K=10,T=0.8: jnp . square ( jax . nn . relu ( x 1 - x 2 ) ) / dispersion return jnp . exp ( d 2 - jnp . ( d , d 2 ) ) def _ swap _ prob _ entropy _ reg _ ( x 1 , x 2 , dispersion = 1 . 0 , norm _ p = 1 . 0 ) : d = 2 * jnp . ( jax . nn . relu ( x 2 - x 1 ) , norm _ p K=10,T=0.8: small will a large learning rate and will results . _ : if true , if gradient . initial _ const : the initial - constant to use to the of distance and confidence . should be set to a small value ( but ) . _ const : the constant to use we . should ( _ . _ _ _ K=10,T=0.8: jnp . square ( jax . nn . relu ( x 1 - x 2 ) ) / dispersion return jnp . exp ( d 2 - jnp . ( d , d 2 ) ) def _ swap _ prob _ entropy _ reg _ ( x 1 , x 2 , dispersion = 1 . 0 , norm _ p = 1 . 0 ) : d = 2 * jnp . ( jax . nn . relu ( x 2 - x 1 ) , norm _ p = . ' . _ . . _ . ) . _ K=10,T=0.8: real _ length ) ) else : starting _ point = [ 0 ] else : if self . params [ " try _ _ starting " ] : starting _ point = random . sample ( range ( real _ length ) , min ( self . params [ " num _ _ starting " ] , real _ length ) ) 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 ' ] [ ' ] , 1 , ' , ( 2 2 2 , 0 ' , ' [ ' , ' , [ ' ] ) output , K=10,T=0.8: " , caption ) caption = re . sub ( r " [ \ u 3 1 f 0 - \ u 3 1 ff ] + " , " " , caption ) caption = re . sub ( r " [ \ u 3 2 0 0 - \ u 3 2 ff ] + " , " " , caption ) caption = re . sub ( r " [ \ u 3 3 0 0 - \ u 3 3 ff ] + " , " " , caption ) if def _ " ) for i ( " , if _ _ " , 1 2 " , 1 0 . 2 ) if K=10,T=0.8: " b 1 _ grad " : grad _ b 1 _ last , " w 2 _ grad " : grad _ w 2 _ last , " b 2 _ grad " : grad _ b 2 _ last , } return last _ param _ dict , xqw _ mini _ batch if last _ mini _ batch _ params _ dict is not none : init _ params _ ( 1 2 " ) : self . path = true : def _ dir = , K=10,T=0.8: if self . params [ ' use _ edge _ bias ' ] : m + = self . weights [ ' edge _ biases ' + scope _ name + str ( iter _ idx ) ] [ edge _ type ] m = tf . reshape ( m , [ - 1 , v , h _ dim ] ) if self ) self . ' ] for self . K=10,T=0.8: max _ iteration _ num = max ( d [ ' number _ iteration ' ] , max _ iteration _ num ) batch _ data = { ' adj _ mat ' : [ ] , ' init ' : [ ] , ' labels ' : [ ] , ' edge _ type _ masks ' : [ ] , ' edge _ type _ labels ' : [ ] , ' edge _ masks ' : [ ] , ' edge _ labels ' ] ) for i [ ' : res _ train _ params [ ' ] self . append ( self . conv _ loss _ type _ ' ] [ ' ] ) self . shape [ ' ' ] )