使用Pytorch训练two-head网络的操作

   f_out_y0, _ = net(x0)
            _, f_out_y1 = net(x1)
            #实例化损失函数
            criterion0 = Loss()
            criterion1 = Loss()
            loss0 = criterion0(f_y0, f_out_y0, w0)
            loss1 = criterion1(f_y1, f_out_y1, w1)
            print(loss0.item(), loss1.item())
            #对网络参数进行初始化
            optimizer.zero_grad()
            loss0.backward()
            loss1.backward()
            #对网络的参数进行更新
            optimizer.step()

   f_out_y0, cf_out_y0 = net(x0)
            cf_out_y1, f_out_y1 = net(x1)
            #按照t=0和t=1的索引拼接向量
            y_pred = torch.zeros([len(x), 1])
            y_pred[index0] = f_out_y0
            y_pred[index1] = f_out_y1
            
   criterion = Loss()
            loss = criterion(f_y, y_pred, w) + 0.01 * (l2_regularization0 + l2_regularization1)
            #print(loss.item())
            viz.line([float(loss)], [epoch], win='train_loss', update='append')
            optimizer.zero_grad()
            loss.backward()
            #对网络的参数进行更新
            optimizer.step()

import torch 
print(torch.tensor([1,2,3],dtype=torch.float))#将一个列表强制转换为torch.Tensor类型
print(torch.randn(5,3))#生成torch.Tensor类型的5X3的随机数

import torch
from torch.autograd import Variable
 
#初步认识构建Tensor数据
def one():
    print(torch.tensor([1,2,3],dtype=torch.float))#将一个列表强制转换为torch.Tensor类型
    print(torch.randn(5,3))#生成torch.Tensor类型的5X3的随机数
    print(torch.zeros((2,3)))#生成一个2X3的全零矩阵
    print(torch.ones((2,3)))#生成一个2X3的全一矩阵
    a = torch.randn((2,3))
    b = a.numpy()#将一个torch.Tensor转换为numpy
    c = torch.from_numpy(b)#将numpy转换为Tensor
    print(a)
    print(b)
    print(c)
 
#使用Variable自动求导
def two():
    # 构建Variable
    x = Variable(torch.Tensor([1, 2, 3]), requires_grad=True)
    w = Variable(torch.Tensor([4, 5, 6]), requires_grad=True)
    b = Variable(torch.Tensor([7, 8, 9]), requires_grad=True)
    # 函数等式
    y = w * x ** 2 + b
    # 使用梯度下降计算各变量的偏导数
    y.backward(torch.Tensor([1, 1, 1]))
    print(x.grad)
    print(w.grad)
    print(b.grad)

import torch
from torch.autograd import Variable
import numpy as np
import matplotlib.pyplot as plt
from torch import nn
 
x = torch.unsqueeze(torch.linspace(-1,1,100),dim=1)
y = 3*x+10+torch.rand(x.size())
class LinearRegression(nn.Module):
    def __init__(self):
        super(LinearRegression,self).__init__()
        self.Linear = nn.Linear(1,1)
    def forward(self,x):
        return self.Linear(x)
model = LinearRegression()
Loss = nn.MSELoss()
Opt = torch.optim.SGD(model.parameters(),lr=0.01)
for i in range(1000):
    inputs = Variable(x)
    targets = Variable(y)
    outputs = model(inputs)
    loss = Loss(outputs,targets)
    Opt.zero_grad()
    loss.backward()
    Opt.step()
model.eval()
predict = model(Variable(x))
plt.plot(x.numpy(),y.numpy(),'ro')
plt.plot(x.numpy(),predict.data.numpy())
plt.show()