| K=10,T=0.8: <data>1 , ) ) . astype ( ' float 3 2 ' ) params [ _ p ( prefix , ' c _ tt ' ) ] = c _ att return params def gru _ cond _ decoder ( tparams , state _ below , options , prefix = ' gru _ cond _ decoder ' , mask = none , context = none , one _ step = false , init _ state = none , context _ mask = none , * * kwargs ) : assert context , ' context must be provided ' assert context </data>_ mask = = none , * * kwargs [ " encoder _ mask " ] = none if context _ mask is not none or context _ mask is not none : return none def forward _ encoder ( self , x , x ) : return none def get _ model ( self , x ) : x = self . _ encoder _ encoder ( x , x ) return x def forward ( self , x , x ) : x = self . _ decoder ( x , x ) for x in x . | |
| K=10,T=0.8: <data>) : param _ num = sum ( [ int ( p . numel ( ) ) for p in param _ dict [ ' params ' ] ] ) print ( f ' optim param dict { i } params num : { param _ num } ' ) optimizer = torch . optim . adam ( param _ dicts , lr = self . config . base _ lr ) step _ lr = torch . optim . lr _ scheduler . multi step lr ( optimizer , milestones = [ 9 , ] , gamma = 0 </data>. 0 0 , gamma = 0 . 6 0 0 , beta = 0 . 0 0 , beta = 0 . 0 0 1 , beta = 0 . 0 0 , beta = 0 . 0 , gamma = 0 . 0 , beta = 0 . 0 , gamma = 0 . 0 , beta = 0 . , beta = 0 . 0 , beta = 0 . 0 5 , beta = 0 . 0 5 , beta = 1 , beta = 0 . , beta = 0 . 0 5 , beta | |
| K=10,T=0.8: <data>r _ ret ) . cuda ( ) return r _ ret def <UNK> 8 7 _ np _ v 2 ( src , tgt , weight = none ) : if len ( src . shape ) = = 2 and len ( tgt . shape ) = = 2 : src , tgt = src [ np . newaxis , : ] , tgt [ np . newaxis , : ] assert ( src . shape [ 2 ] = = tgt . shape [ 2 ] ) n pts = src . shape [ 2 ] k </data>= src [ np . newaxis , : , : , : ] tgt . shape [ 0 ] src = src [ np . newaxis , : , : , : , : ] trg [ np . newaxis , : , : , : , : ] trg [ np . newaxis , - 1 , : , : ] = trg [ np . newaxis , : , : , : , : , : , : ] trg [ np . newaxis , : , : , : , : , : ] trg = src | |
| K=10,T=0.8: <data>( ) . split ( " \ t " ) if not h in word 2 id : word 2 id [ h ] = len ( word 2 id ) ff . write ( " % s \ t % d \ n " % ( h , word 2 id [ h ] ) ) if not t in word 2 id : word 2 id [ t ] = len ( word 2 id ) ff . write ( " % s \ t % d \ n " % ( t , word 2 id [ </data>w ] ) ) ) if len ( word 2 id [ w ] ) > len ( word 2 id [ w ] ) : word 2 id [ word 2 id [ w ] = word 2 id [ w ] return word 2 id def convert _ word 2 word ( word 2 id ) : word 2 id [ w ] = word 2 id [ w ] word 2 id [ w ] = word 2 id [ w ] word 2 id = word 2 id [ w ] word 2 id [ w | |
| K=10,T=0.8: <data>attr _ dict [ " losses " ] ) ) val _ mse . append ( np . mean ( ( val _ attr _ dict [ " targets " ] - val _ attr _ dict [ " preds " ] ) * * 2 ) ) else : self . scheduler . step ( np . average ( train _ attr _ dict [ " losses " ] ) ) if np . mean ( train _ attr _ dict [ " losses " ] ) < best _ train _ loss : best _ train _ loss </data>+ = ( epoch - start _ epoch + 1 ) / best _ train _ loss + = ( epoch - start _ epoch + 1 ) / best _ eval _ loss + = ( epoch - start _ epoch + 1 ) / best _ train _ loss + = ( epoch - start _ epoch + 1 ) / best _ val _ loss + = ( epoch - start _ epoch + 1 ) / best _ val _ loss + = ( epoch - start _ epoch + 1 ) / best _ val | |
