos: 0.05487858131527901
Inter Cos: 0.1061849296092987
Norm Quadratic Average: 104.41006469726562
Nearest Class Center Accuracy: 0.6035

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.780662536621094
Linear Weight Rank: 3670
Intra Cos: 0.11384032666683197
Inter Cos: 0.20473560690879822
Norm Quadratic Average: 52.679588317871094
Nearest Class Center Accuracy: 0.584

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.532667636871338
Linear Weight Rank: 10
Intra Cos: 0.18292568624019623
Inter Cos: 0.32201674580574036
Norm Quadratic Average: 36.03017044067383
Nearest Class Center Accuracy: 0.5715

Output Layer:
Intra Cos: 0.27134186029434204
Inter Cos: 0.5033679008483887
Norm Quadratic Average: 24.559362411499023
Nearest Class Center Accuracy: 0.556

