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)