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Python session_run_hook.SessionRunContext方法代码示例

本文整理汇总了Python中tensorflow.python.training.session_run_hook.SessionRunContext方法的典型用法代码示例。如果您正苦于以下问题:Python session_run_hook.SessionRunContext方法的具体用法?Python session_run_hook.SessionRunContext怎么用?Python session_run_hook.SessionRunContext使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在tensorflow.python.training.session_run_hook的用法示例。


在下文中一共展示了session_run_hook.SessionRunContext方法的3个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: run

# 需要导入模块: from tensorflow.python.training import session_run_hook [as 别名]
# 或者: from tensorflow.python.training.session_run_hook import SessionRunContext [as 别名]
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'] 
开发者ID:ryfeus,项目名称:lambda-packs,代码行数:35,代码来源:monitored_session.py

示例2: end

# 需要导入模块: from tensorflow.python.training import session_run_hook [as 别名]
# 或者: from tensorflow.python.training.session_run_hook import SessionRunContext [as 别名]
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) 
开发者ID:mlperf,项目名称:training_results_v0.5,代码行数:7,代码来源:in_memory_eval.py

示例3: run

# 需要导入模块: from tensorflow.python.training import session_run_hook [as 别名]
# 或者: from tensorflow.python.training.session_run_hook import SessionRunContext [as 别名]
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'] 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:30,代码来源:monitored_session.py


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