1 a = torch.rand(1,10,4)，生成的Tensor在0-1之间，且tensor的shape为（1,10,4）
2 torch.cat(tensors, dim=0, out=None) → Tensor
>>> x = torch.randn(2, 3)
>>> x
tensor([[ 0.6580, -1.0969, -0.4614],
[-0.1034, -0.5790, 0.1497]])
>>> torch.cat((x, x, x), 0)
tensor([[ 0.6580, -1.0969, -0.4614],
[-0.1034, -0.5790, 0.1497],
[ 0.6580, -1.0969, -0.4614],
[-0.1034, -0.5790, 0.1497],
[ 0.6580, -1.0969, -0.4614],
[-0.1034, -0.5790, 0.1497]])
>>> torch.cat((x, x, x), 1)
tensor([[ 0.6580, -1.0969, -0.4614, 0.6580, -1.0969, -0.4614, 0.6580,
-1.0969, -0.4614],
[-0.1034, -0.5790, 0.1497, -0.1034, -0.5790, 0.1497, -0.1034,
-0.5790, 0.1497]])
3 torch.index_select(input, dim, index, out=None) → Tensor
>>> x = torch.randn(3, 4)
>>> x
tensor([[ 0.1427, 0.0231, -0.5414, -1.0009],
[-0.4664, 0.2647, -0.1228, -1.1068],
[-1.1734, -0.6571, 0.7230, -0.6004]])
>>> indices = torch.tensor([0, 2])
>>> torch.index_select(x, 0, indices)
tensor([[ 0.1427, 0.0231, -0.5414, -1.0009],
[-1.1734, -0.6571, 0.7230, -0.6004]])
>>> torch.index_select(x, 1, indices)
tensor([[ 0.1427, -0.5414],
[-0.4664, -0.1228],
[-1.1734, 0.7230]])
4 torch.masked_select(input, mask, out=None) → Tensor
Returns a new 1-D tensor which indexes the input tensor according to the binary mask mask which is a ByteTensor.
>>> x = torch.randn(3, 4)
>>> x
tensor([[ 0.3552, -2.3825, -0.8297, 0.3477],
[-1.2035, 1.2252, 0.5002, 0.6248],
[ 0.1307, -2.0608, 0.1244, 2.0139]])
>>> mask = x.ge(0.5)
>>> mask
tensor([[ 0, 0, 0, 0],
[ 0, 1, 1, 1],
[ 0, 0, 0, 1]], dtype=torch.uint8)
>>> torch.masked_select(x, mask)
tensor([ 1.2252, 0.5002, 0.6248, 2.0139])
5 torch.squeeze(input, dim=None, out=None) → Tensor
Returns a tensor with all the dimensions of input of size 1 removed.
>>> x = torch.zeros(2, 1, 2, 1, 2)
>>> x.size()
torch.Size([2, 1, 2, 1, 2])
>>> y = torch.squeeze(x)
>>> y.size()
torch.Size([2, 2, 2])
>>> y = torch.squeeze(x, 0)
>>> y.size()
torch.Size([2, 1, 2, 1, 2])
>>> y = torch.squeeze(x, 1)
>>> y.size()
torch.Size([2, 2, 1, 2])
6 torch.transpose(input, dim0, dim1) → Tensor
Returns a tensor that is a transposed version of input. The given dimensions dim0 and dim1 are swapped.
The resulting out tensor shares it’s underlying storage with the input tensor, so changing the content of one would change the content of the other.
>>> x = torch.randn(2, 3)
>>> x
tensor([[ 1.0028, -0.9893, 0.5809],
[-0.1669, 0.7299, 0.4942]])
>>> torch.transpose(x, 0, 1)
tensor([[ 1.0028, -0.1669],
[-0.9893, 0.7299],
[ 0.5809, 0.4942]])
7 torch.unsqueeze(input, dim, out=None) → Tensor
Returns a new tensor with a dimension of size one inserted at the specified position.
>>> x = torch.tensor([1, 2, 3, 4])
>>> torch.unsqueeze(x, 0)
tensor([[ 1, 2, 3, 4]])
>>> torch.unsqueeze(x, 1)
tensor([[ 1],
[ 2],
[ 3],
[ 4]])
8 torch.where(condition, x, y) → Tensor
Return a tensor of elements selected from either x or y, depending on condition.
