Linear regression pytorch-优快云博客

本文介绍了如何使用PyTorch库进行线性回归模型的构建，包括数据初始化、数据加载、定义网络结构、参数初始化、损失函数选择以及优化算法应用。作者详细展示了从数据预处理到模型训练的全过程。

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import torch
import torch.nn as nn
import torch.utils.data as Data
import torch.optim as optim
import torch.nn.init as init


def data_init():
    true_w = [2, -3.4]
    true_b = 3
    num_features = 2
    num_data = 1000
    num_output = 1
    features = torch.normal(0, 1, (num_data, num_features))
    labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_b
    e = torch.normal(0, 0.01, size=labels.size())
    labels += e
    return features, labels, num_features, num_output


def linear_test():
    features, labels, num_features, num_output = data_init()
    # get data set
    data_set = Data.TensorDataset(features, labels)
    batch_size = 10
    # get data iterator
    data_iter = Data.DataLoader(data_set, batch_size, shuffle=True)
    # define hyper parameter
    epoch = 3
    lr = 0.03
    # define model
    net = nn.Sequential()
    net.add_module('linear1', nn.Linear(num_features, num_output))
    # init model parameter
    init.normal_(net[0].weight, 0, 0.01)
    init.constant_(net[0].bias, val=0)
    # define loss function
    loss_func = nn.MSELoss()
    # define optimization
    optimization = optim.SGD(net[0].parameters(), lr=lr)

    # begin train
    for i in range(1, epoch+1):
        loss = 0
        for x, y in data_iter:
            # get the predict result
            out_put = net(x)
            # calculate loss
            loss = loss_func(out_put, y.view(-1, 1))
            # backpropagation
            loss.backward()
            # optimize parameters
            optimization.step()
            # clear gradient
            optimization.zero_grad()
        print('epoch', i, 'loss:', loss)
    dense = net[0]
    print(dense.weight)
    print(dense.bias)


if __name__ == '__main__':
    print('start')
    linear_test()