mindspore.ops.ReduceProd
- class mindspore.ops.ReduceProd(keep_dims=False)[source]
Reduces a dimension of a tensor by multiplying all elements in the dimension, by Default. And also can reduces 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, don’t keep these reduced dimensions.
- 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 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.
ValueError – 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 averaging 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]]]