mindspore.ops.Greater
- class mindspore.ops.Greater[source]
Compare the value of the input parameters \(x,y\) element-wise, and the output result is a bool value.
\[\begin{split}out_{i} =\begin{cases} & \text{True, if } x_{i}>y_{i} \\ & \text{False, if } x_{i}<=y_{i} \end{cases}\end{split}\]Note
Inputs of x and y comply with the implicit type conversion rules to make the data types consistent.
The inputs must be two tensors or one tensor and one scalar.
When the inputs are two tensors, dtypes of them cannot be bool at the same time, and the shapes of them can be broadcast.
When the inputs are one tensor and one scalar, the scalar could only be a constant.
Broadcasting is supported.
If the input Tensor can be broadcast, the low dimension will be extended to the corresponding high dimension in another input by copying the value of the dimension.
- Inputs:
x (Union[Tensor, number.Number, bool]) - The first input is a number.Number or a bool or a tensor whose data type is number or bool_ .
y (Union[Tensor, number.Number, bool]) - The second input, when the first input is a Tensor, the second input should be a number.Number or bool value, or a Tensor whose data type is number or bool_. When the first input is Scalar, the second input must be a Tensor whose data type is number or bool_.
- Outputs:
Tensor, the shape is the same as the one after broadcasting, and the data type is bool.
- Raises
TypeError – If neither x nor y is a Tensor.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> y = Tensor(np.array([1, 1, 4]), mindspore.int32) >>> greater = ops.Greater() >>> output = greater(x, y) >>> print(output) [False True False]