Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_846264_weight_decay_0.005.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: 0.024556394666433334
Inter Cos: 0.09688210487365723
Norm Quadratic Average: 32.77166748046875
Nearest Class Center Accuracy: 0.301125

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.030775029212236404
Inter Cos: 0.10077575594186783
Norm Quadratic Average: 25.34333038330078
Nearest Class Center Accuracy: 0.36875

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0363704189658165
Inter Cos: 0.10225848108530045
Norm Quadratic Average: 28.464479446411133
Nearest Class Center Accuracy: 0.414375

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05499379336833954
Inter Cos: 0.13162748515605927
Norm Quadratic Average: 16.81892967224121
Nearest Class Center Accuracy: 0.439875

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06700108200311661
Inter Cos: 0.13206355273723602
Norm Quadratic Average: 13.555631637573242
Nearest Class Center Accuracy: 0.464375

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09463723748922348
Inter Cos: 0.150928795337677
Norm Quadratic Average: 6.621942520141602
Nearest Class Center Accuracy: 0.5185

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1404937505722046
Inter Cos: 0.18788687884807587
Norm Quadratic Average: 4.392062187194824
Nearest Class Center Accuracy: 0.715875

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 79.79938507080078
Linear Weight Rank: 4031
Intra Cos: 0.40047571063041687
Inter Cos: 0.36379584670066833
Norm Quadratic Average: 18.655107498168945
Nearest Class Center Accuracy: 0.963125

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 32.33146667480469
Linear Weight Rank: 3671
Intra Cos: 0.667712926864624
Inter Cos: 0.5219788551330566
Norm Quadratic Average: 18.15441131591797
Nearest Class Center Accuracy: 0.997875

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.108205795288086
Linear Weight Rank: 10
Intra Cos: 0.7629144191741943
Inter Cos: 0.6062131524085999
Norm Quadratic Average: 21.998130798339844
Nearest Class Center Accuracy: 0.998625

Output Layer:
Intra Cos: 0.8475072979927063
Inter Cos: 0.7443031072616577
Norm Quadratic Average: 28.16273307800293
Nearest Class Center Accuracy: 0.9965

Test Set:
Average Loss: 2.5218798141479493
Accuracy: 0.577
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.24102190136909485, Weights: 0.05053073540329933
NC2 Equiangle: Features: 0.454332521226671, Weights: 0.1950160132514106
NC3 Self-Duality: 0.4037880003452301
NC4 NCC Mismatch: 0.16500000000000004

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: 0.026038985699415207
Inter Cos: 0.07937487959861755
Norm Quadratic Average: 32.53561019897461
Nearest Class Center Accuracy: 0.3165

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03235401213169098
Inter Cos: 0.09013637900352478
Norm Quadratic Average: 25.215131759643555
Nearest Class Center Accuracy: 0.377

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03609572350978851
Inter Cos: 0.09016039222478867
Norm Quadratic Average: 28.387264251708984
Nearest Class Center Accuracy: 0.436

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05036738142371178
Inter Cos: 0.11688810586929321
Norm Quadratic Average: 16.787078857421875
Nearest Class Center Accuracy: 0.452

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06015115603804588
Inter Cos: 0.11591394990682602
Norm Quadratic Average: 13.54769515991211
Nearest Class Center Accuracy: 0.463

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07640326023101807
Inter Cos: 0.14000405371189117
Norm Quadratic Average: 6.6085991859436035
Nearest Class Center Accuracy: 0.484

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09294935315847397
Inter Cos: 0.17588670551776886
Norm Quadratic Average: 4.352875232696533
Nearest Class Center Accuracy: 0.5265

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 79.79938507080078
Linear Weight Rank: 4031
Intra Cos: 0.174428790807724
Inter Cos: 0.309810072183609
Norm Quadratic Average: 17.852642059326172
Nearest Class Center Accuracy: 0.5775

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 32.33146667480469
Linear Weight Rank: 3671
Intra Cos: 0.24883326888084412
Inter Cos: 0.4238014817237854
Norm Quadratic Average: 16.894073486328125
Nearest Class Center Accuracy: 0.564

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.108205795288086
Linear Weight Rank: 10
Intra Cos: 0.26798608899116516
Inter Cos: 0.48523402214050293
Norm Quadratic Average: 20.374475479125977
Nearest Class Center Accuracy: 0.557

Output Layer:
Intra Cos: 0.2956103980541229
Inter Cos: 0.5761476159095764
Norm Quadratic Average: 25.94423484802246
Nearest Class Center Accuracy: 0.528

