Model save path: ./New_Models/bn_True_dataset_CIFAR10_epochs_200_lr_0.01_model_type_vgg11_rand_seed_979323_test_samples_None_train_samples_None_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: 0.0198909230530262
Inter Cos: 0.10477276891469955
Norm Quadratic Average: 27.59718894958496
Nearest Class Center Accuracy: 0.26968

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.019535180181264877
Inter Cos: 0.07268165796995163
Norm Quadratic Average: 6.5020670890808105
Nearest Class Center Accuracy: 0.40642

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.020171908661723137
Inter Cos: 0.05468082055449486
Norm Quadratic Average: 3.173607587814331
Nearest Class Center Accuracy: 0.53528

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.014502240344882011
Inter Cos: 0.04094231128692627
Norm Quadratic Average: 2.2926390171051025
Nearest Class Center Accuracy: 0.61456

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.020719004794955254
Inter Cos: 0.038503844290971756
Norm Quadratic Average: 1.57941734790802
Nearest Class Center Accuracy: 0.75976

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.036461420357227325
Inter Cos: 0.04348147660493851
Norm Quadratic Average: 1.0238354206085205
Nearest Class Center Accuracy: 0.88846

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.20551829040050507
Inter Cos: 0.13357806205749512
Norm Quadratic Average: 0.7235687375068665
Nearest Class Center Accuracy: 0.9906

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.8024768829345703
Inter Cos: 0.11147299408912659
Norm Quadratic Average: 0.8495330214500427
Nearest Class Center Accuracy: 1.0

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 2.3074989318847656
Linear Weight Rank: 2866
Intra Cos: 0.9750954508781433
Inter Cos: 0.003136956598609686
Norm Quadratic Average: 23.744644165039062
Nearest Class Center Accuracy: 1.0

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 2.3531718254089355
Linear Weight Rank: 860
Intra Cos: 0.9863126277923584
Inter Cos: 0.03399945795536041
Norm Quadratic Average: 16.82830810546875
Nearest Class Center Accuracy: 1.0

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.296675443649292
Linear Weight Rank: 9
Intra Cos: 0.9884364008903503
Inter Cos: 0.07518905401229858
Norm Quadratic Average: 12.268742561340332
Nearest Class Center Accuracy: 1.0

Output Layer:
Intra Cos: 0.9911271333694458
Inter Cos: 0.11599648743867874
Norm Quadratic Average: 9.371156692504883
Nearest Class Center Accuracy: 1.0

Test Set:
Average Loss: 0.44557330231666564
Accuracy: 0.868
NC1 Within Class Collapse: 3.3174338340759277
NC2 Equinorm: Features: 0.12667594850063324, Weights: 0.006542355287820101
NC2 Equiangle: Features: 0.12438805898030598, Weights: 0.021895970238579643
NC3 Self-Duality: 0.055123768746852875
NC4 NCC Mismatch: 0.016700000000000048

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018526077270507812
Inter Cos: 0.1042705774307251
Norm Quadratic Average: 27.550140380859375
Nearest Class Center Accuracy: 0.284

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018360959365963936
Inter Cos: 0.07435710728168488
Norm Quadratic Average: 6.497597694396973
Nearest Class Center Accuracy: 0.4263

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.019017187878489494
Inter Cos: 0.05607590079307556
Norm Quadratic Average: 3.174125909805298
Nearest Class Center Accuracy: 0.5453

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01350456103682518
Inter Cos: 0.04185391217470169
Norm Quadratic Average: 2.295032501220703
Nearest Class Center Accuracy: 0.6188

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.017526278272271156
Inter Cos: 0.039435457438230515
Norm Quadratic Average: 1.579649567604065
Nearest Class Center Accuracy: 0.7252

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0268566831946373
Inter Cos: 0.04513491690158844
Norm Quadratic Average: 1.015489935874939
Nearest Class Center Accuracy: 0.7852

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.13991263508796692
Inter Cos: 0.14405852556228638
Norm Quadratic Average: 0.7025831341743469
Nearest Class Center Accuracy: 0.8308

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.43616339564323425
Inter Cos: 0.21232566237449646
Norm Quadratic Average: 0.7777611613273621
Nearest Class Center Accuracy: 0.8641

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 2.3074989318847656
Linear Weight Rank: 2866
Intra Cos: 0.5803403854370117
Inter Cos: 0.23307840526103973
Norm Quadratic Average: 20.795927047729492
Nearest Class Center Accuracy: 0.8669

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 2.3531718254089355
Linear Weight Rank: 860
Intra Cos: 0.5939883589744568
Inter Cos: 0.24198977649211884
Norm Quadratic Average: 14.68619155883789
Nearest Class Center Accuracy: 0.8671

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.296675443649292
Linear Weight Rank: 9
Intra Cos: 0.5997943878173828
Inter Cos: 0.2549137473106384
Norm Quadratic Average: 10.718680381774902
Nearest Class Center Accuracy: 0.8678

Output Layer:
Intra Cos: 0.6114728450775146
Inter Cos: 0.2741876244544983
Norm Quadratic Average: 8.195330619812012
Nearest Class Center Accuracy: 0.8681

