Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_200_lr_0.01_model_type_vgg11_rand_seed_314159_test_samples_None_train_samples_None_weight_decay_0.005.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: 0.10477276146411896
Norm Quadratic Average: 27.59718132019043
Nearest Class Center Accuracy: 0.26968

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.023742325603961945
Inter Cos: 0.10273529589176178
Norm Quadratic Average: 28.589584350585938
Nearest Class Center Accuracy: 0.33862

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.030779579654335976
Inter Cos: 0.11701783537864685
Norm Quadratic Average: 27.092796325683594
Nearest Class Center Accuracy: 0.40332

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03308762609958649
Inter Cos: 0.09835004806518555
Norm Quadratic Average: 27.256303787231445
Nearest Class Center Accuracy: 0.47266

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03472816199064255
Inter Cos: 0.08280552923679352
Norm Quadratic Average: 9.379586219787598
Nearest Class Center Accuracy: 0.5726

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07637002319097519
Inter Cos: 0.12400168180465698
Norm Quadratic Average: 2.756359815597534
Nearest Class Center Accuracy: 0.66206

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.3167959749698639
Inter Cos: 0.4025958478450775
Norm Quadratic Average: 0.8510703444480896
Nearest Class Center Accuracy: 0.7735

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.5494316816329956
Inter Cos: 0.4874521791934967
Norm Quadratic Average: 0.9824520349502563
Nearest Class Center Accuracy: 0.94326

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 3.41744065284729
Linear Weight Rank: 49
Intra Cos: 0.6879379749298096
Inter Cos: 0.45315617322921753
Norm Quadratic Average: 7.764993667602539
Nearest Class Center Accuracy: 0.98588

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 3.4232215881347656
Linear Weight Rank: 2861
Intra Cos: 0.7421467900276184
Inter Cos: 0.3892909288406372
Norm Quadratic Average: 9.766127586364746
Nearest Class Center Accuracy: 0.9955

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.417423963546753
Linear Weight Rank: 9
Intra Cos: 0.7731746435165405
Inter Cos: 0.30002665519714355
Norm Quadratic Average: 11.895751953125
Nearest Class Center Accuracy: 0.99776

Output Layer:
Intra Cos: 0.7850850224494934
Inter Cos: 0.36107268929481506
Norm Quadratic Average: 16.716602325439453
Nearest Class Center Accuracy: 0.99898

Test Set:
Average Loss: 0.9810106744766235
Accuracy: 0.7657
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.24345479905605316, Weights: 0.07280447334051132
NC2 Equiangle: Features: 0.3628534528944227, Weights: 0.2126887003580729
NC3 Self-Duality: 0.2246788591146469
NC4 NCC Mismatch: 0.07179999999999997

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018526073545217514
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: 0.024198509752750397
Inter Cos: 0.10342902690172195
Norm Quadratic Average: 28.545215606689453
Nearest Class Center Accuracy: 0.3571

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.030162263661623
Inter Cos: 0.11855513602495193
Norm Quadratic Average: 27.09468650817871
Nearest Class Center Accuracy: 0.4129

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.031615011394023895
Inter Cos: 0.09910227358341217
Norm Quadratic Average: 27.286428451538086
Nearest Class Center Accuracy: 0.4754

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.031511951237916946
Inter Cos: 0.08359559625387192
Norm Quadratic Average: 9.396676063537598
Nearest Class Center Accuracy: 0.5697

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06764095276594162
Inter Cos: 0.12490205466747284
Norm Quadratic Average: 2.760791063308716
Nearest Class Center Accuracy: 0.6346

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.2525585889816284
Inter Cos: 0.3885002136230469
Norm Quadratic Average: 0.8510375618934631
Nearest Class Center Accuracy: 0.6566

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.38288548588752747
Inter Cos: 0.47383567690849304
Norm Quadratic Average: 0.974797785282135
Nearest Class Center Accuracy: 0.7276

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 3.41744065284729
Linear Weight Rank: 49
Intra Cos: 0.44626152515411377
Inter Cos: 0.45324838161468506
Norm Quadratic Average: 7.663459777832031
Nearest Class Center Accuracy: 0.7528

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 3.4232215881347656
Linear Weight Rank: 2861
Intra Cos: 0.44062331318855286
Inter Cos: 0.41140010952949524
Norm Quadratic Average: 9.593412399291992
Nearest Class Center Accuracy: 0.7562

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.417423963546753
Linear Weight Rank: 9
Intra Cos: 0.4211447536945343
Inter Cos: 0.36932092905044556
Norm Quadratic Average: 11.630661010742188
Nearest Class Center Accuracy: 0.7585

Output Layer:
Intra Cos: 0.40298184752464294
Inter Cos: 0.34100639820098877
Norm Quadratic Average: 16.295530319213867
Nearest Class Center Accuracy: 0.7526

