Pytorch实现Boston房产数据回归分析

要求提前安装了torch、sklearn库

1.简单回归网络的定义

# 定义简单回归网络
class Net(torch.nn.Module):
    def __init__(self,n_feature,n_output):
        super(Net,self).__init__()
        self.hidden = torch.nn.Linear(n_feature,100)
        self.predict = torch.nn.Linear(100,n_output)
    def forward(self,x):
        out = self.hidden(x)
        out = torch.relu(out)
        out = self.predict(out)
        return out

2.主函数部分 2.1 配置部分 2.1.1 导入数据

# 导入数据
    boston = load_boston()
    base_data = boston.data
    target_data = boston.target
    X,Y = base_data,target_data

2.1.2 划分数据集与训练集

# 划分训练集与测试集
    X_train = X[0:496,:]
    Y_train = Y[0:496]
    X_test = X[496:,:]
    Y_test = Y[496:]

2.1.3 配置网络结构

# net
    net = Net(13,1)

2.1.4 配置损失函数、求解器设置

 # loss
    loss_func = torch.nn.MSELoss()
    # optimiter
    optimizer = torch.optim.Adam(net.parameters(),lr=0.01)

2.2 训练部分

包含测试与验证

 for i in  range(10000):
        # training
        X_data = torch.tensor(X_train,dtype=torch.float32)
        Y_data = torch.tensor(Y_train,dtype=torch.float32)
        pred = net.forward(X_data)
        pred = torch.squeeze(pred)
        loss = loss_func(pred,Y_data)*0.001
        # Adam梯度下降优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        print("ite:{},loss:{}".format(i,loss*1000))

        # test
        X_test = torch.tensor(X_test, dtype=torch.float32)
        Y_test = torch.tensor(Y_test, dtype=torch.float32)
        pred = net.forward(X_test)
        pred = torch.squeeze(pred)
        loss_test = loss_func(pred, Y_test)
        print('ite:{},test_loss:{}'.format(i,loss_test))

2.3 模型保存

# save model
    torch.save(net,"data_model.pkl")

3. 结果可视化

4.完整代码

import torch
from sklearn.datasets import load_boston

# 定义简单回归网络
class Net(torch.nn.Module):
    def __init__(self,n_feature,n_output):
        super(Net,self).__init__()
        self.hidden = torch.nn.Linear(n_feature,100)
        self.predict = torch.nn.Linear(100,n_output)
    def forward(self,x):
        out = self.hidden(x)
        out = torch.relu(out)
        out = self.predict(out)
        return out

if __name__ == "__main__":
    # 导入数据
    boston = load_boston()
    base_data = boston.data
    target_data = boston.target
    X,Y = base_data,target_data
    # 划分训练集与测试集
    X_train = X[0:496,:]
    Y_train = Y[0:496]
    X_test = X[496:,:]
    Y_test = Y[496:]
    # net
    net = Net(13,1)
    # loss
    loss_func = torch.nn.MSELoss()
    # optimiter
    optimizer = torch.optim.Adam(net.parameters(),lr=0.01)

    for i in  range(10000):
        # training
        X_data = torch.tensor(X_train,dtype=torch.float32)
        Y_data = torch.tensor(Y_train,dtype=torch.float32)
        pred = net.forward(X_data)
        pred = torch.squeeze(pred)
        loss = loss_func(pred,Y_data)*0.001
        # Adam梯度下降优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        print("ite:{},loss:{}".format(i,loss*1000))

        # test
        X_test = torch.tensor(X_test, dtype=torch.float32)
        Y_test = torch.tensor(Y_test, dtype=torch.float32)
        pred = net.forward(X_test)
        pred = torch.squeeze(pred)
        loss_test = loss_func(pred, Y_test)
        print('ite:{},test_loss:{}'.format(i,loss_test))

    # save model
    torch.save(net,"data_model.pkl")

Pytorch实现Boston房产数据回归分析

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