Adding Lab 4.
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{
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"seed": 42,
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"latent_dim": 32,
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"diversity_weights": [
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0.0,
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0.1,
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0.5,
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1.0
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],
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"batch_size": 256,
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"learning_rate": 0.0002,
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"classifier_epochs": 10,
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"pretrain_epochs": 16,
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"collapse_epochs": 8,
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"recovery_epochs": 5,
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"collapse_digits": [
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1,
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7
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],
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"mode_frequency_threshold": 0.01,
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"evaluation_samples": 2500,
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"device": "cpu"
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}
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epoch,generator,adversarial,diversity,discriminator
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1,3.902446909713745,3.9426742090861002,-0.4022729824066162,4.2679691879311577e-05
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2,4.145200756327311,4.184955227152506,-0.397544775835673,1.7615805166618278e-05
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3,4.139080067443848,4.17894912923177,-0.3986907406489054,1.4372343609769207e-05
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4,4.3337121688842775,4.373950477091471,-0.40238287711143494,1.139676257832131e-05
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5,4.437606961568196,4.4788080523173015,-0.4120109644730886,9.15210223950756e-06
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epoch,generator,adversarial,diversity,discriminator
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1,3.9779018229166665,4.181639106241862,-0.4074745655536652,3.138090945431031e-05
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2,4.384994900258382,4.590004500834147,-0.410019177420934,1.108799576322781e-05
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3,4.442870346577962,4.649945588429769,-0.41415047810872396,8.148233926234146e-06
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4,4.501311336263021,4.709003438568115,-0.41538419888814293,6.7271921303472485e-06
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5,4.567720675404867,4.779216910552979,-0.42299244691530863,5.819472274743021e-06
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epoch,generator,adversarial,diversity,discriminator
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1,3.792231286239624,3.792231286239624,-0.39822079056104026,5.601679666045432e-05
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2,3.7167933046976724,3.7167933046976724,-0.38705200486183167,2.9127169354857565e-05
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3,3.774656072998047,3.774656072998047,-0.3904325690587362,2.4593643052503468e-05
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4,4.0028360649108885,4.0028360649108885,-0.39604260533650715,1.812106814662305e-05
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5,4.161207312520345,4.161207312520345,-0.404033615175883,1.4115302633338919e-05
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epoch,generator,adversarial,diversity,discriminator
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1,4.073459280141194,4.48742034072876,-0.4139610550562541,2.2277594858314842e-05
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2,4.556306339263916,4.980417476145426,-0.4241111308892568,6.92474058305379e-06
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3,4.663601414998372,5.0912053082784015,-0.42760391337076825,4.824752171892518e-06
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4,4.601830291493734,5.031373355865479,-0.42954305254618325,4.1199866602255495e-06
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5,4.665722238413493,5.104604846191406,-0.4388826152801514,3.713102867671599e-06
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Configuration,Lambda diversity,mode_count,normalized_entropy,classifier_confidence,feature_diversity,within_class_diversity,Recovery seconds
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Collapsed start,,1,0.010357953608036041,0.9805793166160583,16.11749267578125,11.458220958709717,
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lambda=0,0.0,1,0.021068770438432693,0.9541524648666382,16.436365127563477,11.539350509643555,775.4761435985565
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lambda=0.1,0.1,2,0.05791272968053818,0.9331758618354797,16.686689376831055,11.85418701171875,772.15576171875
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lambda=0.5,0.5,2,0.052982352674007416,0.9251078963279724,17.057252883911133,12.603004455566406,753.808944940567
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lambda=1,1.0,2,0.04722726345062256,0.9086208939552307,17.2882137298584,13.533077716827393,775.8287467956543
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\begin{tabular}{lrrrrrr}
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\toprule
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Configuration & $\lambda_{div}$ & Modes & Entropy & Confidence & Feature diversity & Within-class diversity \\
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\midrule
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Collapsed start & NaN & 1 & 0.010 & 0.981 & 16.117 & 11.458 \\
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lambda=0 & 0.000 & 1 & 0.021 & 0.954 & 16.436 & 11.539 \\
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lambda=0.1 & 0.100 & 2 & 0.058 & 0.933 & 16.687 & 11.854 \\
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lambda=0.5 & 0.500 & 2 & 0.053 & 0.925 & 17.057 & 12.603 \\
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lambda=1 & 1.000 & 2 & 0.047 & 0.909 & 17.288 & 13.533 \\
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\bottomrule
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\end{tabular}
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