Python tensorflow.python.training.session_run_hook.SessionRunContext() Examples

The following are 8 code examples of tensorflow.python.training.session_run_hook.SessionRunContext(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.training.session_run_hook , or try the search function .
Example #1
Source File: monitored_session.py    From lambda-packs with MIT License 5 votes vote down vote up 
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 vote down vote up 
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: in_memory_eval.py    From training_results_v0.5 with Apache License 2.0 5 votes vote down vote up 
def end(self, session):  # pylint: disable=unused-argument
    """Runs evaluator for final model."""
    step = session.run(self._global_step_tensor)
    run_ctx = session_run_hook.SessionRunContext({}, session)
    self._predict(run_ctx, step)
Example #4
Source File: in_memory_eval.py    From training_results_v0.5 with Apache License 2.0 5 votes vote down vote up 
def end(self, session):  # pylint: disable=unused-argument
    """Runs evaluator for final model."""
    step = session.run(self._global_step_tensor)
    run_ctx = session_run_hook.SessionRunContext({}, session)
    self._predict(run_ctx, step)
Example #5
Source File: in_memory_eval.py    From training_results_v0.5 with Apache License 2.0 5 votes vote down vote up 
def end(self, session):  # pylint: disable=unused-argument
    """Runs evaluator for final model."""
    step = session.run(self._global_step_tensor)
    run_ctx = session_run_hook.SessionRunContext({}, session)
    self._predict(run_ctx, step)
Example #6
Source File: monitored_session.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
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 #7
Source File: monitored_session.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
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 #8
Source File: monitored_session.py    From keras-lambda with MIT License 5 votes vote down vote up 
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']