K=10,T=0.8: order ) : tmp = [ ] for o in orders : tmp . append ( { " security " : o . security , " action " : o . action , " price " : o . price , " size " : int ( o . size ) } ) orders = tmp " : " . " " : " . . . . " : . - . . . : . . . . " , nj , 0 , , : , , 4 . , , 0
K=10,T=0.8: else : directory = config . traindir train _ eps = tools . load _ episodes ( directory , limit = config . dataset _ size ) if config . offline _ evaldir : directory = config . offline _ evaldir . format ( * * vars ( config ) ) else : directory = config . evaldir eval _ eps = tools . load _ episodes ( directory , limit = 1 ) make = lambda mode , id : 0 . 0 * * config . get _ fn ( ) * config . data _ size ) if config . get _ fn ( f " { config . output _ size } " ) if config . get _ fn ( f " { config . output _ size } " ) if config . get _ logger ( f " { config . name } _ { config . name } _ { config . name } _ { config . output _ size } _ { config
K=10,T=0.8: 1 , 1 ) , strides = ( 1 , 1 ) , padding = ' same ' , name = ' conv _ 2 1 ' , use _ bias = false ) ( skip _ connection ) skip _ connection = batch normalization ( name = ' norm _ 2 1 ' ) ( skip _ connection ) skip _ connection = leaky re lu ( alpha = 0 . 1 ) ( skip _ connection ) skip _ connection = lambda ( space _ to _ depth _ to _ depth _ to _ depth _ to _ depth [ 0 ] ) ( skip _ connection ) . split ( ' . ' ) ( skip _ connection ) skip _ connection = false skip _ connection = false if skip _ connection = = " " : down _ connection = true if skip _ connection = = " " : down _ connection = false down _ connection = false * block
K=10,T=0.8: = int , help = " part template neural implicit mlp hidden layer size " ) parser . add _ argument ( " - - occupancy _ loss _ weight " , action = " store " , dest = " occupancy _ loss _ multiplier " , default = 0 . 1 , type = float , help = " weight of the loss to encourage binary occupancy " ) parser . add _ argument ( " - - sparse _ loss _ weight " , action = " store " , dest = " sparse _ loss _ weight " , default = 0 . 2 0 , type = float , help = " weight weight norm weight decay rate decay " ) parser . add _ argument ( " - - num _ grad " , action = " store " , dest = " num _ grad " , default = 0 . 2 , type = float , help = " weight decay " ) parser . add _ argument ( " - - num _ grad " , type = int , help = " number weight norm decay
K=10,T=0.8: = address " ) endif ( ) include _ directories ( . ) include _ directories ( $ { cmake _ current _ binary _ dir } ) if ( apple ) set ( cmake _ shared _ linker _ flags " $ { cmake _ shared _ linker _ flags } - undefined dynamic _ lookup - flat _ namespace " ) ( unix ) set ( cmake _ exe _ linker _ flags " $ { cmake _ exe _ linker _ flags } ' ) if ( cmake _ cmake _ cxx _ flags = = " $ " ) : cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _
K=10,T=0.8: n _ collection : step = n _ collection else : cur = collection . find ( { } , skip = ind _ , limit = step ) c = 0 for item in cur : del item [ ' _ id ' ] c + = 1 json _ [ out _ outfield ] . append ( item ) return json _ , ind _ + step def get _ all _ data ( data , data , data ) : data = { } for i in data : if data [ i ] : data [ i + 1 ] + = data [ i ] else : data [ i ] + = data [ i ] return data def get _ all _ data ( data , data ) : data = { }
K=10,T=0.8: weight _ decay = config [ ' wd ' ] ) elif config [ ' optim ' ] = = ' adam w ' : optimizer = torch . optim . adam w ( optim _ paramters , config [ ' lr ' ] , weight _ decay = config [ ' wd ' ] ) elif config [ ' optim ' ] = = ' adam ' : optimizer = torch . optim . adam w ( optim _ parameters , config [ ' optimizer ' ] ) if config [ ' lr ' ] = = ' adam w ' : optimizer = torch . optim . adam w ( optim _ parameters , config [ ' lr ' ] , config [ ' lr ' ] ) optimizer . add _ scalar ( ' lr ' , config [ ' lr ' ] , config [ ' lr ' ] ) elif
K=10,T=0.8: variable ( ' r _ r _ bias ' , [ n _ head , d _ head ] , dtype = tf _ float , initializer = initializer ) bsz = tf . shape ( inp _ k ) [ 1 ] qlen = tf . shape ( inp _ k ) [ 0 ] mlen = tf . shape ( mems [ 0 ] ) [ 0 ] if mems is not none else 0 klen = mlen + qlen bsz = tf . shape ( inp _ k ) [ 0 ] if bsz = = qlen : bsz = tf . shape ( inp _ k ) [ 1 ] else : bsz = [ bsz ] * ( bsz / inp _ k ) [ 1 ] return bsz def pad _ to _ tensor ( inp _ k ) : if bsz in [ bsz , bsz , bsz , bsz ] : bsz =
K=10,T=0.8: ' , ' ' , ' ' , ' cdr ' , ' cdw ' , ' ' , ' ' , ' ' , ' ce ' , ' cea ' , ' cece ' , ' cee ' , ' ' , ' cel ' , ' ' , ' ' , ' cent ' , ' ' , ' ' , ' ceo ' , ' ' , ' ' , ' ' , ' ' , ' cert ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , '
K=10,T=0.8: s [ " village " ] = [ " village " , " " ] s [ " " ] = [ " " , " settlement " ] s [ " " ] = [ " " , " plants " , " vegetation " , " flora " ] s [ " vegetation " ] = [ " " , " plants " , " vegetation " , " flora " ] s [ " fields " ] = [ " fields " , " " ] s [ " " , " " , " " , " " , " " ] s [ " " , " " ] = [ " " , " " , " " , " " , " " , " " , " " ] s [ " " , " " , " " , " " , " " , " " ,
K=10,T=0.8: / movie / { r [ ' tmdb id ' ] } " ) ) elif kind = = " book " and r [ " links " ] : for link in r [ " links " ] : keyboard nav row . append ( inline keyboard button ( link [ " name " ] , url = link [ " url " ] )