梯度计算

求参数w进行求解梯度有两种方式1.

mse.backward()
w.grad
方式2.

torch.autograd.grad(mse,[w])

#损失函数的梯度
import torch
import torch.nn.functional as F
x=torch.ones(1)   
w=torch.full([1],2)
 
mse=F.mse_loss(torch.ones(1),x*w)
w.requires_grad_()
mse=F.mse_loss(torch.ones(1),x*w)
#第一种方式
mse.backward()
w.grad

第二种方式
torch.autograd.grad(mse,[w])

#计算softmax函数
import torch
import torch.nn.functional as F
a=torch.rand(3)
p=F.softmax(a,dim=0)
a.requires_grad_()
p=F.softmax(a,dim=0)
torch.autograd.grad(p[2],[a],retain_graph=True)

import torch
import torch.nn.functional as F
x=torch.randn(1,10)
w=torch.randn(2,10,requires_grad=True)
o=torch.sigmoid(x@w.t())
o.shape

loss=F.mse_loss(torch.ones(1,2),o)

loss.backward()
w.grad

 

 

#两层函数求解梯度值
import torch
import torch.nn.functional as F

x=torch.tensor(1.)
w1=torch.tensor(2.,requires_grad=True)
b1=torch.tensor(1.)
w2=torch.tensor(2.,requires_grad=True)
b2=torch.tensor(1.)
y1=x*w1+b1
y2=y1*w2+b2
day2_dy1=torch.autograd.grad(y2,[y1],retain_graph=True)[0]
day1_dw1=torch.autograd.grad(y1,[w1],retain_graph=True)[0]
day2_dw1=torch.autograd.grad(y2,[w1],retain_graph=True)[0]
day2_dy1*day1_dw1

tensor(2.)

day2_dw1
tensor(2.)

 

import torch
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D


def himmelblau(x):
    return (x[0]**2+x[1]-11)**2+(x[0]+x[1]**2-7)**2
 
x=np.arange(-6,6,0.1)
y=np.arange(-6,6,0.1)
print('x,y range',x.shape,y.shape)
X,Y=np.meshgrid(x,y)
print('X,Y maps:',X.shape,Y.shape)
Z=himmelblau([X,Y])
fig=plt.figure('himmelblau')
ax=fig.gca(projection='3d')
ax.plot_surface(X,Y,Z)
ax.view_init(60,-30)
ax.set_xlabel('x')
ax.set_ylabel('y')
plt.show()

x=torch.tensor([0.,0.],requires_grad=True)
optimizer=torch.optim.Adam([x],lr=0.001) #lr=le-3表示学习率为0.001  
for step in range(20000):
    pred=himmelblau(x)
    optimizer.zero_grad()
    pred.backward()
    optimizer.step()
    if step%2000==0:
        print('step {}:x={},f(x)={}'.format(step,x.tolist(),pred.item()))

 

 

step 0:x=[0.0009999999310821295, 0.0009999999310821295],f(x)=170.0
step 2000:x=[2.3331806659698486, 1.9540694952011108],f(x)=13.730916023254395
step 4000:x=[2.9820079803466797, 2.0270984172821045],f(x)=0.014858869835734367
step 6000:x=[2.999983549118042, 2.0000221729278564],f(x)=1.1074007488787174e-08
step 8000:x=[2.9999938011169434, 2.0000083446502686],f(x)=1.5572823031106964e-09
step 10000:x=[2.999997854232788, 2.000002861022949],f(x)=1.8189894035458565e-10
step 12000:x=[2.9999992847442627, 2.0000009536743164],f(x)=1.6370904631912708e-11
step 14000:x=[2.999999761581421, 2.000000238418579],f(x)=1.8189894035458565e-12
step 16000:x=[3.0, 2.0],f(x)=0.0
step 18000:x=[3.0, 2.0],f(x)=0.0