def norm_weight(nin, nout=None, scale=0.01, ortho=True):
 if (nout is None):
  nout = nin
 if ((nout == nin) and ortho):
  W = ortho_weight(nin)
 else:
  W = (scale * numpy.random.randn(nin, nout))
 return W.astype('float32')

def norm_weight ( nin , nout = None , scale = 0.01 , ortho = True ) : if ( nout is None ) : nout = nin if ( ( nout == nin ) and ortho ) : W = ortho_weight ( nin ) else : W = ( scale * numpy . random . randn ( nin , nout ) ) return W . astype ( 'float32' )
uniform initalization from [-scale .