parameters: (x,
                             x_shape,
                             y,
                             y_shape,
                             x_init_value=None,
                             delta=1e-3,
                             init_targets=None,
                             extra_feed_dict=None)
return_statement: return _compute_error(grad)
docstring: 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.
docstring_summary: Computes the gradient error.
docstring_tokens: Computes the gradient error .
function: 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)
function_tokens: def compute_gradient_error ( x , x_shape , y , y_shape , x_init_value = None , delta = 1e-3 , init_targets = None , extra_feed_dict = None ) : 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 )