Autograd 自動求導

import torch

# 建立張量並設定requires_grad=true來追蹤其計算歷史
x = torch.ones(2,
2, requires_grad=
true
)print
(x)

tensor([[1., 1.],

[1., 1.]], requires_grad=true)

y = x +

2print
(y)

tensor([[3., 3.],

[3., 3.]], grad_fn=)

print

(y.grad_fn)

z = y * y *

3out = z.mean(
)print
(z, out)

tensor([[27., 27.],

[27., 27.]], grad_fn=) tensor(27., grad_fn=)

# .requires_grad_(...)可以改變現有張量的requires_grad屬性，預設flag為false

a = torch.randn(2,
2)a =(
(a *3)
/(a -1)
)print
(a.requires_grad)
a.requires_grad_(
true
)print
(a.requires_grad)
b =(a * a)
.sum()
print
(b.grad_fn)

false

true

x1 = torch.ones(2,

2,requires_grad=
true
)z1 =3*
(x1 +2)
*(x1 +2)
out = z1.mean(
)# z1 = 3(x1+2)^2, out = mean(z1)
print
("x1 = "
, x1)
print
("z1 = "
, z1)
# out是乙個純量(scalar)，out.backward()相當於out.backward(torch,tensor(1))
out.backward(
)# x.grad 梯度d(out)/dx
print
(x1.grad)
# 清零
x1.grad.data.zero_(
)

x1 =  tensor([[1., 1.],

[1., 1.]], requires_grad=true)
z1 = tensor([[27., 27.],
[27., 27.]], grad_fn=)
tensor([[4.5000, 4.5000],
[4.5000, 4.5000]])
tensor([[0., 0.],
[0., 0.]])

x = torch.randn(

3, requires_grad=
true
)y = x *
2while y.data.norm(
)<
1000
:# .data.norm() 張量l2範數
y = y *
2print
("y ="
,y)gradients = torch.tensor(
[0.1
,1.0
,0.0001
], dtype=torch.
float
)y.backward(gradients)
print
("grad ="
, x.grad)

y = tensor([1203.4269, 1136.8713,  854.0352], grad_fn=)

grad = tensor([2.0480e+02, 2.0480e+03, 2.0480e-01])

print

(x.requires_grad)
print
((x **2)
.requires_grad)
# 將變數包裹在with torch.no_grad()中，可以暫時遮蔽autograd計算
with torch.no_grad():
print
((x **2)
.requires_grad)
print
((x **2)
.requires_grad)

true

true
false
true

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