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_0.005.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: 0.03895308077335358
Inter Cos: 0.06986457109451294
Norm Quadratic Average: 36.322967529296875
Nearest Class Center Accuracy: 0.0428

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03793368861079216
Inter Cos: 0.03679109737277031
Norm Quadratic Average: 36.43424987792969
Nearest Class Center Accuracy: 0.05346

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03345315158367157
Inter Cos: 0.03459278866648674
Norm Quadratic Average: 32.498435974121094
Nearest Class Center Accuracy: 0.06272

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03860967978835106
Inter Cos: 0.03499249741435051
Norm Quadratic Average: 8.058075904846191
Nearest Class Center Accuracy: 0.0737

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06714256852865219
Inter Cos: 0.05161260813474655
Norm Quadratic Average: 1.675874948501587
Nearest Class Center Accuracy: 0.08504

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.6095001101493835
Inter Cos: 0.5062262415885925
Norm Quadratic Average: 0.6649205684661865
Nearest Class Center Accuracy: 0.09748

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.8618937730789185
Inter Cos: 0.623478889465332
Norm Quadratic Average: 1.674625039100647
Nearest Class Center Accuracy: 0.09834

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 4.944418430328369
Linear Weight Rank: 133
Intra Cos: 0.9109517931938171
Inter Cos: 0.6405748724937439
Norm Quadratic Average: 21.815364837646484
Nearest Class Center Accuracy: 0.09894

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 5.045917987823486
Linear Weight Rank: 2789
Intra Cos: 0.9079169631004333
Inter Cos: 0.6329241991043091
Norm Quadratic Average: 39.60784149169922
Nearest Class Center Accuracy: 0.09982

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 5.141852378845215
Linear Weight Rank: 96
Intra Cos: 0.8823862075805664
Inter Cos: 0.5751996636390686
Norm Quadratic Average: 52.28977584838867
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.933627188205719
Inter Cos: 0.6493109464645386
Norm Quadratic Average: 68.81901550292969
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 3.6165312152862548
Accuracy: 0.4265
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.2657139301300049, Weights: 0.058336444199085236
NC2 Equiangle: Features: 0.31632472182765153, Weights: 0.15527994791666666
NC3 Self-Duality: 0.47712552547454834
NC4 NCC Mismatch: 0.30479999999999996

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: 0.013713306747376919
Inter Cos: 0.30873903632164
Norm Quadratic Average: 36.512481689453125
Nearest Class Center Accuracy: 0.2032

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.020230848342180252
Inter Cos: 0.32461029291152954
Norm Quadratic Average: 36.65508270263672
Nearest Class Center Accuracy: 0.2812

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022514332085847855
Inter Cos: 0.2616783678531647
Norm Quadratic Average: 32.70930862426758
Nearest Class Center Accuracy: 0.3777

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02396523579955101
Inter Cos: 0.26037532091140747
Norm Quadratic Average: 8.114806175231934
Nearest Class Center Accuracy: 0.5044

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03805010765790939
Inter Cos: 0.33703574538230896
Norm Quadratic Average: 1.6766412258148193
Nearest Class Center Accuracy: 0.5609

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.1846942901611328
Inter Cos: 0.7674046754837036
Norm Quadratic Average: 0.6441897749900818
Nearest Class Center Accuracy: 0.4297

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.22071291506290436
Inter Cos: 0.8023641109466553
Norm Quadratic Average: 1.5998317003250122
Nearest Class Center Accuracy: 0.4055

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 4.944418430328369
Linear Weight Rank: 133
Intra Cos: 0.21604213118553162
Inter Cos: 0.7862481474876404
Norm Quadratic Average: 20.682666778564453
Nearest Class Center Accuracy: 0.4071

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 5.045917987823486
Linear Weight Rank: 2789
Intra Cos: 0.2060367465019226
Inter Cos: 0.7548009157180786
Norm Quadratic Average: 37.34673309326172
Nearest Class Center Accuracy: 0.4096

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 5.141852378845215
Linear Weight Rank: 96
Intra Cos: 0.220782071352005
Inter Cos: 0.6985980868339539
Norm Quadratic Average: 49.62541961669922
Nearest Class Center Accuracy: 0.408

Output Layer:
Intra Cos: 0.23354840278625488
Inter Cos: 0.7230944633483887
Norm Quadratic Average: 64.69276428222656
Nearest Class Center Accuracy: 0.4082

