Model save path: ./New_Models/bn_True_dataset_CIFAR100_epochs_200_lr_0.01_model_type_vgg11_rand_seed_265358_test_samples_None_train_samples_None_weight_decay_0.001.pth.tar
Training Set:
Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022893192246556282
Inter Cos: 0.07011394202709198
Norm Quadratic Average: 29.326324462890625
Nearest Class Center Accuracy: 0.03258

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.025597311556339264
Inter Cos: 0.028741005808115005
Norm Quadratic Average: 19.786426544189453
Nearest Class Center Accuracy: 0.04908

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02278624102473259
Inter Cos: 0.021789370104670525
Norm Quadratic Average: 10.239112854003906
Nearest Class Center Accuracy: 0.06044

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01790323480963707
Inter Cos: 0.018851418048143387
Norm Quadratic Average: 8.210259437561035
Nearest Class Center Accuracy: 0.06886

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02340082824230194
Inter Cos: 0.01990531198680401
Norm Quadratic Average: 5.462634086608887
Nearest Class Center Accuracy: 0.07988

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02697158046066761
Inter Cos: 0.022368771955370903
Norm Quadratic Average: 4.137584209442139
Nearest Class Center Accuracy: 0.08582

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07014159113168716
Inter Cos: 0.04367201030254364
Norm Quadratic Average: 2.8634800910949707
Nearest Class Center Accuracy: 0.09796

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.3092867136001587
Inter Cos: 0.11236633360385895
Norm Quadratic Average: 1.6739197969436646
Nearest Class Center Accuracy: 0.1

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 7.376066207885742
Linear Weight Rank: 4020
Intra Cos: 0.7586610913276672
Inter Cos: 0.2178187072277069
Norm Quadratic Average: 38.02613830566406
Nearest Class Center Accuracy: 0.1

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 6.944424629211426
Linear Weight Rank: 3521
Intra Cos: 0.8722935318946838
Inter Cos: 0.2462814748287201
Norm Quadratic Average: 31.41079330444336
Nearest Class Center Accuracy: 0.1

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 6.751813888549805
Linear Weight Rank: 98
Intra Cos: 0.8875937461853027
Inter Cos: 0.27788957953453064
Norm Quadratic Average: 30.99639320373535
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.9119246006011963
Inter Cos: 0.34260863065719604
Norm Quadratic Average: 37.52768325805664
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 2.0603894094467163
Accuracy: 0.56
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.23767592012882233, Weights: 0.02874929830431938
NC2 Equiangle: Features: 0.18750776811079545, Weights: 0.1064678770123106
NC3 Self-Duality: 0.3742183744907379
NC4 NCC Mismatch: 0.15510000000000002

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.006621266715228558
Inter Cos: 0.4067547917366028
Norm Quadratic Average: 29.42218589782715
Nearest Class Center Accuracy: 0.1136

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.011793163605034351
Inter Cos: 0.25241532921791077
Norm Quadratic Average: 19.925167083740234
Nearest Class Center Accuracy: 0.2703

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.014893871732056141
Inter Cos: 0.2004605233669281
Norm Quadratic Average: 10.31318473815918
Nearest Class Center Accuracy: 0.3994

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.013079321943223476
Inter Cos: 0.14424516260623932
Norm Quadratic Average: 8.245451927185059
Nearest Class Center Accuracy: 0.5127

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01290332991629839
Inter Cos: 0.1360611766576767
Norm Quadratic Average: 5.476700305938721
Nearest Class Center Accuracy: 0.6129

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.012047766707837582
Inter Cos: 0.11501136422157288
Norm Quadratic Average: 4.126003742218018
Nearest Class Center Accuracy: 0.6576

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02191302180290222
Inter Cos: 0.1779860258102417
Norm Quadratic Average: 2.8042547702789307
Nearest Class Center Accuracy: 0.6502

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05790364369750023
Inter Cos: 0.33146223425865173
Norm Quadratic Average: 1.497269630432129
Nearest Class Center Accuracy: 0.5994

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 7.376066207885742
Linear Weight Rank: 4020
Intra Cos: 0.19889266788959503
Inter Cos: 0.4564754068851471
Norm Quadratic Average: 30.30031394958496
Nearest Class Center Accuracy: 0.5565

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 6.944424629211426
Linear Weight Rank: 3521
Intra Cos: 0.21711580455303192
Inter Cos: 0.49585631489753723
Norm Quadratic Average: 24.731199264526367
Nearest Class Center Accuracy: 0.5541

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 6.751813888549805
Linear Weight Rank: 98
Intra Cos: 0.21547190845012665
Inter Cos: 0.5257471203804016
Norm Quadratic Average: 24.907135009765625
Nearest Class Center Accuracy: 0.5558

Output Layer:
Intra Cos: 0.22706986963748932
Inter Cos: 0.5841395854949951
Norm Quadratic Average: 30.403322219848633
Nearest Class Center Accuracy: 0.5559

