Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_338327_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: 0.021450400352478027
Inter Cos: 0.11371058225631714
Norm Quadratic Average: 27.691619873046875
Nearest Class Center Accuracy: 0.279125

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.023963456973433495
Inter Cos: 0.09607070684432983
Norm Quadratic Average: 35.17855453491211
Nearest Class Center Accuracy: 0.30125

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.029713626950979233
Inter Cos: 0.10374939441680908
Norm Quadratic Average: 28.11374855041504
Nearest Class Center Accuracy: 0.36

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.034970201551914215
Inter Cos: 0.1049124151468277
Norm Quadratic Average: 34.06032943725586
Nearest Class Center Accuracy: 0.405875

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05129648372530937
Inter Cos: 0.13219474256038666
Norm Quadratic Average: 21.99285316467285
Nearest Class Center Accuracy: 0.4345

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.056492872536182404
Inter Cos: 0.13278008997440338
Norm Quadratic Average: 20.046924591064453
Nearest Class Center Accuracy: 0.4565

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07213751971721649
Inter Cos: 0.13978485763072968
Norm Quadratic Average: 10.891860008239746
Nearest Class Center Accuracy: 0.506875

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09772784262895584
Inter Cos: 0.14810368418693542
Norm Quadratic Average: 8.088754653930664
Nearest Class Center Accuracy: 0.66625

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 99.95556640625
Linear Weight Rank: 4031
Intra Cos: 0.26885274052619934
Inter Cos: 0.24704883992671967
Norm Quadratic Average: 31.503915786743164
Nearest Class Center Accuracy: 0.9625

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.440711975097656
Linear Weight Rank: 3670
Intra Cos: 0.5462026000022888
Inter Cos: 0.38955527544021606
Norm Quadratic Average: 26.26866340637207
Nearest Class Center Accuracy: 0.99875

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.221074342727661
Linear Weight Rank: 10
Intra Cos: 0.7130239009857178
Inter Cos: 0.5006597638130188
Norm Quadratic Average: 30.10546875
Nearest Class Center Accuracy: 0.99975

Output Layer:
Intra Cos: 0.8271551132202148
Inter Cos: 0.6674929857254028
Norm Quadratic Average: 35.79549789428711
Nearest Class Center Accuracy: 0.999125

Test Set:
Average Loss: 3.173635940551758
Accuracy: 0.5885
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.2383159101009369, Weights: 0.04619121551513672
NC2 Equiangle: Features: 0.4368528578016493, Weights: 0.15019852320353191
NC3 Self-Duality: 0.46298497915267944
NC4 NCC Mismatch: 0.14900000000000002

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018480459228157997
Inter Cos: 0.09352367371320724
Norm Quadratic Average: 27.53066635131836
Nearest Class Center Accuracy: 0.2895

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.024888206273317337
Inter Cos: 0.07949893176555634
Norm Quadratic Average: 34.9291877746582
Nearest Class Center Accuracy: 0.316

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03275367245078087
Inter Cos: 0.08940417319536209
Norm Quadratic Average: 27.955644607543945
Nearest Class Center Accuracy: 0.3745

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0363893024623394
Inter Cos: 0.09250801056623459
Norm Quadratic Average: 33.930023193359375
Nearest Class Center Accuracy: 0.4305

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04997667670249939
Inter Cos: 0.11925467848777771
Norm Quadratic Average: 21.936861038208008
Nearest Class Center Accuracy: 0.4535

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.05370640382170677
Inter Cos: 0.11808644980192184
Norm Quadratic Average: 20.035423278808594
Nearest Class Center Accuracy: 0.467

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06025231257081032
Inter Cos: 0.132719948887825
Norm Quadratic Average: 10.875476837158203
Nearest Class Center Accuracy: 0.488

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06937024742364883
Inter Cos: 0.1298377364873886
Norm Quadratic Average: 8.045368194580078
Nearest Class Center Accuracy: 0.515

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 99.95556640625
Linear Weight Rank: 4031
Intra Cos: 0.12617361545562744
Inter Cos: 0.22396641969680786
Norm Quadratic Average: 30.453250885009766
Nearest Class Center Accuracy: 0.5805

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.440711975097656
Linear Weight Rank: 3670
Intra Cos: 0.21221975982189178
Inter Cos: 0.34116438031196594
Norm Quadratic Average: 24.570995330810547
Nearest Class Center Accuracy: 0.586

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.221074342727661
Linear Weight Rank: 10
Intra Cos: 0.2570846974849701
Inter Cos: 0.4225998818874359
Norm Quadratic Average: 27.848424911499023
Nearest Class Center Accuracy: 0.574

Output Layer:
Intra Cos: 0.2964727580547333
Inter Cos: 0.5288657546043396
Norm Quadratic Average: 32.9333381652832
Nearest Class Center Accuracy: 0.5525

