1
import torch
2

3
a = torch.ones(8)
4
b = torch.ones(2, 4)
5
c = torch.ones(2, 3, 4)
6

7
print("a:\n{}".format(a))
8
print("b:\n{}".format(b))
9
print("c:\n{}".format(c))
10

11
# norm1 = sum(|x|)
12
print("a.norm(1): {}, b.norm(1): {}, c.norm(1): {}".format(
13
    a.norm(1), b.norm(1), c.norm(1)
14
))
15

16
# norm2 = sqrt(sum(x**2))
17
print("a.norm(2): {}\nb.norm(2): {}\nc.norm(2): {}".format(
18
    a.norm(2), b.norm(2), c.norm(2)
19
))
20

21
print("b.norm(1, dim=1): ", b.norm(1, dim=1))
22
print("b.norm(2, dim=1): ", b.norm(2, dim=1))
23
print("c.norm(1, dim=0): \n", c.norm(1, dim=0))
24
print("c.norm(2, dim=0): \n", c.norm(2, dim=1))

1
import torch
2

3
a = torch.arange(8).view(2, 4).float() + 1
4
print("a:\n{}\n".format(a))
5

6
# 最小值
7
print("a.min(): ", a.min())
8
# 最小值
9
print("a.max(): ", a.max())
10
# 平均值
11
print("a.mean(): ", a.mean())
12
# 累乘
13
print("a.prod(): ", a.prod())
14
# 求和
15
print("a.sum(): ", a.sum())
16
# 最大值的索引
17
# 若不带参数，argmax和argmin会把矩阵打平
18
print("a.argmax(): ", a.argmax())
19
# 最小值的索引
20
print("a.argmin(): ", a.argmin())

1
import torch
2

3
a = torch.arange(8).view(2, 4).float() + 1
4
print("a:\n{}\n".format(a))
5

6
print("a.argmax(): ",a.argmax())
7
# dim 指定在哪一位上取最大值或最小值索引
8
print("a.argmax(dim=0): ", a.argmax(dim=0))
9
print("a.argmin(dim=1): ", a.argmin(dim=1))

1
import torch
2

3
a = torch.randn(4, 10)
4
print("a.max(dim=1):\n{}\n".format(a.max(dim=1)))
5
print("a.argmax(dim=1): ", a.argmax(dim=1), "\n")
6

7
# keepdim=True 会使结果维数不变
8
# 有时可以方便运算
9
print("a.max(dim=1, keepdim=True):\n{}\n".format(
10
    a.max(dim=1, keepdim=True))
11
)
12

13
print("a.argmax(dim=0, keepdim=True):\n{}\n".format(
14
    a.argmax(dim=0, keepdim=True))
15
)

1
import torch
2

3
a = torch.randn(4, 10)
4
# 在第二维最大前三个
5
print("a.topk(3, dim=1):\n{}\n".format(a.topk(3, dim=1)))
6
# 在第二维取最小前三个
7
print("a.topk(3, dim=1, largest=False):\n{}\n".format(
8
    a.topk(3, dim=1, largest=False))
9
)
10

11
# 在第二维上取第八大的值
12
print("a.kthvalue(8, dim=1):\n{}\n".format(
13
    a.kthvalue(8, dim=1)
14
))
15

16
# 在第一维上取第三大的值
17
print("a.kthvalue(3, dim=0):\n{}\n".format(
18
    a.kthvalue(3, dim=0)
19
))

1
import torch
2

3
a = torch.arange(-4, 4).view(2, 4)
4

5
print("a:\n{}\n".format(a))
6
print("a>0:\n{}\n".format(a>0))
7
# gt -> greater 大于
8
print("torch.equal(a>0, torch.gt(a, 0)):",
9
      torch.equal(a>0, torch.gt(a, 0)))
10

11
print("a==0:\n{}\n".format(a==0))
12
# eq(a, b) 会返回一个矩阵，矩阵每个元素表示该位置的元素是否相等
13
# equal(a, b) 会返回一个 布尔值，表示两矩阵是否完全相等
14
print("torch.equal(a==0, torch.eq(a, 0)):",
15
      torch.equal(a==0, torch.eq(a, 0)))
16

17
print("a>=0:\n{}\n".format(a>=0))
18
# ge greater or equal
19
print("torch.equal(a>=0, torch.ge(a, 0)):",
20
      torch.equal(a>=0, torch.ge(a, 0)))