>>> x = torch.randn(3, 2)
>>> y = torch.ones(3, 2)
>>> x
tensor([[-0.4620, 0.3139],
[ 0.3898, -0.7197],
[ 0.0478, -0.1657]])
>>> torch.where(x > 0, x, y)
tensor([[ 1.0000, 0.3139],
[ 0.3898, 1.0000],
[ 0.0478, 1.0000]])
9 torch.rand(*sizes, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor
>>> torch.rand(4)
tensor([ 0.5204, 0.2503, 0.3525, 0.5673])
>>> torch.rand(2, 3)
tensor([[ 0.8237, 0.5781, 0.6879],
[ 0.3816, 0.7249, 0.0998]])
10 torch.randint(low=0, high, size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor
>>> torch.randint(3, 5, (3,))
tensor([4, 3, 4])
>>> torch.randint(10, (2, 2))
tensor([[0, 2],
[5, 5]])
>>> torch.randint(3, 10, (2, 2))
tensor([[4, 5],
[6, 7]])
10 torch.randn(*sizes, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor
Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 (also called the standard normal distribution).
>>> torch.randn(4)
tensor([-2.1436, 0.9966, 2.3426, -0.6366])
>>> torch.randn(2, 3)
tensor([[ 1.5954, 2.8929, -1.0923],
[ 1.1719, -0.4709, -0.1996]])
11 torch.clamp(input, min, max, out=None) → Tensor
>>> a = torch.randn(4)
>>> a
tensor([-1.7120, 0.1734, -0.0478, -0.0922])
>>> torch.clamp(a, min=-0.5, max=0.5)
tensor([-0.5000, 0.1734, -0.0478, -0.0922])
12 torch.argmax(input, dim, keepdim=False) → LongTensor
torch.argmin(input, dim, keepdim=False, out=None) → LongTensor
>>> a = torch.randn(4, 4)
>>> a
tensor([[ 0.1139, 0.2254, -0.1381, 0.3687],
[ 1.0100, -1.1975, -0.0102, -0.4732],
[-0.9240, 0.1207, -0.7506, -1.0213],
[ 1.7809, -1.2960, 0.9384, 0.1438]])
>>> torch.argmin(a, dim=1)
tensor([ 2, 1, 3, 1])
13 torch.unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None)
>>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long))
>>> output
tensor([ 2, 3, 1])
>>> output, inverse_indices = torch.unique(
torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True)
>>> output
tensor([ 1, 2, 3])
>>> inverse_indices
tensor([ 0, 2, 1, 2])
>>> output, inverse_indices = torch.unique(
torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True)
>>> output
tensor([ 1, 2, 3])
>>> inverse_indices
tensor([[ 0, 2],
[ 1, 2]])
14 torch.argsort(input, dim=-1, descending=False, out=None) → LongTensor
>>> a = torch.randn(4, 4)
>>> a
tensor([[ 0.0785, 1.5267, -0.8521, 0.4065],
[ 0.1598, 0.0788, -0.0745, -1.2700],
[ 1.2208, 1.0722, -0.7064, 1.2564],
[ 0.0669, -0.2318, -0.8229, -0.9280]])
>>> torch.argsort(a, dim=1)
tensor([[2, 0, 3, 1],
[3, 2, 1, 0],
[2, 1, 0, 3],
[3, 2, 1, 0]])
15 torch.eq(input, other, out=None) → Tensor（ByteTensor）
Computes element-wise equality
The second argument can be a number or a tensor whose shape is broadcastable with the first argument.
>>> torch.eq(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]]))
tensor([[ 1, 0],
[ 0, 1]], dtype=torch.uint8)
16 torch.ge(input, other, out=None) → Tensor（ByteTensor）
Computes \text{input} \geq \text{other}input≥other element-wise.
The second argument can be a number or a tensor whose shape is broadcastable with the first argument.
>>> torch.ge(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]]))
tensor([[ 1, 1],
[ 0, 1]], dtype=torch.uint8)
17 torch.le(input, other, out=None) → Tensor（ByteTensor）
>>> torch.le(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]]))
tensor([[ 1, 0],
[ 1, 1]], dtype=torch.uint8)
18 torch.max(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor)（同torch.min(), torch.ne()）
>>> a = torch.randn(4, 4)
>>> a
tensor([[-1.2360, -0.2942, -0.1222, 0.8475],
[ 1.1949, -1.1127, -2.2379, -0.6702],
[ 1.5717, -0.9207, 0.1297, -1.8768],
[-0.6172, 1.0036, -0.6060, -0.2432]])
>>> torch.max(a, 1)
torch.return_types.max(values=tensor([0.8475, 1.1949, 1.5717, 1.0036]), indices=tensor([3, 0, 0, 1]))
19 torch.sort(input, dim=-1, descending=False, out=None) -> (Tensor, LongTensor)
>>> x = torch.randn(3, 4)
>>> sorted, indices = torch.sort(x)
>>> sorted
tensor([[-0.2162, 0.0608, 0.6719, 2.3332],
[-0.5793, 0.0061, 0.6058, 0.9497],
[-0.5071, 0.3343, 0.9553, 1.0960]])
>>> indices
tensor([[ 1, 0, 2, 3],
[ 3, 1, 0, 2],
[ 0, 3, 1, 2]])
>>> sorted, indices = torch.sort(x, 0)
>>> sorted
tensor([[-0.5071, -0.2162, 0.6719, -0.5793],
[ 0.0608, 0.0061, 0.9497, 0.3343],
[ 0.6058, 0.9553, 1.0960, 2.3332]])
>>> indices
tensor([[ 2, 0, 0, 1],
[ 0, 1, 1, 2],
[ 1, 2, 2, 0]])
20 torch.topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTensor)
>>> x = torch.arange(1., 6.)
