mindspore.Tensor.xdivy

Tensor.xdivy(y)[source]

Divides self tensor by the input tensor element-wise. Returns zero when self is zero. The dtype of original Tensor must be one of float, complex or bool. For simplicity, denote the original Tensor by x.

\[out_i = x_{i}\y_{i}\]

x and y comply with the implicit type conversion rules to make the data types consistent. ‘y’ must be tensor or scalar, when y is tensor, dtypes of x and y cannot be bool at the same time, and the shapes of them could be broadcast. When y is scalar, the scalar can only be a constant.

Parameters

y (Union[Tensor, number.Number, bool]) – The second input y is a Number, or a bool when the first input x is a tensor, or a tensor whose data type is float16, float32, float64, complex64, complex128 or bool.

Returns

Tensor, the shape is the same as the one after broadcasting, and the data type is the one with higher precision or higher digits among the two inputs.

Raises

  • TypeError – If y is not one of the following: Tensor, Number, bool.

  • TypeError – If dtype of self and ‘y’ is not in [float16, float32, float64, complex64, complex128, bool].

  • ValueError – If self could not be broadcast to a tensor with shape of y.

  • RuntimeError – If the data type of y conversion of Parameter is given but data type conversion of Parameter is not supported.

Supported Platforms:

Ascend GPU CPU

Examples

>>> x = Tensor(np.array([2, 4, -1]), mindspore.float32)
>>> y = Tensor(np.array([2, 2, 2]), mindspore.float32)
>>> output = x.xdivy(y)
>>> print(output)
[ 1.   2.  -0.5]