Method build_results

tensorflow/python/client/session.py:528–573

Build results matching the original fetch shape. `tensor_values` must be a list of the same length as the one returned by `fetches()`, and holding the requested fetch values. This method builds a struct with the same shape as the original `fetches` passed to the constructor, in

(self, session, tensor_values)

    return self._targets

  def build_results(self, session, tensor_values):
    """Build results matching the original fetch shape.

    `tensor_values` must be a list of the same length as
    the one returned by `fetches()`, and holding the requested
    fetch values.

    This method builds a struct with the same shape as the original `fetches`
    passed to the constructor, in which the fetches are replaced by their
    fetched value.

    Args:
      session: The enclosing session.  Used for tensor handles.
      tensor_values: List of values matching the list returned by fetches().

    Returns:
      A structure of the same shape as the original `fetches` argument but
        containing tensors or None (for fetched ops).
    """
    full_values = []
    assert len(self._final_fetches) == len(tensor_values)
    i = 0
    j = 0
    for is_op in self._ops:
      if is_op:
        full_values.append(None)
      else:
        # If the fetch was in the feeds, use the fed value, otherwise
        # use the returned value.
        if self._fetches[i] in self._feed_handles:
          # A fetch had a corresponding direct TensorHandle feed. Call eval()
          # to obtain the Tensor value from the TensorHandle.
          value = self._feed_handles[self._fetches[i]].eval()
        else:
          value = self._feeds.get(self._fetches[i])
        if value is None:
          value = tensor_values[j]
          j += 1
        dtype = self._fetch_handles.get(self._fetches[i])
        if dtype:
          full_values.append(session_ops.TensorHandle(value, dtype, session))
        else:
          full_values.append(value)
        i += 1
    assert j == len(tensor_values)
    return self._fetch_mapper.build_results(full_values)


def _name_list(tensor_list):