Tensor——基本运算
文章目录
-
- broadcasting
- 加减乘除
- 矩阵运算
-
- torch.mm( )
- torch.matmul( )/@
-
- 二维：
- >二维
- pow
- sqrt/sqrt
- exp/log
- 近似值
- 梯度裁剪
-
- 最大、最小值：
- .clamp( )
broadcasting
expand、without copying data
key idea
- insert 1 dim ahead
- expand dims with size 1 to same size
- feature maps:[4,32,14,14]
- bias:[32,1,1] =>[1,32,1,1] => [4,32,14,14]
加减乘除
add(+)
>>> import torch
>>> a = torch.rand(3,4)
>>> b = torch.rand(4)
>>> a+b,torch.add(a,b)
(tensor([[1.1636, 1.1454, 1.3575, 1.1242],
[1.6967, 1.2132, 0.9738, 1.2507],
[0.9603, 1.6887, 1.3814, 0.7453]]),
tensor([[1.1636, 1.1454, 1.3575, 1.1242],
[1.6967, 1.2132, 0.9738, 1.2507],
[0.9603, 1.6887, 1.3814, 0.7453]]))
可见使用“+”和使用add函数结果是相同的。也可以验证一下：
>>> import torch
>>> a = torch.rand(3,4)
>>> b = torch.rand(4)
>>> torch.all(torch.eq(a+b,torch.add(a,b)))
tensor(True)
sub(-)
mul(*)
div(/)
矩阵运算
torch.mm( )
只能用于二维矩阵的运算。
>>> import torch
>>> a = torch.full([2,2],3)
>>> b = torch.ones(2,2)
>>> torch.mm(a,b)
tensor([[6., 6.],
[6., 6.]])
torch.matmul( )/@
可以用于二维矩阵计算，也可以是多维。
二维：
>>> import torch
>>> a = torch.full([2,2],3)
>>> b = torch.ones(2,2)
>>> torch.matmul(a,b)
tensor([[6., 6.],
[6., 6.]])
>>> import torch
>>> a = torch.full([2,2],3)
>>> b = torch.ones(2,2)
>>> a@b
tensor([[6., 6.],
[6., 6.]])
降维度
>>> import torch
>>> a = torch.rand(4,784)
>>> x = torch.rand(4,784)
>>> w = torch.rand(512,784)
>>> ([email protected]()).shape
torch.Size([4, 512])
>二维
比如四维，计算的时候就是前两维不变，后两维进行计算。
>>> import torch
>>> a = torch.rand(4,3,28,64)
>>> b = torch.rand(4,3,64,32)
>>> torch.matmul(a,b).shape
torch.Size([4, 3, 28, 32])
broadcast
通道数目不同时，符合broadcast情况：
>>> import torch
>>> a = torch.rand(4,3,28,64)
>>> b = torch.rand(4,1,64,32)
>>> torch.matmul(a,b).shape
torch.Size([4, 3, 28, 32])
维度不同、通道数目也不等时。不符合broadcast时会报错：
>>> import torch
>>> a = torch.rand(4,3,28,64)
>>> b = torch.rand(4,64,32)
>>> torch.matmul(a,b).shape
Traceback (most recent call last):
File "", line 1, in
RuntimeError: The size of tensor a (3) must match the size of tensor b (4) at non-singleton dimension 1
pow
两种方法：
①任意次幂
>>> import torch
>>> a = torch.full([2,2],6)
>>> a.pow(3)
tensor([[216., 216.],
[216., 216.]])
②仅限平方
>>> import torch
>>> a = torch.full([2,2],6)
>>> a**2
tensor([[36., 36.],
[36., 36.]])
sqrt/sqrt
平方根：
>>> import torch
>>> a = torch.full([2,2],1024)
>>> a.sqrt(),a.rsqrt, a**0.5
(tensor([[32., 32.],
[32., 32.]]),
tensor([[32., 32.],
[32., 32.]]))
>>>
平方根的倒数：
>>> import torch
>>> a = torch.full([2,2],1024)
>>> a.rsqrt()
( tensor([[0.0312, 0.0312],
[0.0312, 0.0312]]))
exp/log
我变懒了因为我困了！！三种：exp、log、log2写在一起吧：
>>> import torch
>>> a = torch.exp(torch.ones(2,2))
>>> a,torch.log(a), torch.log2(a)
(tensor([[2.7183, 2.7183],
[2.7183, 2.7183]]),
tensor([[1., 1.],
[1., 1.]]),
tensor([[1.4427, 1.4427],
[1.4427, 1.4427]]))
近似值
- .floor()——往下近似
- .ceil()——往上近似
- .trunc()——裁剪为整数部分
- .frac()——裁剪成小数部分
>>> import torch
>>> a = torch.tensor(3.1415926)
>>> a.floor(), a.ceil(), a.trunc(), a.frac()
(tensor(3.), tensor(4.), tensor(3.), tensor(0.1416))
- torch.round()——四舍五入
>>> import torch
>>> a = torch.tensor(3.499)
>>> b = torch.tensor(4.501)
>>> a.round(), b.round()
(tensor(3.), tensor(5.))
梯度裁剪
最大、最小值：
>>> import torch
>>> grad = torch.rand(2,3)*15
>>> grad.max(), grad.min()
(tensor(12.5018), tensor(4.8845))
.clamp( )
>>> import torch
>>> grad = torch.rand(2,3)*15
>>> grad, grad.clamp(10),grad.clamp(5,10)
(tensor([[11.7561, 0.7950, 7.1729],
[11.2285, 5.1940, 14.5741]]),
tensor([[11.7561, 10.0000, 10.0000],
[11.2285, 10.0000, 14.5741]]),
tensor([[10.0000, 5.0000, 7.1729],
[10.0000, 5.1940, 10.0000]]))
没睡午觉真的太困了，最后这点代码就放在一起打印吧。