本文整理汇总了Python中tensorflow.python.ops.variable_scope.variable_op_scope方法的典型用法代码示例。如果您正苦于以下问题：Python variable_scope.variable_op_scope方法的具体用法？Python variable_scope.variable_op_scope怎么用？Python variable_scope.variable_op_scope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.ops.variable_scope的用法示例。
在下文中一共展示了variable_scope.variable_op_scope方法的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: weighted_moving_average
# 需要导入模块: from tensorflow.python.ops import variable_scope [as 别名]
# 或者: from tensorflow.python.ops.variable_scope import variable_op_scope [as 别名]
def weighted_moving_average(value,
decay,
weight,
truediv=True,
collections=None,
name=None):
"""Compute the weighted moving average of `value`.
Conceptually, the weighted moving average is:
`moving_average(value * weight) / moving_average(weight)`,
where a moving average updates by the rule
`new_value = decay * old_value + (1 - decay) * update`
Internally, this Op keeps moving average variables of both `value * weight`
and `weight`.
Args:
value: A numeric `Tensor`.
decay: A float `Tensor` or float value. The moving average decay.
weight: `Tensor` that keeps the current value of a weight.
Shape should be able to multiply `value`.
truediv: Boolean, if `True`, dividing by `moving_average(weight)` is
floating point division. If `False`, use division implied by dtypes.
collections: List of graph collections keys to add the internal variables
`value * weight` and `weight` to. Defaults to `[GraphKeys.VARIABLES]`.
name: Optional name of the returned operation.
Defaults to "WeightedMovingAvg".
Returns:
An Operation that updates and returns the weighted moving average.
"""
# Unlike assign_moving_average, the weighted moving average doesn't modify
# user-visible variables. It is the ratio of two internal variables, which are
# moving averages of the updates. Thus, the signature of this function is
# quite different than assign_moving_average.
if collections is None:
collections = [ops.GraphKeys.VARIABLES]
with variable_scope.variable_op_scope(
[value, weight, decay], name, "WeightedMovingAvg") as scope:
value_x_weight_var = variable_scope.get_variable(
"value_x_weight",
initializer=init_ops.zeros_initializer(value.get_shape(),
dtype=value.dtype),
trainable=False,
collections=collections)
weight_var = variable_scope.get_variable(
"weight",
initializer=init_ops.zeros_initializer(weight.get_shape(),
dtype=weight.dtype),
trainable=False,
collections=collections)
numerator = assign_moving_average(value_x_weight_var, value * weight, decay)
denominator = assign_moving_average(weight_var, weight, decay)
if truediv:
return math_ops.truediv(numerator, denominator, name=scope.name)
else:
return math_ops.div(numerator, denominator, name=scope.name)