Model save path: /content/drive/MyDrive/Neural Collapse/New_Models/bn_True_dataset_MNIST_epochs_150_lr_0.001_model_type_vgg11_train_samples_None_weight_decay_0.005.pth.tar
Training Set:
Layer 0: Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09116753190755844
Inter Cos: 0.10967154055833817
Norm Quadratic Average: 23.567678451538086
Nearest Class Center Accuracy: 0.8079833333333334

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09850166738033295
Inter Cos: 0.11726351082324982
Norm Quadratic Average: 13.869457244873047
Nearest Class Center Accuracy: 0.8358833333333333

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1646682471036911
Inter Cos: 0.14020948112010956
Norm Quadratic Average: 8.809064865112305
Nearest Class Center Accuracy: 0.87915

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.18446461856365204
Inter Cos: 0.1426190733909607
Norm Quadratic Average: 8.256636619567871
Nearest Class Center Accuracy: 0.9110166666666667

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.24878132343292236
Inter Cos: 0.13163092732429504
Norm Quadratic Average: 5.267523765563965
Nearest Class Center Accuracy: 0.9680166666666666

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.3243841528892517
Inter Cos: 0.16940739750862122
Norm Quadratic Average: 5.357837200164795
Nearest Class Center Accuracy: 0.9886333333333334

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.4755420386791229
Inter Cos: 0.1643066704273224
Norm Quadratic Average: 3.5541207790374756
Nearest Class Center Accuracy: 0.99935

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.829107940196991
Inter Cos: 0.11446298658847809
Norm Quadratic Average: 3.040884017944336
Nearest Class Center Accuracy: 1.0

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 14.81345272064209
Linear Weight Rank: 4031
Intra Cos: 0.9813165068626404
Inter Cos: 0.12043066322803497
Norm Quadratic Average: 39.12929153442383
Nearest Class Center Accuracy: 1.0

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 6.213501453399658
Linear Weight Rank: 3668
Intra Cos: 0.993170976638794
Inter Cos: 0.14514541625976562
Norm Quadratic Average: 22.764822006225586
Nearest Class Center Accuracy: 1.0

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 1.8475136756896973
Linear Weight Rank: 10
Intra Cos: 0.99270099401474
Inter Cos: 0.1532973200082779
Norm Quadratic Average: 15.007268905639648
Nearest Class Center Accuracy: 1.0

Output Layer:
Intra Cos: 0.9943106174468994
Inter Cos: 0.2908537983894348
Norm Quadratic Average: 10.859210968017578
Nearest Class Center Accuracy: 1.0

Test Set:
Average Loss: 0.021459842890501023
Accuracy: 0.993
NC1 Within Class Collapse: 0.25301116704940796
NC2 Equinorm: Features: 0.07004628330469131, Weights: 0.024877237156033516
NC2 Equiangle: Features: 0.19893663194444444, Weights: 0.13945020039876302
NC3 Self-Duality: 0.049179382622241974
NC4 NCC Mismatch: 0.0004999999999999449

Layer 0: Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.10133807361125946
Inter Cos: 0.11957792937755585
Norm Quadratic Average: 22.82430076599121
Nearest Class Center Accuracy: 0.809

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.12232237309217453
Inter Cos: 0.12313847243785858
Norm Quadratic Average: 13.669638633728027
Nearest Class Center Accuracy: 0.8335

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.16768884658813477
Inter Cos: 0.1526288390159607
Norm Quadratic Average: 8.70426082611084
Nearest Class Center Accuracy: 0.8735

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.19349037110805511
Inter Cos: 0.14484503865242004
Norm Quadratic Average: 8.158408164978027
Nearest Class Center Accuracy: 0.9125

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.2600148022174835
Inter Cos: 0.1258164942264557
Norm Quadratic Average: 5.226772785186768
Nearest Class Center Accuracy: 0.964

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.3321843445301056
Inter Cos: 0.182317852973938
Norm Quadratic Average: 5.333085060119629
Nearest Class Center Accuracy: 0.983

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.46281901001930237
Inter Cos: 0.17390109598636627
Norm Quadratic Average: 3.533700704574585
Nearest Class Center Accuracy: 0.9905

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.8042680025100708
Inter Cos: 0.13465720415115356
Norm Quadratic Average: 3.0084939002990723
Nearest Class Center Accuracy: 0.993

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 14.81345272064209
Linear Weight Rank: 4031
Intra Cos: 0.9493004083633423
Inter Cos: 0.13375253975391388
Norm Quadratic Average: 38.54661178588867
Nearest Class Center Accuracy: 0.993

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 6.213501453399658
Linear Weight Rank: 3668
Intra Cos: 0.9591394662857056
Inter Cos: 0.15388816595077515
Norm Quadratic Average: 22.438339233398438
Nearest Class Center Accuracy: 0.993

Layer 10: Linear(in_features=4096, out_features=10, bias=True)
Linear Weight Norm: 1.8475136756896973
Linear Weight Rank: 10
Intra Cos: 0.9557662606239319
Inter Cos: 0.15302464365959167
Norm Quadratic Average: 14.808906555175781
Nearest Class Center Accuracy: 0.9935

Output Layer:
Intra Cos: 0.9542171359062195
Inter Cos: 0.28623124957084656
Norm Quadratic Average: 10.713576316833496
Nearest Class Center Accuracy: 0.9935

