Method gradient

tensorflow/python/eager/backprop.py:935–1021

Computes the gradient using operations recorded in context of this tape. Args: target: a list or nested structure of Tensors or Variables to be differentiated. sources: a list or nested structure of Tensors or Variables. `target` will be differentiated against elemen

(self,
               target,
               sources,
               output_gradients=None,
               unconnected_gradients=UnconnectedGradients.NONE)

    return self._tape.watched_variables()

  def gradient(self,
               target,
               sources,
               output_gradients=None,
               unconnected_gradients=UnconnectedGradients.NONE):
    """Computes the gradient using operations recorded in context of this tape.

    Args:
      target: a list or nested structure of Tensors or Variables to be
        differentiated.
      sources: a list or nested structure of Tensors or Variables. `target`
        will be differentiated against elements in `sources`.
      output_gradients: a list of gradients, one for each element of
        target. Defaults to None.
      unconnected_gradients: a value which can either hold 'none' or 'zero' and
        alters the value which will be returned if the target and sources are
        unconnected. The possible values and effects are detailed in
        'UnconnectedGradients' and it defaults to 'none'.

    Returns:
      a list or nested structure of Tensors (or IndexedSlices, or None),
      one for each element in `sources`. Returned structure is the same as
      the structure of `sources`.

    Raises:
      RuntimeError: if called inside the context of the tape, or if called more
       than once on a non-persistent tape.
      ValueError: if the target is a variable or if unconnected gradients is
       called with an unknown value.
    """
    if self._tape is None:
      raise RuntimeError("GradientTape.gradient can only be called once on "
                         "non-persistent tapes.")
    if self._recording:
      if not self._persistent:
        self._pop_tape()
      else:
        logging.log_first_n(
            logging.WARN, "Calling GradientTape.gradient on a persistent "
            "tape inside its context is significantly less "
            "efficient than calling it outside the context (it "
            "causes the gradient ops to be recorded on the "
            "tape, leading to increased CPU and memory usage). "
            "Only call GradientTape.gradient inside the "
            "context if you actually want to trace the "
            "gradient in order to compute higher order "
            "derivatives.", 1)

    flat_targets = []
    for t in nest.flatten(target):
      if not t.dtype.is_floating:
        logging.vlog(
            logging.WARN, "The dtype of the target tensor must be "
            "floating (e.g. tf.float32) when calling GradientTape.gradient, "
            "got %r", t.dtype)
      if resource_variable_ops.is_resource_variable(t):
        with self:
          t = ops.convert_to_tensor(t)