Model save path: ./New_Models/bn_False_dataset_CIFAR10_epochs_100_lr_0.001_model_type_vgg11_rand_seed_265358_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: 0.021450400352478027
Inter Cos: 0.11371058970689774
Norm Quadratic Average: 27.691621780395508
Nearest Class Center Accuracy: 0.279125

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.026181500405073166
Inter Cos: 0.10413137823343277
Norm Quadratic Average: 31.167165756225586
Nearest Class Center Accuracy: 0.310875

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.035160403698682785
Inter Cos: 0.11512572318315506
Norm Quadratic Average: 23.865604400634766
Nearest Class Center Accuracy: 0.35975

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03922928869724274
Inter Cos: 0.10601218789815903
Norm Quadratic Average: 27.71919059753418
Nearest Class Center Accuracy: 0.408625

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0535813607275486
Inter Cos: 0.1275155395269394
Norm Quadratic Average: 17.50363540649414
Nearest Class Center Accuracy: 0.4415

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06614863872528076
Inter Cos: 0.13190865516662598
Norm Quadratic Average: 16.47551727294922
Nearest Class Center Accuracy: 0.47

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08684319257736206
Inter Cos: 0.14374470710754395
Norm Quadratic Average: 9.460034370422363
Nearest Class Center Accuracy: 0.51375

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.11775068938732147
Inter Cos: 0.1527339369058609
Norm Quadratic Average: 7.1630635261535645
Nearest Class Center Accuracy: 0.6905

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 100.92411041259766
Linear Weight Rank: 4031
Intra Cos: 0.30755725502967834
Inter Cos: 0.2764001488685608
Norm Quadratic Average: 29.010425567626953
Nearest Class Center Accuracy: 0.968

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.848114013671875
Linear Weight Rank: 3671
Intra Cos: 0.5887490510940552
Inter Cos: 0.43667641282081604
Norm Quadratic Average: 25.248199462890625
Nearest Class Center Accuracy: 0.999125

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.2955453395843506
Linear Weight Rank: 10
Intra Cos: 0.7361316084861755
Inter Cos: 0.5463135838508606
Norm Quadratic Average: 29.790910720825195
Nearest Class Center Accuracy: 0.999625

Output Layer:
Intra Cos: 0.8288199305534363
Inter Cos: 0.719312846660614
Norm Quadratic Average: 37.1626091003418
Nearest Class Center Accuracy: 0.999375

Test Set:
Average Loss: 3.238515380859375
Accuracy: 0.5915
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.23925313353538513, Weights: 0.04044013470411301
NC2 Equiangle: Features: 0.44131893581814235, Weights: 0.1654062059190538
NC3 Self-Duality: 0.46194902062416077
NC4 NCC Mismatch: 0.13049999999999995

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.018480459228157997
Inter Cos: 0.09352368116378784
Norm Quadratic Average: 27.530664443969727
Nearest Class Center Accuracy: 0.2895

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.025032229721546173
Inter Cos: 0.09722353518009186
Norm Quadratic Average: 31.063798904418945
Nearest Class Center Accuracy: 0.3325

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03664600849151611
Inter Cos: 0.11166268587112427
Norm Quadratic Average: 23.788049697875977
Nearest Class Center Accuracy: 0.3755

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04002590477466583
Inter Cos: 0.103730209171772
Norm Quadratic Average: 27.65555763244629
Nearest Class Center Accuracy: 0.4305

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.052187178283929825
Inter Cos: 0.11577253043651581
Norm Quadratic Average: 17.444217681884766
Nearest Class Center Accuracy: 0.46

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06291139125823975
Inter Cos: 0.12095940858125687
Norm Quadratic Average: 16.430904388427734
Nearest Class Center Accuracy: 0.4745

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07522913813591003
Inter Cos: 0.13127319514751434
Norm Quadratic Average: 9.421233177185059
Nearest Class Center Accuracy: 0.475

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08441169559955597
Inter Cos: 0.13908492028713226
Norm Quadratic Average: 7.099547386169434
Nearest Class Center Accuracy: 0.52

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 100.92411041259766
Linear Weight Rank: 4031
Intra Cos: 0.14058876037597656
Inter Cos: 0.2396424412727356
Norm Quadratic Average: 27.93899917602539
Nearest Class Center Accuracy: 0.582

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 40.848114013671875
Linear Weight Rank: 3671
Intra Cos: 0.21708877384662628
Inter Cos: 0.37120702862739563
Norm Quadratic Average: 23.619312286376953
Nearest Class Center Accuracy: 0.5855

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 3.2955453395843506
Linear Weight Rank: 10
Intra Cos: 0.2522507607936859
Inter Cos: 0.46447160840034485
Norm Quadratic Average: 27.63776397705078
Nearest Class Center Accuracy: 0.5765

Output Layer:
Intra Cos: 0.2837269604206085
Inter Cos: 0.598472535610199
Norm Quadratic Average: 34.35707473754883
Nearest Class Center Accuracy: 0.5585

