Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_979323_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: 0.021450400352478027
Inter Cos: 0.11371058970689774
Norm Quadratic Average: 27.691617965698242
Nearest Class Center Accuracy: 0.279125

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.025504838675260544
Inter Cos: 0.10929802805185318
Norm Quadratic Average: 29.25735092163086
Nearest Class Center Accuracy: 0.315875

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.027869608253240585
Inter Cos: 0.11133650690317154
Norm Quadratic Average: 23.142623901367188
Nearest Class Center Accuracy: 0.37725

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03556269407272339
Inter Cos: 0.11821334064006805
Norm Quadratic Average: 27.09665298461914
Nearest Class Center Accuracy: 0.4185

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.055516742169857025
Inter Cos: 0.15291310846805573
Norm Quadratic Average: 16.655498504638672
Nearest Class Center Accuracy: 0.444125

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07047288119792938
Inter Cos: 0.16212980449199677
Norm Quadratic Average: 14.270285606384277
Nearest Class Center Accuracy: 0.475375

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09378787130117416
Inter Cos: 0.1707456260919571
Norm Quadratic Average: 7.374792575836182
Nearest Class Center Accuracy: 0.524375

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.13082382082939148
Inter Cos: 0.184038445353508
Norm Quadratic Average: 5.136859893798828
Nearest Class Center Accuracy: 0.704375

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 87.81970977783203
Linear Weight Rank: 4031
Intra Cos: 0.35764771699905396
Inter Cos: 0.3036193549633026
Norm Quadratic Average: 20.679094314575195
Nearest Class Center Accuracy: 0.970125

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 35.56235122680664
Linear Weight Rank: 3670
Intra Cos: 0.6444775462150574
Inter Cos: 0.4549369215965271
Norm Quadratic Average: 18.93840980529785
Nearest Class Center Accuracy: 0.998875

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.15627384185791
Linear Weight Rank: 10
Intra Cos: 0.7538453936576843
Inter Cos: 0.5462476015090942
Norm Quadratic Average: 22.737064361572266
Nearest Class Center Accuracy: 0.99925

Output Layer:
Intra Cos: 0.7950924038887024
Inter Cos: 0.6765400171279907
Norm Quadratic Average: 28.369274139404297
Nearest Class Center Accuracy: 0.99675

Test Set:
Average Loss: 2.4748628005981446
Accuracy: 0.595
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.24923454225063324, Weights: 0.04859134182333946
NC2 Equiangle: Features: 0.4262934366861979, Weights: 0.17976525624593098
NC3 Self-Duality: 0.41641965508461
NC4 NCC Mismatch: 0.14400000000000002

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018480457365512848
Inter Cos: 0.09352368116378784
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: 0.02530524879693985
Inter Cos: 0.09278574585914612
Norm Quadratic Average: 29.06610870361328
Nearest Class Center Accuracy: 0.334

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.029276849702000618
Inter Cos: 0.0971384197473526
Norm Quadratic Average: 22.997386932373047
Nearest Class Center Accuracy: 0.3955

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03570467233657837
Inter Cos: 0.10467968881130219
Norm Quadratic Average: 26.974441528320312
Nearest Class Center Accuracy: 0.4485

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05206412076950073
Inter Cos: 0.13530033826828003
Norm Quadratic Average: 16.581174850463867
Nearest Class Center Accuracy: 0.46

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06327065825462341
Inter Cos: 0.14195257425308228
Norm Quadratic Average: 14.223198890686035
Nearest Class Center Accuracy: 0.4765

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07679934799671173
Inter Cos: 0.14721596240997314
Norm Quadratic Average: 7.3393988609313965
Nearest Class Center Accuracy: 0.4905

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09054357558488846
Inter Cos: 0.1544450968503952
Norm Quadratic Average: 5.0880632400512695
Nearest Class Center Accuracy: 0.536

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 87.81970977783203
Linear Weight Rank: 4031
Intra Cos: 0.15834370255470276
Inter Cos: 0.27840203046798706
Norm Quadratic Average: 19.89212989807129
Nearest Class Center Accuracy: 0.6025

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 35.56235122680664
Linear Weight Rank: 3670
Intra Cos: 0.23380981385707855
Inter Cos: 0.40396034717559814
Norm Quadratic Average: 17.72380828857422
Nearest Class Center Accuracy: 0.5995

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.15627384185791
Linear Weight Rank: 10
Intra Cos: 0.25569888949394226
Inter Cos: 0.4806371033191681
Norm Quadratic Average: 21.119054794311523
Nearest Class Center Accuracy: 0.5915

Output Layer:
Intra Cos: 0.2866310775279999
Inter Cos: 0.5819544792175293
Norm Quadratic Average: 26.239946365356445
Nearest Class Center Accuracy: 0.5715

