Pytorch 自动微分

参考 http://pytorch123.com/

• Tensor.requires_grad = True 记录对Tensor的所有操作，后序.backward() 自动计算所有梯度到 .grad 属性

import torch
x = torch.ones(2,2, requires_grad=True) # 默认是False
print(x)

tensor([[1., 1.],
        [1., 1.]], requires_grad=True)

• 停止记录调用.detach()

x.detach_()
print(x.requires_grad) # False

• .grad_fn 保存了创建张量的 Function 的引用

x = torch.ones(2,2, requires_grad=True)
y = x + 2
print(y)
print(y.grad_fn)

tensor([[3., 3.],
        [3., 3.]], grad_fn=<AddBackward0>)
<AddBackward0 object at 0x0000015716529D68>

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

tensor([[27., 27.],
        [27., 27.]], grad_fn=<MulBackward0>) 

tensor(27., grad_fn=<MeanBackward0>)

# requires_grad 默认为 False
a = torch.randn(2, 2)
a = ((a*3)/(a-1))
print(a.requires_grad)  # False
b = (a*a).sum()
print(b.grad_fn)  # None

a.requires_grad_(True)  # 设置为 True
print(a.requires_grad)  # True
b = (a*a).sum()
print(b.grad_fn)
# <SumBackward0 object at 0x0000015717DC69E8>

• backward() 后向传播

z = y*y*3
y = x+2
计算 d(out)/dx

o u t = 1 4 ( ∑ 3 ( x i + 2 ) 2 ) → d o u t d x i = 3 2 ( x i + 2 ) out = \frac{1}{4}(\sum3(x_i+2)^2) \rightarrow \frac{d_{out}}{dx_i} = \frac{3}{2}(x_i+2) out=41​(∑3(xi​+2)2)→dxi​dout​​=23​(xi​+2) x i = 1 , d o u t / d x i = 4.5 x_i = 1, d_{out}/dx_i = 4.5 xi​=1,dout​/dxi​=4.5

out.backward()
print(y.grad) # None, 为什么？是 None
print(x.grad)
tensor([[4.5000, 4.5000],
        [4.5000, 4.5000]])

J = ( ∂ y 1 ∂ x 1 ⋯ ∂ y m ∂ x 1 ⋮ ⋱ ⋮ ∂ y 1 ∂ x n ⋯ ∂ y m ∂ x n ) J=\left(\begin{array}{ccc}\frac{\partial y_{1}}{\partial x_{1}} & \cdots & \frac{\partial y_{m}}{\partial x_{1}} \\ \vdots & \ddots & \vdots \\ \frac{\partial y_{1}}{\partial x_{n}} & \cdots & \frac{\partial y_{m}}{\partial x_{n}}\end{array}\right) J=⎝⎜⎛​∂x1​∂y1​​⋮∂xn​∂y1​​​⋯⋱⋯​∂x1​∂ym​​⋮∂xn​∂ym​​​⎠⎟⎞​

• 当又使用了一个函数 l = g ( y ) l = g(y) l=g(y)，v 是 l l l 对 y y y 的导数，链式求导相乘，得到 l l l 对 x x x 的导数 J ⋅ v = ( ∂ y 1 ∂ x 1 ⋯ ∂ y m ∂ x 1 ⋮ ⋱ ⋮ ∂ y 1 ∂ x n ⋯ ∂ y m ∂ x n ) ( ∂ l ∂ y 1 ⋮ ∂ l ∂ y m ) = ( ∂ l ∂ x 1 ⋮ ∂ l ∂ x n ) J \cdot v=\left(\begin{array}{ccc}\frac{\partial y_{1}}{\partial x_{1}} & \cdots & \frac{\partial y_{m}}{\partial x_{1}} \\ \vdots & \ddots & \vdots \\ \frac{\partial y_{1}}{\partial x_{n}} & \cdots & \frac{\partial y_{m}}{\partial x_{n}}\end{array}\right)\left(\begin{array}{c}\frac{\partial l}{\partial y_{1}} \\ \vdots \\ \frac{\partial l}{\partial y_{m}}\end{array}\right)=\left(\begin{array}{c}\frac{\partial l}{\partial x_{1}} \\ \vdots \\ \frac{\partial l}{\partial x_{n}}\end{array}\right) J⋅v=⎝⎜⎛​∂x1​∂y1​​⋮∂xn​∂y1​​​⋯⋱⋯​∂x1​∂ym​​⋮∂xn​∂ym​​​⎠⎟⎞​⎝⎜⎛​∂y1​∂l​⋮∂ym​∂l​​⎠⎟⎞​=⎝⎜⎛​∂x1​∂l​⋮∂xn​∂l​​⎠⎟⎞​

上面代码改为：

v = torch.tensor(2, dtype=torch.float)
out.backward(v)
print(x.grad)

# 梯度乘以了 2
tensor([[9., 9.],
        [9., 9.]])

• 评估阶段可以使用 with torch.no_grad(): 不需要梯度计算和更新

print(x.requires_grad) # True
print((x ** 2).requires_grad) # True

# 取消梯度记录
with torch.no_grad():
    print((x ** 2).requires_grad) # False