mindspore.ops.ReduceMax
- class mindspore.ops.ReduceMax(keep_dims=False)[source]
Reduces a dimension of a tensor by the maximum value in this 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.
Note
The axis with tensor type is only used for compatibility with older versions and is not recommended.
- Parameters
keep_dims (bool) – If
True
, keep these reduced dimensions and the length is 1. IfFalse
, don't keep these dimensions. Default:False
.
- Inputs:
x (Tensor[Number]) - The input tensor.
axis (Union[int, tuple(int), list(int), tensor]) - The dimensions to reduce. Default:
()
, reduce all dimensions. Must be in the range [-r, r).
- Outputs:
output(Tensor): has the same dtype as the x.
If axis is
()
, and keep_dims isFalse
, the output is a 0-D tensor representing the maximum of all elements in the input tensor.If axis is int, set as 1, and keep_dims is
False
, the shape of output is \((x_0, x_2, ..., x_R)\).If axis is tuple(int) or list(int), set as (1, 2), and keep_dims is
False
, the shape of output is \((x_0, x_3, ..., x_R)\).If axis is 1-D Tensor, set as [1, 2], and keep_dims is
False
, the shape of output is \((x_0, x_3, ..., x_R)\).
- 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, list or Tensor.
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.ReduceMax(keep_dims=True)(x, 1) >>> result = output.shape >>> print(result) (3, 1, 5, 6) >>> # case 1: Reduces a dimension by the maximum value of 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.ReduceMax(keep_dims=True)(x, ()) >>> print(output) [[[9.]]] >>> print(output.shape) (1, 1, 1) >>> # case 2: Reduces a dimension along axis 0. >>> output = ops.ReduceMax(keep_dims=True)(x, 0) >>> print(output) [[[7. 7. 7. 7. 7. 7.] [8. 8. 8. 8. 8. 8.] [9. 9. 9. 9. 9. 9.]]] >>> # case 3: Reduces a dimension along axis 1. >>> output = ops.ReduceMax(keep_dims=True)(x, 1) >>> print(output) [[[3. 3. 3. 3. 3. 3.]] [[6. 6. 6. 6. 6. 6.]] [[9. 9. 9. 9. 9. 9.]]] >>> # case 4: Reduces a dimension along axis 2. >>> output = ops.ReduceMax(keep_dims=True)(x, 2) >>> print(output) [[[1.] [2.] [3.]] [[4.] [5.] [6.]] [[7.] [8.] [9.]]]