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正统之独孤求败
mindspore
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75c38a08
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体验新版 GitCode,发现更多精彩内容 >>
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75c38a08
编写于
7月 21, 2020
作者:
L
liuxiao93
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差异文件
Fix bug about API of ops.
上级
28c8a5cc
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
9 addition
and
9 deletion
+9
-9
mindspore/ops/operations/array_ops.py
mindspore/ops/operations/array_ops.py
+3
-2
mindspore/ops/operations/nn_ops.py
mindspore/ops/operations/nn_ops.py
+6
-7
未找到文件。
mindspore/ops/operations/array_ops.py
浏览文件 @
75c38a08
...
...
@@ -435,7 +435,7 @@ class Squeeze(PrimitiveWithInfer):
ValueError: If the corresponding dimension of the specified axis does not equal to 1.
Args:
axis (
int
): Specifies the dimension indexes of shape to be removed, which will remove
axis (
Union[int, tuple(int)]
): Specifies the dimension indexes of shape to be removed, which will remove
all the dimensions that are equal to 1. If specified, it must be int32 or int64.
Default: (), an empty tuple.
...
...
@@ -1427,7 +1427,8 @@ class UnsortedSegmentProd(PrimitiveWithInfer):
Inputs:
- **input_x** (Tensor) - The shape is :math:`(x_1, x_2, ..., x_R)`.
With float16, float32 or int32 data type.
- **segment_ids** (Tensor) - A `1-D` tensor whose shape is :math:`(x_1)`. Data type must be int32.
- **segment_ids** (Tensor) - A `1-D` tensor whose shape is :math:`(x_1)`, the value should be >= 0.
Data type must be int32.
- **num_segments** (int) - The value spcifies the number of distinct `segment_ids`,
should be greater than 0.
...
...
mindspore/ops/operations/nn_ops.py
浏览文件 @
75c38a08
...
...
@@ -3760,12 +3760,12 @@ class ApplyAdagradV2(PrimitiveWithInfer):
update_slots (bool): If `True`, `accum` will be updated. Default: True.
Inputs:
- **var** (Parameter) - Variable to be updated. With float32
or float16
data type.
- **var** (Parameter) - Variable to be updated. With float32 data type.
- **accum** (Parameter) - Accum to be updated. The shape and dtype should be the same as `var`.
With float32
or float16
data type.
- **lr** (Union[Number, Tensor]) - The learning rate value, should be scalar. With float32
or float16
data type.
With float32 data type.
- **lr** (Union[Number, Tensor]) - The learning rate value, should be scalar. With float32 data type.
- **grad** (Tensor) - A tensor for gradient. The shape and dtype should be the same as `var`.
With float32
or float16
data type.
With float32 data type.
Outputs:
Tuple of 2 Tensor, the updated parameters.
...
...
@@ -3817,9 +3817,8 @@ class ApplyAdagradV2(PrimitiveWithInfer):
def
infer_dtype
(
self
,
var_dtype
,
accum_dtype
,
lr_dtype
,
grad_dtype
):
args
=
{
'var'
:
var_dtype
,
'accum'
:
accum_dtype
,
'grad'
:
grad_dtype
}
valid_types
=
[
mstype
.
float16
,
mstype
.
float32
]
validator
.
check_tensor_type_same
(
args
,
valid_types
,
self
.
name
)
validator
.
check_scalar_or_tensor_type_same
({
'lr'
:
lr_dtype
},
valid_types
,
self
.
name
)
validator
.
check_tensor_type_same
(
args
,
[
mstype
.
float32
],
self
.
name
)
validator
.
check_scalar_or_tensor_type_same
({
'lr'
:
lr_dtype
},
[
mstype
.
float32
],
self
.
name
)
return
var_dtype
,
accum_dtype
...
...
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