K=10,T=0.8: ; parser . add _ argument ( ' - - max _ epoch ' , type = int , default = 5 0 0 , help = ' maximum number of epochs ' ) ; parser . add _ argument ( ' - - _ stride ' , type = int , default = 1 , help = ' ' ) ; parser . add _ argument ( ' - - model ' , type = str , default = " " , help = ' model name ' ) ; ) K=10,T=0.8: ' ' ( and drop _ remainder is false ) . ' % ( batch _ size , key , data [ ' sizes ' ] [ key ] , ) ) def _ batch _ sets ( config : collections . config dict , data : dict [ str , any ] , drop _ remainder : bool ) : if data [ ' sizes ' ] [ ' train ' ] % config . batch _ dir _ _ ' _ ) K=10,T=0.8: ( dataset ) : with open ( ' generated _ smiles _ % s ' % dataset , ' rb ' ) as f : all _ smiles = set ( pickle . load ( f ) ) count = 0 for smiles in all _ smiles : mol = chem . mol from smiles ( smiles ) if mol is not none : count + = 1 return len ( all _ smiles ) , count ) . = _ " _ K=10,T=0.8: inputs else : shortcut = conv _ shortcut ( name + ' . shortcut ' , input _ dim = input _ dim , output _ dim = output _ dim , filter _ size = 1 , he _ init = false , biases = true , inputs = inputs ) output = inputs output = normalize ( name + ' . n 1 ' , output , labels = labels ) output = nonlinearity ( output ) output = conv _ 1 ( name ) K=10,T=0.8: ) : @ parameterized . parameters ( [ dict ( cifar _ augmentation = ' standard + autoaugment + cutout ' ) , ] ) def test _ apply _ cifar _ augmentation ( self , cifar _ augmentation ) : batch _ size = 1 0 0 data = cpdata . load _ data _ split ( ' cifar 1 0 ' , val _ examples = 5 0 0 0 0 - batch _ size , data _ dir = data _ dir ) K=10,T=0.8: device ) binary _ tensor = torch . ( random _ tensor ) output = inputs / keep _ prob * binary _ tensor return output def add _ dropout ( network , p , prefix = ' ' ) : for _ str in dir ( network ) : target _ = ( network , _ str ) if isinstance ( target _ , torch . nn . conv 2 d = _ , , , = " _ ) = = 2 _ , . K=10,T=0.8: . ops . linear . linear ( ' discriminator . 4 ' , dim , 1 , output ) return tf . reshape ( output , [ - 1 ] ) real _ data = tf . placeholder ( tf . float 3 2 , shape = [ none , 2 ] ) fake _ data = generator ( batch _ size , real _ data ) disc _ real = discriminator ( real _ data ) disc _ fake = discriminator ( fake _ data ) disc _ , = ' _ _ . . _ = ( 0 ( [ _ K=10,T=0.8: by the number of gpus , gradient accumulation steps , and batch size . " , default = true , ) , lr _ scheduler : str = input ( description = " the scheduler type to use " , = [ " linear " , " cosine " , " cosine _ with _ " , " " _ = ) ' ] = _ = " , K=10,T=0.8: 1 2 8 , 1 9 2 , 0 ) : 1 9 , ( 0 , 6 4 , 1 2 8 ) : 2 0 } return palette def palette _ ( ) : palette _ list = pascal _ palette ( ) . keys ( ) palette = ( ) for color in palette _ list : palette + = color return palette def convert _ from _ color _ segmentation ( arr _ 3 d ) : if ( ) ) self ) ' ( ' , self . = = ' : ' ( ' ) , ) ' ) if _ , = . ( ' . :