本文整理汇总了Python中tensorflow.python.training.training_ops.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.apply_ftrl方法的具体用法？Python training_ops.apply_ftrl怎么用？Python training_ops.apply_ftrl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_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_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_ftrl [as 别名]
def _apply_dense(self, grad, var):
accum = self.get_slot(var, "accum")
linear = self.get_slot(var, "linear")
return training_ops.apply_ftrl(
var,
accum,
linear,
grad,
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: _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_ftrl [as 别名]
def _apply_dense(self, grad, var):
accum = self.get_slot(var, "accum")
linear = self.get_slot(var, "linear")
return training_ops.apply_ftrl(
var, accum, linear, grad,
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3: _testTypesForFtrl
# 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_ftrl [as 别名]
def _testTypesForFtrl(self, x, y, z, lr, grad, use_gpu=None, l1=0.0,
l2=0.0, lr_power=-0.5):
self.setUp()
with self.test_session(use_gpu=use_gpu):
var = variables.Variable(x)
accum = variables.Variable(y)
linear = variables.Variable(z)
variables.global_variables_initializer().run()
self.assertAllCloseAccordingToType(x, var.eval())
apply_ftrl = training_ops.apply_ftrl(var, accum, linear, grad, lr, l1, l2,
lr_power)
out = apply_ftrl.eval()
self.assertShapeEqual(out, apply_ftrl)
accum_update = y + grad * grad
linear_update = z + grad - (accum_update ** (-lr_power) - y ** (
-lr_power)) / lr * x
quadratic = 1.0 / (accum_update ** (lr_power) * lr) + 2 * l2
expected_out = np.array([(np.sign(
linear_update[i]) * l1 - linear_update[i]) / (
quadratic[i]) if np.abs(
linear_update[i]) > l1 else 0.0 for i in range(
linear_update.size)])
self.assertAllCloseAccordingToType(accum_update, accum.eval())
if x.dtype == np.float16:
# The calculations here really are not very precise in float16.
self.assertAllClose(linear_update, linear.eval(), rtol=2e-2, atol=2e-2)
self.assertAllClose(expected_out, out, rtol=2e-2, atol=2e-2)
else:
self.assertAllClose(linear_update, linear.eval())
self.assertAllClose(expected_out, out) 示例4: _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_ftrl [as 别名]
def _apply_dense(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.apply_ftrl(
var,
accum,
linear,
grad,
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.apply_ftrl_v2(
var,
accum,
linear,
grad,
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,
var.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:33，代码来源:ftrl.py