直接看代码：
import torch
import numpy as np
import random
from IPython import display
from matplotlib import pyplot as plt
import torchvision
import torchvision.transforms as transforms mnist_train = torchvision.datasets.MNIST(root='/MNIST', train=True, download=True, transform=transforms.ToTensor())
mnist_test = torchvision.datasets.MNIST(root='./MNIST', train=False,download=True, transform=transforms.ToTensor()) batch_size = 256 train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True,num_workers=0)
test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False,num_workers=0) num_inputs,num_hiddens,num_outputs =784, 256,10def init_param():W1 = torch.tensor(np.random.normal(0, 0.01, (num_hiddens,num_inputs)), dtype=torch.float32) b1 = torch.zeros(1, dtype=torch.float32) W2 = torch.tensor(np.random.normal(0, 0.01, (num_outputs,num_hiddens)), dtype=torch.float32) b2 = torch.zeros(1, dtype=torch.float32) params =[W1,b1,W2,b2]for param in params:param.requires_grad_(requires_grad=True) return W1,b1,W2,b2def relu(x):x = torch.max(input=x,other=torch.tensor(0.0)) return xdef net(X): X = X.view((-1,num_inputs)) H = relu(torch.matmul(X,W1.t())+b1) #myrelu =((matmal x,w1)+b1),return matmal(myrelu,w2 )+ b2return relu(torch.matmul(H,W2.t())+b2 )return torch.matmul(H,W2.t())+b2def SGD(paras,lr): for param in params: param.data -= lr * param.grad def l2_penalty(w):return (w**2).sum()/2def train(net,train_iter,test_iter,loss,num_epochs,batch_size,lr=None,optimizer=None,mylambda=0): train_ls, test_ls = [], []for epoch in range(num_epochs):ls, count = 0, 0for X,y in train_iter :X = X.reshape(-1,num_inputs)l=loss(net(X),y)+ mylambda*l2_penalty(W1) + mylambda*l2_penalty(W2)optimizer.zero_grad()l.backward()optimizer.step()ls += l.item()count += y.shape[0]train_ls.append(ls)ls, count = 0, 0for X,y in test_iter:X = X.reshape(-1,num_inputs)l=loss(net(X),y) + mylambda*l2_penalty(W1) + mylambda*l2_penalty(W2)ls += l.item()count += y.shape[0]test_ls.append(ls)if(epoch)%2==0:print('epoch: %d, train loss: %f, test loss: %f'%(epoch+1,train_ls[-1],test_ls[-1]))return train_ls,test_lslr = 0.01num_epochs = 20Lamda = [0,0.1,0.2,0.3,0.4,0.5]Train_ls, Test_ls = [], []for lamda in Lamda:print("current lambda is %f"%lamda)W1,b1,W2,b2 = init_param()loss = torch.nn.CrossEntropyLoss()optimizer = torch.optim.SGD([W1,b1,W2,b2],lr = 0.001)train_ls, test_ls = train(net,train_iter,test_iter,loss,num_epochs,batch_size,lr,optimizer,lamda) Train_ls.append(train_ls)Test_ls.append(test_ls)x = np.linspace(0,len(Train_ls[1]),len(Train_ls[1]))plt.figure(figsize=(10,8))for i in range(0,len(Lamda)):plt.plot(x,Train_ls[i],label= f'L2_Regularization:{Lamda [i]}',linewidth=1.5)plt.xlabel('different epoch')plt.ylabel('loss')plt.legend(loc=2, bbox_to_anchor=(1.1,1.0),borderAxesPad = 0.)plt.title('train loss with L2_penalty')plt.show()
运行结果：
疑问和心得：
- 画图的实现和细节还是有些模糊。
- 正则化系数一般是一个可以根据算法有一定变动的常数。
- 前馈神经网络中，二分类最后使用logistic函数返回，多分类一般返回softmax值，若是一般的回归任务，一般是直接relu返回。
- 前馈神经网络的实现，从物理层上应该是全连接的，但是网上的代码一般都是两层单个神经元，这个容易产生误解。个人感觉，还是要使用nn封装的函数比较正宗。
