<s> import os <EOL> import numpy as np <EOL> from time import time <EOL> from sklearn . externals import joblib <EOL> import theano <EOL> import theano . tensor as T <EOL> from theano_utils import sharedX , floatX , intX <EOL> from rng import np_rng <EOL> class W2VEmbedding ( object ) : <EOL> def __init__ ( self , data_dir ) : <EOL> self . data_dir = data_dir <EOL> def __call__ ( self , vocab , name = None ) : <EOL> t = time ( ) <EOL> w2v_vocab = joblib . load ( os . path . join ( self . data_dir , '<STR_LIT>' ) ) <EOL> w2v_embed = joblib . load ( os . path . join ( self . data_dir , '<STR_LIT>' ) ) <EOL> mapping = { } <EOL> for i , w in enumerate ( w2v_vocab ) : <EOL> w = w . lower ( ) <EOL> if w in mapping : <EOL> mapping [ w ] . append ( i ) <EOL> else : <EOL> mapping [ w ] = [ i ] <EOL> widxs = [ ] <EOL> w2vidxs = [ ] <EOL> for i , w in enumerate ( vocab ) : <EOL> w = w . replace ( '<STR_LIT>' , "<STR_LIT:'>" ) <EOL> if w in mapping : <EOL> w2vi = min ( mapping [ w ] ) <EOL> w2vidxs . append ( w2vi ) <EOL> widxs . append ( i ) <EOL> w = np . zeros ( ( len ( vocab ) , w2v_embed . shape [ <NUM_LIT:1> ] ) ) <EOL> w [ widxs , : ] = w2v_embed [ w2vidxs , : ] / <NUM_LIT> <EOL> return sharedX ( w , name = name ) <EOL> class Uniform ( object ) : <EOL> def __init__ ( self , scale = <NUM_LIT> ) : <EOL> self . scale = <NUM_LIT> <EOL> def __call__ ( self , shape ) : <EOL> return sharedX ( np_rng . uniform ( low = - self . scale , high = self . scale , size = shape ) ) <EOL> class Normal ( object ) : <EOL> def __init__ ( self , loc = <NUM_LIT:0.> , scale = <NUM_LIT> ) : <EOL> self . scale = scale <EOL> self . loc = loc <EOL> def __call__ ( self , shape , name = None ) : <EOL> return sharedX ( np_rng . normal ( loc = self . loc , scale = self . scale , size = shape ) , name = name ) <EOL> class Orthogonal ( object ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , scale = <NUM_LIT> ) : <EOL> self . scale = scale <EOL> def __call__ ( self , shape , name = None ) : <EOL> flat_shape = ( shape [ <NUM_LIT:0> ] , np . prod ( shape [ <NUM_LIT:1> : ] ) ) <EOL> a = np_rng . normal ( <NUM_LIT:0.0> , <NUM_LIT:1.0> , flat_shape ) <EOL> u , _ , v = np . linalg . svd ( a , full_matrices = False ) <EOL> q = u if u . shape == flat_shape else v <EOL> q = q . reshape ( shape ) <EOL> return sharedX ( self . scale * q [ : shape [ <NUM_LIT:0> ] , : shape [ <NUM_LIT:1> ] ] , name = name ) <EOL> class Frob ( object ) : <EOL> def __init__ ( self ) : <EOL> pass <EOL> def __call__ ( self , shape , name = None ) : <EOL> r = np_rng . normal ( loc = <NUM_LIT:0> , scale = <NUM_LIT> , size = shape ) <EOL> r = r / np . sqrt ( np . sum ( r ** <NUM_LIT:2> ) ) * np . sqrt ( shape [ <NUM_LIT:1> ] ) <EOL> return sharedX ( r , name = name ) <EOL> class Constant ( object ) : <EOL> def __init__ ( self , c = <NUM_LIT:0.> ) : <EOL> self . c = c <EOL> def __call__ ( self , shape ) : <EOL> return sharedX ( np . ones ( shape ) * self . c ) <EOL> class Identity ( object ) : <EOL> def __init__ ( self , scale = <NUM_LIT> ) : <EOL> self . scale = scale <EOL> def __call__ ( self , shape ) : <EOL> return sharedX ( np . identity ( shape [ <NUM_LIT:0> ] ) * self . scale ) <EOL> class ReluInit ( object ) : <EOL> def __init__ ( self ) : <EOL> pass <EOL> def __call__ ( self , shape ) : <EOL> if len ( shape ) == <NUM_LIT:2> : <EOL> scale = np . sqrt ( <NUM_LIT> / shape [ <NUM_LIT:0> ] ) <EOL> elif len ( shape ) == <NUM_LIT:4> : <EOL> scale = np . sqrt ( <NUM_LIT> / np . prod ( shape [ <NUM_LIT:1> : ] ) ) <EOL> else : <EOL> raise NotImplementedError <EOL> return sharedX ( np_rng . normal ( size = shape , scale = scale ) ) </s>
