Model save path: ./New_Models/bn_True_dataset_CIFAR100_epochs_200_lr_0.01_model_type_vgg11_rand_seed_979323_test_samples_None_train_samples_None_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: 0.022893190383911133
Inter Cos: 0.07011394202709198
Norm Quadratic Average: 29.326318740844727
Nearest Class Center Accuracy: 0.03258

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02645130828022957
Inter Cos: 0.025554127991199493
Norm Quadratic Average: 72.39088439941406
Nearest Class Center Accuracy: 0.04866

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022691085934638977
Inter Cos: 0.02276359498500824
Norm Quadratic Average: 38.94482421875
Nearest Class Center Accuracy: 0.0607

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.019642416387796402
Inter Cos: 0.021831337362527847
Norm Quadratic Average: 37.681270599365234
Nearest Class Center Accuracy: 0.06918

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.026070991531014442
Inter Cos: 0.02623966708779335
Norm Quadratic Average: 23.77170753479004
Nearest Class Center Accuracy: 0.07762

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03509677201509476
Inter Cos: 0.0337119959294796
Norm Quadratic Average: 27.745214462280273
Nearest Class Center Accuracy: 0.08308

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08079664409160614
Inter Cos: 0.060640592128038406
Norm Quadratic Average: 21.24321746826172
Nearest Class Center Accuracy: 0.09284

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.2519989013671875
Inter Cos: 0.15054410696029663
Norm Quadratic Average: 17.72758674621582
Nearest Class Center Accuracy: 0.09864

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 74.57171630859375
Linear Weight Rank: 4031
Intra Cos: 0.5381295084953308
Inter Cos: 0.2417670041322708
Norm Quadratic Average: 49.41700744628906
Nearest Class Center Accuracy: 0.09996

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 33.91197204589844
Linear Weight Rank: 3661
Intra Cos: 0.745682418346405
Inter Cos: 0.25513955950737
Norm Quadratic Average: 41.259765625
Nearest Class Center Accuracy: 0.1

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 14.089248657226562
Linear Weight Rank: 98
Intra Cos: 0.8163048624992371
Inter Cos: 0.2850431203842163
Norm Quadratic Average: 42.10606002807617
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.9058216214179993
Inter Cos: 0.5415347218513489
Norm Quadratic Average: 83.79674530029297
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 4.24040530090332
Accuracy: 0.5342
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.30759868025779724, Weights: 0.032878633588552475
NC2 Equiangle: Features: 0.15531184649226643, Weights: 0.1008248870541351
NC3 Self-Duality: 0.5806363821029663
NC4 NCC Mismatch: 0.17420000000000002

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.006621268577873707
Inter Cos: 0.4067547619342804
Norm Quadratic Average: 29.42218780517578
Nearest Class Center Accuracy: 0.1136

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.012087232433259487
Inter Cos: 0.25776320695877075
Norm Quadratic Average: 72.91175079345703
Nearest Class Center Accuracy: 0.2612

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01562450546771288
Inter Cos: 0.21426476538181305
Norm Quadratic Average: 39.24227523803711
Nearest Class Center Accuracy: 0.3849

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.015338978730142117
Inter Cos: 0.16409340500831604
Norm Quadratic Average: 37.87693405151367
Nearest Class Center Accuracy: 0.4914

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.015255262143909931
Inter Cos: 0.16609592735767365
Norm Quadratic Average: 23.856266021728516
Nearest Class Center Accuracy: 0.5668

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.015564906410872936
Inter Cos: 0.1510886698961258
Norm Quadratic Average: 27.735700607299805
Nearest Class Center Accuracy: 0.5851

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02694767341017723
Inter Cos: 0.2078665792942047
Norm Quadratic Average: 21.070846557617188
Nearest Class Center Accuracy: 0.5776

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06314755976200104
Inter Cos: 0.36967143416404724
Norm Quadratic Average: 17.05632972717285
Nearest Class Center Accuracy: 0.5553

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 74.57171630859375
Linear Weight Rank: 4031
Intra Cos: 0.1175600215792656
Inter Cos: 0.4795418679714203
Norm Quadratic Average: 43.988224029541016
Nearest Class Center Accuracy: 0.5381

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 33.91197204589844
Linear Weight Rank: 3661
Intra Cos: 0.15294452011585236
Inter Cos: 0.4531804025173187
Norm Quadratic Average: 33.59599304199219
Nearest Class Center Accuracy: 0.5274

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 14.089248657226562
Linear Weight Rank: 98
Intra Cos: 0.16093772649765015
Inter Cos: 0.5347710847854614
Norm Quadratic Average: 33.38335037231445
Nearest Class Center Accuracy: 0.525

Output Layer:
Intra Cos: 0.1893942803144455
Inter Cos: 0.7155861854553223
Norm Quadratic Average: 66.40071868896484
Nearest Class Center Accuracy: 0.517

