本文整理汇总了Python中tensorflow.python.training.evaluation._get_or_create_eval_step方法的典型用法代码示例。如果您正苦于以下问题：Python evaluation._get_or_create_eval_step方法的具体用法？Python evaluation._get_or_create_eval_step怎么用？Python evaluation._get_or_create_eval_step使用的例子？那么恭喜您, 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类tensorflow.python.training.evaluation的用法示例。
在下文中一共展示了evaluation._get_or_create_eval_step方法的4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: _increase_eval_step_op
# 需要导入模块: from tensorflow.python.training import evaluation [as 别名]
# 或者: from tensorflow.python.training.evaluation import _get_or_create_eval_step [as 别名]
def _increase_eval_step_op(iterations_per_loop):
"""Returns an op to increase the eval step for TPU evaluation.
Args:
iterations_per_loop: Tensor. The number of eval steps running in TPU system
before returning to CPU host for each `Session.run`.
Returns:
An operation
"""
eval_step = evaluation._get_or_create_eval_step() # pylint: disable=protected-access
# Estimator evaluate increases 1 by default. So, we increase the difference.
return state_ops.assign_add(
eval_step,
math_ops.cast(iterations_per_loop - 1, dtype=eval_step.dtype),
use_locking=True) 示例2: _increase_eval_step_op
# 需要导入模块: from tensorflow.python.training import evaluation [as 别名]
# 或者: from tensorflow.python.training.evaluation import _get_or_create_eval_step [as 别名]
def _increase_eval_step_op(iterations_per_loop):
"""Returns an op to increase the eval step for TPU evaluation.
Args:
iterations_per_loop: Tensor. The number of eval steps running in TPU
system before returning to CPU host for each `Session.run`.
Returns:
An operation
"""
eval_step = evaluation._get_or_create_eval_step() # pylint: disable=protected-access
# Estimator evaluate increases 1 by default. So, we increase the difference.
return state_ops.assign_add(
eval_step,
math_ops.cast(iterations_per_loop - 1, dtype=eval_step.dtype),
use_locking=True) 示例3: _increase_eval_step_op
# 需要导入模块: from tensorflow.python.training import evaluation [as 别名]
# 或者: from tensorflow.python.training.evaluation import _get_or_create_eval_step [as 别名]
def _increase_eval_step_op(iterations_per_loop):
"""Returns an op to increase the eval step for TPU evaluation.
Args:
iterations_per_loop: Tensor. The number of eval steps running in TPU system
before returning to CPU host for each `Session.run`.
Returns:
An operation
"""
eval_step = evaluation._get_or_create_eval_step() # pylint: disable=protected-access
# Estimator evaluate increases 1 by default. So, we increase the difference.
return tf.compat.v1.assign_add(
eval_step,
tf.cast(iterations_per_loop - 1, dtype=eval_step.dtype),
use_locking=True) 示例4: _evaluate_build_graph
# 需要导入模块: from tensorflow.python.training import evaluation [as 别名]
# 或者: from tensorflow.python.training.evaluation import _get_or_create_eval_step [as 别名]
def _evaluate_build_graph(self, input_fn, hooks=None, checkpoint_path=None):
"""Builds the graph and related hooks to run evaluation."""
tf.compat.v1.random.set_random_seed(self._config.tf_random_seed)
self._create_and_assert_global_step(tf.compat.v1.get_default_graph())
if self._eval_distribution:
(scaffold, evaluation_hooks, input_hooks, update_op, eval_dict) = (
self._call_model_fn_eval_distributed(input_fn, self.config))
else:
(scaffold, evaluation_hooks, input_hooks, update_op, eval_dict) = (
self._call_model_fn_eval(input_fn, self.config))
global_step_tensor = tf.compat.v1.train.get_global_step(
tf.compat.v1.get_default_graph())
# Call to warm_start has to be after model_fn is called.
self._maybe_warm_start(checkpoint_path)
if tf.compat.v1.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[tf.compat.v1.GraphKeys.GLOBAL_STEP] = global_step_tensor
all_hooks = list(input_hooks)
all_hooks.extend(hooks)
all_hooks.extend(list(evaluation_hooks or []))
# New local variables have been added, so update the estimator spec's
# local init op if it was defined.
if scaffold and scaffold.local_init_op:
# Ensure that eval step has been created before updating local init op.
evaluation._get_or_create_eval_step() # pylint: disable=protected-access
scaffold = tf.compat.v1.train.Scaffold(
local_init_op=tf.group(
scaffold.local_init_op,
tf.compat.v1.train.Scaffold.default_local_init_op()),
copy_from_scaffold=scaffold)
return scaffold, update_op, eval_dict, all_hooks