mindspore.ops.TensorScatterMax
- class mindspore.ops.TensorScatterMax[source]
By comparing the value at the position indicated by indices in 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.
Refer to
mindspore.ops.tensor_scatter_max()
for more details.- Inputs:
input_x (Tensor) - The target tensor. The dimension of input_x must be no less than indices.shape[-1].
indices (Tensor) - The index of input tensor whose data type is int32 or int64. The rank must be at least 2.
updates (Tensor) - The tensor to update the input tensor, has the same type as input, and updates.shape should be equal to \(indices.shape[:-1] + input\_x.shape[indices.shape[-1]:]\).
- Outputs:
Tensor, has the same shape and type as input_x.
- 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) >>> op = ops.TensorScatterMax() >>> # 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]] >>> output = op(input_x, indices, updates) >>> print(output) [[ 2.2 0.3 3.6] [ 0.4 0.5 -3.2]]