mindspore.ops.ge

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

Computes the boolean value of \(input >= other\) element-wise.

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.

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

  • input (Union[Tensor, Number, bool]) – The first input is a number or a bool or a tensor whose data type is number or bool.

  • other (Union[Tensor, Number, bool]) – The second input is a number or a bool when the first input is a tensor or 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.ge(x, y)
>>> print(output)
[True True False]