pytorch-API实现线性回归

示例：

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

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

x= torch.rand([500,1])
y_true = 3*x+0.8
#1.实例化模型
model = MyModel()
#2.实例化优化器
optimizer = optim.Adam(model.parameters(),lr=0.1)
#3.实例化损失函数
loss_fn = nn.MSELoss()

for i in range(500):
    #4.梯度置为0
    optimizer.zero_grad()
    #5.调用模型得到预测值
    y_predict = model(x)
    #6.通过损失函数，计算得到损失
    loss = loss_fn(y_predict,y_true)
    #7.反向传播，计算梯度
    loss.backward()
    #8.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())

　　

使用英伟达显卡CUDA模式加速计算：

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

x= torch.rand([500,1]).to(device=device)
y_true = 3*x+0.8
#1.实例化模型
model = MyModel().to(device)
#2.实例化优化器
optimizer = optim.Adam(model.parameters(),lr=0.1)
#3.实例化损失函数
loss_fn = nn.MSELoss()
start = time.time()
for i in range(500):
    #4.梯度置为0
    optimizer.zero_grad()
    #5.调用模型得到预测值
    y_predict = model(x)
    #6.通过损失函数，计算得到损失
    loss = loss_fn(y_predict,y_true)
    #7.反向传播，计算梯度
    loss.backward()
    #8.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())

end = time.time()

print(end-start)