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mindspore.ops.Tril

class mindspore.ops.Tril(diagonal=0)

Returns the lower triangular portion of the 2-D matrix or the set of matrices in a batch. The remaining elements of the resulting Tensor are assigned a value of 0. The lower triangular section of the matrix comprises of the elements present on and below the main diagonal.

Warning

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

Parameters

diagonal (int, optional) – An optional attribute indicates the diagonal to consider, default: 0 , indicating the main didiagonal.

Inputs:

• x (Tensor) - The input tensor with shape $(M,N,\ast )$ where $\ast$ means any number of additional dimensions.

Outputs:

Tensor, the same shape and data type as the input x.

Raises

• TypeError – If x is not a Tensor.

• TypeError – If diagonal is not an int.

• ValueError – If the rank of x is less than 2.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([[ 1,  2,  3,  4],
...                      [ 5,  6,  7,  8],
...                      [10, 11, 12, 13],
...                      [14, 15, 16, 17]]))
>>> tril = ops.Tril()
>>> result = tril(x)
>>> print(result)
[[ 1  0  0  0]
 [ 5  6  0  0]
 [10 11 12  0]
 [14 15 16 17]]
>>> x = Tensor(np.array([[ 1,  2,  3,  4],
...                      [ 5,  6,  7,  8],
...                      [10, 11, 12, 13],
...                      [14, 15, 16, 17]]))
>>> tril = ops.Tril(diagonal=1)
>>> result = tril(x)
>>> print(result)
[[ 1  2  0  0]
 [ 5  6  7  0]
 [10 11 12 13]
 [14 15 16 17]]
>>> x = Tensor(np.array([[ 1,  2,  3,  4],
...                      [ 5,  6,  7,  8],
...                      [10, 11, 12, 13],
...                      [14, 15, 16, 17]]))
>>> tril = ops.Tril(diagonal=-1)
>>> result = tril(x)
>>> print(result)
[[ 0  0  0  0]
 [ 5  0  0  0]
 [10 11  0  0]
 [14 15 16  0]]