def before_run(self, run_context): # pylint: disable=unused-argument
# extend session.run(ops) so the ops can be excute parallel
if self.rank != self.root_rank:
return basic_session_run_hooks.SessionRunArgs(fetches={})
# only root print log
return basic_session_run_hooks.SessionRunArgs(fetches=self.fetches)
def before_run(self, run_context): # pylint: disable=unused-argument
# extend session.run(ops) so the ops can be excute parallel
self.fetches.update({
'global_step': self.global_step,
'run_ops': self.run_ops
})
return basic_session_run_hooks.SessionRunArgs(fetches=self.fetches)
def before_run(self, run_context): # pylint: disable=unused-argument
requests = {"global_episode": self._global_episode_tensor}
if can_run_hook(run_context):
self._request_summary = self._current_episode == self._next_episode
if self._request_summary:
if self._get_summary_op() is not None:
requests["summary"] = self._get_summary_op()
return basic_session_run_hooks.SessionRunArgs(requests)
def before_run(self, run_context): # pylint: disable=unused-argument
if can_run_hook(run_context) and self._timer.last_triggered_episode() is None:
# We do write graph and saver_def at the first call of before_run.
# We cannot do this in begin, since we let other hooks to change graph and
# add variables in begin. Graph is finalized after all begin calls.
training_util.write_graph(
tf.get_default_graph().as_graph_def(add_shapes=True),
self._checkpoint_dir,
"graph.pbtxt")
saver_def = self._get_saver().saver_def if self._get_saver() else None
graph = tf.get_default_graph()
meta_graph_def = meta_graph.create_meta_graph_def(
graph_def=graph.as_graph_def(add_shapes=True),
saver_def=saver_def)
self._summary_writer.add_graph(graph)
self._summary_writer.add_meta_graph(meta_graph_def)
return basic_session_run_hooks.SessionRunArgs(self._global_episode_tensor)
def before_run(self, run_context): # pylint: disable=unused-argument
# extend session.run(ops) so the ops can be excute parallel
return basic_session_run_hooks.SessionRunArgs(fetches=self.fetches)
def before_run(self, run_context):
fetches = {
'summary': self.merged_ops,
'gloal_step': self._global_step_tensor
}
return basic_session_run_hooks.SessionRunArgs(fetches=fetches)
def before_run(self, run_context): # pylint: disable=unused-argument
return basic_session_run_hooks.SessionRunArgs(self._global_step_tensor)
def before_run(self, run_context): # pylint: disable=unused-argument
return basic_session_run_hooks.SessionRunArgs(self._metrics.values())
def before_run(self, run_context):
total_losses = tf.add_n(tf.get_collection("total_losses"))
#self._global_step_tensor,
return basic_session_run_hooks.SessionRunArgs(
[self._global_step_tensor, total_losses])
def before_run(self, run_context):
return basic_session_run_hooks.SessionRunArgs(self._global_step_tensor)
