Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_200_lr_0.01_model_type_vgg11_rand_seed_265358_test_samples_None_train_samples_None_weight_decay_0.0003.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: 0.10477277636528015
Norm Quadratic Average: 27.59717559814453
Nearest Class Center Accuracy: 0.26968

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022560304030776024
Inter Cos: 0.08469895273447037
Norm Quadratic Average: 27.898181915283203
Nearest Class Center Accuracy: 0.3943

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02522563375532627
Inter Cos: 0.07100387662649155
Norm Quadratic Average: 24.062580108642578
Nearest Class Center Accuracy: 0.50616

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.023317785933613777
Inter Cos: 0.05631949007511139
Norm Quadratic Average: 25.147945404052734
Nearest Class Center Accuracy: 0.59208

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.030166340991854668
Inter Cos: 0.047380249947309494
Norm Quadratic Average: 11.7330904006958
Nearest Class Center Accuracy: 0.69456

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.048938535153865814
Inter Cos: 0.04845404997467995
Norm Quadratic Average: 7.1317949295043945
Nearest Class Center Accuracy: 0.7736

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.15209217369556427
Inter Cos: 0.10636559873819351
Norm Quadratic Average: 2.876567840576172
Nearest Class Center Accuracy: 0.8954

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.5076620578765869
Inter Cos: 0.19207040965557098
Norm Quadratic Average: 1.8937335014343262
Nearest Class Center Accuracy: 0.99476

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 38.57484817504883
Linear Weight Rank: 4031
Intra Cos: 0.847962498664856
Inter Cos: 0.16460496187210083
Norm Quadratic Average: 12.668180465698242
Nearest Class Center Accuracy: 0.99878

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 16.028560638427734
Linear Weight Rank: 3669
Intra Cos: 0.9061756730079651
Inter Cos: 0.10194593667984009
Norm Quadratic Average: 13.079465866088867
Nearest Class Center Accuracy: 0.9998

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.759873390197754
Linear Weight Rank: 10
Intra Cos: 0.9197043776512146
Inter Cos: 0.12491464614868164
Norm Quadratic Average: 14.501561164855957
Nearest Class Center Accuracy: 1.0

Output Layer:
Intra Cos: 0.9505799412727356
Inter Cos: 0.2934514880180359
Norm Quadratic Average: 17.672340393066406
Nearest Class Center Accuracy: 1.0

Test Set:
Average Loss: 0.9641950148105621
Accuracy: 0.8177
NC1 Within Class Collapse: 5.611151695251465
NC2 Equinorm: Features: 0.219153493642807, Weights: 0.027121275663375854
NC2 Equiangle: Features: 0.17829449971516928, Weights: 0.07077094184027778
NC3 Self-Duality: 0.17915856838226318
NC4 NCC Mismatch: 0.05589999999999995

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018526077270507812
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: 0.020900307223200798
Inter Cos: 0.0855104848742485
Norm Quadratic Average: 27.876094818115234
Nearest Class Center Accuracy: 0.4115

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02353384904563427
Inter Cos: 0.0721106007695198
Norm Quadratic Average: 24.0738582611084
Nearest Class Center Accuracy: 0.514

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.021356696262955666
Inter Cos: 0.05719419941306114
Norm Quadratic Average: 25.181989669799805
Nearest Class Center Accuracy: 0.5936

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.026303235441446304
Inter Cos: 0.04857978597283363
Norm Quadratic Average: 11.742175102233887
Nearest Class Center Accuracy: 0.6686

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04106884449720383
Inter Cos: 0.05050092190504074
Norm Quadratic Average: 7.114467620849609
Nearest Class Center Accuracy: 0.7145

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.11569834500551224
Inter Cos: 0.11412333697080612
Norm Quadratic Average: 2.852802276611328
Nearest Class Center Accuracy: 0.7511

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.2966512441635132
Inter Cos: 0.23775970935821533
Norm Quadratic Average: 1.8371143341064453
Nearest Class Center Accuracy: 0.7967

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 38.57484817504883
Linear Weight Rank: 4031
Intra Cos: 0.4797510504722595
Inter Cos: 0.31563687324523926
Norm Quadratic Average: 11.985738754272461
Nearest Class Center Accuracy: 0.7978

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 16.028560638427734
Linear Weight Rank: 3669
Intra Cos: 0.5008583068847656
Inter Cos: 0.2998277544975281
Norm Quadratic Average: 12.257441520690918
Nearest Class Center Accuracy: 0.8034

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.759873390197754
Linear Weight Rank: 10
Intra Cos: 0.49876099824905396
Inter Cos: 0.2982029616832733
Norm Quadratic Average: 13.577601432800293
Nearest Class Center Accuracy: 0.8088

Output Layer:
Intra Cos: 0.5330162644386292
Inter Cos: 0.36451223492622375
Norm Quadratic Average: 16.493362426757812
Nearest Class Center Accuracy: 0.8122

