forked from huawei/mindspore2022
76 lines
2.3 KiB
ReStructuredText
76 lines
2.3 KiB
ReStructuredText
mindspore.nn.Tril
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=================
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.. py:class:: mindspore.nn.Tril
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返回一个Tensor，其中第 `k` 个对角线以上的元素被置为零。
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矩阵的下三角把矩阵分成对角线上和对角线下的元素。
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参数 `k` 控制着矩阵的对角线。如果 `k` 为0，则保留主对角线上和下面的所有元素。正值包括主对角线上方尽可能多的对角线，类似地，负值排除主对角线下方尽可能多的对角线。
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**输入：**
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- **x** (Tensor)：输入Tensor。数据类型为Number。shape为 :math:`(N,*)`，其中 :math:`*` 表示任意的附加维度数。
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- **k** (Int)：对角线的索引。默认值：0。
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**输出：**
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Tensor，shape和数据类型与 `x` 相同。
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**异常：**
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- **TypeError：** `k` 不是int。
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- **ValueError：** `x` 的维度小于1。
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**支持平台：**
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``Ascend`` ``GPU`` ``CPU``
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**样例：**
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>>> x = Tensor(np.array([[ 1, 2, 3, 4],
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... [ 5, 6, 7, 8],
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... [10, 11, 12, 13],
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... [14, 15, 16, 17]]))
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>>> tril = nn.Tril()
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>>> result = tril(x)
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>>> print(result)
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[[ 1 0 0 0]
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[ 5 6 0 0]
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[10 11 12 0]
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[14 15 16 17]]
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>>> x = Tensor(np.array([[ 1, 2, 3, 4],
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... [ 5, 6, 7, 8],
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... [10, 11, 12, 13],
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... [14, 15, 16, 17]]))
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>>> tril = nn.Tril()
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>>> result = tril(x, 1)
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>>> print(result)
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[[ 1 2 0 0]
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[ 5 6 7 0]
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[10 11 12 13]
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[14 15 16 17]]
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>>> x = Tensor(np.array([[ 1, 2, 3, 4],
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... [ 5, 6, 7, 8],
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... [10, 11, 12, 13],
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... [14, 15, 16, 17]]))
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>>> tril = nn.Tril()
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>>> result = tril(x, 2)
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>>> print(result)
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[[ 1 2 3 0]
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[ 5 6 7 8]
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[10 11 12 13]
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[14 15 16 17]]
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>>> x = Tensor(np.array([[ 1, 2, 3, 4],
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... [ 5, 6, 7, 8],
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... [10, 11, 12, 13],
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... [14, 15, 16, 17]]))
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>>> tril = nn.Tril()
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>>> result = tril(x, -1)
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>>> print(result)
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[[ 0 0 0 0]
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[ 5 0 0 0]
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[10 11 0 0]
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[14 15 16 0]]
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