mindspore.ops.amax
- mindspore.ops.amax(x, axis=(), 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.
- Parameters
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. 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.
- Returns
Tensor, has the same data type as input tensor.
If axis is () and keep_dims is False, output a 0-dimensional Tensor representing the maximum value of all elements in the input Tensor.
If axis is int, takes the value 1, and keep_dims is False, the shape of the output is \((x_0, x_2, ..., x_R)\) .
If axis is tuple(int) or list(int), the value is (1, 2), and keep_dims is False, the shape of the output Tensor is: math:(x_0, x_3, … , x_R) .
- Raises
TypeError – If x is not a Tensor.
TypeError – If axis is not one of the following: int, tuple or list.
TypeError – If keep_dims is not a bool.
ValueError – If axis is out of range.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) >>> output = ops.amax(x, 1, keep_dims=True) >>> 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.amax(x) >>> print(output) 9.0 >>> print(output.shape) () >>> # case 2: Reduces a dimension along axis 0. >>> output = ops.amax(x, 0, True) >>> 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.amax(x, 1, True) >>> 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.amax(x, 2, True) >>> print(output) [[[1.] [2.] [3.]] [[4.] [5.] [6.]] [[7.] [8.] [9.]]]