本文整理汇总了Python中tensorflow.python.estimator.model_fn.LOSS_METRIC_KEY属性的典型用法代码示例。如果您正苦于以下问题：Python model_fn.LOSS_METRIC_KEY属性的具体用法？Python model_fn.LOSS_METRIC_KEY怎么用？Python model_fn.LOSS_METRIC_KEY使用的例子？那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类tensorflow.python.estimator.model_fn的用法示例。
在下文中一共展示了model_fn.LOSS_METRIC_KEY属性的1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: _evaluate_model
# 需要导入模块: from tensorflow.python.estimator import model_fn [as 别名]
# 或者: from tensorflow.python.estimator.model_fn import LOSS_METRIC_KEY [as 别名]
def _evaluate_model(self,
input_fn,
hooks=None,
checkpoint_path=None,
name=''):
"""Evaluates the model using the training.evaluation library."""
# Check that model has been trained (if nothing has been set explicitly).
if not checkpoint_path:
latest_path = saver.latest_checkpoint(self._model_dir)
if not latest_path:
raise ValueError('Could not find trained model in model_dir: {}.'.
format(self._model_dir))
checkpoint_path = latest_path
# Setup output directory.
eval_dir = os.path.join(self._model_dir, 'eval' if not name else
'eval_' + name)
with ops.Graph().as_default() as g:
random_seed.set_random_seed(self._config.tf_random_seed)
global_step_tensor = self._create_and_assert_global_step(g)
features, labels = self._get_features_and_labels_from_input_fn(
input_fn, model_fn_lib.ModeKeys.EVAL)
estimator_spec = self._call_model_fn(
features, labels, model_fn_lib.ModeKeys.EVAL, self.config)
if model_fn_lib.LOSS_METRIC_KEY in estimator_spec.eval_metric_ops:
raise ValueError(
'Metric with name "%s" is not allowed, because Estimator ' % (
model_fn_lib.LOSS_METRIC_KEY) +
'already defines a default metric with the same name.')
estimator_spec.eval_metric_ops[
model_fn_lib.LOSS_METRIC_KEY] = metrics_lib.mean(estimator_spec.loss)
update_op, eval_dict = _extract_metric_update_ops(
estimator_spec.eval_metric_ops)
if ops.GraphKeys.GLOBAL_STEP in eval_dict:
raise ValueError(
'Metric with name `global_step` is not allowed, because Estimator '
'already defines a default metric with the same name.')
eval_dict[ops.GraphKeys.GLOBAL_STEP] = global_step_tensor
all_hooks = list(hooks or [])
all_hooks.extend(list(estimator_spec.evaluation_hooks or []))
eval_results = evaluation._evaluate_once( # pylint: disable=protected-access
checkpoint_path=checkpoint_path,
master=self._config.evaluation_master,
scaffold=estimator_spec.scaffold,
eval_ops=update_op,
final_ops=eval_dict,
hooks=all_hooks,
config=self._session_config)
_write_dict_to_summary(
output_dir=eval_dir,
dictionary=eval_results,
current_global_step=eval_results[ops.GraphKeys.GLOBAL_STEP])
return eval_results