B站-刘老师-PyTorch深度学习实践-用pytorch实现线性回归课后作业-学习笔记（4）

本次课程老师通过使用torch的模块来完成对线性模型的训练。

 视频如下：《PyTorch深度学习实践》完结合集_哔哩哔哩_bilibili

部分代码如下:

import torch
import matplotlib.pyplot as plt

# 构建数据集（线性方程：2.763*x-1.121）
def getDataset():
    x = torch.Tensor([[1.0],[2.0],[3.0]])
    y = torch.Tensor([[1.642],[4.404],[7.168]])
    return x,y

# 构建线性模型
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


def run():
    x_data,y_data = getDataset()
    model = LinearModel()

    # 保存loss值    
    ls_SGD = []
    # ls_Adagrad = []
    # ls_Adam = []
    # ls_Adamax = []


    # 计算loss值
    criterion = torch.nn.MSELoss(size_average=False)
    # 构建优化器
    optimizer_SGD = torch.optim.SGD(model.parameters(),lr=0.01)
    # optimizer_Adagrad = torch.optim.Adagrad(model.parameters(),lr=0.01)
    # optimizer_Adam = torch.optim.Adam(model.parameters(),lr=0.01)
    # optimizer_Adamax = torch.optim.Adamax(model.parameters(),lr=0.01)


    for epoch in range(200):
        y_pred = model(x_data)
        loss = criterion(y_pred,y_data)
        ls_SGD.append(loss)
        # ls_Adagrad.append(loss)
        # ls_Adam.append(loss)
        # ls_Adamax.append(loss)

        # 参数梯度置0
        optimizer_SGD.zero_grad()
        # optimizer_Adagrad.zero_grad()
        # optimizer_Adam.zero_grad()
        # optimizer_Adamax.zero_grad()

        # 反向传播构建参数梯度值
        loss.backward()

        # 根据梯度下降执行一步参数更新
        optimizer_SGD.step()
        # optimizer_Adagrad.step()
        # optimizer_Adam.step()
        # optimizer_Adamax.step()


    plt.plot(range(1,201),ls_SGD)
    # plt.show()
    plt.savefig('SGD.jpg', dpi=300)

if __name__=="__main__":
    run()

SGD:

 Adagrad:

 Adam:

 Adamax: