PyTorch 深度学习实践 第5讲 用PyTorch实现线性回归

import torch

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

class LinearModel(torch.nn.Module):
    def __init__(self):
        super(LinearModel, self).__init__()
        # (1,1)是指输入x和输出y的特征维度，这里数据集中的x和y的特征都是1维的
        # 该线性层需要学习的参数是w和b  获取w/b的方式分别是~linear.weight/linear.bias
        self.linear = torch.nn.Linear(1, 1)
        
    def forward(self, x):
        y_pred = self.linear(x)
        return y_pred
    
model = LinearModel()

# criterion = torch.nn.MSELoss(size_average = False)
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:", epoch, "loss:", loss.item())
    
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    
print("w:", model.linear.weight.item(), "b:", model.linear.bias.item())
print("when x = 4, y=", model(torch.tensor([[4.0]])).item())