Model save path: ./New_Models/bn_True_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_846264_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: 0.021450398489832878
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: 0.023541387170553207
Inter Cos: 0.07734493166208267
Norm Quadratic Average: 88.50666046142578
Nearest Class Center Accuracy: 0.347375

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03010370209813118
Inter Cos: 0.08314523100852966
Norm Quadratic Average: 66.05998229980469
Nearest Class Center Accuracy: 0.373375

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02577395550906658
Inter Cos: 0.06653683632612228
Norm Quadratic Average: 69.3741455078125
Nearest Class Center Accuracy: 0.40375

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03568146005272865
Inter Cos: 0.08415517210960388
Norm Quadratic Average: 44.615196228027344
Nearest Class Center Accuracy: 0.423125

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03458937630057335
Inter Cos: 0.06887980550527573
Norm Quadratic Average: 45.37173080444336
Nearest Class Center Accuracy: 0.45575

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04681747034192085
Inter Cos: 0.08298065513372421
Norm Quadratic Average: 28.9781551361084
Nearest Class Center Accuracy: 0.535

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06137208640575409
Inter Cos: 0.07667000591754913
Norm Quadratic Average: 20.513572692871094
Nearest Class Center Accuracy: 0.816375

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 100.90843200683594
Linear Weight Rank: 4031
Intra Cos: 0.17689670622348785
Inter Cos: 0.09627779573202133
Norm Quadratic Average: 108.93415069580078
Nearest Class Center Accuracy: 0.99975

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.79174041748047
Linear Weight Rank: 3671
Intra Cos: 0.3984246850013733
Inter Cos: 0.1807701587677002
Norm Quadratic Average: 56.945709228515625
Nearest Class Center Accuracy: 1.0

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.525991439819336
Linear Weight Rank: 10
Intra Cos: 0.6269538402557373
Inter Cos: 0.28535088896751404
Norm Quadratic Average: 39.94401168823242
Nearest Class Center Accuracy: 1.0

Output Layer:
Intra Cos: 0.8625420928001404
Inter Cos: 0.5090008974075317
Norm Quadratic Average: 27.624967575073242
Nearest Class Center Accuracy: 1.0

Test Set:
Average Loss: 2.677209716796875
Accuracy: 0.5985
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.21333979070186615, Weights: 0.019716162234544754
NC2 Equiangle: Features: 0.4390629662407769, Weights: 0.08854536480373806
NC3 Self-Duality: 0.6345816254615784
NC4 NCC Mismatch: 0.14149999999999996

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: 0.023257242515683174
Inter Cos: 0.0700886994600296
Norm Quadratic Average: 88.26958465576172
Nearest Class Center Accuracy: 0.359

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02973599173128605
Inter Cos: 0.08087892085313797
Norm Quadratic Average: 65.8866958618164
Nearest Class Center Accuracy: 0.403

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.025168217718601227
Inter Cos: 0.06168441101908684
Norm Quadratic Average: 69.26373291015625
Nearest Class Center Accuracy: 0.4385

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03236156702041626
Inter Cos: 0.08355297148227692
Norm Quadratic Average: 44.50796127319336
Nearest Class Center Accuracy: 0.441

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.030414236709475517
Inter Cos: 0.068123959004879
Norm Quadratic Average: 45.24168395996094
Nearest Class Center Accuracy: 0.461

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03493321314454079
Inter Cos: 0.08147551119327545
Norm Quadratic Average: 28.82918930053711
Nearest Class Center Accuracy: 0.4845

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03577745705842972
Inter Cos: 0.0667511373758316
Norm Quadratic Average: 20.318254470825195
Nearest Class Center Accuracy: 0.554

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 100.90843200683594
Linear Weight Rank: 4031
Intra Cos: 0.06378458440303802
Inter Cos: 0.10019775480031967
Norm Quadratic Average: 105.08806610107422
Nearest Class Center Accuracy: 0.6035

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.79174041748047
Linear Weight Rank: 3671
Intra Cos: 0.12429719418287277
Inter Cos: 0.19466346502304077
Norm Quadratic Average: 52.82331085205078
Nearest Class Center Accuracy: 0.599

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.525991439819336
Linear Weight Rank: 10
Intra Cos: 0.1923464685678482
Inter Cos: 0.31039687991142273
Norm Quadratic Average: 35.79048538208008
Nearest Class Center Accuracy: 0.5835

Output Layer:
Intra Cos: 0.2810608148574829
Inter Cos: 0.48788636922836304
Norm Quadratic Average: 24.078773498535156
Nearest Class Center Accuracy: 0.5635