| K=10,T=0.8: <data>, use _ bert _ input = true , bert _ type = bert _ type , bert _ trainable = false , bert _ layer _ num = 1 , use _ bichar _ input = use _ bichar _ input , bichar _ embed _ type = ' bic 2 v ' , bichar _ embed _ trainable = false , use _ word _ input = true , word _ embed _ type = ' w 2 v ' , word _ embed _ trainable = false , use _ charpos _ input = use _ charpos </data>_ input , word _ embed _ type = ' w 2 v ' , word _ embed _ type = ' w 3 v ' , word _ embed _ type = ' w 3 v ' , word _ embed _ type = ' w 3 v ' , word _ embed _ type = ' w 4 v ' , word _ embed _ type = ' w 3 v ' , word _ embed _ type = ' w 3 v ' , word _ embed _ type = ' w 4 v ' , word | |
| K=10,T=0.8: <data>] ) pred _ max = tf . reduce _ max ( predictions , axis = - 1 ) pred _ max _ indices = tf . argmax ( predictions , axis = - 1 ) pred _ max _ x , pred _ max _ y = tf . cast ( tf . <UNK> ( pred _ max _ indices , heatmap _ size ) , tf . float 3 2 ) , tf . cast ( tf . floordiv ( pred _ max _ indices , heatmap _ size ) , tf . float 3 2 ) mask </data>_ max _ indices = tf . cast ( tf . cast ( mask ( mask _ max _ indices , heatmap _ size ) , tf . float 3 2 ) , tf . float 3 2 ) mask _ max _ indices = tf . cast ( tf . cast ( mask _ max _ indices , heatmap _ size ) , tf . float 3 2 ) mask _ max _ indices = tf . cast ( mask _ max _ indices , heatmap _ size ) mask _ max _ indices = tf . cast ( | |
| K=10,T=0.8: <data>, ' rgb ' ) image . save ( " result . jpg " ) files . download ( " result . jpg " ) import argparse import os from exp . exp _ <UNK> _ dad import exp _ <UNK> _ dad parser = argparse . argument parser ( description = ' [ <UNK> ] lstm - vae for time series modeling ' ) parser . add _ argument ( ' - - model ' , type = str , required = true , default = ' <UNK> ' , help = ' model of the experiment ' ) </data>parser . add _ argument ( ' - - model ' , type = str , required = true , default = ' vgg ' , help = ' model of the model ' ) parser . add _ argument ( ' - - model ' , type = str , default = ' vgg ' , help = ' model for the model ' ) parser . add _ argument ( ' - - optimizer ' , type = str , default = ' sgd ' ) parser . add _ argument ( ' - - lr ' , | |
| K=10,T=0.8: <data>' store . oppo . com ' , ' connection ' : ' keep - alive ' , ' source _ type ' : ' 5 0 1 ' , ' client package ' : ' com . oppo . store ' , ' accept ' : ' application / json , text / plain , * / * ' , ' accept - encoding ' : ' gzip , deflate ' , ' accept - language ' : ' zh - cn , en - us ; q = 0 . 9 ' , ' x - requested - </data>with ' : ' xmlhttp request ' , ' accept - encoding ' : ' gzip , deflate ' , ' accept - encoding ' : ' gzip , deflate ' , ' accept - encoding ' : ' gzip , deflate ' , ' accept - encoding ' : ' gzip , deflate ' , ' accept - encoding ' : ' gzip , deflate ' , ' accept - encoding ' : ' gzip , deflate ' , ' accept - language ' : ' en - us ; q = 0 . 9 ' , ' accept | |