mindspore.Tensor.remainder

Tensor.remainder(other) → Tensor

Computes the remainder of self divided by other element-wise. The result has the same sign as the divisor and its absolute value is less than that of other.

Supports broadcasting to a common shape and implicit type promotion.

remainder(input, other) == input - input.div(other, rounding_mode="floor") * other

Note

Complex inputs are not supported. At least one input need to be tensor, but not both are bool tensors.

The dividend self is a tensor whose data type is number or bool_.

Parameters

other (Union[Tensor, numbers.Number, bool]) – The divisor is a numbers.Number or a bool or a tensor whose data type is number or bool_ when the dividend is a tensor.

Returns

Tensor, with dtype promoted and shape broadcasted.

Raises

  • TypeError – If self and other are not of types: (tensor, tensor), (tensor, number), (tensor, bool), (number, tensor) or (bool, tensor).

  • ValueError – If self and other are not broadcastable.

Supported Platforms:

Ascend

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> x = Tensor(np.array([-4.0, 5.0, 6.0]).astype(np.float32))
>>> y = Tensor(np.array([3.0, 2.0, 3.0]).astype(np.float64))
>>> output = x.remainder(y)
>>> print(output)
[2.  1.  0.]
Tensor.remainder(divisor) → Tensor

Computes the remainder of dividing the first input tensor by the second input tensor element-wise.

Inputs of self and divisor 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, both dtypes cannot be bool, and the shapes of them could be broadcast. When the inputs are one tensor and one scalar, the scalar could only be a constant.

remainder(input, other) == input - input.div(other, rounding_mode="floor") * other

Warning

  • When the elements of input exceed 2048, there might be accuracy problems.

  • The calculation results of this operator on Ascend and CPU might be inconsistent.

  • If shape is expressed as (D1,D2… ,Dn), then D1*D2… *DN<=1000000,n<=8.

Note

The first input self is a tensor whose data type is number.

Parameters

divisor (Union[Tensor, numbers.Number, bool]) – When the first input is a tensor, The second input could be a number, a bool or a tensor whose data type is number.

Returns

Tensor, the shape is the same as the one after broadcasting, and the data type is the one with higher precision.

Raises

  • TypeError – If neither self nor divisor is one of the following: Tensor, number, bool.

  • ValueError – If the shape of self and divisor cannot be broadcasted to each other.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([-4.0, 5.0, 6.0]).astype(np.float16))
>>> y = Tensor(np.array([3.0, 2.0, 3.0]).astype(np.float16))
>>> output = x.remainder(divisor=y)
>>> print(output)
[2.  1.  0.]