Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_265358_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: 27.691621780395508
Nearest Class Center Accuracy: 0.279125

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.025915823876857758
Inter Cos: 0.10311979055404663
Norm Quadratic Average: 30.550552368164062
Nearest Class Center Accuracy: 0.31

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03567826375365257
Inter Cos: 0.1187235563993454
Norm Quadratic Average: 23.18528938293457
Nearest Class Center Accuracy: 0.3595

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04021424427628517
Inter Cos: 0.1125284880399704
Norm Quadratic Average: 25.802160263061523
Nearest Class Center Accuracy: 0.411

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.055094458162784576
Inter Cos: 0.13112357258796692
Norm Quadratic Average: 15.4314603805542
Nearest Class Center Accuracy: 0.441875

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06991232186555862
Inter Cos: 0.14213848114013672
Norm Quadratic Average: 13.35651969909668
Nearest Class Center Accuracy: 0.467125

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09401530772447586
Inter Cos: 0.1612926721572876
Norm Quadratic Average: 7.091714382171631
Nearest Class Center Accuracy: 0.51575

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.13220320641994476
Inter Cos: 0.17140647768974304
Norm Quadratic Average: 4.983366966247559
Nearest Class Center Accuracy: 0.70025

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 87.8261947631836
Linear Weight Rank: 4031
Intra Cos: 0.3750472664833069
Inter Cos: 0.32897302508354187
Norm Quadratic Average: 20.85247230529785
Nearest Class Center Accuracy: 0.96075

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 35.568111419677734
Linear Weight Rank: 3671
Intra Cos: 0.6560197472572327
Inter Cos: 0.4929589629173279
Norm Quadratic Average: 19.48107147216797
Nearest Class Center Accuracy: 0.999

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.1686742305755615
Linear Weight Rank: 10
Intra Cos: 0.7629023194313049
Inter Cos: 0.5902487635612488
Norm Quadratic Average: 23.56455421447754
Nearest Class Center Accuracy: 0.999625

Output Layer:
Intra Cos: 0.8226234316825867
Inter Cos: 0.7495301961898804
Norm Quadratic Average: 29.941808700561523
Nearest Class Center Accuracy: 0.99825

Test Set:
Average Loss: 2.5690284423828125
Accuracy: 0.585
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.2326555699110031, Weights: 0.04268224537372589
NC2 Equiangle: Features: 0.4410975138346354, Weights: 0.18797859615749782
NC3 Self-Duality: 0.4199664890766144
NC4 NCC Mismatch: 0.14700000000000002

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018480459228157997
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: 0.024826182052493095
Inter Cos: 0.09715379774570465
Norm Quadratic Average: 30.443239212036133
Nearest Class Center Accuracy: 0.3295

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03727082163095474
Inter Cos: 0.11408631503582001
Norm Quadratic Average: 23.109851837158203
Nearest Class Center Accuracy: 0.3775

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.040941838175058365
Inter Cos: 0.10567454248666763
Norm Quadratic Average: 25.73587417602539
Nearest Class Center Accuracy: 0.4265

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.053810037672519684
Inter Cos: 0.11751851439476013
Norm Quadratic Average: 15.369194984436035
Nearest Class Center Accuracy: 0.455

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06682582944631577
Inter Cos: 0.12877944111824036
Norm Quadratic Average: 13.310894966125488
Nearest Class Center Accuracy: 0.469

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08149788528680801
Inter Cos: 0.14551787078380585
Norm Quadratic Average: 7.060093402862549
Nearest Class Center Accuracy: 0.4775

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09271371364593506
Inter Cos: 0.15293031930923462
Norm Quadratic Average: 4.931962013244629
Nearest Class Center Accuracy: 0.5195

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 87.8261947631836
Linear Weight Rank: 4031
Intra Cos: 0.16836363077163696
Inter Cos: 0.28130027651786804
Norm Quadratic Average: 20.008928298950195
Nearest Class Center Accuracy: 0.583

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 35.568111419677734
Linear Weight Rank: 3671
Intra Cos: 0.24426491558551788
Inter Cos: 0.4076137840747833
Norm Quadratic Average: 18.20317268371582
Nearest Class Center Accuracy: 0.5815

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.1686742305755615
Linear Weight Rank: 10
Intra Cos: 0.2667802572250366
Inter Cos: 0.488107830286026
Norm Quadratic Average: 21.87873649597168
Nearest Class Center Accuracy: 0.5795

Output Layer:
Intra Cos: 0.2971230149269104
Inter Cos: 0.6116981506347656
Norm Quadratic Average: 27.658119201660156
Nearest Class Center Accuracy: 0.564

