Method partial_run

tensorflow/python/client/session.py:976–1021

Continues the execution with more feeds and fetches. This is EXPERIMENTAL and subject to change. To use partial execution, a user first calls `partial_run_setup()` and then a sequence of `partial_run()`. `partial_run_setup` specifies the list of feeds and fetches that will be used

(self, handle, fetches, feed_dict=None)

    return result

  def partial_run(self, handle, fetches, feed_dict=None):
    """Continues the execution with more feeds and fetches.

    This is EXPERIMENTAL and subject to change.

    To use partial execution, a user first calls `partial_run_setup()` and
    then a sequence of `partial_run()`. `partial_run_setup` specifies the
    list of feeds and fetches that will be used in the subsequent
    `partial_run` calls.

    The optional `feed_dict` argument allows the caller to override
    the value of tensors in the graph. See run() for more information.

    Below is a simple example:

    ```python
    a = array_ops.placeholder(dtypes.float32, shape=[])
    b = array_ops.placeholder(dtypes.float32, shape=[])
    c = array_ops.placeholder(dtypes.float32, shape=[])
    r1 = math_ops.add(a, b)
    r2 = math_ops.multiply(r1, c)

    h = sess.partial_run_setup([r1, r2], [a, b, c])
    res = sess.partial_run(h, r1, feed_dict={a: 1, b: 2})
    res = sess.partial_run(h, r2, feed_dict={c: res})
    ```

    Args:
      handle: A handle for a sequence of partial runs.
      fetches: A single graph element, a list of graph elements, or a dictionary
        whose values are graph elements or lists of graph elements (see
        documentation for `run`).
      feed_dict: A dictionary that maps graph elements to values (described
        above).

    Returns:
      Either a single value if `fetches` is a single graph element, or
      a list of values if `fetches` is a list, or a dictionary with the
      same keys as `fetches` if that is a dictionary
      (see documentation for `run`).

    Raises:
      tf.errors.OpError: Or one of its subclasses on error.
    """
    # TODO(touts): Support feeding and fetching the same tensor.
    return self._run(handle, fetches, feed_dict, None, None)

  def partial_run_setup(self, fetches, feeds=None):
    """Sets up a graph with feeds and fetches for partial run.