mindspore.mint.prod

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

Multiplying all elements of input.

Parameters: input (Tensor[Number]) – The input tensor. The dtype of the tensor to be reduced is number. \((N, *)\) where \(*\) means, any number of additional dimensions.
Keyword Arguments: dtype (mindspore.dtype, optional) – The desired data type of returned Tensor. Default: None .
Returns: Tensor.
Raises: TypeError – If input is not a Tensor.

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([[[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 = mint.prod(x)
>>> print(output)
2.2833798e+33
>>> print(output.shape)
()

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

Reduces 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. The dtype of the tensor to be reduced is number. \((N, *)\) where \(*\) means, any number of additional dimensions.
dim (int) – The dimensions to reduce. Only constant value is allowed. Assume the rank of x is r, and the value range is [-r,r).
keepdim (bool) – If True , keep these reduced dimensions and the length is 1. If False , don't keep these dimensions. Default: False .

Keyword Arguments

dtype (mindspore.dtype, optional) – The desired data type of returned Tensor. 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

TypeError – If input is not a Tensor.
TypeError – If dim is not int.
TypeError – If keepdim is not a bool.
ValueError – If dim is out of range.

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([[[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 = mint.prod(x, 0, True)
>>> print(output)
[[[ 28.  28.  28.  28.  28.  28.]
  [ 80.  80.  80.  80.  80.  80.]
  [162. 162. 162. 162. 162. 162.]]]