Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_846264_weight_decay_0.007.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: 0.024602651596069336
Inter Cos: 0.09665808826684952
Norm Quadratic Average: 33.069583892822266
Nearest Class Center Accuracy: 0.29975

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.030805986374616623
Inter Cos: 0.10243861377239227
Norm Quadratic Average: 25.836091995239258
Nearest Class Center Accuracy: 0.36625

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03721443936228752
Inter Cos: 0.10784260928630829
Norm Quadratic Average: 28.402467727661133
Nearest Class Center Accuracy: 0.414

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.054806359112262726
Inter Cos: 0.13654379546642303
Norm Quadratic Average: 16.490055084228516
Nearest Class Center Accuracy: 0.438875

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06804209202528
Inter Cos: 0.14034394919872284
Norm Quadratic Average: 12.569436073303223
Nearest Class Center Accuracy: 0.465

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09420119225978851
Inter Cos: 0.1623535007238388
Norm Quadratic Average: 5.877530097961426
Nearest Class Center Accuracy: 0.519

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.14579911530017853
Inter Cos: 0.19665338099002838
Norm Quadratic Average: 3.7324113845825195
Nearest Class Center Accuracy: 0.723625

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 72.5027847290039
Linear Weight Rank: 4031
Intra Cos: 0.42656102776527405
Inter Cos: 0.396470844745636
Norm Quadratic Average: 16.43769645690918
Nearest Class Center Accuracy: 0.961875

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 29.395652770996094
Linear Weight Rank: 3670
Intra Cos: 0.6719504594802856
Inter Cos: 0.5563647150993347
Norm Quadratic Average: 16.785593032836914
Nearest Class Center Accuracy: 0.9965

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.068732738494873
Linear Weight Rank: 10
Intra Cos: 0.7529852986335754
Inter Cos: 0.6369157433509827
Norm Quadratic Average: 20.597984313964844
Nearest Class Center Accuracy: 0.998625

Output Layer:
Intra Cos: 0.8308236598968506
Inter Cos: 0.7703166604042053
Norm Quadratic Average: 27.070924758911133
Nearest Class Center Accuracy: 0.993

Test Set:
Average Loss: 2.4680283966064454
Accuracy: 0.5715
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.24245834350585938, Weights: 0.059721216559410095
NC2 Equiangle: Features: 0.45425372653537327, Weights: 0.21256065368652344
NC3 Self-Duality: 0.3756301999092102
NC4 NCC Mismatch: 0.16900000000000004

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: 0.026129545643925667
Inter Cos: 0.0790700763463974
Norm Quadratic Average: 32.82912063598633
Nearest Class Center Accuracy: 0.3155

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03254168853163719
Inter Cos: 0.09110534936189651
Norm Quadratic Average: 25.70337677001953
Nearest Class Center Accuracy: 0.3745

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.036993447691202164
Inter Cos: 0.09522997587919235
Norm Quadratic Average: 28.337249755859375
Nearest Class Center Accuracy: 0.434

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05026599019765854
Inter Cos: 0.1213143989443779
Norm Quadratic Average: 16.466594696044922
Nearest Class Center Accuracy: 0.4495

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06086454167962074
Inter Cos: 0.12280359864234924
Norm Quadratic Average: 12.569011688232422
Nearest Class Center Accuracy: 0.4615

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07576287537813187
Inter Cos: 0.14423754811286926
Norm Quadratic Average: 5.870665550231934
Nearest Class Center Accuracy: 0.486

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09525860846042633
Inter Cos: 0.1824020892381668
Norm Quadratic Average: 3.697671890258789
Nearest Class Center Accuracy: 0.5275

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 72.5027847290039
Linear Weight Rank: 4031
Intra Cos: 0.19034618139266968
Inter Cos: 0.3207239508628845
Norm Quadratic Average: 15.684962272644043
Nearest Class Center Accuracy: 0.5725

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 29.395652770996094
Linear Weight Rank: 3670
Intra Cos: 0.25769805908203125
Inter Cos: 0.4277317523956299
Norm Quadratic Average: 15.601162910461426
Nearest Class Center Accuracy: 0.5675

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.068732738494873
Linear Weight Rank: 10
Intra Cos: 0.2690306603908539
Inter Cos: 0.4860214591026306
Norm Quadratic Average: 19.05901336669922
Nearest Class Center Accuracy: 0.559

Output Layer:
Intra Cos: 0.2888292372226715
Inter Cos: 0.5763906240463257
Norm Quadratic Average: 24.867918014526367
Nearest Class Center Accuracy: 0.5335

