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体验新版 GitCode,发现更多精彩内容 >>
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56e03fb0
编写于
10月 10, 2022
作者:
O
OccupyMars2025
提交者:
GitHub
10月 10, 2022
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[typo] correct typo errors in python\paddle\vision\datasets\cifar.py (#46738)
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python/paddle/nn/functional/activation.py
python/paddle/nn/functional/activation.py
+2
-2
python/paddle/vision/datasets/cifar.py
python/paddle/vision/datasets/cifar.py
+2
-2
python/paddle/vision/transforms/transforms.py
python/paddle/vision/transforms/transforms.py
+1
-1
未找到文件。
python/paddle/nn/functional/activation.py
浏览文件 @
56e03fb0
...
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@@ -1058,9 +1058,9 @@ def softmax(x, axis=-1, dtype=None, name=None):
Parameters:
x (Tensor): The input Tensor with data type float32, float64.
axis (int, optional): The axis along which to perform
log_
softmax
axis (int, optional): The axis along which to perform softmax
calculations. It should be in range [-D, D), where D is the
dimensions
of ``x`` . If ``axis`` < 0, it works the same way as
rank
of ``x`` . If ``axis`` < 0, it works the same way as
:math:`axis + D` . Default is -1.
dtype (str, optional): The data type of the output tensor, can be float32, float64.
name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
...
...
python/paddle/vision/datasets/cifar.py
浏览文件 @
56e03fb0
...
...
@@ -109,8 +109,8 @@ class Cifar10(Dataset):
transform
=
None
,
download
=
True
,
backend
=
None
):
assert
mode
.
lower
()
in
[
'train'
,
'test'
,
'train'
,
'test'
],
\
"mode
should be 'train10', 'test10', 'train100' or 'test100
', but got {}"
.
format
(
mode
)
assert
mode
.
lower
()
in
[
'train'
,
'test'
],
\
"mode
.lower() should be 'train' or 'test
', but got {}"
.
format
(
mode
)
self
.
mode
=
mode
.
lower
()
if
backend
is
None
:
...
...
python/paddle/vision/transforms/transforms.py
浏览文件 @
56e03fb0
...
...
@@ -300,7 +300,7 @@ class ToTensor(BaseTransform):
Converts a PIL.Image or numpy.ndarray (H x W x C) to a paddle.Tensor of shape (C x H x W).
If input is a grayscale image (H x W), it will be converted to a image of shape (H x W x 1).
If input is a grayscale image (H x W), it will be converted to a
n
image of shape (H x W x 1).
And the shape of output tensor will be (1 x H x W).
If you want to keep the shape of output tensor as (H x W x C), you can set data_format = ``HWC`` .
...
...
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