mindspore.ops.scatter_nd_min
- mindspore.ops.scatter_nd_min(input_x, indices, updates, use_locking=False)[source]
Applying sparse minimum to individual values or slices in a tensor.
Using given values to update tensor value through the min 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.
input_x has rank P and indices has rank Q where Q >= 2.
indices has shape \((i_0, i_1, ..., i_{Q-2}, N)\) where N <= P.
The last dimension of indices (with length N ) indicates slices along the N th dimension of input_x.
updates is a tensor of rank Q-1+P-N. Its shape is: \((i_0, i_1, ..., i_{Q-2}, x\_shape_N, ..., x\_shape_{P-1})\).
- Parameters
input_x (Parameter) – The target tensor, with data type of Parameter. The shape is \((N,*)\), where \(*\) means any number of additional dimensions.
indices (Tensor) – The index to do min operation whose data type must be mindspore.int32 or mindspore.int64. The rank of indices must be at least 2 and indices.shape[-1] <= len(shape).
updates (Tensor) – The tensor to do the min operation with input_x. The data type is same as input_x, and the shape is indices.shape[:-1] + x.shape[indices.shape[-1]:].
use_locking (bool) – Whether to protect the assignment by a lock. Default: False.
- Returns
Tensor, the updated input_x, has the same shape and type as input_x.
- Raises
TypeError – If the dtype of use_locking is not bool.
TypeError – If the dtype of indices is not int32 or int64.
TypeError – If dtype of input_x and updates are not the same.
ValueError – If the shape of updates is not equal to indices.shape[:-1] + x.shape[indices.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.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> input_x = Parameter(Tensor(np.ones(8) * 10, mindspore.float32), name="x") >>> indices = Tensor(np.array([[2], [4], [1], [7]]), mindspore.int32) >>> updates = Tensor(np.array([6, 7, 8, 9]), mindspore.float32) >>> output = ops.scatter_nd_min(input_x, indices, updates, False) >>> print(output) [10. 8. 6. 10. 7. 10. 10. 9.] >>> input_x = Parameter(Tensor(np.ones((4, 4, 4)) * 10, mindspore.int32)) >>> indices = Tensor(np.array([[0], [2]]), mindspore.int32) >>> updates = Tensor(np.array([[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], ... [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]]]), mindspore.int32) >>> output = ops.scatter_nd_min(input_x, indices, updates, False) >>> print(output) [[[ 1 1 1 1] [ 2 2 2 2] [ 3 3 3 3] [ 4 4 4 4]] [[10 10 10 10] [10 10 10 10] [10 10 10 10] [10 10 10 10]] [[ 5 5 5 5] [ 6 6 6 6] [ 7 7 7 7] [ 8 8 8 8]] [[10 10 10 10] [10 10 10 10] [10 10 10 10] [10 10 10 10]]]