def before_run(self, run_context):
del run_context # Unused by feature importance summary saver hook.
requests = {
"global_step": self._global_step_tensor,
"feature_names": self._feature_names_tensor,
"feature_usage_counts": self._feature_usage_counts_tensor,
"feature_gains": self._feature_gains_tensor
}
return SessionRunArgs(requests)
def before_run(self, run_context): # pylint: disable=unused-argument
requests = {"global_step": self._global_step_tensor}
if self._request_summary:
if self._summary_op is not None:
requests["summary"] = self._summary_op
elif self._scaffold.summary_op is not None:
requests["summary"] = self._scaffold.summary_op
return SessionRunArgs(requests)
def before_run(self, run_context): # pylint: disable=unused-argument
if self._last_saved_time is None:
# Write graph in the first call.
training_util.write_graph(
ops.get_default_graph().as_graph_def(add_shapes=True),
self._checkpoint_dir, "graph.pbtxt")
self._summary_writer.add_graph(ops.get_default_graph())
return SessionRunArgs(self._global_step_tensor)
def before_run(self, run_context): # pylint: disable=unused-argument
if self._iter_count % self._every_n_iter == 0:
return SessionRunArgs(self._current_tensors)
else:
return None
def before_run(self, run_context): # pylint: disable=unused-argument
return SessionRunArgs(self._loss_tensor)