linersimple.py

import torch
from IPython import display
from matplotlib import pyplot as plt
import numpy as np
import random
import torch.utils.data as Data
import torch.nn as nn
from torch.nn import init
import torch.optim as optim


class LinearNet(nn.Module):
    def __init__(self, n_feature):
        super(LinearNet, self).__init__()
        self.linear = nn.Linear(n_feature, 1)
    # forward 定义前向传播
    def forward(self, x):
        y = self.linear(x)
        return y



if __name__ == '__main__':
    num_inputs = 2
    num_examples = 1000
    true_w = [2, -3.4]
    true_b = 4.2
    features = torch.tensor(np.random.normal(0, 1, (num_examples, num_inputs)), dtype=torch.float)
    labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_b
    labels += torch.tensor(np.random.normal(0, 0.01, size=labels.size()), dtype=torch.float)
    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)




    # 写法一
    net = nn.Sequential(
        nn.Linear(num_inputs, 1)
        # 此处还可以传入其他层
    )



    print(net)
    print(net[0])

    for param in net.parameters():
        print(param)
    init.normal_(net[0].weight, mean=0, std=0.01)
    init.constant_(net[0].bias, val=0)  # 也可以直接修改bias的data: net[0].bias.data.fill_(0)

    loss = nn.MSELoss()
    optimizer = optim.SGD(net.parameters(), lr=0.03)
    print(optimizer)
    
    num_epochs = 3
    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()))