Model save path: ./New_Models/bn_True_dataset_CIFAR100_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: 0.022893192246556282
Inter Cos: 0.07011394202709198
Norm Quadratic Average: 29.326324462890625
Nearest Class Center Accuracy: 0.03258

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02345217578113079
Inter Cos: 0.029620617628097534
Norm Quadratic Average: 4.443872928619385
Nearest Class Center Accuracy: 0.04822

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.021397985517978668
Inter Cos: 0.024076737463474274
Norm Quadratic Average: 2.300865888595581
Nearest Class Center Accuracy: 0.06058

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018245643004775047
Inter Cos: 0.017117267474532127
Norm Quadratic Average: 1.5878020524978638
Nearest Class Center Accuracy: 0.0708

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.025850092992186546
Inter Cos: 0.022515906020998955
Norm Quadratic Average: 1.1055481433868408
Nearest Class Center Accuracy: 0.08228

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03781501203775406
Inter Cos: 0.030034366995096207
Norm Quadratic Average: 0.8645817041397095
Nearest Class Center Accuracy: 0.092

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.18460936844348907
Inter Cos: 0.10052292048931122
Norm Quadratic Average: 0.6912444233894348
Nearest Class Center Accuracy: 0.09934

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.7339318990707397
Inter Cos: 0.24136687815189362
Norm Quadratic Average: 1.0109343528747559
Nearest Class Center Accuracy: 0.1

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 3.9129486083984375
Linear Weight Rank: 222
Intra Cos: 0.934090256690979
Inter Cos: 0.37460076808929443
Norm Quadratic Average: 38.625003814697266
Nearest Class Center Accuracy: 0.1

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 3.930222988128662
Linear Weight Rank: 1897
Intra Cos: 0.9498942494392395
Inter Cos: 0.3921007215976715
Norm Quadratic Average: 32.454742431640625
Nearest Class Center Accuracy: 0.1

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 3.9907948970794678
Linear Weight Rank: 95
Intra Cos: 0.9530508518218994
Inter Cos: 0.38901278376579285
Norm Quadratic Average: 29.94098663330078
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.957802414894104
Inter Cos: 0.4287458062171936
Norm Quadratic Average: 29.709213256835938
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 1.5902844526290894
Accuracy: 0.6147
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.18628190457820892, Weights: 0.016077760607004166
NC2 Equiangle: Features: 0.21542621034564394, Weights: 0.185880065301452
NC3 Self-Duality: 0.1463821679353714
NC4 NCC Mismatch: 0.12029999999999996

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.006621266715228558
Inter Cos: 0.4067547917366028
Norm Quadratic Average: 29.42218589782715
Nearest Class Center Accuracy: 0.1136

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.011173396371304989
Inter Cos: 0.2447790950536728
Norm Quadratic Average: 4.476700782775879
Nearest Class Center Accuracy: 0.2657

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.015592421405017376
Inter Cos: 0.1963418871164322
Norm Quadratic Average: 2.3178799152374268
Nearest Class Center Accuracy: 0.4

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.013917830772697926
Inter Cos: 0.13875825703144073
Norm Quadratic Average: 1.594472050666809
Nearest Class Center Accuracy: 0.531

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.013843021355569363
Inter Cos: 0.14966560900211334
Norm Quadratic Average: 1.1073052883148193
Nearest Class Center Accuracy: 0.6402

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01445209514349699
Inter Cos: 0.17105107009410858
Norm Quadratic Average: 0.8569946885108948
Nearest Class Center Accuracy: 0.681

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.051429130136966705
Inter Cos: 0.3765542209148407
Norm Quadratic Average: 0.658921480178833
Nearest Class Center Accuracy: 0.6372

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.17216473817825317
Inter Cos: 0.5552569627761841
Norm Quadratic Average: 0.8735198378562927
Nearest Class Center Accuracy: 0.6304

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 3.9129486083984375
Linear Weight Rank: 222
Intra Cos: 0.24335284531116486
Inter Cos: 0.6039831042289734
Norm Quadratic Average: 31.709165573120117
Nearest Class Center Accuracy: 0.6216

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 3.930222988128662
Linear Weight Rank: 1897
Intra Cos: 0.2601846754550934
Inter Cos: 0.6120837330818176
Norm Quadratic Average: 26.89468002319336
Nearest Class Center Accuracy: 0.6183

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 3.9907948970794678
Linear Weight Rank: 95
Intra Cos: 0.2706633508205414
Inter Cos: 0.6098636388778687
Norm Quadratic Average: 25.026582717895508
Nearest Class Center Accuracy: 0.6154

Output Layer:
Intra Cos: 0.2702706456184387
Inter Cos: 0.6155412197113037
Norm Quadratic Average: 24.85796546936035
Nearest Class Center Accuracy: 0.6117

