Model save path: ./New_Models/bn_False_dataset_CIFAR100_epochs_200_lr_0.01_model_type_vgg11_rand_seed_979323_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: 0.022893190383911133
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: 0.035750336945056915
Inter Cos: 0.04768183454871178
Norm Quadratic Average: 35.46011734008789
Nearest Class Center Accuracy: 0.04502

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.037967998534440994
Inter Cos: 0.036172930151224136
Norm Quadratic Average: 44.638248443603516
Nearest Class Center Accuracy: 0.0539

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.034384068101644516
Inter Cos: 0.03559431806206703
Norm Quadratic Average: 74.62657165527344
Nearest Class Center Accuracy: 0.06298

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03587530180811882
Inter Cos: 0.03184564784169197
Norm Quadratic Average: 49.2230339050293
Nearest Class Center Accuracy: 0.07168

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0360482893884182
Inter Cos: 0.03318847715854645
Norm Quadratic Average: 34.97218322753906
Nearest Class Center Accuracy: 0.07506

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07455752044916153
Inter Cos: 0.04916807636618614
Norm Quadratic Average: 13.294055938720703
Nearest Class Center Accuracy: 0.08628

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.2285350114107132
Inter Cos: 0.1402471661567688
Norm Quadratic Average: 7.865527629852295
Nearest Class Center Accuracy: 0.09778

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 39.159976959228516
Linear Weight Rank: 4031
Intra Cos: 0.5667388439178467
Inter Cos: 0.304694265127182
Norm Quadratic Average: 35.990013122558594
Nearest Class Center Accuracy: 0.09998

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 17.910829544067383
Linear Weight Rank: 3662
Intra Cos: 0.6687776446342468
Inter Cos: 0.31765881180763245
Norm Quadratic Average: 38.01213455200195
Nearest Class Center Accuracy: 0.1

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 8.957038879394531
Linear Weight Rank: 98
Intra Cos: 0.6915813684463501
Inter Cos: 0.3174237012863159
Norm Quadratic Average: 45.07140350341797
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.7329612970352173
Inter Cos: 0.40091365575790405
Norm Quadratic Average: 73.98966217041016
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 4.20777018623352
Accuracy: 0.4823
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.2231701761484146, Weights: 0.03240422159433365
NC2 Equiangle: Features: 0.2215655332623106, Weights: 0.10679080847537879
NC3 Self-Duality: 0.5074807405471802
NC4 NCC Mismatch: 0.23109999999999997

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: 0.015580707229673862
Inter Cos: 0.28184741735458374
Norm Quadratic Average: 35.67485046386719
Nearest Class Center Accuracy: 0.2298

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02074417844414711
Inter Cos: 0.3141317665576935
Norm Quadratic Average: 44.944705963134766
Nearest Class Center Accuracy: 0.2914

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02300519123673439
Inter Cos: 0.270374596118927
Norm Quadratic Average: 75.19300842285156
Nearest Class Center Accuracy: 0.368

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02320381999015808
Inter Cos: 0.24579748511314392
Norm Quadratic Average: 49.66153335571289
Nearest Class Center Accuracy: 0.4565

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022052716463804245
Inter Cos: 0.18233677744865417
Norm Quadratic Average: 35.18890380859375
Nearest Class Center Accuracy: 0.5019

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.029442567378282547
Inter Cos: 0.21666404604911804
Norm Quadratic Average: 13.243189811706543
Nearest Class Center Accuracy: 0.5085

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.059341106563806534
Inter Cos: 0.38221997022628784
Norm Quadratic Average: 7.6805100440979
Nearest Class Center Accuracy: 0.5068

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 39.159976959228516
Linear Weight Rank: 4031
Intra Cos: 0.12080962210893631
Inter Cos: 0.5438640713691711
Norm Quadratic Average: 33.62993240356445
Nearest Class Center Accuracy: 0.4958

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 17.910829544067383
Linear Weight Rank: 3662
Intra Cos: 0.14099469780921936
Inter Cos: 0.5811905264854431
Norm Quadratic Average: 35.189491271972656
Nearest Class Center Accuracy: 0.4858

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 8.957038879394531
Linear Weight Rank: 98
Intra Cos: 0.14101077616214752
Inter Cos: 0.598313570022583
Norm Quadratic Average: 41.88288497924805
Nearest Class Center Accuracy: 0.4759

Output Layer:
Intra Cos: 0.14916731417179108
Inter Cos: 0.6783335208892822
Norm Quadratic Average: 69.1373519897461
Nearest Class Center Accuracy: 0.4575

