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!39030 [MS][DOC]fix bug of doc 

Merge pull request !39030 from mengyuanli/code_docs_bug_fix
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i-robot 2022-07-28 03:33:13 +00:00 committed by Gitee
parent 365225789e 7721de7529
commit 12c19dd0c0
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2 changed files with 9 additions and 7 deletions
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12
mindspore/python/mindspore/ops/function/array_func.py
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@ -2529,9 +2529,10 @@ def tensor_scatter_elements(input_x, indices, updates, axis=0, reduction="none")
output[i][j][indices[i][j][k]] = updates[i][j][k] # if axis == 2, reduction == "none" output[i][j][indices[i][j][k]] = updates[i][j][k] # if axis == 2, reduction == "none"
.. warning:: .. warning::
The order in which updates are applied is nondeterministic, meaning that if there - The order in which updates are applied is nondeterministic, meaning that if there are multiple index vectors
are multiple index vectors in `indices` that correspond to the same position, the in `indices` that correspond to the same position, the value of that position in the output will be
value of that position in the output will be nondeterministic. nondeterministic.
- On Ascend, the reduction only support set to "none" for now.
.. note:: .. note::
If some values of the `indices` are out of bound, instead of raising an index error, If some values of the `indices` are out of bound, instead of raising an index error,
@ -2562,12 +2563,13 @@ def tensor_scatter_elements(input_x, indices, updates, axis=0, reduction="none")
``Ascend`` ``GPU`` ``CPU`` ``Ascend`` ``GPU`` ``CPU``
Examples: Examples:
>>> from mindspore.ops import functional as F
>>> input_x = Parameter(Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), mindspore.float32), name="input_x") >>> input_x = Parameter(Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), mindspore.float32), name="input_x")
>>> indices = Tensor(np.array([[1, 0, 2], [0, 2, 1]]), mindspore.int32) >>> indices = Tensor(np.array([[1, 0, 2], [0, 2, 1]]), mindspore.int32)
>>> updates = Tensor(np.array([[1, 1, 1], [1, 1, 1]]), mindspore.float32) >>> updates = Tensor(np.array([[1, 1, 1], [1, 1, 1]]), mindspore.float32)
>>> axis = 0 >>> axis = 0
>>> reduction = "add" >>> reduction = "add"
>>> output = tensor_scatter_elements(input_x, indices, updates, axis, reduction) >>> output = F.tensor_scatter_elements(input_x, indices, updates, axis, reduction)
>>> print(output) >>> print(output)
[[ 2.0 3.0 3.0] [[ 2.0 3.0 3.0]
[ 5.0 5.0 7.0] [ 5.0 5.0 7.0]
@ -2577,7 +2579,7 @@ def tensor_scatter_elements(input_x, indices, updates, axis=0, reduction="none")
>>> updates = Tensor(np.array([[8, 8]]), mindspore.int32) >>> updates = Tensor(np.array([[8, 8]]), mindspore.int32)
>>> axis = 1 >>> axis = 1
>>> reduction = "none" >>> reduction = "none"
>>> output = tensor_scatter_elements(input_x, indices, updates, axis, reduction) >>> output = F.tensor_scatter_elements(input_x, indices, updates, axis, reduction)
>>> print(output) >>> print(output)
[[ 1 2 8 4 8]] [[ 1 2 8 4 8]]
""" """

4
mindspore/python/mindspore/ops/function/nn_func.py
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@ -1465,10 +1465,10 @@ def smooth_l1_loss(logits, labels, beta=1.0, reduction='none'):
``Ascend`` ``GPU`` ``CPU`` ``Ascend`` ``GPU`` ``CPU``
Examples: Examples:
>>> loss = ops.SmoothL1Loss() >>> from mindspore.ops import functional as F
>>> logits = Tensor(np.array([1, 2, 3]), mindspore.float32) >>> logits = Tensor(np.array([1, 2, 3]), mindspore.float32)
>>> labels = Tensor(np.array([1, 2, 2]), mindspore.float32) >>> labels = Tensor(np.array([1, 2, 2]), mindspore.float32)
>>> output = loss(logits, labels) >>> output = F.smooth_l1_loss(logits, labels)
>>> print(output) >>> print(output)
[0. 0. 0.5] [0. 0. 0.5]
""" """