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_0.005.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: 0.024736402556300163
Inter Cos: 0.027946259826421738
Norm Quadratic Average: 4.050092697143555
Nearest Class Center Accuracy: 0.04714

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02199495956301689
Inter Cos: 0.024541813880205154
Norm Quadratic Average: 2.0525388717651367
Nearest Class Center Accuracy: 0.06078

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01719563454389572
Inter Cos: 0.01763158291578293
Norm Quadratic Average: 1.4896316528320312
Nearest Class Center Accuracy: 0.0697

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02367786318063736
Inter Cos: 0.021055489778518677
Norm Quadratic Average: 1.0968092679977417
Nearest Class Center Accuracy: 0.08132

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03137378767132759
Inter Cos: 0.02748420089483261
Norm Quadratic Average: 0.9078487753868103
Nearest Class Center Accuracy: 0.091

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.13036306202411652
Inter Cos: 0.08043055981397629
Norm Quadratic Average: 0.7202395796775818
Nearest Class Center Accuracy: 0.09964

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.7159105539321899
Inter Cos: 0.20655745267868042
Norm Quadratic Average: 1.08445143699646
Nearest Class Center Accuracy: 0.1

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 4.16268253326416
Linear Weight Rank: 423
Intra Cos: 0.9470294117927551
Inter Cos: 0.31302952766418457
Norm Quadratic Average: 40.21343231201172
Nearest Class Center Accuracy: 0.1

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 4.233567237854004
Linear Weight Rank: 1701
Intra Cos: 0.9652356505393982
Inter Cos: 0.3374714255332947
Norm Quadratic Average: 31.993696212768555
Nearest Class Center Accuracy: 0.1

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 4.272308826446533
Linear Weight Rank: 96
Intra Cos: 0.9646866917610168
Inter Cos: 0.342146098613739
Norm Quadratic Average: 29.34085464477539
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.9655467867851257
Inter Cos: 0.3844153583049774
Norm Quadratic Average: 28.995479583740234
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 1.608461771774292
Accuracy: 0.6102
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.22006665170192719, Weights: 0.012764292769134045
NC2 Equiangle: Features: 0.2051602450284091, Weights: 0.16859792998342804
NC3 Self-Duality: 0.19139324128627777
NC4 NCC Mismatch: 0.14170000000000005

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: 0.012721204198896885
Inter Cos: 0.24684782326221466
Norm Quadratic Average: 4.078277111053467
Nearest Class Center Accuracy: 0.2553

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.015650883316993713
Inter Cos: 0.20761263370513916
Norm Quadratic Average: 2.0680418014526367
Nearest Class Center Accuracy: 0.3975

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.013567048124969006
Inter Cos: 0.14683912694454193
Norm Quadratic Average: 1.4961680173873901
Nearest Class Center Accuracy: 0.5266

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.012938368134200573
Inter Cos: 0.14863444864749908
Norm Quadratic Average: 1.0989805459976196
Nearest Class Center Accuracy: 0.6323

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.012904799543321133
Inter Cos: 0.15330664813518524
Norm Quadratic Average: 0.9019291996955872
Nearest Class Center Accuracy: 0.6912

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03753187879920006
Inter Cos: 0.31384000182151794
Norm Quadratic Average: 0.6934049129486084
Nearest Class Center Accuracy: 0.6647

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.12542015314102173
Inter Cos: 0.5212472081184387
Norm Quadratic Average: 0.9056551456451416
Nearest Class Center Accuracy: 0.6267

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 4.16268253326416
Linear Weight Rank: 423
Intra Cos: 0.249410018324852
Inter Cos: 0.5541515350341797
Norm Quadratic Average: 31.224897384643555
Nearest Class Center Accuracy: 0.6148

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 4.233567237854004
Linear Weight Rank: 1701
Intra Cos: 0.2699541449546814
Inter Cos: 0.5684042572975159
Norm Quadratic Average: 25.138479232788086
Nearest Class Center Accuracy: 0.6097

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 4.272308826446533
Linear Weight Rank: 96
Intra Cos: 0.25646188855171204
Inter Cos: 0.5638638734817505
Norm Quadratic Average: 23.340099334716797
Nearest Class Center Accuracy: 0.608

Output Layer:
Intra Cos: 0.2467888742685318
Inter Cos: 0.5745282173156738
Norm Quadratic Average: 22.905681610107422
Nearest Class Center Accuracy: 0.6042

