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Specifications and Common Mistakes

- Specifications and Common Mistakes:

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mindspore.ops.scatter_max

mindspore.ops.scatter_max(input_x, indices, updates)

Using given values to update tensor value through the max operation, along with the input indices. This operation outputs the input_x after the update is done, which makes it convenient to use the updated value.

for each $i,...,j$ in indices.shape:

$$\text{input\_x}[\text{indices}[i,...,j],:]=max(\text{input\_x}[\text{indices}[i,...,j],:],\text{updates}[i,...,j,:])$$

Inputs of input_x and updates follow the implicit type conversion rules to keep the data types consistent. If they have different data types, the lower priority data type will be converted to the relatively highest priority data type. A RuntimeError will be reported when updates does not support conversion to the data type required by input_x.

Parameters

• input_x (Parameter) – The target tensor, with data type of Parameter. The shape is $(N,\ast )$ where $\ast$ means, any number of additional dimensions.

• indices (Tensor) – The index to do max operation whose data type must be mindspore.int32.

• updates (Tensor) – The tensor doing the max operation with input_x, the data type is same as input_x, the shape is indices.shape + x.shape[1:].

Returns

Tensor, the updated input_x, the type and shape same as input_x.

Raises

• TypeError – If indices is not an int32 or int64.

• ValueError – If the shape of updates is not equal to indices.shape + input_x.shape[1:].

• RuntimeError – If the data type of input_x and updates conversion of Parameter is required when data type conversion of Parameter is not supported.

• RuntimeError – On the Ascend platform, the input data dimension of input_x , indices and updates is greater than 8 dimensions.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops, Parameter
>>> input_x = Parameter(Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), mindspore.float32), name="input_x")
>>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32)
>>> updates = Tensor(np.ones([2, 2, 3]) * 88, mindspore.float32)
>>> output = ops.scatter_max(input_x, indices, updates)
>>> print(output)
[[88. 88. 88.]
 [88. 88. 88.]]