[人工智能]线性回归的简洁实现

导入包：

import torch
import numpy as np
import torch.utils.data as Data    # 数据读取
import torch.nn as nn
from torch.nn import init     # 初始化
import torch.optim as optim   # 优化器

数据集的生成

num_inputs = 2     # 2个维度
num_examples = 1000     # 1000调数据
# 标准的参数
true_w = [2, -3.4]
true_b = 4.2
features = torch.tensor(np.random.normal(0, 1, (num_examples,num_inputs)), dtype=torch.float)
label = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] +true_b
label += torch.tensor(np.random.normal(0, 0.01,size=label.size()), dtype=torch.float)

数据的读取

batch_size = 30
dataset = Data.TensorDataset(features,label)
data_it = Data.DataLoader(dataset,batch_size,shuffle =True)

定义模型

定义模型有多种方法：
方法一：继承nn.Module

class LinearNet(nn.Module):
    def __init__(self,n_feature):
        super(LinearNet, self).__init__()
        self.linear = nn.Linear(n_feature,1)
    
    def forward(self, x):
        y = self.linear(x)
        return y

net = LinearNet(num_inputs)
for param in net.parameters():
    print(param)

方法二：nn.Sequential

net = nn.Sequential(
    nn.Linear(num_inputs, 1)
 )

方法三：nn.Sequential()+add_module

net = nn.Sequential()
net.add_module('linear', nn.Linear(num_inputs, 1))

方法四：导入OrderedDict

from collections import OrderedDict
net = nn.Sequential(OrderedDict([
    ('linear', nn.Linear(num_inputs, 1))]))

初始化模型参数

init.normal_(net[0].weight, mean=0, std=0.01)
init.constant_(net[0].bias,val=2)

MSE损失函数

loss = nn.MSELoss()

定义优化算法

optimizer = optim.SGD(net.parameters(), lr=0.03)

模型的优化

num_epochs = 3
for epoch in range(1, num_epochs + 1):
    for X, y in data_it:
        output = net(X)
        l = loss(output, y.view(-1, 1))
        optimizer.zero_grad() # 梯度清零，等价于net.zero_grad()
        l.backward()
        optimizer.step()
    print(epoch, l.item())

最后输出epoch和loss：

1 0.3572668433189392
2 0.005662666633725166
3 0.00011592111695790663

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