tfdbg.watch_graph_with_blacklists( run_options, graph, debug_ops='DebugIdentity', debug_urls=None, node_name_regex_blacklist=None, op_type_regex_blacklist=None, tensor_dtype_regex_blacklist=None, tolerate_debug_op_creation_failures=False, global_step=-1 )
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.: 1. Under certain circumstances, the
Tensor may not get actually watched (e.g., if the node of the
Tensor is constant-folded during runtime). 2. For debugging purposes, the
parallel_iteration attribute of all
tf.while_loops in the graph are set to 1 to prevent any node from being executed multiple times concurrently. This change does not affect subsequent non-debugged runs of the same
run_options: An instance of
config_pb2.RunOptionsto be modified.
graph: An instance of
str) name(s) of the debug op(s) to use. See the documentation of
watch_graphfor more details.
debug_urls: URL(s) to send debug values to, e.g.,
node_name_regex_blacklist: Regular-expression blacklist for node_name. This should be a string, e.g.,
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
ORrelation. 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.
tensor_dtype_regex_blacklist: Regular-expression blacklist for Tensor data type, e.g.,
"^int.*". This blacklist operates in logical
ORrelations to the two whitelists above.
bool) whether debug op creation failures (e.g., due to dtype incompatibility) are to be tolerated by not throwing exceptions.
int) Optional global_step count for this debug tensor watch.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.