每日总结(2)

今天学习了用pytorch框架进行线性模型的搭建。

import  torch


x_data = torch.tensor([[1.0], [2.0], [3.0]])
y_data = torch.tensor([[2.0], [4.0], [6.0]])


class LinerModel(torch.nn.Module):
    def __init__(self):
        super(LinerModel, self).__init__()#父类的初始化函数
        self.linear = torch.nn.Linear(1, 1)
     # def __init__(self, in_features, out_features, bias=True):
     #形参的含义：in：输入样本是几维的即x的是几次方的  out：输出样本是几维的 ，bias是要不要加上偏执量


    def forward(self, x):
        y_pred = self.linear(x) #linera函数做的就是 （w*x+b）的操作
        return y_pred

model = LinerModel()

criterion = torch.nn.MSELoss(reduction='sum')
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

for epoch in range(1000):
    y_pred = model(x_data)
    loss = criterion(y_pred, y_data)
    print(epoch, loss.item())

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

print('w=', model.linear.weight.item())
print('b=', model.linear.bias.item())

x_test = torch.tensor([[4.0]])
y_test = model(x_data)
print('y_pred = ', y_test.detach())