Model save path: ./New_Models/bn_False_dataset_CIFAR100_epochs_200_lr_0.01_model_type_vgg11_rand_seed_265358_test_samples_None_train_samples_None_weight_decay_0.0005.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: 0.03385678306221962
Inter Cos: 0.04562981426715851
Norm Quadratic Average: 38.06759262084961
Nearest Class Center Accuracy: 0.04556

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.039047908037900925
Inter Cos: 0.03447294607758522
Norm Quadratic Average: 46.95086669921875
Nearest Class Center Accuracy: 0.05422

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03325797989964485
Inter Cos: 0.032080430537462234
Norm Quadratic Average: 69.38941192626953
Nearest Class Center Accuracy: 0.06416

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03339600935578346
Inter Cos: 0.02775845304131508
Norm Quadratic Average: 41.62366485595703
Nearest Class Center Accuracy: 0.0722

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03282550349831581
Inter Cos: 0.02710537612438202
Norm Quadratic Average: 25.88680648803711
Nearest Class Center Accuracy: 0.07618

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07622085511684418
Inter Cos: 0.049843158572912216
Norm Quadratic Average: 9.228534698486328
Nearest Class Center Accuracy: 0.08614

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.31425756216049194
Inter Cos: 0.1688269078731537
Norm Quadratic Average: 4.947950839996338
Nearest Class Center Accuracy: 0.09884

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 21.434804916381836
Linear Weight Rank: 4030
Intra Cos: 0.6535595059394836
Inter Cos: 0.30971938371658325
Norm Quadratic Average: 26.06659507751465
Nearest Class Center Accuracy: 0.1

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 11.86001968383789
Linear Weight Rank: 3652
Intra Cos: 0.7035170793533325
Inter Cos: 0.3103843629360199
Norm Quadratic Average: 30.90043830871582
Nearest Class Center Accuracy: 0.1

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 8.860928535461426
Linear Weight Rank: 98
Intra Cos: 0.7237094640731812
Inter Cos: 0.3087528645992279
Norm Quadratic Average: 38.94355773925781
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.7747204899787903
Inter Cos: 0.3747299611568451
Norm Quadratic Average: 65.1262435913086
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 3.71235778503418
Accuracy: 0.4858
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.22570857405662537, Weights: 0.0369071364402771
NC2 Equiangle: Features: 0.2204550879892677, Weights: 0.1134045286852904
NC3 Self-Duality: 0.4704664647579193
NC4 NCC Mismatch: 0.22519999999999996

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: 0.013063515536487103
Inter Cos: 0.2711120843887329
Norm Quadratic Average: 38.30916213989258
Nearest Class Center Accuracy: 0.2366

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02105872705578804
Inter Cos: 0.30123409628868103
Norm Quadratic Average: 47.26973342895508
Nearest Class Center Accuracy: 0.2994

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02260293811559677
Inter Cos: 0.25415998697280884
Norm Quadratic Average: 69.88801574707031
Nearest Class Center Accuracy: 0.3829

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022039536386728287
Inter Cos: 0.21861346065998077
Norm Quadratic Average: 41.98007583618164
Nearest Class Center Accuracy: 0.4812

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0199684277176857
Inter Cos: 0.1547618806362152
Norm Quadratic Average: 26.014902114868164
Nearest Class Center Accuracy: 0.5322

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02906643971800804
Inter Cos: 0.2501528859138489
Norm Quadratic Average: 9.151978492736816
Nearest Class Center Accuracy: 0.5192

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07486388832330704
Inter Cos: 0.4499928951263428
Norm Quadratic Average: 4.75366735458374
Nearest Class Center Accuracy: 0.5136

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 21.434804916381836
Linear Weight Rank: 4030
Intra Cos: 0.13476844131946564
Inter Cos: 0.5749484896659851
Norm Quadratic Average: 23.78887176513672
Nearest Class Center Accuracy: 0.4971

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 11.86001968383789
Linear Weight Rank: 3652
Intra Cos: 0.1395287662744522
Inter Cos: 0.5972804427146912
Norm Quadratic Average: 28.097076416015625
Nearest Class Center Accuracy: 0.4901

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 8.860928535461426
Linear Weight Rank: 98
Intra Cos: 0.13071732223033905
Inter Cos: 0.607248067855835
Norm Quadratic Average: 35.578285217285156
Nearest Class Center Accuracy: 0.482

Output Layer:
Intra Cos: 0.132609024643898
Inter Cos: 0.6773711442947388
Norm Quadratic Average: 59.71730041503906
Nearest Class Center Accuracy: 0.4686

