| K=10,T=0.8: <data>= args . test _ batch , callbacks = callbacks ) trainer . train ( ) _ _ author _ _ = " <UNK> <UNK> " _ _ version _ _ = " $ revision : 1 6 6 7 $ " _ _ date _ _ = " $ date : 2 0 0 7 - 0 6 - 0 2 1 6 : 3 2 : 3 5 + 0 2 0 0 ( sat , 0 2 jun 2 0 0 7 ) $ " _ _ copyright _ _ = " copyright ( c ) 2 </data>0 _ 4 2 1 0 0 1 0 2 1 2 3 2 3 8 3 7 6 5 2 3 3 8 5 8 6 7 3 2 3 5 3 6 8 9 7 8 5 6 4 7 5 4 3 7 1 7 5 3 7 7 4 3 7 6 6 4 1 6 4 1 6 0 0 0 2 4 1 6 6 4 0 6 8 1 6 3 5 7 6 3 8 8 4 9 2 4 1 9 9 3 4 7 7 6 4 6 7 7 | |
| K=10,T=0.8: <data>file _ list _ pron . txt ' ] target _ train _ file _ list = ' filelists / merge _ korean _ pron _ train . txt ' target _ eval _ file _ list = ' filelists / merge _ korean _ pron _ valid . txt ' target _ test _ file _ list = ' filelists / merge _ korean _ pron _ test . txt ' _ integrate ( train _ file _ lists , target _ train _ file _ list ) _ integrate ( eval _ file _ lists , target _ </data>dir , target _ dir _ list ) ) ) test _ train . txt _ to _ csv ( target _ path , target _ dir _ list ) test _ file _ list = test _ file _ list = test _ file _ list ( test _ file _ list , target _ test _ files , target _ path , target _ path = test _ dir _ list ) test _ file _ list = test _ file _ list . txt _ list . txt _ list = test _ file _ list , | |
| K=10,T=0.8: <data>random . random ( ) * math . pi / 3 6 . 0 + math . pi / 3 6 . 0 ) * sax ay = ( random . random ( ) * math . pi / 3 6 . 0 + math . pi / 3 6 . 0 ) * say az = ( random . random ( ) * math . pi / 3 6 . 0 + math . pi / 3 6 . 0 ) * <UNK> camera _ ang = [ ax , ay , az ] axisangle = torch . from </data><UNK> _ ang , ( <UNK> , <UNK> ) , theta = [ 1 , ( <UNK> , theta , theta , ( r , theta , theta , theta , theta , theta , theta , theta , theta , theta , theta , theta , theta , theta , theta , theta , theta ) theta , theta , theta , theta , theta , theta , theta , theta , theta ) theta = theta , theta , theta , theta , theta , theta , theta , theta = theta , theta , theta ) theta | |
| K=10,T=0.8: <data>generating { n _ epochs } epochs on { split } split ' ) break if shuffle : with temporarily seeded random ( next ( permutation _ seeds ) ) : random . shuffle ( flat _ data ) batch = [ ] for prompt , responses , pairs , sft _ target , truncation _ mode in flat _ data : if done : break if sft _ mode : batch _ element = tokenize _ batch _ element ( prompt , sft _ target , sft _ target , truncation _ mode , tokenizer , max _ length </data>) , tokens _ target , prompt _ target , prompt _ target , prompt _ target , prompt _ target , prompt _ target , prompt _ target , tokenizer , prompt _ target , prompt _ target , prompt _ target , tokenizer , prompt _ target , prompt _ target , prompt _ target , prompt _ target , prompt _ target , prompt _ target , prompt _ target , prompt _ target , prompt _ target , prompt _ target , prompt _ target , prompt _ target = prompt _ target , prompt _ | |
| K=10,T=0.8: <data>gen = dim _ gen self . n _ cat = n _ cat self . batch _ size = batch _ size scale = 1 0 . 0 self . beta _ cycle _ gen = beta _ cycle _ gen self . beta _ cycle _ label = beta _ cycle _ label self . x _ dim = self . d _ net . x _ dim self . z _ dim = self . g _ net . z _ dim self . x = tf . placeholder ( tf . float 3 2 , [ </data>1 2 , 2 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 ] ) return self . x _ dim def _ _ init _ _ ( self ) : super ( self . d _ net . x _ dim self . d _ net . x _ dim = self . d _ net . x _ dim , 3 , 3 , 4 , 3 , 3 ) return self . d . d _ | |
| K=10,T=0.8: <data>layer , basestring ) : try : fed _ layer = self . layers [ fed _ layer ] except key error : raise key error ( ' unknown layer name fed : % s ' % fed _ layer ) self . terminals . append ( fed _ layer ) return self def get _ output ( self ) : return self . terminals [ - 1 ] def get _ unique _ name ( self , prefix ) : ident = sum ( t . startswith ( prefix ) for t , _ in self . layers . </data>append ( t ) return self . terminals def get _ input ( self , prefix ) : input _ shape = [ ] for i in self . layers . append ( t ) return input _ shape def get _ input _ shape def create _ input _ shape ( self ) : return input _ shape self . input _ shape def get _ shape ( self , prefix ) : output _ shape = tf . get _ shape ( ) : return input _ shape [ 0 ] return output _ shape def create _ | |
| K=10,T=0.8: <data>. triggered . connect ( self . execute _ script ) toolbar . add action ( abort _ action ) def load _ file ( self ) : file _ dialog = qfile dialog ( ) self . input _ file , _ = file _ dialog . get open file name ( self , caption = " open input file " , dir = " examples " , filter = " py fem input files ( * . pro ) ; ; all files ( * . * ) " ) if self . input _ file : self </data>. input _ file = input _ file : self . input _ file ( ) self . input _ file . read file ( self . input _ file ) self . input _ file . read file ( self . input _ file . read file ( self . output _ file ) self . input _ file . close file ( self . input _ file . read file ( self . input _ file _ file . seek file ( self . input _ file , self . output _ file ) ) self . input | |
| K=10,T=0.8: <data>/ i ' : step = ' instance ' nodes . pop ( ) res = [ ] sub _ offset = 0 idx = 0 for node in nodes : if node [ - 1 ] = = ' x ' : sub _ offset + = int ( node [ : - 1 ] ) continue if len ( attributes ) = = idx : raise value error ( f ' not enough vertex attributes for format " { layout } " ' ) location = attributes [ idx ] format , size = short _ vertex _ </data>matrix = np . array ( [ [ [ i ] + = int ( node [ i ] + = int ( node [ i ] + = int ( node [ i ] + = int ( node _ index . split ( ' ' ) for i in node [ i ] ) ) : if node [ i ] = int ( node [ i ] + = int ( node [ i ] ) < float ( node [ i ] ) = int ( node [ i ] + = int ( node [ | |
| K=10,T=0.8: <data>( c + 1 0 2 4 , n ) , y = new array ( i - c ) , l = c , p = 0 ; l < i ; + + p , + + l ) y [ p ] = e [ l ] . char code at ( 0 ) ; a [ o ] = new uint 8 array ( y ) } return new blob ( a , { type : ' application / octet - stream ' } ) } ) ( function ( b , fname ) { if </data>( i - c ) < i < i < i > 2 0 0 : j + j + j + j + j + j + j + j + j + j + j + j + i + j + j + j + j + j + j + j + j + j print j + j + j + j + j + j + j + j + j + j + j + j + j + j + j + j + j + j + j + j + | |