mdn.py 文件源码

def _create_constant_uas_across_datapoints(self):
        """
        Helper function. Creates and returns new theano variables representing noise, where noise is the same across
        datapoints in the minibatch. Useful for binding the original noise variables in evaluation function where
        randomness is required but same predictions are needed across minibatch.
        """

        n_data = tt.iscalar('n_data')

        net_uas = [tt.tile(self.srng.normal((n_units,), dtype=dtype), [n_data, 1]) for n_units in self.net.n_units[1:]]
        uaa = tt.tile(self.srng.normal((self.n_components,), dtype=dtype), [n_data, 1])
        uams = [tt.tile(self.srng.normal((self.n_outputs,), dtype=dtype), [n_data, 1]) for _ in xrange(self.n_components)]
        uaUs = [tt.tile(self.srng.normal((self.n_outputs**2,), dtype=dtype), [n_data, 1]) for _ in xrange(self.n_components)]

        # NOTE: order matters here
        uas = net_uas + [uaa] + uams + uaUs

        return n_data, uas