d1ac7b831ad36cd0e4bdd7980819f83208345148,gpflow/expectations.py,,_expectation,#Any#Any#Any#Any#Any#,172

Before Change

        D_in = p.mu.shape[1].value

        exKxz = _expectation(p, rbf_kern, feat, mean_functions.Identity(D_in), None)
        eKxz = _expectation(p, rbf_kern, feat, None, None)

        eAxKxz = tf.reduce_sum(exKxz[:, :, None, :]
                               * tf.transpose(linear_mean.A)[None, None, :, :], axis=3)
        ebKxz = eKxz[..., None] * linear_mean.b[None, None, :]

After Change

    with params_as_tensors_for(linear_mean):
        N = p.mu.shape[0].value
        D = p.mu.shape[1].value
        exKxz = expectation(p, mean_functions.Identity(D), (kern, feat))
        eKxz = expectation(p, (kern, feat))
        eAxKxz = tf.matmul(tf.tile(linear_mean.A[None, :, :], (N, 1, 1)),
                           exKxz, transpose_a=True)
        ebKxz = linear_mean.b[None, :, None] * eKxz[:, None, :]

Instances