>>> x
tensor([ 1., 2., 3., 4., 5.])
>>> torch.topk(x, 3)
torch.return_types.topk(values=tensor([5., 4., 3.]), indices=tensor([4, 3, 2]))
21 torch.diag(input, diagonal=0, out=None) → Tensor
If input is a vector (1-D tensor), then returns a 2-D square tensor with the elements of input as the diagonal.
If input is a matrix (2-D tensor), then returns a 1-D tensor with the diagonal elements of input.
>>> a = torch.randn(3)
>>> a
tensor([ 0.5950,-0.0872, 2.3298])
>>> torch.diag(a)
tensor([[ 0.5950, 0.0000, 0.0000],
[ 0.0000,-0.0872, 0.0000],
[ 0.0000, 0.0000, 2.3298]])
>>> torch.diag(a, 1)
tensor([[ 0.0000, 0.5950, 0.0000, 0.0000],
[ 0.0000, 0.0000,-0.0872, 0.0000],
[ 0.0000, 0.0000, 0.0000, 2.3298],
[ 0.0000, 0.0000, 0.0000, 0.0000]])
# ******************
>>> a = torch.randn(3, 3)
>>> a
tensor([[-0.4264, 0.0255,-0.1064],
[ 0.8795,-0.2429, 0.1374],
[ 0.1029,-0.6482,-1.6300]])
>>> torch.diag(a, 0)
tensor([-0.4264,-0.2429,-1.6300])
>>> torch.diag(a, 1)
tensor([ 0.0255, 0.1374])
就是对tensor根据index进行选择，例子如下，这里需要保证a是long，byte，bool类型
In [26]: a = a.long()
In [27]: b[a]
Out[27]:
tensor([[0.2214, 0.9189, 0.2356, 0.0580],
[0.2214, 0.9189, 0.2356, 0.0580],
[0.2214, 0.9189, 0.2356, 0.0580],
[0.2214, 0.9189, 0.2356, 0.0580]])
In [28]: a = torch.ones(4).long()
In [29]: b = torch.rand(2,4)
In [30]: b
Out[30]:
tensor([[0.5651, 0.5041, 0.2411, 0.9585],
[0.0191, 0.7716, 0.2444, 0.5113]])
In [31]: b[a]
Out[31]:
tensor([[0.0191, 0.7716, 0.2444, 0.5113],
[0.0191, 0.7716, 0.2444, 0.5113],
[0.0191, 0.7716, 0.2444, 0.5113],
[0.0191, 0.7716, 0.2444, 0.5113]])
22 torch.linspace(start, end, steps, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor
>>> torch.linspace(3, 10, steps=5)
tensor([ 3.0000, 4.7500, 6.5000, 8.2500, 10.0000])
>>> torch.linspace(-10, 10, steps=5)
tensor([-10., -5., 0., 5., 10.])
>>> torch.linspace(start=-10, end=10, steps=5)
tensor([-10., -5., 0., 5., 10.])
>>> torch.linspace(start=-10, end=10, steps=1)
tensor([-10.])
23 torch.meshgrid(*tensors, indexing=None) 功能是生成网格，主要用于生成图像坐标；输入两个数据类型和维度相同的一维tensor，输出两个tensor（输出tensor的行数为第一个输入tensor元素个数，输出tensor的列数为第二个输入tensor元素个数）
第一个输出tensor，填充第一个输入tensor中的元素，各行元素相同；第二个输出tensor，填充第二个输入tensor中的元素，各列元素相同
import torch
a = torch.tensor([1, 2, 3, 4])
print(a)
b = torch.tensor([4, 5, 6])
print(b)
x, y = torch.meshgrid(a, b)
print(x)
print(y)
结果显示：
tensor([1, 2, 3, 4])
tensor([4, 5, 6])
tensor([[1, 1, 1],
[2, 2, 2],
[3, 3, 3],
[4, 4, 4]])
tensor([[4, 5, 6],
[4, 5, 6],
[4, 5, 6],
[4, 5, 6]])
24 torch.stack
官方解释：沿着一个新维度对输入张量序列进行连接。 序列中所有的张量都应该为相同形状。
浅显说法：把多个2维的张量凑成一个3维的张量；多个3维的凑成一个4维的张量…以此类推，也就是在增加新的维度进行堆叠。
具体参考https://blog.youkuaiyun.com/xinjieyuan/article/details/105205326
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