Model save path: ./New_Models/bn_False_dataset_CIFAR100_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: 0.022893192246556282
Inter Cos: 0.07011394202709198
Norm Quadratic Average: 29.326324462890625
Nearest Class Center Accuracy: 0.03258

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03324059396982193
Inter Cos: 0.04250185564160347
Norm Quadratic Average: 35.333961486816406
Nearest Class Center Accuracy: 0.04584

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03723817691206932
Inter Cos: 0.03498772904276848
Norm Quadratic Average: 43.700218200683594
Nearest Class Center Accuracy: 0.05362

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.034590527415275574
Inter Cos: 0.03344675898551941
Norm Quadratic Average: 70.4247055053711
Nearest Class Center Accuracy: 0.0632

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.036395393311977386
Inter Cos: 0.030956126749515533
Norm Quadratic Average: 45.63042449951172
Nearest Class Center Accuracy: 0.07036

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03740152716636658
Inter Cos: 0.030129358172416687
Norm Quadratic Average: 32.13869094848633
Nearest Class Center Accuracy: 0.07506

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0783362165093422
Inter Cos: 0.053659629076719284
Norm Quadratic Average: 12.320713996887207
Nearest Class Center Accuracy: 0.0848

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.246959388256073
Inter Cos: 0.14772862195968628
Norm Quadratic Average: 7.318599224090576
Nearest Class Center Accuracy: 0.09778

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 39.18346405029297
Linear Weight Rank: 4031
Intra Cos: 0.5812214016914368
Inter Cos: 0.3138904571533203
Norm Quadratic Average: 34.282501220703125
Nearest Class Center Accuracy: 0.1

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 17.969280242919922
Linear Weight Rank: 3662
Intra Cos: 0.6740447878837585
Inter Cos: 0.3351929485797882
Norm Quadratic Average: 36.67625045776367
Nearest Class Center Accuracy: 0.1

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 9.099804878234863
Linear Weight Rank: 98
Intra Cos: 0.7023642063140869
Inter Cos: 0.347329318523407
Norm Quadratic Average: 43.732940673828125
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.7636992931365967
Inter Cos: 0.46259915828704834
Norm Quadratic Average: 72.89753723144531
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 4.029720239257813
Accuracy: 0.488
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.22443678975105286, Weights: 0.034129973500967026
NC2 Equiangle: Features: 0.22221655026830808, Weights: 0.10656881313131313
NC3 Self-Duality: 0.5025668740272522
NC4 NCC Mismatch: 0.21889999999999998

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.006621266715228558
Inter Cos: 0.4067547917366028
Norm Quadratic Average: 29.42218589782715
Nearest Class Center Accuracy: 0.1136

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.012526378966867924
Inter Cos: 0.25810566544532776
Norm Quadratic Average: 35.55644226074219
Nearest Class Center Accuracy: 0.2377

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.019401326775550842
Inter Cos: 0.30632299184799194
Norm Quadratic Average: 43.99541473388672
Nearest Class Center Accuracy: 0.2964

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022686321288347244
Inter Cos: 0.2636723518371582
Norm Quadratic Average: 70.92595672607422
Nearest Class Center Accuracy: 0.3716

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.021999511867761612
Inter Cos: 0.2249712347984314
Norm Quadratic Average: 46.01097869873047
Nearest Class Center Accuracy: 0.4617

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.020664386451244354
Inter Cos: 0.176980659365654
Norm Quadratic Average: 32.311676025390625
Nearest Class Center Accuracy: 0.5059

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03076063096523285
Inter Cos: 0.22949904203414917
Norm Quadratic Average: 12.245462417602539
Nearest Class Center Accuracy: 0.5123

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.061900798231363297
Inter Cos: 0.4056284725666046
Norm Quadratic Average: 7.122005939483643
Nearest Class Center Accuracy: 0.514

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 39.18346405029297
Linear Weight Rank: 4031
Intra Cos: 0.13064374029636383
Inter Cos: 0.5721583962440491
Norm Quadratic Average: 31.8709716796875
Nearest Class Center Accuracy: 0.4989

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 17.969280242919922
Linear Weight Rank: 3662
Intra Cos: 0.15252597630023956
Inter Cos: 0.6004169583320618
Norm Quadratic Average: 33.816341400146484
Nearest Class Center Accuracy: 0.4921

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 9.099804878234863
Linear Weight Rank: 98
Intra Cos: 0.15240348875522614
Inter Cos: 0.620651364326477
Norm Quadratic Average: 40.50705337524414
Nearest Class Center Accuracy: 0.4838

Output Layer:
Intra Cos: 0.15499454736709595
Inter Cos: 0.6982842087745667
Norm Quadratic Average: 67.92990112304688
Nearest Class Center Accuracy: 0.4647

