def linear_transform_weights(input_dim, output_dim, param_list=None, name=''):
 weight_inialization = uniform(numpy.sqrt((2.0 / input_dim)), (input_dim, output_dim))
 W = theano.shared(weight_inialization, name=name)
 assert (param_list is not None)
 param_list.append(W)
 return W

def linear_transform_weights ( input_dim , output_dim , param_list = None , name = '' ) : weight_inialization = uniform ( numpy . sqrt ( ( 2.0 / input_dim ) ) , ( input_dim , output_dim ) ) W = theano . shared ( weight_inialization , name = name ) assert ( param_list is not None ) param_list . append ( W ) return W
theano shared variable given input and output dimension .