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. IfFalse
, 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 the input.
If axis is
None
, and keep_dims isFalse
, 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]]]