Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_200_lr_0.01_model_type_vgg11_rand_seed_265358_test_samples_None_train_samples_None_weight_decay_0.01.pth.tar
Training Set:
Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01989092119038105
Inter Cos: 0.10477277636528015
Norm Quadratic Average: 27.59717559814453
Nearest Class Center Accuracy: 0.26968

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.023120468482375145
Inter Cos: 0.10289976000785828
Norm Quadratic Average: 23.245655059814453
Nearest Class Center Accuracy: 0.3465

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.030349379405379295
Inter Cos: 0.10637285560369492
Norm Quadratic Average: 16.149999618530273
Nearest Class Center Accuracy: 0.43224

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.032801344990730286
Inter Cos: 0.08461882174015045
Norm Quadratic Average: 8.746613502502441
Nearest Class Center Accuracy: 0.54438

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.054249852895736694
Inter Cos: 0.09579598903656006
Norm Quadratic Average: 1.6753305196762085
Nearest Class Center Accuracy: 0.6481

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.27389469742774963
Inter Cos: 0.367937296628952
Norm Quadratic Average: 0.5477950572967529
Nearest Class Center Accuracy: 0.79444

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.5405795574188232
Inter Cos: 0.49519678950309753
Norm Quadratic Average: 0.4796319603919983
Nearest Class Center Accuracy: 0.97726

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.7560216188430786
Inter Cos: 0.6241433024406433
Norm Quadratic Average: 0.8999821543693542
Nearest Class Center Accuracy: 0.99514

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 2.9051992893218994
Linear Weight Rank: 5
Intra Cos: 0.7953423857688904
Inter Cos: 0.6061116456985474
Norm Quadratic Average: 8.345913887023926
Nearest Class Center Accuracy: 0.9984

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 2.904923439025879
Linear Weight Rank: 2700
Intra Cos: 0.8028155565261841
Inter Cos: 0.5883415937423706
Norm Quadratic Average: 10.987372398376465
Nearest Class Center Accuracy: 0.99902

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.9031639099121094
Linear Weight Rank: 9
Intra Cos: 0.817165732383728
Inter Cos: 0.5005976557731628
Norm Quadratic Average: 12.726544380187988
Nearest Class Center Accuracy: 0.99962

Output Layer:
Intra Cos: 0.8298057913780212
Inter Cos: 0.5395586490631104
Norm Quadratic Average: 16.267597198486328
Nearest Class Center Accuracy: 0.99996

Test Set:
Average Loss: 0.8954098798155785
Accuracy: 0.7583
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.25134989619255066, Weights: 0.06555691361427307
NC2 Equiangle: Features: 0.42283041212293837, Weights: 0.24943555196126302
NC3 Self-Duality: 0.26457449793815613
NC4 NCC Mismatch: 0.0685

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: 27.550134658813477
Nearest Class Center Accuracy: 0.284

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.023335721343755722
Inter Cos: 0.10363214462995529
Norm Quadratic Average: 23.213943481445312
Nearest Class Center Accuracy: 0.3624

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.029209503903985023
Inter Cos: 0.10762505978345871
Norm Quadratic Average: 16.159381866455078
Nearest Class Center Accuracy: 0.442

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03117583692073822
Inter Cos: 0.08499310910701752
Norm Quadratic Average: 8.762434959411621
Nearest Class Center Accuracy: 0.5474

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.049600813537836075
Inter Cos: 0.09552552551031113
Norm Quadratic Average: 1.67850923538208
Nearest Class Center Accuracy: 0.6361

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.23248158395290375
Inter Cos: 0.3646470308303833
Norm Quadratic Average: 0.5467917323112488
Nearest Class Center Accuracy: 0.6983

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.3626058101654053
Inter Cos: 0.43369224667549133
Norm Quadratic Average: 0.4729140102863312
Nearest Class Center Accuracy: 0.7384

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.4919475018978119
Inter Cos: 0.5510237812995911
Norm Quadratic Average: 0.8827006816864014
Nearest Class Center Accuracy: 0.7503

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 2.9051992893218994
Linear Weight Rank: 5
Intra Cos: 0.4775042235851288
Inter Cos: 0.5366658568382263
Norm Quadratic Average: 8.152819633483887
Nearest Class Center Accuracy: 0.754

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 2.904923439025879
Linear Weight Rank: 2700
Intra Cos: 0.47089287638664246
Inter Cos: 0.5352926850318909
Norm Quadratic Average: 10.69961929321289
Nearest Class Center Accuracy: 0.7535

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 2.9031639099121094
Linear Weight Rank: 9
Intra Cos: 0.4702965021133423
Inter Cos: 0.48911207914352417
Norm Quadratic Average: 12.362078666687012
Nearest Class Center Accuracy: 0.7543

Output Layer:
Intra Cos: 0.44983208179473877
Inter Cos: 0.505810558795929
Norm Quadratic Average: 15.771530151367188
Nearest Class Center Accuracy: 0.7537

