import torch
from torch import nn
from torch import optim
import numpy as np
from matplotlib import pyplot as plt

# 1.定义数据
x = torch.randn([50,1])
y = 3*x+0.8

# 2.定义模型
class Lr(nn.Module):
    def __init__(self):
        super(Lr, self).__init__()
        self.linear = nn.Linear(1,1)

    def forward(self,x):
        out = self.linear(x)
        return out


# 3.实例化模型，loss，和优化器
model = Lr()
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(),lr=1e-3)

# 4.训练模型
for i in range(3000):
    out = model(x) #获取预测值
    loss = criterion(y,out) #计算损失
    optimizer.zero_grad() #梯度归0
    loss.backward() #计算梯度
    optimizer.step() #更新梯度
    if(i-1)%20 ==0:
        print("Epoch[{}/{}],loss:{:.6f}".format(i,3000,loss.data))

# 5.模型评估
model.eval() #设置模型为评估模型，即预测模式
predict = model(x)
predict = predict.data.numpy()
plt.scatter(x.data.numpy(),y.data.numpy(),c="r")
plt.plot(x.data.numpy(),predict)
plt.show()