动手学深度学习——线性回归的简单实现

线性回归的简单实现"/>

动手学深度学习——线性回归的简单实现

import torch
from torch import nntrue_w = [2, -3.4]
true_b = 6
num_input = 2
num_examples = 1000#创建数据
import numpy as np
features = torch.tensor(np.random.normal(0, 1, size=(num_examples, num_input)), dtype=torch.float)
labels = features[:, 0] * true_w[0] + features[:, 1] * true_w[1] + true_b
labels += torch.tensor(np.random.normal(0, 0.01, size=labels.size()), dtype=torch.float)#创建用于学习的数据
import torch.utils.data as Data
batch_size = 10
dataset = Data.TensorDataset(features, labels)
data_iter = Data.DataLoader(dataset, batch_size, shuffle=True)#定义神经网路
class LinearNet(nn.Module):def __init__(self, n_features):super(LinearNet, self).__init__()self.linear = nn.Linear(n_features, 1)def forward(self, x):return self.linear(x)net = LinearNet(num_input)#初始化神经网络
from torch.nn import initinit.normal_(net.linear.weight, mean=0, std=0.01)
init.constant_(net.linear.bias, val=0)
#定于损失函数
loss = nn.MSELoss()
#定义优化函数
import torch.optim as Optimoptimizer = Optim.SGD(net.parameters(), lr=0.03)
#进行训练
for i in range(1, 4):for X, y in data_iter:output = net(X)l = loss(output, y.view(-1 , 1))optimizer.zero_grad()l.backward()optimizer.step()print(net.linear.weight)
print(net.linear.bias)