p60975123
/
mindspore
forked from mindspore-Ecosystem/mindspore
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

!20138 update the result of example of Split, AdaptiveAvgPool2D and ReduceMean operators. 

Merge pull request !20138 from wangshuide/code_docs_wsd_master_new
This commit is contained in:
i-robot 2021-07-13 03:37:20 +00:00 committed by Gitee
parent 63672de8e6 b2b66abdb0
commit d24c508bf0
5 changed files with 21 additions and 21 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
mindspore/ops/operations/_inner_ops.py
View File

@ -723,10 +723,10 @@ class SequenceMask(PrimitiveWithCheck):
>>> sequence_mask = ops.SequenceMask() >>> sequence_mask = ops.SequenceMask()
>>> output = sequence_mask(x, 3) >>> output = sequence_mask(x, 3)
>>> print(output) >>> print(output)
[[[True, False, False], [[[True False False]
[True, True, True]], [True True True]]
[[True, True, False], [[True True False]
[False, False, False]]] [False False False]]]
""" """
@prim_attr_register @prim_attr_register

8
mindspore/ops/operations/array_ops.py
View File

@ -1040,10 +1040,10 @@ class Split(PrimitiveWithCheck):
>>> output = split(x) >>> output = split(x)
>>> print(output) >>> print(output)
(Tensor(shape=[2, 2], dtype=Int32, value= (Tensor(shape=[2, 2], dtype=Int32, value=
[[1 1] [[1, 1],
[2 2]]), Tensor(shape=[2, 2], dtype=Int32, value= [2, 2]]), Tensor(shape=[2, 2], dtype=Int32, value=
[[1 1] [[1, 1],
[2 2]])) [2, 2]]))
>>> split = ops.Split(1, 4) >>> split = ops.Split(1, 4)
>>> output = split(x) >>> output = split(x)
>>> print(output) >>> print(output)

4
mindspore/ops/operations/math_ops.py
View File

@ -460,8 +460,8 @@ class ReduceMean(_Reduce):
[5. ] [5. ]
[6. ]] [6. ]]
[[7.0000005] [[7.0000005]
[5. ] [8. ]
[6. ]]] [9. ]]]
""" """

20
mindspore/ops/operations/nn_ops.py
View File

@ -179,15 +179,15 @@ class AdaptiveAvgPool2D(PrimitiveWithInfer):
>>> adaptive_avg_pool_2d = ops.AdaptiveAvgPool2D((None, 2)) >>> adaptive_avg_pool_2d = ops.AdaptiveAvgPool2D((None, 2))
>>> output = adaptive_avg_pool_2d(input_x) >>> output = adaptive_avg_pool_2d(input_x)
>>> print(output) >>> print(output)
[[[2.5 3.5] [[[1.5 2.5]
[4.5 5.5] [4.5 5.5]
[6.5 7.5]] [7.5 8.5]]
[[2.5 3.5] [[1.5 2.5]
[4.5 5.5] [4.5 5.5]
[6.5 7.5]] [7.5 8.5]]
[[2.5 3.5] [[1.5 2.5]
[4.5 5.5] [4.5 5.5]
[6.5 7.5]]] [7.5 8.5]]]
>>> # case 2: output_size=2 >>> # case 2: output_size=2
>>> adaptive_avg_pool_2d = ops.AdaptiveAvgPool2D(2) >>> adaptive_avg_pool_2d = ops.AdaptiveAvgPool2D(2)
>>> output = adaptive_avg_pool_2d(input_x) >>> output = adaptive_avg_pool_2d(input_x)
@ -202,9 +202,9 @@ class AdaptiveAvgPool2D(PrimitiveWithInfer):
>>> adaptive_avg_pool_2d = ops.AdaptiveAvgPool2D((1, 2)) >>> adaptive_avg_pool_2d = ops.AdaptiveAvgPool2D((1, 2))
>>> output = adaptive_avg_pool_2d(input_x) >>> output = adaptive_avg_pool_2d(input_x)
>>> print(output) >>> print(output)
[[[3.5 6.5]] [[[4.5 5.5]]
[[3.5 6.5]] [[4.5 5.5]]
[[3.5 6.5]]] [[4.5 5.5]]]
""" """
@prim_attr_register @prim_attr_register
@ -6723,7 +6723,7 @@ class ApplyFtrl(PrimitiveWithInfer):
>>> net = ApplyFtrlNet() >>> net = ApplyFtrlNet()
>>> input_x = Tensor(np.array([[0.3, 0.7], [0.1, 0.8]]).astype(np.float32)) >>> input_x = Tensor(np.array([[0.3, 0.7], [0.1, 0.8]]).astype(np.float32))
>>> output = net(input_x) >>> output = net(input_x)
>>> print(output) >>> print(net.var.asnumpy())
[[ 0.0390525, 0.11492836] [[ 0.0390525, 0.11492836]
[ 0.00066425, 0.15075898]] [ 0.00066425, 0.15075898]]
""" """

2
mindspore/ops/primitive.py
View File

@ -236,7 +236,7 @@ class Primitive(Primitive_):
>>> input_x = Tensor(np.array([1, 2, 3]), mindspore.float32) >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.float32)
>>> output = addn.check_elim((input_x,)) >>> output = addn.check_elim((input_x,))
>>> print(output) >>> print(output)
(True, Tensor(shape = [3], dtype = Float32, value = [1.0000000e+00,2.0000000e+00，3.0000000e+00])) (True, Tensor(shape = [3], dtype = Float32, value = [1.0000000e+00,2.0000000e+00,3.0000000e+00]))
""" """
return (False, None) return (False, None)