mindspore.ops.prod

mindspore.ops.prod(input, axis=None, keep_dims=False, dtype=None)[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 axis. Determine whether the dimensions of the output and input are the same by controlling keep_dims.

Note

The axis with tensor type is only used for compatibility with older versions and is not recommended.

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.

  • axis (Union[int, tuple(int), list(int), Tensor]) – The dimensions to reduce. Default: None , reduce all dimensions. Only constant value is allowed. Assume the rank of x is r, and the value range is [-r,r).

  • keep_dims (bool) – If True , keep these reduced dimensions and the length is 1. If False , don’t keep these dimensions. Default: False .

  • dtype (mindspore.dtype) – The desired data type of returned Tensor. Default: None .

Returns

Tensor, has the same data type as input tensor.

  • If axis is None , and keep_dims is False , the output is a 0-D tensor representing the product of all elements in the input tensor.

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

  • If axis is tuple(int), set as (1, 2), and keep_dims is False , the shape of output is \((input_0, input_3, ..., input_R)\).

  • If axis is 1-D Tensor, set as [1, 2], and keep_dims is False , the shape of output is \((input_0, input_3, ..., input_R)\).

Raises

  • TypeError – If input is not a Tensor.

  • TypeError – If axis is not one of the following: int, tuple, list or Tensor.

  • TypeError – If keep_dims is not a bool.

  • ValueError – If axis is out of range.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> output = ops.prod(x, 1, keep_dims=True)
>>> result = output.shape
>>> print(result)
(3, 1, 5, 6)
>>> # case 1: Reduces a dimension by multiplying all elements in the dimension.
>>> 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 = ops.prod(x)
>>> print(output)
2.2833798e+33
>>> print(output.shape)
()
>>> # case 2: Reduces a dimension along axis 0.
>>> output = ops.prod(x, 0, True)
>>> print(output)
[[[ 28.  28.  28.  28.  28.  28.]
  [ 80.  80.  80.  80.  80.  80.]
  [162. 162. 162. 162. 162. 162.]]]
>>> # case 3: Reduces a dimension along axis 1.
>>> output = ops.prod(x, 1, True)
>>> print(output)
[[[  6.   6.   6.   6.   6.   6.]]
 [[120. 120. 120. 120. 120. 120.]]
 [[504. 504. 504. 504. 504. 504.]]]
>>> # case 4: Reduces a dimension along axis 2.
>>> output = ops.prod(x, 2, True)
>>> print(output)
[[[1.00000e+00]
  [6.40000e+01]
  [7.29000e+02]]
 [[4.09600e+03]
  [1.56250e+04]
  [4.66560e+04]]
 [[1.17649e+05]
  [2.62144e+05]
  [5.31441e+05]]]