【深度学习】|线性回归的简单实现

import numpy as np
import torch
from torch.utils import data
from d2l import torch as d2l
true_w = torch.tensor([2,-3.4])
true_b = 4.2
features, labels = d2l.synthetic_data(true_w, true_b,1000)


def load_array(data_arrays, batch_size, is_train = True):
'''构造一个PyTorch数据迭代器'''
dataset = data.TensorDataset(*data_arrays)# 此处*的作用
return data.DataLoader(dataset, batch_size, shuffle = is_train)
batch_size = 10
data_iter = load_array((features, labels), batch_size)
next(iter(data_iter))


from torch import nn
net = nn.Sequential(nn.Linear(2, 1))


net[0].weight.data.normal_(0, 0.01)
net[0].bias.data.fill_(0)


loss = nn.MSELoss()


trainer = torch.optim.SGD(net.parameters(),lr = 0.03)


# 训练
num_epochs = 3
for epoch in range(num_epochs):
for X, y in data_iter:
# print(X.shape)
l = loss(net(X), y)# 一个批次的损失
trainer.zero_grad()
l.backward()
trainer.step()
# print(features.shape)
l = loss(net(features), labels)# 整个数据集的损失
print(f'epoch {epoch + 1}, loss {l:f}')


w = net[0].weight.data
print('w的估计误差：', true_w - w.reshape(true_w.shape))
b = net[0].bias.data
print('b的估计误差：', true_b - b)