K=10,T=0.8: = ' ' , loss = ' ' , lr = ' ' ) : super ( ) . _ _ init _ _ ( ) device = torch . device ( " cuda " if torch . cuda . is _ available ( ) else " cpu " ) self . layer = layer [ 0 ] self . res _ seq = list ( layer [ 1 ] ) for idx , i in enumerate ( self . res _ seq ) _ _ ) _ ) K=10,T=0.8: kl _ trade _ off _ lambda * self . ops [ ' kl _ loss ' ] ) \ + self . params [ " qed _ trade _ off _ lambda " ] * self . ops [ ' total _ qed _ loss ' ] def gated _ regression ( self , last _ h , regression _ gate , regression _ transform , hidden _ size , projection _ weight , projection _ bias , v , mask ) : ( ) ( = K=10,T=0.8: argparse . argument parser ( description = ' py torch image net validation ' ) parser . add _ argument ( ' data ' , metavar = ' dir ' , help = ' path to dataset ' ) parser . add _ argument ( ' - - model ' , ' - m ' , metavar = ' model ' , default = ' dpn 9 2 ' , help = ' model architecture ( default : dpn 9 2 ) ' ) parser ) , ) = ( ) = ) ) _ ) ) K=10,T=0.8: ( ( ' train ' , ' test ' ) , loc = 0 ) ax 1 . grid ( b = true , which = ' ' ) plt . ( ' / ' + network _ choice + ' . ' ) print ( ' plot save as ' + network _ choice + ' . in the / folder ' ) from keras . models import sequential from keras . layers . import dense , dropout , activation , flatten from keras , = _ _ K=10,T=0.8: else : raise cnn not found error ( " cnn name not found ! " ) rnn = self . config [ ' train ' ] [ ' rnn ' ] [ ' name ' ] self . hidden _ num = int ( self . config [ ' train ' ] [ ' lstm ' ] [ ' hidden _ num ' ] ) dropout = int ( self . config [ ' train ' ] [ ' lstm ' ] [ . _ ) K=10,T=0.8: config . epochs : logging . info ( ' epoch % d : ' , checkpoint . state . epoch ) for b , ( inputs , labels ) in enumerate ( cpdata . load _ batches ( self . data [ ' train ' ] ) ) : loss , trainable _ params , new _ model _ state , optimizer _ state , mixed = update _ fn ( trainable _ params , fixed _ params , inputs , labels , model _ state , _ . . , , _ , _ ) . = = , , _ ( _ = ' _ , ) ] _ , = , ( _ , K=10,T=0.8: bound _ size = config . size _ bound , bound _ weight = config . size _ bound _ weight , transform = size _ transform _ fn ) elif config . size _ loss = = ' ' : selected _ size _ loss _ fn = cputils . compute _ _ size _ loss else : raise value error ( ' invalid size loss . ' ) classes = self . data [ ' classes ' ] if . _ _ = _ _ ) K=10,T=0.8: ax . set _ ( [ f ' { a } ' for a in [ 2 0 , 3 0 , 4 0 , 4 5 ] ] ) elif args . nasspace = = ' nasbench 1 0 1 ' and args . dataset = = ' cifar 1 0 ' : ax . set _ ( [ scale ( float ( a ) / 1 0 0 . ) for a in [ 5 0 , 8 0 , 9 0 , 9 5 ] ] ) ax . ) return : K=10,T=0.8: model _ params , lr = args . lr , weight _ decay = args . weight _ decay ) elif args . optimizer = = " " : optimizer = torch . optim . rmsprop ( model _ params , lr = args . lr , weight _ decay = args . weight _ decay ) elif args . optimizer = = " sgd " : optimizer = torch . optim . sgd ( return _ = _ = _ = [ . _ _ return _ self . = = [ = . = ) " _ [ . " ) " _ ( ) self .