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E.g.--learning-rate-scales=AffineComponent=0.5If set, will truncate the neural net to this many components by removing the last components.Set this to true to remove any dropout components.If set, set the dropout scale in any dropout components to this value. Note: in traditional dropout, this is always zero; you can set it to any value between zero and one.Set this to true to replace components of type AffineComponentPreconditioned with AffineComponent.Before copying neural net, copy the statistics in any layer of type NonlinearComponent, from this neural network: provide the extended filename.If true, collapse sequences of AffineComponents and FixedAffineComponents to compactify modelOnly relevant if collapse=true; set this to false to collapse mixed types.Expected --learning-rates option to be a Unknown format for --learning-rate-scales option. --learning-rate-scales=AffineComponent=0.1:AffineComponentPreconditioned=0.5 Expected --scales option to be a 88888.-8/.8//80/800810821 8 2 2 8 3 2 8 3 3 8 4 3 8 4 4854A888:5867Y6k6676 8 7"##8$7$a8'7()7)8):8*8*T8.9 99}mainN5kaldi15KaldiFatalErrorE?std^ $rJ( M< .(~ffqkfy\&xN zwz X& eM PK'WITY&eZ&[&]&^j'`)a*b*l )(.)EK)bh{  ))  2B yVf y~+  y5  y>    F3>y<O^hzy <X~hzy <ahz y<thz<xhz<}hzhzhz<hzyyy=  Q\p  y   ; 3 H]  r  y y y%.:J*2_o6yy y9D]h*08$end@$endHQ Z " c; A lZ ` uy  }         3 9 N ^  ys ~           + 6  vO Z  $at2r }  $atGv  Wv b mv x  5 @ Y d y} y *     ' y @ K yd t  y *; yJ Z q.  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