def _get_node(self, element):
"""Get the node of a graph element.
Args:
element: A graph element (Op, Tensor or Node)
Returns:
The node associated with element in the graph.
"""
node_name, _ = debug_graphs.parse_node_or_tensor_name(element.name)
return self._sess.graph.as_graph_element(node_name)
def _get_node(self, element):
"""Get the node of a graph element.
Args:
element: A graph element (Op, Tensor or Node)
Returns:
The node associated with element in the graph.
"""
node_name, _ = debug_graphs.parse_node_or_tensor_name(element.name)
return self._sess.graph.as_graph_element(node_name)
def gradient_values_from_dump(grad_debugger, x_tensor, dump):
"""Find gradient values from a `DebugDumpDir` object.
Args:
grad_debugger: the `tf_debug.GradientsDebugger` instance to be used.
x_tensor: (`tf.Tensor`, `tf.Variable` or `str`) The x-tensor object or its
name. x-tensor refers to the independent `tf.Tensor`, i.e., the tensor
on the denominator of the differentiation.
dump: A `tfdbg.DebugDumpDir` object.
Returns:
If this `GradientsDebugger` instance has the gradient tensor of `x_tensor`
registered: a list of `numpy.ndarray` representing the value of the
gradient tensor from `dump`. The list could be empty, if the gradient
tensor is not executed in the `tf.Session.run()` call that generated
the `dump`. The list could also contain multiple values of the gradient
tensor, e.g., if gradient tensor is computed repeatedly in a
`tf.while_loop` during the run that generated the `dump`.
Raises:
LookupError: If this `GradientsDebugger` instance does not have the
gradient tensor of `x_tensor` registered.
ValueError: If this `GradientsDebugger` has a `tf.Graph` object that
does not match the `tf.Graph` object of the `dump`.
TypeError: If `x_tensor` is not a `tf.Tensor`, `tf.Variable` or `str`.
"""
# TODO(cais): Use this method in LocalCLIDebugWrapperSession to present the
# gradient tensors to the TFDBG CLI.
# If possible, verify that the Python graph of the dump and that of this
# GradientsDebugger match.
if (dump.python_graph and grad_debugger.graph and
dump.python_graph != grad_debugger.graph):
raise ValueError(
"This GradientsDebugger instance has a graph (%s) that differs from "
"the graph of the DebugDumpDir object (%s)." %
(grad_debugger.graph, dump.python_graph))
gradient_tensor = grad_debugger.gradient_tensor(x_tensor)
node_name, output_slot = debug_graphs.parse_node_or_tensor_name(
gradient_tensor.name)
try:
return dump.get_tensors(node_name, output_slot, "DebugIdentity")
except debug_data.WatchKeyDoesNotExistInDebugDumpDirError:
return []
def _tensor_to_grad_debug_op_name(tensor, grad_debugger_uuid): op_name, slot = debug_graphs.parse_node_or_tensor_name(tensor.name) return "%s_%d/%s%s" % (op_name, slot, _GRADIENT_DEBUG_TAG, grad_debugger_uuid)
def testParseTensorName(self):
node_name, slot = debug_graphs.parse_node_or_tensor_name(
"namespace1/node_2:3")
self.assertEqual("namespace1/node_2", node_name)
self.assertEqual(3, slot)
def testParseNodeName(self):
node_name, slot = debug_graphs.parse_node_or_tensor_name(
"namespace1/node_1")
self.assertEqual("namespace1/node_1", node_name)
self.assertIsNone(slot)
