Model save path: ./New_Models/bn_True_dataset_CIFAR100_epochs_200_lr_0.01_model_type_vgg11_rand_seed_314159_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: 0.022893192246556282
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: 0.023558584973216057
Inter Cos: 0.030278772115707397
Norm Quadratic Average: 3.1916189193725586
Nearest Class Center Accuracy: 0.04772

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02229866199195385
Inter Cos: 0.023923756554722786
Norm Quadratic Average: 1.7586647272109985
Nearest Class Center Accuracy: 0.06086

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.019155306741595268
Inter Cos: 0.018525103107094765
Norm Quadratic Average: 1.3754831552505493
Nearest Class Center Accuracy: 0.071

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.027751611545681953
Inter Cos: 0.023196270689368248
Norm Quadratic Average: 1.003493309020996
Nearest Class Center Accuracy: 0.08258

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.043194279074668884
Inter Cos: 0.032784536480903625
Norm Quadratic Average: 0.8281970620155334
Nearest Class Center Accuracy: 0.09196

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.20619657635688782
Inter Cos: 0.11441367864608765
Norm Quadratic Average: 0.6401480436325073
Nearest Class Center Accuracy: 0.0993

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.7848075032234192
Inter Cos: 0.2820785343647003
Norm Quadratic Average: 0.9765743613243103
Nearest Class Center Accuracy: 0.1

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 3.8733625411987305
Linear Weight Rank: 196
Intra Cos: 0.9548652172088623
Inter Cos: 0.41280806064605713
Norm Quadratic Average: 38.04537582397461
Nearest Class Center Accuracy: 0.1

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 3.8821959495544434
Linear Weight Rank: 1851
Intra Cos: 0.9662892818450928
Inter Cos: 0.3974752724170685
Norm Quadratic Average: 32.09580612182617
Nearest Class Center Accuracy: 0.1

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 3.908363103866577
Linear Weight Rank: 94
Intra Cos: 0.9681289792060852
Inter Cos: 0.3854435384273529
Norm Quadratic Average: 29.726953506469727
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.9703851342201233
Inter Cos: 0.41724881529808044
Norm Quadratic Average: 29.451740264892578
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 1.5869570779800415
Accuracy: 0.6184
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.20057937502861023, Weights: 0.013811808079481125
NC2 Equiangle: Features: 0.21325853752367424, Weights: 0.19195852568655303
NC3 Self-Duality: 0.1436842978000641
NC4 NCC Mismatch: 0.12549999999999994

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: 0.011956746689975262
Inter Cos: 0.2427849918603897
Norm Quadratic Average: 3.2147622108459473
Nearest Class Center Accuracy: 0.2615

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.016242850571870804
Inter Cos: 0.20170651376247406
Norm Quadratic Average: 1.771545648574829
Nearest Class Center Accuracy: 0.3963

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.014880110509693623
Inter Cos: 0.14532503485679626
Norm Quadratic Average: 1.3808449506759644
Nearest Class Center Accuracy: 0.5268

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.014604161493480206
Inter Cos: 0.16208486258983612
Norm Quadratic Average: 1.0049686431884766
Nearest Class Center Accuracy: 0.6323

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.016384437680244446
Inter Cos: 0.18701714277267456
Norm Quadratic Average: 0.8219037055969238
Nearest Class Center Accuracy: 0.6753

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05974116548895836
Inter Cos: 0.43510711193084717
Norm Quadratic Average: 0.6122828125953674
Nearest Class Center Accuracy: 0.6472

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1891855150461197
Inter Cos: 0.5959136486053467
Norm Quadratic Average: 0.840770423412323
Nearest Class Center Accuracy: 0.6389

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 3.8733625411987305
Linear Weight Rank: 196
Intra Cos: 0.2441263049840927
Inter Cos: 0.6299079656600952
Norm Quadratic Average: 31.203662872314453
Nearest Class Center Accuracy: 0.6306

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 3.8821959495544434
Linear Weight Rank: 1851
Intra Cos: 0.2579957842826843
Inter Cos: 0.6444298028945923
Norm Quadratic Average: 26.51730728149414
Nearest Class Center Accuracy: 0.6287

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 3.908363103866577
Linear Weight Rank: 94
Intra Cos: 0.26309528946876526
Inter Cos: 0.6456016898155212
Norm Quadratic Average: 24.728666305541992
Nearest Class Center Accuracy: 0.6245

Output Layer:
Intra Cos: 0.25706198811531067
Inter Cos: 0.6514683961868286
Norm Quadratic Average: 24.47406768798828
Nearest Class Center Accuracy: 0.6163

