Tensor 基本運算

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]]))

>>> import torch

>>> a = torch.rand(3,4)
>>> b = torch.rand(4)
>>> torch.all(torch.eq(a+b,torch.add(a,b)))
tensor(true)

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.]])

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.]])

>>> import torch

>>> a = torch.rand(4,784)
>>> x = torch.rand(4,784)
>>> w = torch.rand(512,784)
>>> ([email protected]()).shape
torch.size([4, 512])

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])

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])

>>> 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

①任意次冪

>>> import torch

>>> a = torch.full([2,2],6)
>>> a.pow(3)
tensor([[216., 216.],
[216., 216.]])

>>> import torch

>>> a = torch.full([2,2],6)
>>> a**2
tensor([[36., 36.],
[36., 36.]])

>>> 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.]]))
>>>

>>> import torch

>>> a = torch.full([2,2],1024)
>>> a.rsqrt()
( tensor([[0.0312, 0.0312],
[0.0312, 0.0312]]))

>>> 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]]))

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