Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_338327_weight_decay_0.001.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: 0.023972593247890472
Inter Cos: 0.09689341485500336
Norm Quadratic Average: 34.84645080566406
Nearest Class Center Accuracy: 0.301375

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02943670004606247
Inter Cos: 0.10480675101280212
Norm Quadratic Average: 27.691097259521484
Nearest Class Center Accuracy: 0.359625

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03406909108161926
Inter Cos: 0.10566870123147964
Norm Quadratic Average: 33.166786193847656
Nearest Class Center Accuracy: 0.405125

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05016087368130684
Inter Cos: 0.1311941146850586
Norm Quadratic Average: 21.225128173828125
Nearest Class Center Accuracy: 0.433

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05692308396100998
Inter Cos: 0.13174526393413544
Norm Quadratic Average: 19.023883819580078
Nearest Class Center Accuracy: 0.4565

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0728069469332695
Inter Cos: 0.1373310536146164
Norm Quadratic Average: 10.076887130737305
Nearest Class Center Accuracy: 0.51175

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09952948242425919
Inter Cos: 0.14965049922466278
Norm Quadratic Average: 7.386272430419922
Nearest Class Center Accuracy: 0.67125

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 96.65791320800781
Linear Weight Rank: 4031
Intra Cos: 0.2809246778488159
Inter Cos: 0.2602873146533966
Norm Quadratic Average: 28.644744873046875
Nearest Class Center Accuracy: 0.964875

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 39.11161804199219
Linear Weight Rank: 3670
Intra Cos: 0.5583640336990356
Inter Cos: 0.4078693985939026
Norm Quadratic Average: 24.281230926513672
Nearest Class Center Accuracy: 0.999125

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.1907382011413574
Linear Weight Rank: 10
Intra Cos: 0.7153095602989197
Inter Cos: 0.5177549719810486
Norm Quadratic Average: 28.00523567199707
Nearest Class Center Accuracy: 0.999625

Output Layer:
Intra Cos: 0.8240484595298767
Inter Cos: 0.6816715002059937
Norm Quadratic Average: 33.45451736450195
Nearest Class Center Accuracy: 0.9995

Test Set:
Average Loss: 2.9285937957763672
Accuracy: 0.586
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.23927012085914612, Weights: 0.047107961028814316
NC2 Equiangle: Features: 0.43864356146918404, Weights: 0.15306625366210938
NC3 Self-Duality: 0.45569002628326416
NC4 NCC Mismatch: 0.15000000000000002

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: 0.024988148361444473
Inter Cos: 0.08016733080148697
Norm Quadratic Average: 34.593894958496094
Nearest Class Center Accuracy: 0.315

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03250402957201004
Inter Cos: 0.09025920182466507
Norm Quadratic Average: 27.5335636138916
Nearest Class Center Accuracy: 0.377

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0355033278465271
Inter Cos: 0.09288986027240753
Norm Quadratic Average: 33.03312301635742
Nearest Class Center Accuracy: 0.4285

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04875266179442406
Inter Cos: 0.11516984552145004
Norm Quadratic Average: 21.160903930664062
Nearest Class Center Accuracy: 0.4525

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.053973566740751266
Inter Cos: 0.11507699638605118
Norm Quadratic Average: 19.00095558166504
Nearest Class Center Accuracy: 0.471

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06169481948018074
Inter Cos: 0.12761656939983368
Norm Quadratic Average: 10.055429458618164
Nearest Class Center Accuracy: 0.488

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07170677930116653
Inter Cos: 0.13734355568885803
Norm Quadratic Average: 7.340734481811523
Nearest Class Center Accuracy: 0.5145

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 96.65791320800781
Linear Weight Rank: 4031
Intra Cos: 0.1373329907655716
Inter Cos: 0.23914596438407898
Norm Quadratic Average: 27.665931701660156
Nearest Class Center Accuracy: 0.5765

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 39.11161804199219
Linear Weight Rank: 3670
Intra Cos: 0.22672948241233826
Inter Cos: 0.36411216855049133
Norm Quadratic Average: 22.711397171020508
Nearest Class Center Accuracy: 0.5865

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.1907382011413574
Linear Weight Rank: 10
Intra Cos: 0.2674957811832428
Inter Cos: 0.44729188084602356
Norm Quadratic Average: 25.941776275634766
Nearest Class Center Accuracy: 0.572

Output Layer:
Intra Cos: 0.30282700061798096
Inter Cos: 0.5554718971252441
Norm Quadratic Average: 30.817123413085938
Nearest Class Center Accuracy: 0.557

