tfdbg.watch_graph_with_blacklists(run_options, graph, debug_ops='DebugIdentity', debug_urls=None, node_name_regex_blacklist=None, op_type_regex_blacklist=None)

tfdbg.watch_graph_with_blacklists(run_options, graph, debug_ops='DebugIdentity', debug_urls=None, node_name_regex_blacklist=None, op_type_regex_blacklist=None)

See the guide: TensorFlow Debugger > Functions for adding debug watches

Add debug tensor watches, blacklisting nodes and op types.

This is similar to watch_graph(), but the node names and op types are blacklisted, instead of whitelisted.

N.B.: Under certain circumstances, not all specified Tensors will be actually watched (e.g., nodes that are constant-folded during runtime will not be watched).

Args:

  • run_options: An instance of config_pb2.RunOptions to be modified.
  • graph: An instance of ops.Graph.
  • debug_ops: (str or list of str) name(s) of the debug op(s) to use.
  • debug_urls: URL(s) to send ebug values to, e.g., file:///tmp/tfdbg_dump_1, grpc://localhost:12345.
  • node_name_regex_blacklist: Regular-expression blacklist for node_name. This should be a string, e.g., "(weight_[0-9]+|bias_.*)".
  • op_type_regex_blacklist: Regular-expression blacklist for the op type of nodes, e.g., "(Variable|Add)". If both node_name_regex_blacklist and op_type_regex_blacklist are set, the two filtering operations will occur in a logical OR relation. In other words, a node will be excluded if it hits either of the two blacklists; a node will be included if and only if it hits neither of the blacklists.

Defined in tensorflow/python/debug/lib/debug_utils.py.