本文整理汇总了Python中tensorflow.python.training.training_ops.apply_adagrad_da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_adagrad_da方法的具体用法？Python training_ops.apply_adagrad_da怎么用？Python training_ops.apply_adagrad_da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_adagrad_da方法的2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_adagrad_da [as 别名]
def _apply_dense(self, grad, var):
g_acc = self.get_slot(var, "gradient_accumulator")
gg_acc = self.get_slot(var, "gradient_squared_accumulator")
# Performance optimization so that worker creates a copy of the global step
# to avoid overloading the parameter server holding the global step.
with ops.device(grad[0].device):
global_step = array_ops.identity(self._global_step) + 1
return training_ops.apply_adagrad_da(
var,
g_acc,
gg_acc,
grad,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
math_ops.cast(self._l1_regularization_strength, var.dtype.base_dtype),
math_ops.cast(self._l2_regularization_strength, var.dtype.base_dtype),
global_step,
use_locking=self._use_locking) 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_adagrad_da [as 别名]
def _apply_dense(self, grad, var):
g_acc = self.get_slot(var, "gradient_accumulator")
gg_acc = self.get_slot(var, "gradient_squared_accumulator")
with ops.device(var.device):
global_step = array_ops.identity(self._global_step_on_worker)
return training_ops.apply_adagrad_da(
var,
g_acc,
gg_acc,
grad,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
math_ops.cast(self._l1_regularization_strength, var.dtype.base_dtype),
math_ops.cast(self._l2_regularization_strength, var.dtype.base_dtype),
global_step,
use_locking=self._use_locking) 开发者ID:PacktPublishing，项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代码行数:17，代码来源:adagrad_da.py