Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_314159_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: 0.021450400352478027
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: 0.025055520236492157
Inter Cos: 0.09400875121355057
Norm Quadratic Average: 33.733707427978516
Nearest Class Center Accuracy: 0.302125

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03173236548900604
Inter Cos: 0.10520630329847336
Norm Quadratic Average: 26.341421127319336
Nearest Class Center Accuracy: 0.3605

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.036367230117321014
Inter Cos: 0.10490606725215912
Norm Quadratic Average: 30.988391876220703
Nearest Class Center Accuracy: 0.41225

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05304796248674393
Inter Cos: 0.13398593664169312
Norm Quadratic Average: 19.67255210876465
Nearest Class Center Accuracy: 0.441125

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06533222645521164
Inter Cos: 0.1381576657295227
Norm Quadratic Average: 18.339271545410156
Nearest Class Center Accuracy: 0.472125

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08474420011043549
Inter Cos: 0.16161464154720306
Norm Quadratic Average: 10.214024543762207
Nearest Class Center Accuracy: 0.51775

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.11554086208343506
Inter Cos: 0.16917365789413452
Norm Quadratic Average: 7.52658224105835
Nearest Class Center Accuracy: 0.693375

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 100.91815948486328
Linear Weight Rank: 4031
Intra Cos: 0.32094821333885193
Inter Cos: 0.2684383988380432
Norm Quadratic Average: 29.95311737060547
Nearest Class Center Accuracy: 0.964625

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.8388786315918
Linear Weight Rank: 3670
Intra Cos: 0.6245492100715637
Inter Cos: 0.41767948865890503
Norm Quadratic Average: 25.7681941986084
Nearest Class Center Accuracy: 0.998625

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.293491840362549
Linear Weight Rank: 10
Intra Cos: 0.7678614854812622
Inter Cos: 0.5194001197814941
Norm Quadratic Average: 30.170753479003906
Nearest Class Center Accuracy: 0.999375

Output Layer:
Intra Cos: 0.8676044344902039
Inter Cos: 0.680260419845581
Norm Quadratic Average: 37.150245666503906
Nearest Class Center Accuracy: 0.999625

Test Set:
Average Loss: 3.311737121582031
Accuracy: 0.592
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.24543681740760803, Weights: 0.039266977459192276
NC2 Equiangle: Features: 0.4175755394829644, Weights: 0.1652034123738607
NC3 Self-Duality: 0.4498305916786194
NC4 NCC Mismatch: 0.136

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: 0.025268904864788055
Inter Cos: 0.08866846561431885
Norm Quadratic Average: 33.560001373291016
Nearest Class Center Accuracy: 0.3175

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03394153341650963
Inter Cos: 0.10205613821744919
Norm Quadratic Average: 26.22115135192871
Nearest Class Center Accuracy: 0.377

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.036901965737342834
Inter Cos: 0.09437817335128784
Norm Quadratic Average: 30.857105255126953
Nearest Class Center Accuracy: 0.4375

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.051317375153303146
Inter Cos: 0.12139095366001129
Norm Quadratic Average: 19.6087703704834
Nearest Class Center Accuracy: 0.4555

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.060948166996240616
Inter Cos: 0.1241391971707344
Norm Quadratic Average: 18.3144474029541
Nearest Class Center Accuracy: 0.478

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07127851992845535
Inter Cos: 0.14258229732513428
Norm Quadratic Average: 10.191917419433594
Nearest Class Center Accuracy: 0.4745

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08142360299825668
Inter Cos: 0.14509142935276031
Norm Quadratic Average: 7.4788818359375
Nearest Class Center Accuracy: 0.5205

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 100.91815948486328
Linear Weight Rank: 4031
Intra Cos: 0.13422976434230804
Inter Cos: 0.23476354777812958
Norm Quadratic Average: 28.850542068481445
Nearest Class Center Accuracy: 0.584

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.8388786315918
Linear Weight Rank: 3670
Intra Cos: 0.22213207185268402
Inter Cos: 0.3549928367137909
Norm Quadratic Average: 24.041576385498047
Nearest Class Center Accuracy: 0.588

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.293491840362549
Linear Weight Rank: 10
Intra Cos: 0.2643156349658966
Inter Cos: 0.43733248114585876
Norm Quadratic Average: 27.867198944091797
Nearest Class Center Accuracy: 0.586

Output Layer:
Intra Cos: 0.3047561049461365
Inter Cos: 0.5517659187316895
Norm Quadratic Average: 34.08773422241211
Nearest Class Center Accuracy: 0.567

