fb43d1f4a64b77f8b64134460b0c77cb5f79e185,t3f/autodiff.py,,hessian_vector_product,#,72

Before Change

    left = decompositions.orthogonalize_tt_cores(x)
    right = decompositions.orthogonalize_tt_cores(left, left_to_right=False)
    vector_projected = riemannian.project(vector, x)
    deltas = [right.tt_cores[0]] + [tf.zeros_like(cc) for cc in right.tt_cores[1:]]
    x_projection = riemannian.deltas_to_tangent_space(deltas, x, left, right)
    cores_grad = tf.gradients(func(x_projection), deltas)
    vec_deltas = riemannian.tangent_space_to_deltas(vector_projected)
    products = [tf.reduce_sum(a * b) for a, b in zip(cores_grad, vec_deltas)]
    grad_times_vec = tf.add_n(products)
    second_cores_grad = tf.gradients(grad_times_vec, deltas)

After Change

  with tf.name_scope(name, values=all_cores):
    left = decompositions.orthogonalize_tt_cores(x)
    right = decompositions.orthogonalize_tt_cores(left, left_to_right=False)
    deltas = [right.tt_cores[0]]
    deltas += [tf.zeros_like(cc) for cc in right.tt_cores[1:]]
    x_projection = riemannian.deltas_to_tangent_space(deltas, x, left, right)
    function_value = func(x_projection)
    if debug:
      assert_op = _is_invariant_to_input_transforms(function_value, func(right))
    else:
      assert_op = None
    with tf.control_dependencies([assert_op]):
      vector_projected = riemannian.project(vector, x)
    cores_grad = tf.gradients(function_value, deltas)
    vec_deltas = riemannian.tangent_space_to_deltas(vector_projected)

Instances