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_0.0001.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: 0.025410765781998634
Inter Cos: 0.027625784277915955
Norm Quadratic Average: 71.67017364501953
Nearest Class Center Accuracy: 0.04826

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.021842559799551964
Inter Cos: 0.024570779874920845
Norm Quadratic Average: 38.14877700805664
Nearest Class Center Accuracy: 0.06034

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01949063502252102
Inter Cos: 0.022493679076433182
Norm Quadratic Average: 37.73823928833008
Nearest Class Center Accuracy: 0.0691

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.025937199592590332
Inter Cos: 0.025205615907907486
Norm Quadratic Average: 23.726932525634766
Nearest Class Center Accuracy: 0.07898

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03498775139451027
Inter Cos: 0.03474114462733269
Norm Quadratic Average: 27.860689163208008
Nearest Class Center Accuracy: 0.08358

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07866694778203964
Inter Cos: 0.06168413534760475
Norm Quadratic Average: 21.19529151916504
Nearest Class Center Accuracy: 0.09366

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.2420457899570465
Inter Cos: 0.15432588756084442
Norm Quadratic Average: 17.711210250854492
Nearest Class Center Accuracy: 0.09884

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 74.59564971923828
Linear Weight Rank: 4031
Intra Cos: 0.536306619644165
Inter Cos: 0.25291192531585693
Norm Quadratic Average: 50.08262634277344
Nearest Class Center Accuracy: 0.09996

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 33.878570556640625
Linear Weight Rank: 3661
Intra Cos: 0.7084634900093079
Inter Cos: 0.21939131617546082
Norm Quadratic Average: 41.0693359375
Nearest Class Center Accuracy: 0.1

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 13.997340202331543
Linear Weight Rank: 98
Intra Cos: 0.7950114607810974
Inter Cos: 0.25738993287086487
Norm Quadratic Average: 41.879642486572266
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.8917253613471985
Inter Cos: 0.48565369844436646
Norm Quadratic Average: 83.01422882080078
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 4.162028121948242
Accuracy: 0.541
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.304945707321167, Weights: 0.03232520818710327
NC2 Equiangle: Features: 0.15591012665719697, Weights: 0.10056384154040404
NC3 Self-Duality: 0.5759074091911316
NC4 NCC Mismatch: 0.16359999999999997

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: 0.010025459341704845
Inter Cos: 0.26371318101882935
Norm Quadratic Average: 72.19303894042969
Nearest Class Center Accuracy: 0.2634

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.014673816971480846
Inter Cos: 0.1982823610305786
Norm Quadratic Average: 38.43421936035156
Nearest Class Center Accuracy: 0.3867

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.013762486167252064
Inter Cos: 0.1411491185426712
Norm Quadratic Average: 37.92961120605469
Nearest Class Center Accuracy: 0.492

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.014925089664757252
Inter Cos: 0.1527106612920761
Norm Quadratic Average: 23.790435791015625
Nearest Class Center Accuracy: 0.5665

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.015732411295175552
Inter Cos: 0.14562442898750305
Norm Quadratic Average: 27.828777313232422
Nearest Class Center Accuracy: 0.5888

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.026608500629663467
Inter Cos: 0.2181130349636078
Norm Quadratic Average: 21.01239776611328
Nearest Class Center Accuracy: 0.5889

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06711872667074203
Inter Cos: 0.3717513382434845
Norm Quadratic Average: 17.004840850830078
Nearest Class Center Accuracy: 0.559

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 74.59564971923828
Linear Weight Rank: 4031
Intra Cos: 0.12344750016927719
Inter Cos: 0.4552483856678009
Norm Quadratic Average: 44.648536682128906
Nearest Class Center Accuracy: 0.546

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 33.878570556640625
Linear Weight Rank: 3661
Intra Cos: 0.1663264036178589
Inter Cos: 0.433497816324234
Norm Quadratic Average: 33.542823791503906
Nearest Class Center Accuracy: 0.5389

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 13.997340202331543
Linear Weight Rank: 98
Intra Cos: 0.17248283326625824
Inter Cos: 0.4951269030570984
Norm Quadratic Average: 33.32429885864258
Nearest Class Center Accuracy: 0.5363

Output Layer:
Intra Cos: 0.19511261582374573
Inter Cos: 0.6659676432609558
Norm Quadratic Average: 66.09671783447266
Nearest Class Center Accuracy: 0.5261

