mindspore.mint.amax

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mindspore.mint.amax(input, dim=(), keepdim=False)[source]

Computes the maximum value of of all elements along the specified dim dimension of the input, and retains the dimension based on the keepdim parameter.

Warning

This is an experimental API that is subject to change or deletion.

Parameters

  • input (Tensor) – Input tensor.

  • dim (Union[int, tuple(int), list(int)], optional) – The dimension to be reduced, the value should be within [-len(input.shape), len(input.shape) - 1], when the dim is (), all dimensions are reduced, default: ().

  • keepdim (bool, optional) – Whether the output tensor retains the dimension dim, default: False.

Returns

Tensor, has same type as input, and the shape changes according to the values of dim and keepdim.

  • If dim is (), and keepdim is False, the output is a 0-D tensor representing the maximum value of all elements in the input tensor.

  • If dim is 1, and keepdim is False, the shape of output is \((input.shape[0], input.shape[2], ..., input.shape[n])\).

  • If dim is (1, 2), and keepdim is False, the shape of output is \((input.shape[0], input.shape[3], ..., input.shape[n])\).

Raises

  • TypeError – If input is not a Tensor.

  • TypeError – If dim is not an int or tuple(int) or list(int).

  • TypeError – If keepdim is not a bool.

  • ValueError – If the value of any elements of dim is not in the range [-len(input.shape), len(input.shape) - 1].

  • RuntimeError – If any element of dim is repeated.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore import mint
>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> output = mint.amax(x, 1, keepdim=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 = mint.amax(x)
>>> print(output)
9.0
>>> print(output.shape)
()
>>> # case 2: Reduces a dimension along axis 0.
>>> output = mint.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 = mint.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 = mint.amax(x, 2, True)
>>> print(output)
[[[1.]
  [2.]
  [3.]]
 [[4.]
  [5.]
  [6.]]
 [[7.]
  [8.]
  [9.]]]