def bias_weights(length, param_list=None, name=''):
 bias_initialization = numpy.zeros(length).astype(theano.config.floatX)
 bias = theano.shared(bias_initialization, name=name)
 if (param_list is not None):
  param_list.append(bias)
 return bias

def bias_weights ( length , param_list = None , name = '' ) : bias_initialization = numpy . zeros ( length ) . astype ( theano . config . floatX ) bias = theano . shared ( bias_initialization , name = name ) if ( param_list is not None ) : param_list . append ( bias ) return bias
theano shared variable for bias unit .