Model save path: ./New_Models/bn_False_dataset_CIFAR100_epochs_200_lr_0.01_model_type_vgg11_rand_seed_314159_test_samples_None_train_samples_None_weight_decay_0.0005.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: 29.326322555541992
Nearest Class Center Accuracy: 0.03258

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03692255914211273
Inter Cos: 0.048574890941381454
Norm Quadratic Average: 38.67353057861328
Nearest Class Center Accuracy: 0.04504

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04181332141160965
Inter Cos: 0.03710487112402916
Norm Quadratic Average: 49.258697509765625
Nearest Class Center Accuracy: 0.05368

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03360781446099281
Inter Cos: 0.03405566141009331
Norm Quadratic Average: 74.7175064086914
Nearest Class Center Accuracy: 0.06332

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.034157514572143555
Inter Cos: 0.028361443430185318
Norm Quadratic Average: 45.020912170410156
Nearest Class Center Accuracy: 0.07184

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.032997969537973404
Inter Cos: 0.02666894905269146
Norm Quadratic Average: 27.61509895324707
Nearest Class Center Accuracy: 0.07618

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07241412997245789
Inter Cos: 0.050574831664562225
Norm Quadratic Average: 9.633500099182129
Nearest Class Center Accuracy: 0.08792

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.2942531704902649
Inter Cos: 0.17069710791110992
Norm Quadratic Average: 5.126017093658447
Nearest Class Center Accuracy: 0.09908

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 21.41811752319336
Linear Weight Rank: 4030
Intra Cos: 0.6373286247253418
Inter Cos: 0.3029441833496094
Norm Quadratic Average: 26.664575576782227
Nearest Class Center Accuracy: 0.1

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 11.814920425415039
Linear Weight Rank: 3652
Intra Cos: 0.7100164294242859
Inter Cos: 0.31119027733802795
Norm Quadratic Average: 31.261383056640625
Nearest Class Center Accuracy: 0.1

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 8.768722534179688
Linear Weight Rank: 98
Intra Cos: 0.7279345989227295
Inter Cos: 0.32234811782836914
Norm Quadratic Average: 39.1429557800293
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.7638340592384338
Inter Cos: 0.406848669052124
Norm Quadratic Average: 64.8487319946289
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 3.7395611488342286
Accuracy: 0.4868
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.23520949482917786, Weights: 0.03906111419200897
NC2 Equiangle: Features: 0.22398888790246213, Weights: 0.11305392795138888
NC3 Self-Duality: 0.48087412118911743
NC4 NCC Mismatch: 0.2207

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.006621268577873707
Inter Cos: 0.4067547917366028
Norm Quadratic Average: 29.42218780517578
Nearest Class Center Accuracy: 0.1136

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.015483771450817585
Inter Cos: 0.2941923141479492
Norm Quadratic Average: 38.9083137512207
Nearest Class Center Accuracy: 0.2293

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.023559413850307465
Inter Cos: 0.3020014464855194
Norm Quadratic Average: 49.58954620361328
Nearest Class Center Accuracy: 0.296

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02284017950296402
Inter Cos: 0.24674344062805176
Norm Quadratic Average: 75.26264953613281
Nearest Class Center Accuracy: 0.3776

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022356398403644562
Inter Cos: 0.21653960645198822
Norm Quadratic Average: 45.416748046875
Nearest Class Center Accuracy: 0.4746

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01994207873940468
Inter Cos: 0.1587587594985962
Norm Quadratic Average: 27.75771713256836
Nearest Class Center Accuracy: 0.5276

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.027257952839136124
Inter Cos: 0.2329631894826889
Norm Quadratic Average: 9.542998313903809
Nearest Class Center Accuracy: 0.5198

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06717590987682343
Inter Cos: 0.43210700154304504
Norm Quadratic Average: 4.917182445526123
Nearest Class Center Accuracy: 0.5157

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 21.41811752319336
Linear Weight Rank: 4030
Intra Cos: 0.1420907974243164
Inter Cos: 0.5646501183509827
Norm Quadratic Average: 24.28572654724121
Nearest Class Center Accuracy: 0.4924

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 11.814920425415039
Linear Weight Rank: 3652
Intra Cos: 0.1557491570711136
Inter Cos: 0.5822647213935852
Norm Quadratic Average: 28.36989402770996
Nearest Class Center Accuracy: 0.4841

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 8.768722534179688
Linear Weight Rank: 98
Intra Cos: 0.1503537893295288
Inter Cos: 0.5963608622550964
Norm Quadratic Average: 35.72191619873047
Nearest Class Center Accuracy: 0.4797

Output Layer:
Intra Cos: 0.15112921595573425
Inter Cos: 0.6578958034515381
Norm Quadratic Average: 59.40321350097656
Nearest Class Center Accuracy: 0.4718

