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Add some description of InTopK op in API.

This commit is contained in:
liuxiao93 2020-09-18 15:26:23 +08:00
parent faa0a6ad45
commit 0a28c79fe0
2 changed files with 6 additions and 5 deletions
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2
mindspore/nn/layer/math.py
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@ -41,7 +41,7 @@ class ReduceLogSumExp(Cell):
Default : False.
Inputs:
- **input_x** (Tensor[Number]) - The input tensor.
- **input_x** (Tensor[Number]) - The input tensor. With float16 or float32 data type.
- **axis** (Union[int, tuple(int), list(int)]) - The dimensions to reduce. Default: (), reduce all dimensions.
Only constant value is allowed.

9
mindspore/ops/operations/nn_ops.py
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@ -4365,11 +4365,11 @@ class SparseApplyProximalAdagrad(PrimitiveWithCheck):
Inputs:
- **var** (Parameter) - Variable tensor to be updated. The data type must be float16 or float32.
- **accum** (Parameter) - Variable tensor to be updated, has the same dtype as `var`.
- **lr** (Union[Number, Tensor]) - The learning rate value. should be a float number or
- **lr** (Union[Number, Tensor]) - The learning rate value, should be a float number or
a scalar tensor with float16 or float32 data type.
- **l1** (Union[Number, Tensor]) - l1 regularization strength. should be a float number or
- **l1** (Union[Number, Tensor]) - l1 regularization strength, should be a float number or
a scalar tensor with float16 or float32 data type.
- **l2** (Union[Number, Tensor]) - l2 regularization strength. should be a float number or
- **l2** (Union[Number, Tensor]) - l2 regularization strength, should be a float number or
a scalar tensor with float16 or float32 data type..
- **grad** (Tensor) - A tensor of the same type as `var`, for the gradient.
- **indices** (Tensor) - A vector of indices in the first dimension of `var` and `accum`.
@ -5444,7 +5444,8 @@ class InTopK(PrimitiveWithInfer):
Inputs:
- **x1** (Tensor) - A 2D Tensor defines the predictions of a batch of samples with float16 or float32 data type.
- **x2** (Tensor) - A 1D Tensor defines the labels of a batch of samples with int32 data type. The size of x2
must be equal to x1's first dimension.
must be equal to x1's first dimension. The values of `x2` can not be negative and
must be equal to or less than index of x1's second dimension.
Outputs:
Tensor has 1 dimension of type bool and the same shape with `x2`. For labeling sample `i` in `x2`,