本文整理汇总了Python中tensorflow.python.ops.gen_math_ops._pow方法的典型用法代码示例。如果您正苦于以下问题：Python gen_math_ops._pow方法的具体用法？Python gen_math_ops._pow怎么用？Python gen_math_ops._pow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.gen_math_ops的用法示例。
在下文中一共展示了gen_math_ops._pow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: pow
# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import _pow [as 别名]
def pow(x, y, name=None):
"""Computes the power of one value to another.
Given a tensor `x` and a tensor `y`, this operation computes \\\\(x^y\\\\) for
corresponding elements in `x` and `y`. For example:
```
# tensor 'x' is [[2, 2], [3, 3]]
# tensor 'y' is [[8, 16], [2, 3]]
tf.pow(x, y) ==> [[256, 65536], [9, 27]]
```
Args:
x: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`,
or `complex128`.
y: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`,
or `complex128`.
name: A name for the operation (optional).
Returns:
A `Tensor`.
"""
with ops.name_scope(name, "Pow", [x]) as name:
return gen_math_ops._pow(x, y, name=name) 示例2: pow
# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import _pow [as 别名]
def pow(x, y, name=None):
r"""Computes the power of one value to another.
Given a tensor `x` and a tensor `y`, this operation computes \\\\(x^y\\\\) for
corresponding elements in `x` and `y`. For example:
```
# tensor 'x' is [[2, 2], [3, 3]]
# tensor 'y' is [[8, 16], [2, 3]]
tf.pow(x, y) ==> [[256, 65536], [9, 27]]
```
Args:
x: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`,
or `complex128`.
y: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`,
or `complex128`.
name: A name for the operation (optional).
Returns:
A `Tensor`.
"""
with ops.name_scope(name, "Pow", [x]) as name:
return gen_math_ops._pow(x, y, name=name)
# pylint: disable=redefined-builtin,redefined-outer-name 示例3: pow
# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import _pow [as 别名]
def pow(x, y, name=None):
"""Computes the power of one value to another.
Given a tensor `x` and a tensor `y`, this operation computes \\\\(x^y\\\\) for
corresponding elements in `x` and `y`. For example:
```
# tensor 'x' is [[2, 2], [3, 3]]
# tensor 'y' is [[8, 16], [2, 3]]
tf.pow(x, y) ==> [[256, 65536], [9, 27]]
```
Args:
x: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`,
or `complex128`.
y: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`,
or `complex128`.
name: A name for the operation (optional).
Returns:
A `Tensor`.
"""
with ops.name_scope(name, "Pow", [x]) as name:
return gen_math_ops._pow(x, y, name=name)
# pylint: disable=redefined-builtin,redefined-outer-name 示例4: pow
# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import _pow [as 别名]
def pow(x, y, name=None):
r"""Computes the power of one value to another.
Given a tensor `x` and a tensor `y`, this operation computes \\(x^y\\) for
corresponding elements in `x` and `y`. For example:
```python
x = tf.constant([[2, 2], [3, 3]])
y = tf.constant([[8, 16], [2, 3]])
tf.pow(x, y) # [[256, 65536], [9, 27]]
```
Args:
x: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`,
or `complex128`.
y: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`,
or `complex128`.
name: A name for the operation (optional).
Returns:
A `Tensor`.
"""
with ops.name_scope(name, "Pow", [x]) as name:
return gen_math_ops._pow(x, y, name=name)
# pylint: disable=redefined-builtin,redefined-outer-name 开发者ID:PacktPublishing，项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代码行数:29，代码来源:math_ops.py