Python tensorflow.python.training.session_run_hook.SessionRunValues() Examples
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Example #1
Source File: monitored_session.py From lambda-packs with MIT License | 5 votes |
def run(self, fetches, feed_dict=None, options=None, run_metadata=None): """See base class.""" if self.should_stop(): raise RuntimeError('Run called even after should_stop requested.') actual_fetches = {'caller': fetches} run_context = session_run_hook.SessionRunContext( original_args=session_run_hook.SessionRunArgs(fetches, feed_dict), session=self._sess) options = options or config_pb2.RunOptions() feed_dict = self._call_hook_before_run(run_context, actual_fetches, feed_dict, options) # Do session run. run_metadata = run_metadata or config_pb2.RunMetadata() outputs = _WrappedSession.run(self, fetches=actual_fetches, feed_dict=feed_dict, options=options, run_metadata=run_metadata) for hook in self._hooks: hook.after_run( run_context, session_run_hook.SessionRunValues( results=outputs[hook] if hook in outputs else None, options=options, run_metadata=run_metadata)) self._should_stop = self._should_stop or run_context.stop_requested return outputs['caller']
Example #2
Source File: monitored_session.py From auto-alt-text-lambda-api with MIT License | 5 votes |
def run(self, fetches, feed_dict=None, options=None, run_metadata=None): """See base class.""" if self.should_stop(): raise RuntimeError('Run called even after should_stop requested.') actual_fetches = {'caller': fetches} run_context = session_run_hook.SessionRunContext( original_args=session_run_hook.SessionRunArgs(fetches, feed_dict), session=self._sess) options = options or config_pb2.RunOptions() feed_dict = self._call_hook_before_run(run_context, actual_fetches, feed_dict, options) # Do session run. run_metadata = run_metadata or config_pb2.RunMetadata() outputs = _WrappedSession.run(self, fetches=actual_fetches, feed_dict=feed_dict, options=options, run_metadata=run_metadata) for hook in self._hooks: hook.after_run( run_context, session_run_hook.SessionRunValues( results=outputs[hook] if hook in outputs else None, options=options, run_metadata=run_metadata)) self._should_stop = self._should_stop or run_context.stop_requested return outputs['caller']
Example #3
Source File: monitored_session.py From deep_image_model with Apache License 2.0 | 5 votes |
def run(self, fetches, feed_dict=None, options=None, run_metadata=None): """See base class.""" if self.should_stop(): raise RuntimeError('Run called even after should_stop requested.') actual_fetches = {'caller': fetches} run_context = session_run_hook.SessionRunContext( original_args=session_run_hook.SessionRunArgs(fetches, feed_dict), session=self._sess) feed_dict = self._call_hook_before_run( run_context, actual_fetches, feed_dict) # Do session run. outputs = _WrappedSession.run(self, fetches=actual_fetches, feed_dict=feed_dict, options=options, run_metadata=run_metadata) for hook in self._hooks: hook.after_run( run_context, session_run_hook.SessionRunValues(results=outputs[hook] if hook in outputs else None)) self._should_stop = self._should_stop or run_context.stop_requested return outputs['caller']
Example #4
Source File: monitored_session.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def run(self, fetches, feed_dict=None, options=None, run_metadata=None): """See base class.""" if self.should_stop(): raise RuntimeError('Run called even after should_stop requested.') actual_fetches = {'caller': fetches} run_context = session_run_hook.SessionRunContext( original_args=session_run_hook.SessionRunArgs(fetches, feed_dict), session=self._sess) options = options or config_pb2.RunOptions() feed_dict = self._call_hook_before_run(run_context, actual_fetches, feed_dict, options) # Do session run. run_metadata = run_metadata or config_pb2.RunMetadata() outputs = _WrappedSession.run(self, fetches=actual_fetches, feed_dict=feed_dict, options=options, run_metadata=run_metadata) for hook in self._hooks: hook.after_run( run_context, session_run_hook.SessionRunValues( results=outputs[hook] if hook in outputs else None, options=options, run_metadata=run_metadata)) self._should_stop = self._should_stop or run_context.stop_requested return outputs['caller']
Example #5
Source File: monitored_session.py From keras-lambda with MIT License | 5 votes |
def run(self, fetches, feed_dict=None, options=None, run_metadata=None): """See base class.""" if self.should_stop(): raise RuntimeError('Run called even after should_stop requested.') actual_fetches = {'caller': fetches} run_context = session_run_hook.SessionRunContext( original_args=session_run_hook.SessionRunArgs(fetches, feed_dict), session=self._sess) options = options or config_pb2.RunOptions() feed_dict = self._call_hook_before_run(run_context, actual_fetches, feed_dict, options) # Do session run. run_metadata = run_metadata or config_pb2.RunMetadata() outputs = _WrappedSession.run(self, fetches=actual_fetches, feed_dict=feed_dict, options=options, run_metadata=run_metadata) for hook in self._hooks: hook.after_run( run_context, session_run_hook.SessionRunValues( results=outputs[hook] if hook in outputs else None, options=options, run_metadata=run_metadata)) self._should_stop = self._should_stop or run_context.stop_requested return outputs['caller']