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1ff69f58
编写于
1月 07, 2021
作者:
L
LutaoChu
提交者:
GitHub
1月 07, 2021
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差异文件
fix paddle.pow doc, test=document_fix (#30159)
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c5b415bf
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1
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-11
python/paddle/tensor/math.py
python/paddle/tensor/math.py
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python/paddle/tensor/math.py
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1ff69f58
...
@@ -156,11 +156,11 @@ def pow(x, y, name=None):
...
@@ -156,11 +156,11 @@ def pow(x, y, name=None):
Args:
Args:
x (Tensor): An N-D Tensor, the data type is float32, float64, int32 or int64.
x (Tensor): An N-D Tensor, the data type is float32, float64, int32 or int64.
y (
Tensor): An N-D Tensor with type float32, float64, int32 or int64
.
y (
float|int|Tensor): If it is an N-D Tensor, its data type should be the same as `x`
.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Returns:
N-D Tensor. A location into which the result is stored. Its dimension
equals with $x$
.
N-D Tensor. A location into which the result is stored. Its dimension
and data type are the same as `x`
.
Examples:
Examples:
...
@@ -168,17 +168,24 @@ def pow(x, y, name=None):
...
@@ -168,17 +168,24 @@ def pow(x, y, name=None):
import paddle
import paddle
# example 1: y is a float
x = paddle.to_tensor([1, 2, 3], dtype='float32')
x = paddle.to_tensor([1, 2, 3])
y = 2
res = paddle.pow(x, y)
print(res) # [1 4 9]
# example 2: y is a Tensor
# example 1: y is a float or int
y = paddle.full(shape=[1], fill_value=2, dtype='int64')
res = paddle.pow(x, 2)
print(res)
# Tensor(shape=[3], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [1., 4., 9.])
res = paddle.pow(x, 2.5)
print(res)
# Tensor(shape=[3], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [1. , 5.65685415 , 15.58845711])
# example 2: y is a Tensor
y = paddle.to_tensor([2], dtype='float32')
res = paddle.pow(x, y)
res = paddle.pow(x, y)
print(res) # [1 4 9]
print(res)
# Tensor(shape=[3], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [1., 4., 9.])
"""
"""
# in dynamic graph mode
# in dynamic graph mode
...
...
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