Function compute_gradient_error

tensorflow/python/ops/gradient_checker.py:354–395

Computes the gradient error. Computes the maximum error for dy/dx between the computed Jacobian and the numerically estimated Jacobian. This function will modify the tensors passed in as it adds more operations and hence changing the consumers of the operations of the input tensors. Thi

(x,
                           x_shape,
                           y,
                           y_shape,
                           x_init_value=None,
                           delta=1e-3,
                           init_targets=None,
                           extra_feed_dict=None)

    "support for functions. Note that the two versions have different usage, "
    "so code change is needed.")
def compute_gradient_error(x,
                           x_shape,
                           y,
                           y_shape,
                           x_init_value=None,
                           delta=1e-3,
                           init_targets=None,
                           extra_feed_dict=None):
  """Computes the gradient error.

  Computes the maximum error for dy/dx between the computed Jacobian and the
  numerically estimated Jacobian.

  This function will modify the tensors passed in as it adds more operations
  and hence changing the consumers of the operations of the input tensors.

  This function adds operations to the current session. To compute the error
  using a particular device, such as a GPU, use the standard methods for
  setting a device (e.g. using with sess.graph.device() or setting a device
  function in the session constructor).

  Args:
    x: a tensor or list of tensors
    x_shape: the dimensions of x as a tuple or an array of ints. If x is a list,
    then this is the list of shapes.
    y: a tensor
    y_shape: the dimensions of y as a tuple or an array of ints.
    x_init_value: (optional) a numpy array of the same shape as "x"
      representing the initial value of x. If x is a list, this should be a list
      of numpy arrays.  If this is none, the function will pick a random tensor
      as the initial value.
    delta: (optional) the amount of perturbation.
    init_targets: list of targets to run to initialize model params.
    extra_feed_dict: dict that allows fixing specified tensor values
      during the Jacobian calculation.

  Returns:
    The maximum error in between the two Jacobians.
  """
  grad = compute_gradient(x, x_shape, y, y_shape, x_init_value, delta,
                          init_targets, extra_feed_dict=extra_feed_dict)
  return _compute_error(grad)