mindspore.ops.gt

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

Compute the value of $i n p u t > o t h e r$ element-wise.

\begin{array}{r} o u t_{i} = {\begin{cases} True,    if i n p u t_{i} > o t h e r_{i} \\ False,   if i n p u t_{i} <= o t h e r_{i} \end{cases} \end{array}

Note

Support implicit type conversion.
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, bool]) – The first input.
other (Union[Tensor, Number, bool]) – The second input.

Returns

Tensor

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> # case 1: The shape of two inputs are different
>>> input = mindspore.tensor([1, 2, 3], mindspore.float32)
>>> output = mindspore.ops.gt(input, 2.0)
>>> print(output)
[False False True]
>>> # case 2: The shape of two inputs are the same
>>> input = mindspore.tensor([1, 2, 3], mindspore.int32)
>>> other = mindspore.tensor([1, 2, 4], mindspore.int32)
>>> output = mindspore.ops.gt(input, other)
>>> print(output)
[ False False False]