代码进度

线性回归代码：

import torch
import random
from d2l import torch as d2l

def dataCreat(w, b, numSample):
    X = torch.normal(0, 1, (numSample, len(w)))
    y = torch.matmul(X, w) + b
    y += torch.normal(0, 0.01, y.shape)
    return X, y.reshape(-1, 1)

def dataInit(wsq):
    w=torch.normal(0, wsq, size=(2,1), requires_grad=True)
    b=torch.zeros(1, requires_grad=True)
    return w, b

def dataBatch(batchSize, features, labels):
    numSamples = len(features)
    indices = list(range(numSamples))
    random.shuffle(indices)
    for i in range(0, numSamples, batchSize):
        batchIndices = torch.tensor(indices[i:min(i+batchSize, numSamples)])
        yield features[batchIndices], labels[batchIndices]

def linReg(X, w, b):
    return torch.matmul(X,w)+b

def squLoss(yHat, y):
    return (yHat-y.reshape(yHat.shape))**2/2

def sgdOptim(params, lr, batchSize):
    with torch.no_grad():
        for param in params:
            param -= lr * param.grad/batchSize
            param.grad.zero_()

初始化函数：

def varInit(lr=0.03, numEpochs=10, wsq=0.01):
    trueW = torch.tensor([2, -3.4])
    trueB = 4.2
    numSample = 1000
    batchSize = 10
    lr = 0.03
    numEpochs = 10
    wsq = 0.01
    return [trueW, trueB, numSample, wsq], [batchSize, lr, numEpochs]

回归测试函数：

def testReg(*kwargs):
    kwarg=kwargs[0]
    for epoch in range(kwarg[2]):
        for X, y in dataBatch(kwarg[0], features, labels):
            los = squLoss(linReg(X, w, b), y)
            los.sum().backward()
            sgdOptim([w, b], kwarg[1], kwarg[0])
        with torch.no_grad():
            train1 = squLoss(linReg(features, w, b), labels)
            print(f'epoch{epoch + 1}, loss: {float(train1.mean()):f}')

测试线性回归：

varG, varG1 = varInit()
features, labels = dataCreat(varG[0], varG[1], varG[2])
w, b = dataInit(varG[3])
testReg(varG1)

结果：

epoch1, loss: 0.047491
epoch2, loss: 0.000198
epoch3, loss: 0.000051
epoch4, loss: 0.000050
epoch5, loss: 0.000050
epoch6, loss: 0.000050
epoch7, loss: 0.000050
epoch8, loss: 0.000050
epoch9, loss: 0.000050
epoch10, loss: 0.000050