Tensor 基本運算

2021-10-07 06:22:18 字數 4424 閱讀 9649

powsqrt/sqrt

exp/log

近似值梯度裁剪

expand、without copying data

key idea

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]]))
可見使用「+」和使用add函式結果是相同的。也可以驗證一下：

>>> 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(/)

只能用於二維矩陣的運算。

>>> import torch


>>> a = torch.full([2,2],3)


>>> b = torch.ones(2,2)


>>> torch.mm(a,b)


tensor([[6., 6.],


[6., 6.]])
可以用於二維矩陣計算，也可以是多維。

二維：

>>> 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.]])
降維度

>>> import torch


>>> a = torch.rand(4,784)


>>> x = torch.rand(4,784)


>>> w = torch.rand(512,784)


>>> ([email protected]()).shape


torch.size([4, 512])
>二維

比如四維，計算的時候就是前兩維不變，後兩維進行計算。

>>> 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

通道數目不同時，符合broadcast情況：

>>> 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])
維度不同、通道數目也不等時。不符合broadcast時會報錯：

>>> 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
兩種方法：

①任意次冪

>>> import torch


>>> a = torch.full([2,2],6)


>>> a.pow(3)


tensor([[216., 216.],


[216., 216.]])
②僅限平方

>>> import torch


>>> a = torch.full([2,2],6)


>>> a**2


tensor([[36., 36.],


[36., 36.]])
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.]]))


>>>
平方根的倒數：

>>> import torch


>>> a = torch.full([2,2],1024)


>>> a.rsqrt()


( tensor([[0.0312, 0.0312],


[0.0312, 0.0312]]))
我變懶了因為我困了！！三種：exp、log、log2寫在一起吧：

>>> 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]]))
>>> import torch


>>> a = torch.tensor(3.1415926)


>>> a.floor(), a.ceil(), a.trunc(), a.frac()


(tensor(3.), tensor(4.), tensor(3.), tensor(0.1416))
>>> import torch


>>> a = torch.tensor(3.499)


>>> b = torch.tensor(4.501)


>>> a.round(), b.round()


(tensor(3.), tensor(5.))
>>> import torch


>>> grad = torch.rand(2,3)*15


>>> grad.max(), grad.min()


(tensor(12.5018), tensor(4.8845))
>>> 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]]))
沒睡午覺真的太睏了，最後這點**就放在一起列印吧。

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