def _print_feed_handler(self, args, screen_info=None):
np_printoptions = cli_shared.numpy_printoptions_from_screen_info(
screen_info)
if not self._feed_dict:
return cli_shared.error(
"The feed_dict of the current run is None or empty.")
parsed = self._argparsers["print_feed"].parse_args(args)
tensor_name, tensor_slicing = (
command_parser.parse_tensor_name_with_slicing(parsed.tensor_name))
feed_key = None
feed_value = None
for key in self._feed_dict:
key_name = common.get_graph_element_name(key)
if key_name == tensor_name:
feed_key = key_name
feed_value = self._feed_dict[key]
break
if feed_key is None:
return cli_shared.error(
"The feed_dict of the current run does not contain the key %s" %
tensor_name)
else:
return cli_shared.format_tensor(
feed_value,
feed_key + " (feed)",
np_printoptions,
print_all=parsed.print_all,
tensor_slicing=tensor_slicing,
highlight_options=cli_shared.parse_ranges_highlight(parsed.ranges),
include_numeric_summary=parsed.numeric_summary)
def _print_feed_handler(self, args, screen_info=None):
np_printoptions = cli_shared.numpy_printoptions_from_screen_info(
screen_info)
if not self._feed_dict:
return cli_shared.error(
"The feed_dict of the current run is None or empty.")
parsed = self._argparsers["print_feed"].parse_args(args)
tensor_name, tensor_slicing = (
command_parser.parse_tensor_name_with_slicing(parsed.tensor_name))
feed_key = None
feed_value = None
for key in self._feed_dict:
key_name = common.get_graph_element_name(key)
if key_name == tensor_name:
feed_key = key_name
feed_value = self._feed_dict[key]
break
if feed_key is None:
return cli_shared.error(
"The feed_dict of the current run does not contain the key %s" %
tensor_name)
else:
return cli_shared.format_tensor(
feed_value,
feed_key + " (feed)",
np_printoptions,
print_all=parsed.print_all,
tensor_slicing=tensor_slicing,
highlight_options=cli_shared.parse_ranges_highlight(parsed.ranges),
include_numeric_summary=parsed.numeric_summary)
