1. torch.renorm(input, p, dim, maxnorm, out=None) → Tensor
Returns a tensor where each sub-tensor of input along dimension dim is normalized such that the p-norm of the sub-tensor is lower than the value maxnorm。
解釋：返回一個張量，包含規范化后的各個子張量，使得沿着dim維划分的各子張量的p范數小於maxnorm。
>>> x = torch.Tensor([[1,2,3]])
>>> torch.renorm(x,2,0,1) tensor([[ 0.2673, 0.5345, 0.8018]])
2. torch. scatter_(dim, index, src) → Tensor
將src中的所有值按照index確定的索引寫入本tensor中。其中索引是根據給定的dimension，dim按照gather()描述的規則來確定。
注意，index的值必須是在0到(self.size(dim)-1)之間，
參數：
- input (Tensor)-源tensor
- dim (int)-索引的軸向
- index (LongTensor)-散射元素的索引指數
- src (Tensor or float)-散射的源元素
1 >>> x = torch.rand(2, 5) 2 >>> x 3 0.4319 0.6500 0.4080 0.8760 0.2355 4 0.2609 0.4711 0.8486 0.8573 0.1029 5 [torch.FloatTensor of size 2x5]
6 >>> torch.zeros(3, 5).scatter_(0, torch.LongTensor([[0, 1, 2, 0, 0], [2, 0, 0, 1, 2]]), x) #將 x 按照格式寫入新的Tensor里 7 0.4319 0.4711 0.8486 0.8760 0.2355 8 0.0000 0.6500 0.0000 0.8573 0.0000 9 0.2609 0.0000 0.4080 0.0000 0.1029 10 [torch.FloatTensor of size 3x5]
11 >>> z = torch.zeros(2, 4).scatter_(1, torch.LongTensor([[2], [3]]), 1.23) 12 >>> z 13 0.0000 0.0000 1.2300 0.0000 14 0.0000 0.0000 0.0000 1.2300 15 [torch.FloatTensor of size 2x4]
3. torch.gather(input, dim, index, out=None) → Tensor
沿給定軸dim，將輸入索引張量index指定位置的值進行聚合。
參數:
- input (Tensor) – 源張量
- dim (int) – 索引的軸
- index (LongTensor) – 聚合元素的下標
- out (Tensor, optional) – 目標張量
>>> t = torch.Tensor([[1,2],[3,4]]) >>> torch.gather(t, 1, torch.LongTensor([[0,0],[1,0]])) 1 1 4 3 [torch.FloatTensor of size 2x2]
or：
>>> s=torch.randn(3,6) >>> s tensor([[-0.4857, -0.0982, -0.6532, -1.0273, -0.9205, -0.7440], [-0.6890, -0.3474, -1.4337, -0.3511, -0.2443, -0.6398], [ 1.2902, 1.1210, 1.7374, 0.0902, -0.4524, -0.6898]]) >>> s.gather(1,torch.LongTensor([[1,2,1],[1,2,3],[1,2,3]])) tensor([[-0.0982, -0.6532, -0.0982], [-0.3474, -1.4337, -0.3511], [ 1.1210, 1.7374, 0.0902]])
4. pytorch改變維度的操作