Function grad_fn

tensorflow/python/eager/backprop.py:200–221

Computes the gradient of the wrapped function.

(*args, **kwds)

  # accept lists and np.ndarrays.

  def grad_fn(*args, **kwds):
    """Computes the gradient of the wrapped function."""
    this_tape = tape.push_new_tape()
    try:
      end_node = f(*args, **kwds)
      if end_node is None:
        raise ValueError("Cannot differentiate a function that returns None; "
                         "did you forget to return a value from {}?".format(
                             f.__name__))
    finally:
      tape.pop_tape(this_tape)
    # Note: variables are returned in construction order. This ensures unique
    # order across executions.
    variables = this_tape.watched_variables()
    if not variables:
      raise ValueError("No trainable variables were accessed while the "
                       "function was being computed.")

    sources = [v.handle for v in variables]
    grad = imperative_grad.imperative_grad(this_tape, nest.flatten(end_node),
                                           sources)
    return end_node, list(zip(grad, variables))

  return grad_fn