mindspore.mint.prod

mindspore.mint.prod(input, *, dtype=None) → Tensor[source]

Multiply all elements of input.

Parameters: input (Tensor[Number]) – The input tensor.
Keyword Arguments: dtype (mindspore.dtype, optional) – Specify data type. Default None .
Returns: Tensor.

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> x = mindspore.tensor([[[1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 3, 3]],
...                      [[4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]],
...                      [[7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8], [9, 9, 9, 9, 9, 9]]], mindspore.float32)
>>> output = mindspore.mint.prod(x)
>>> print(output)
2.2833798e+33
>>> print(output.shape)
()

mindspore.mint.prod(input, dim, keepdim=False, *, dtype=None) → Tensor[source]

Reduce a dimension of a tensor by multiplying all elements in the dimension, by default. And also can reduce a dimension of input along the dim. Determine whether the dimensions of the output and input are the same by controlling keepdim.

Parameters

input (Tensor[Number]) – The input tensor.
dim (int) – Specify the dimension to compute.
keepdim (bool) – Whether the output tensor has dim retained. Default False .

Keyword Arguments

dtype (mindspore.dtype, optional) – Specify data type. Default None .

Returns

Tensor.

If dim is int, set as 1, and keepdim is False , the shape of output is \((input_0, input_2, ..., input_R)\).

Raises

ValueError – If dim is out of range.

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> x = mindspore.tensor([[[1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 3, 3]],
...                      [[4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]],
...                      [[7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8], [9, 9, 9, 9, 9, 9]]], mindspore.float32)
>>> mindspore.mint.prod(x, 0, True)
Tensor(shape=[1, 3, 6], dtype=Float32, value=
[[[ 2.80000000e+01,  2.80000000e+01,  2.80000000e+01,  2.80000000e+01,  2.80000000e+01,  2.80000000e+01],
  [ 8.00000000e+01,  8.00000000e+01,  8.00000000e+01,  8.00000000e+01,  8.00000000e+01,  8.00000000e+01],
  [ 1.62000000e+02,  1.62000000e+02,  1.62000000e+02,  1.62000000e+02,  1.62000000e+02,  1.62000000e+02]]])