mindspore.ops.tensor_scatter_max

mindspore.ops.tensor_scatter_max(input_x, indices, updates)[source]

By comparing the value at the position indicated by indices in input_x with the value in the updates, the value at the index will eventually be equal to the largest one to create a new tensor.

The last axis of the index is the depth of each index vector. For each index vector, there must be a corresponding value in updates. The shape of updates should be equal to the shape of input_x[indices].

\[output\left [indices \right ] = \max(input\_x, update)\]

Note

On GPU, if some values of the indices are out of bound, instead of raising an index error, the corresponding updates will not be updated to self tensor.
On CPU, if some values of the indices are out of bound, raising an index error.
On Ascend, out of bound checking is not supported, if some values of the indices are out of bound, unknown errors may be caused.

Parameters

input_x (Tensor) – The input tensor. The dimension of input_x must be no less than indices.shape[-1].
indices (Tensor) – The index of input tensor whose data type must be int32 or int64. The rank must be at least 2.
updates (Tensor) – The tensor to update the input_x tensor, has the same type as input, and updates.shape should be equal to \(indices.shape[:-1] + input\_x.shape[indices.shape[-1]:]\).

Returns

Tensor, has the same shape and type as input_x.

Raises

TypeError – If dtype of indices is neither int32 nor int64.
ValueError – If length of shape of input_x is less than the last dimension of shape of indices.
RuntimeError – If a value of indices is not in input_x on CPU backend.

Supported Platforms:: GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
>>> indices = Tensor(np.array([[0, 0], [0, 0]]), mindspore.int32)
>>> updates = Tensor(np.array([1.0, 2.2]), mindspore.float32)
>>> # Next, demonstrate the approximate operation process of this operator:
>>> # 1, indices[0] = [0, 0], indices[1] = [0, 0]
>>> # 2, And input_x[0, 0] = -0.1
>>> # 3, So input_x[indices] = [-0.1, -0.1]
>>> # 4, Satisfy the above formula: input_x[indices].shape=(2) == updates.shape=(2)
>>> output = ops.tensor_scatter_max(input_x, indices, updates)
>>> # 5, Perform the max operation for the first time:
>>> #      first_input_x = Max(input_x[0][0], updates[0]) = [[1.0, 0.3, 3.6], [0.4, 0.5, -3.2]]
>>> # 6, Perform the max operation for the second time:
>>> #      second_input_x = Max(input_x[0][0], updates[1]) = [[2.2, 0.3, 3.6], [0.4, 0.5, -3.2]]
>>> print(output)
[[ 2.2  0.3  3.6]
 [ 0.4  0.5 -3.2]]