pytorch Model Linear实现线性回归CUDA版本

实验代码

import torch
import torch.nn as nn

#y = wx + b
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel,self).__init__()
        #自定义代码
        # self.w = torch.rand([500,1],requires_grad=True)
        # self.b = torch.tensor(0,dtype=torch.float,requires_grad=True)
        # self.lr = nn.Linear(1,1)
        self.lr1 = nn.Linear(1,10)
        # self.lr2 = nn.Linear(10,20)
        # self.lr3 = nn.Linear(20,1)


    def forward(self,x):   #完成一次前项计算
        # y_predict = x*self.w + self.b
        # return y_predict
        # return self.lr(x)
        out1 = self.lr1(x)
        # out2 = self.lr2(out1)
        # out = self.lr3(out2)
        return out1



if __name__ == '__main__':
    model = MyModel()
    # print(model.parameters())
    for i in model.parameters():
        print(i)
        print("*"*100)
    # y_predict = model(torch.FloatTensor([10]))
    # print(y_predict)

　　Linear实现线性回归，cuda版本

import torch
import torch.nn as nn
from torch import optim

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel,self).__init__()
        self.lr = nn.Linear(1,1)

    def forward(self,x):
        return self.lr(x)

#准备数据  如果使用cuda，数据和模型需要to（device）
x = torch.rand([500,1]).to(device)
y_true = 3*x + 0.8
#实例化模型
model = MyModel().to(device)
#实例化优化器
optimizer = optim.Adam(model.parameters(),lr=0.1)
#实例化损失函数
loss_fn = nn.MSELoss()

for i in range(500):
    #梯度置零
    optimizer.zero_grad()
    #调用模型得到预测值
    y_predict = model(x)
    #损失函数，计算损失
    loss = loss_fn(y_predict,y_true)
    #反向传播计算梯度
    loss.backward()
    #更新参数
    optimizer.step()
    #打印部分数据
    if i%10 ==0:
        print(i,loss.item())

for param in model.parameters():
    print(param.item())