增加Tensor.ne和Tensor.maximum的中文接口文档
修正Tensor.ne和Tensor.maximum的英文接口文档
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mindspore.Tensor.maximum
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========================
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.. py:method:: mindspore.Tensor.maximum(other)
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详情请参考 :func:`mindspore.ops.maximum`。
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mindspore.Tensor.ne
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===================
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.. py:method:: mindspore.Tensor.ne(other)
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详情请参考 :func:`mindspore.ops.ne`。
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@ -188,6 +188,7 @@ mindspore.Tensor
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mindspore.Tensor.masked_select
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mindspore.Tensor.matrix_power
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mindspore.Tensor.max
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mindspore.Tensor.maximum
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mindspore.Tensor.mean
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mindspore.Tensor.median
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mindspore.Tensor.mH
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@ -206,6 +207,7 @@ mindspore.Tensor
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mindspore.Tensor.nbytes
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mindspore.Tensor.ndim
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mindspore.Tensor.ndimension
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mindspore.Tensor.ne
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mindspore.Tensor.negative
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mindspore.Tensor.nelement
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mindspore.Tensor.new_ones
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@ -194,6 +194,7 @@
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mindspore.Tensor.masked_select
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mindspore.Tensor.matrix_power
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mindspore.Tensor.max
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mindspore.Tensor.maximum
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mindspore.Tensor.mean
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mindspore.Tensor.median
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mindspore.Tensor.mH
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@ -212,6 +213,7 @@
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mindspore.Tensor.nbytes
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mindspore.Tensor.ndim
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mindspore.Tensor.ndimension
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mindspore.Tensor.ne
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mindspore.Tensor.negative
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mindspore.Tensor.nelement
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mindspore.Tensor.new_ones
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@ -3941,43 +3941,7 @@ class Tensor(Tensor_):
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def maximum(self, other):
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r"""
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Computes the maximum of input tensors element-wise.
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Note:
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- Inputs of `input` and `other` comply with the implicit type conversion rules to make the data
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types consistent.
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- The inputs must be two tensors or one tensor and one scalar.
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- When the inputs are two tensors,
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dtypes of them cannot be bool at the same time, and the shapes of them could be broadcast.
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- When the inputs are one tensor and one scalar,
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the scalar could only be a constant.
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- Broadcasting is supported.
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- If one of the elements being compared is a NaN, then that element is returned.
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.. math::
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output_i = max(input_i, other_i)
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Args:
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other (Union[Tensor, Number, bool]): The second input is a number or
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a bool when the first input is a tensor or a tensor whose data type is number or bool.
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Returns:
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Tensor, the shape is the same as the one after broadcasting,
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and the data type is the one with higher precision or higher digits among the two inputs.
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Raises:
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TypeError: If `input` and `other` is not one of the following: Tensor, Number, bool.
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ValueError: If `input` and `other` are not the same shape.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> x = Tensor(np.array([1.0, 5.0, 3.0]), mindspore.float32)
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>>> y = Tensor(np.array([4.0, 2.0, 6.0]), mindspore.float32)
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>>> output = x.maximum(y)
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>>> print(output)
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[4. 5. 6.]
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For details, please refer to :func:`mindspore.ops.maximum`.
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"""
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self._init_check()
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return tensor_operator_registry.get('maximum')(self, other)
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@ -4034,35 +3998,7 @@ class Tensor(Tensor_):
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def ne(self, other):
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r"""
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Computes the non-equivalence of two tensors element-wise.
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Note:
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- Input tensor and `other` comply with the implicit type conversion rules to make the data
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types consistent.
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- The inputs must be two tensors or one tensor and one scalar.
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- When the inputs are two tensors, the shapes of them could be broadcast.
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- When the inputs are one tensor and one scalar, the scalar could only be a constant.
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- Broadcasting is supported.
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Args:
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other (Union[Tensor, Number, bool]): The second input is a number or
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a bool when the first input is a tensor or a tensor whose data type is number or bool.
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Returns:
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Tensor, the shape is the same as the one after broadcasting,and the data type is bool.
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Raises:
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TypeError: If input tensor and `other` is not one of the following: Tensor, Number, bool.
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TypeError: If neither input tensor and `other` is a Tensor.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> x = Tensor(np.array([1, 2, 3]), mindspore.float32)
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>>> output = x.ne(2.0)
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>>> print(output)
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[ True False True]
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For details, please refer to :func:`mindspore.ops.ne`.
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"""
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self._init_check()
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return tensor_operator_registry.get('ne')(self, other)
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