本文整理汇总了Python中tensorflow.python.training.training_ops.apply_rms_prop方法的典型用法代码示例。如果您正苦于以下问题：Python training_ops.apply_rms_prop方法的具体用法？Python training_ops.apply_rms_prop怎么用？Python training_ops.apply_rms_prop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类tensorflow.python.training.training_ops的用法示例。
在下文中一共展示了training_ops.apply_rms_prop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: _apply_dense
# 需要导入模块: from tensorflow.python.training import training_ops [as 别名]
# 或者: from tensorflow.python.training.training_ops import apply_rms_prop [as 别名]
def _apply_dense(self, grad, var):
rms = self.get_slot(var, "rms")
mom = self.get_slot(var, "momentum")
if self._centered:
mg = self.get_slot(var, "mg")
return training_ops.apply_centered_rms_prop(
var,
mg,
rms,
mom,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
math_ops.cast(self._decay_tensor, var.dtype.base_dtype),
math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
math_ops.cast(self._epsilon_tensor, var.dtype.base_dtype),
grad,
use_locking=self._use_locking).op
else:
return training_ops.apply_rms_prop(
var,
rms,
mom,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
math_ops.cast(self._decay_tensor, var.dtype.base_dtype),
math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
math_ops.cast(self._epsilon_tensor, var.dtype.base_dtype),
grad,
use_locking=self._use_locking).op 示例2: _apply_dense
# 需要导入模块: from tensorflow.python.training import training_ops [as 别名]
# 或者: from tensorflow.python.training.training_ops import apply_rms_prop [as 别名]
def _apply_dense(self, grad, var):
rms = self.get_slot(var, "rms")
mom = self.get_slot(var, "momentum")
return training_ops.apply_rms_prop(
var, rms, mom,
self._learning_rate_tensor,
self._decay_tensor,
self._momentum_tensor,
self._epsilon_tensor,
grad,
use_locking=False).op
# Apply accumulated gradients to var. 示例3: _apply_dense
# 需要导入模块: from tensorflow.python.training import training_ops [as 别名]
# 或者: from tensorflow.python.training.training_ops import apply_rms_prop [as 别名]
def _apply_dense(self, grad, var):
rms = self.get_slot(var, "rms")
mom = self.get_slot(var, "momentum")
return training_ops.apply_rms_prop(
var, rms, mom,
self._learning_rate_tensor,
self._decay_tensor,
self._momentum_tensor,
self._epsilon_tensor,
grad,
use_locking=False).op
# Apply accumulated gradients to var.