def after_run(self, run_context, run_values):
global_episode = run_values.results
if can_run_hook(
run_context) and self._timer.should_trigger_for_episode(
global_episode):
self._timer.update_last_triggered_episode(global_episode)
self._save(global_episode, run_context.session)
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
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 after_run(self, run_context, run_values):
global_episode = run_values.results['global_episode']
if can_run_hook(run_context):
if self._timer.should_trigger_for_episode(global_episode):
original = np.get_printoptions()
np.set_printoptions(suppress=True)
elapsed_secs, _ = self._timer.update_last_triggered_episode(global_episode)
if self._formatter:
logging.info(self._formatter(run_values.results))
else:
stats = []
for tag in self._tag_order:
stats.append("%s = %s" % (tag, run_values.results[tag]))
if elapsed_secs is not None:
logging.info("%s (%.3f sec)", ", ".join(stats), elapsed_secs)
else:
logging.info("%s", ", ".join(stats))
np.set_printoptions(**original)
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 after_run(self, run_context, run_values):
global_episode = run_values.results['global_episode']
if can_run_hook(run_context):
if self._timer.should_trigger_for_episode(global_episode):
original = np.get_printoptions()
np.set_printoptions(suppress=True)
elapsed_secs, _ = self._timer.update_last_triggered_episode(global_episode)
if self._formatter:
logging.info(self._formatter(run_values.results))
else:
stats = []
for tag in self._tag_order:
stats.append("%s = %s" % (tag, run_values.results[tag]))
if elapsed_secs is not None:
logging.info("%s (%.3f sec)", ", ".join(stats), elapsed_secs)
else:
logging.info("%s", ", ".join(stats))
np.set_printoptions(**original)
def before_run(self, run_context): # pylint: disable=unused-argument
if can_run_hook(run_context):
return session_run_hook.SessionRunArgs(self._current_tensors)
else:
return session_run_hook.SessionRunArgs({'global_episode': self._global_episode_tensor})
def before_run(self, run_context): # pylint: disable=unused-argument
self._should_trigger = can_run_hook(run_context)
if self._should_trigger:
return super(StepLoggingTensorHook, self).before_run(run_context)
else:
return None
def before_run(self, run_context): # pylint: disable=unused-argument
if can_run_hook(run_context):
return super(NanTensorHook, self).before_run(run_context)
return None
def after_run(self, run_context, run_values):
if can_run_hook(run_context):
return super(NanTensorHook, self).after_run(run_context, run_values)
def after_run(self, run_context, run_values):
global_episode = run_values.results
if can_run_hook(run_context) and self._timer.should_trigger_for_episode(global_episode):
self._timer.update_last_triggered_episode(global_episode)
self._save(global_episode, run_context.session)
def before_run(self, run_context): # pylint: disable=unused-argument
if can_run_hook(run_context):
return session_run_hook.SessionRunArgs(self._current_tensors)
else:
return session_run_hook.SessionRunArgs({'global_episode': self._global_episode_tensor})
