本文整理汇总了Python中tensorflow.python.training.training_ops.sparse_apply_ftrl方法的典型用法代码示例。如果您正苦于以下问题：Python training_ops.sparse_apply_ftrl方法的具体用法？Python training_ops.sparse_apply_ftrl怎么用？Python training_ops.sparse_apply_ftrl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.training_ops的用法示例。
在下文中一共展示了training_ops.sparse_apply_ftrl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: _apply_sparse
# 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 sparse_apply_ftrl [as 别名]
def _apply_sparse(self, grad, var):
accum = self.get_slot(var, "accum")
linear = self.get_slot(var, "linear")
return training_ops.sparse_apply_ftrl(
var,
accum,
linear,
grad.values,
grad.indices,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
math_ops.cast(self._l1_regularization_strength_tensor,
var.dtype.base_dtype),
math_ops.cast(self._l2_regularization_strength_tensor,
var.dtype.base_dtype),
math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
use_locking=self._use_locking) 示例2: _testTypesForSparseFtrl
# 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 sparse_apply_ftrl [as 别名]
def _testTypesForSparseFtrl(self, x, y, z, lr, grad, indices, l1=0.0, l2=0.0,
lr_power=-0.5):
self.setUp()
with self.test_session(use_gpu=False):
var = variables.Variable(x)
accum = variables.Variable(y)
linear = variables.Variable(z)
variables.global_variables_initializer().run()
self.assertAllCloseAccordingToType(x, var.eval())
sparse_apply_ftrl = training_ops.sparse_apply_ftrl(
var, accum, linear, grad,
constant_op.constant(indices, self._toType(indices.dtype)),
lr, l1, l2, lr_power=lr_power)
out = sparse_apply_ftrl.eval()
self.assertShapeEqual(out, sparse_apply_ftrl)
for (i, index) in enumerate(indices):
self.assertAllCloseAccordingToType(
x[index] - lr * grad[i] * (y[index] + grad[i] * grad[i]) ** (
lr_power),
var.eval()[index])
self.assertAllCloseAccordingToType(y[index] + grad[i] * grad[i],
accum.eval()[index]) 示例3: _apply_sparse
# 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 sparse_apply_ftrl [as 别名]
def _apply_sparse(self, grad, var):
accum = self.get_slot(var, "accum")
linear = self.get_slot(var, "linear")
return training_ops.sparse_apply_ftrl(
var, accum, linear, grad.values, grad.indices,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
math_ops.cast(self._l1_regularization_strength_tensor,
var.dtype.base_dtype),
math_ops.cast(self._l2_regularization_strength_tensor,
var.dtype.base_dtype),
math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
use_locking=self._use_locking) 示例4: _apply_sparse
# 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 sparse_apply_ftrl [as 别名]
def _apply_sparse(self, grad, var):
accum = self.get_slot(var, "accum")
linear = self.get_slot(var, "linear")
if self._l2_shrinkage_regularization_strength <= 0.0:
return training_ops.sparse_apply_ftrl(
var,
accum,
linear,
grad.values,
grad.indices,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
math_ops.cast(self._l1_regularization_strength_tensor,
var.dtype.base_dtype),
math_ops.cast(self._l2_regularization_strength_tensor,
var.dtype.base_dtype),
math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
use_locking=self._use_locking)
else:
return training_ops.sparse_apply_ftrl_v2(
var,
accum,
linear,
grad.values,
grad.indices,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
math_ops.cast(self._l1_regularization_strength_tensor,
var.dtype.base_dtype),
math_ops.cast(self._l2_regularization_strength_tensor,
var.dtype.base_dtype),
math_ops.cast(self._l2_shrinkage_regularization_strength_tensor,
grad.dtype.base_dtype),
math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
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:35，代码来源:ftrl.py