PytorchZerotoAll学习笔记（四）--线性回归

线性回归

# 导入 torch、torch.autograd的Variable模块
import torch
from torch.autograd import Variable

# 生成需要回归需要的tensor
x_data = Variable(torch.Tensor([[1.0], [2.0], [3.0]]))
y_data = Variable(torch.Tensor([[2.0], [4.0], [6.0]]))

#定义模型类，继承自nn包的Module模块
class Model(torch.nn.Module):
    # 初始化方法
    def __init__(self):
        """
        In the constructor we instantiate two nn.Linear module
        """
        # 子类转换为父类对象并调用初始化方法
        super(Model, self).__init__()
        # 线性回归，一个输入，一个输出
        self.linear = torch.nn.Linear(1, 1)  # One in and one out

    def forward(self, x):
        """
        In the forward function we accept a Variable of input data and we must return
        a Variable of output data. We can use Modules defined in the constructor as
        well as arbitrary operators on Variables.
        """
        # y=K *x 返回结果
        y_pred = self.linear(x)
        return y_pred

# our model

# 调用模型类生成模型的对象
model = Model()

# Construct our loss function and an Optimizer. The call to model.parameters()
# in the SGD constructor will contain the learnable parameters of the two
# nn.Linear modules which are members of the model.

##使用均方误差函数，在多个样本情况下，不计算平均值
criterion = torch.nn.MSELoss(size_average=False)
##使用随机梯度下降，学习率为0.01
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# Training loop
# 训练 500轮
for epoch in range(500):
    # Forward pass: Compute predicted y by passing x to the model
    # 前向推理模型
    y_pred = model(x_data)

    # Compute and print loss
    # 计算损失
    loss = criterion(y_pred, y_data)
    print(epoch, loss.data[0])

    # Zero gradients, perform a backward pass, and update the weights.
    # 由于使用多次训练，每轮结束之后要清零数据
    optimizer.zero_grad()
    # 反向传播求梯度值
    loss.backward()
    # 更新所有的参数
    optimizer.step()

# After training
hour_var = Variable(torch.Tensor([[4.0]]))
y_pred = model(hour_var)
print("predict (after training)",  4, model(hour_var).data[0][0])