mindspore.ops.gt

mindspore.ops.gt(input, other)[source]

Compare the value of the input parameters \(input,other\) element-wise, and the output result is a bool value.

\[\begin{split}out_{i} =\begin{cases} & \text{True, if } input_{i}>other_{i} \\ & \text{False, if } input_{i}<=other_{i} \end{cases}\end{split}\]

Note

  • Inputs of input and other 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.

Parameters

  • input (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_ .

  • other (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_.

Returns

Tensor, the shape is the same as the one after broadcasting, and the data type is bool.

Raises

TypeError – If neither input nor other is a Tensor.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([1, 2, 3]), mindspore.int32)
>>> y = Tensor(np.array([1, 1, 4]), mindspore.int32)
>>> output = ops.gt(x, y)
>>> print(output)
[False True False]