pytorch实现线性回归

pytorch实现线性回归

• 代码
• 结果

代码

import torch
import numpy as np
from torch.nn import init
from torch.utils import data
from torch import nn

# 数据集
num_inputs = 2
num_examples = 1000
true_w = [2, -3.4]
true_b = 4.2
features = torch.from_numpy(np.random.normal(0, 1, (num_examples, num_inputs))).type(torch.float32)  # 1000*2
labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_b
labels += torch.from_numpy(np.random.normal(0, 0.01, size=labels.size()))  # 噪声

batch_size = 10
# 将训练数据的特征和标签组合
dataset = data.TensorDataset(features, labels)
# 随机读取⼩批量
data_iter = data.DataLoader(dataset, batch_size, shuffle=True)

# 使用框架预定义好的层
net = nn.Sequential(nn.Linear(2, 1))  # 输入是二维，输出是一维

# 初始化模型参数
# net[0].weight.data.normal_(0, 0.01)
# net[0].bias.data.fill_(0)
init.normal_(net[0].weight, mean=0, std=0.01)
init.constant_(net[0].bias, val=0)

# 计算均方误差使用的是MELoss类，也称为L_2范数
loss = nn.MSELoss()
# 实例化SGD(随机梯度下降)实例
optimizer = torch.optim.SGD(net.parameters(), lr=0.03)

# 训练
num_epochs = 3
l = 0
for epoch in range(1, num_epochs + 1):
    for X, y in data_iter:
        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 %d, loss: %f' % (epoch, l.item()))

结果