13831dd959789127f9735275757f1d660de22b82,pymc3/variational/stein.py,Stein,dlogp,#Stein#,53

Before Change

    @node_property
    def dlogp(self):
        loc_random = self.input_matrix[..., :self.approx.local_size]
        glob_random = self.input_matrix[..., self.approx.local_size:]
        loc_grad, glob_grad = tt.grad(
            self.logp_norm.sum(),
            [self.approx.symbolic_random_local_matrix,
             self.approx.symbolic_random_global_matrix],
            disconnected_inputs="ignore"
        )
        loc_grad, glob_grad = theano.clone(
            [loc_grad, glob_grad],
            {self.approx.symbolic_random_local_matrix: loc_random,
             self.approx.symbolic_random_global_matrix: glob_random}
        )
        return tt.concatenate([loc_grad, glob_grad], axis=-1)

    @memoize
    @change_flags(compute_test_value="off")

After Change

        dlogp = tt.concatenate(list(map(unpack, gradients_for_rmatrices)), axis=-1)

        if self.use_histogram:
            dlogp = theano.clone(
                dlogp,
                dict(zip(self.approx.symbolic_randoms, self.approx.collect("histogram")))
            )
        return dlogp

    @node_property
    def grad(self):

Instances