PyTorch线性回归模型

import torch
# Prepare dataset
x_data=torch.Tensor([[1.0],[2.0],[3.0]])
y_data=torch.Tensor([[2.0],[4.0],[6.0]])

# Design model using Class
## inherit from nn.Module
class LinearModel(torch.nn.Module):
    def __init__(self):
        super(LinearModel,self).__init__()
        self.linear=torch.nn.Linear(1,1)
    def forward(self,x):
        y_pred=self.linear(x)
        return y_pred
model=LinearModel()

# Construct loss and optimizer 
## using PyTorch API
criterion=torch.nn.MSELoss(size_average=False)
optimizer=torch.optim.SGD(model.parameters(),lr=0.01)

# Training cycle
## forward,backward,update
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_test)
print('y_pred=',y_test.data)

准备数据，构造模型，定义损失和优化，模型训练与测试

通过减小损失，更新模型参数学习到特征和标签之间的关系预测新的特征