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Chapter3 Linear Neural Networks
3.3 Concise Implementations of Linear Regression
import numpy as np
import torch
from torch.utils import data
from d2l import torch as d2ltrue_wtorch.tensor([2,-3.4])
true_b4.2
features,labelsd2l.synthetic_data(true_w,true_b,1000)#构造一个pytorch数据迭代器
def load_array(data_arrays,batch_size,is_trainTrue): #savedatasetdata.TensorDataset(*data_arrays)#TensorDataset is a class provided by the torch.utils.data module which is a dataset wrapper that allows you to create a dataset from a sequence of tensors. #*data_arrays is used to unpack the tuple into individual tensors.#The * operator is used for iterable unpacking.#Here, data_arrays is expected to be a tuple containing the input features and corresponding labels. The *data_arrays syntax is used to unpack the elements of the tuple and pass them as separate arguments.return data.DataLoader(dataset,batch_size,shuffleis_train)#Constructs a PyTorch DataLoader object which is an iterator that provides batches of data during training or testing.
batch_size10
data_iterload_array([features,labels],batch_size)
print(next(iter(data_iter)))#调用next()函数时会返回迭代器的下一个项目并更新迭代器的内部状态以便下次调用#定义模型变量nn是神经网络的缩写
from torch import nn
netnn.Sequential(nn.Linear(2,1))
#Creates a sequential neural network with one linear layer.
#Input size (in_features) is 2, indicating the network expects input with 2 features.
#Output size (out_features) is 1, indicating the network produces 1 output.#初始化模型参数
net[0].weight.data.normal_(0,0.01)#The underscore at the end (normal_) indicates that this operation is performed in-place, modifying the existing tensor in memory.
net[0].bias.data.fill_(0)#定义均方误差损失函数也称平方L2范数返回所有样本损失的平均值
lossnn.MSELoss()#MSE:mean squared error #定义优化算法仍是小批量随机梯度下降
#update the parameters of the neural network (net.parameters()) using gradients computed during backpropagation.
trainertorch.optim.SGD(net.parameters(),lr0.03)#SGD:stochastic gradient descent(随机梯度下降)#训练
num_epochs3
for epoch in range(num_epochs):for X,y in data_iter:lloss(net(X),y)trainer.zero_grad()l.backward()trainer.step()#Updates the model parameters using the computed gradients and the optimization algorithm.lloss(net(features),labels)print(fepoch {epoch1},loss {l:.6f})#{l:.f}表示将变量l格式化为小数点后有6位的浮点数。wnet[0].weight.data
print(w的估计误差,true_w-w.reshape(true_w.shape))
bnet[0].bias.data
print(b的估计误差,true_b-b)
