mindspore.ops.ReduceProd

class mindspore.ops.ReduceProd(keep_dims=False)[源代码]

Reduces a dimension of a tensor by multiplying all elements in the dimension, by default. And also can reduce a dimension of x along the axis. Determine whether the dimensions of the output and input are the same by controlling keep_dims.

Parameters

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

Inputs:

  • x (Tensor[Number]) - The input tensor. The dtype of the tensor to be reduced is number. \((N,*)\) where \(*\) means, any number of additional dimensions, its rank should be less than 8.

  • axis (Union[int, tuple(int), list(int)]) - The dimensions to reduce. Default: (), reduce all dimensions. Only constant value is allowed. Must be in the range [-rank(x), rank(x)).

Outputs:

Tensor, has the same dtype as the x.

  • If axis is (), 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 2, and keep_dims is False, the shape of output is \((x_1, x_3, ..., x_R)\).

  • If axis is tuple(int), set as (2, 3), and keep_dims is False, the shape of output is \((x_1, x_4, ..., x_R)\).

Raises

  • TypeError – If keep_dims is not a bool.

  • TypeError – If x is not a Tensor.

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

Supported Platforms:

Ascend GPU

Examples

>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> op = ops.ReduceProd(keep_dims=True)
>>> output = op(x, 1)
>>> 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 = op(x)
>>> print(output)
[[[2.2833798e+33]]]
>>> print(output.shape)
(1, 1, 1)
>>> # case 2: Reduces a dimension along axis 0.
>>> output = op(x, 0)
>>> 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 = op(x, 1)
>>> 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 = op(x, 2)
>>> 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]]]