本文主要是介绍深度学习本科课程 实验3 网络优化，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
一、在多分类任务实验中实现momentum、rmsprop、adam优化器
1.1 任务内容
- 在手动实现多分类的任务中手动实现三种优化算法，并补全Adam中计算部分的内容
- 在torch.nn实现多分类的任务中使用torch.nn实现各种优化器，并对比其效果
1.2 任务思路及代码
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import torchvision
from torchvision import transforms
import time
from torch.nn import CrossEntropyLoss
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 如果有gpu则在gpu上计算 加快计算速度
print(f'当前使用的device为{device}')
# 多分类任务
mnist_train = torchvision.datasets.FashionMNIST(root='./FashionMNIST', train=True, download=True, transform=transforms.ToTensor())
mnist_test = torchvision.datasets.FashionMNIST(root='./FashionMNIST', 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)
# 定义绘图函数
import matplotlib.pyplot as plt
def draw(name, trainl, testl,xlabel='Epoch',ylabel='Loss'):plt.rcParams["font.sans-serif"]=["SimHei"] #设置字体plt.rcParams["axes.unicode_minus"]=False #该语句解决图像中的“-”负号的乱码问题plt.figure(figsize=(8, 3))plt.title(name[-1]) # 命名color = ['g','r','b','c']if trainl is not None:plt.subplot(121)for i in range(len(name)-1):plt.plot(trainl[i], c=color[i],label=name[i])plt.xlabel(xlabel)plt.ylabel(ylabel)plt.legend()if testl is not None:plt.subplot(122)for i in range(len(name)-1):plt.plot(testl[i], c=color[i], label=name[i])plt.xlabel(xlabel)plt.ylabel(ylabel)plt.legend()# 自定义实现
class Net():def __init__(self):# 设置隐藏层和输出层的节点数num_inputs, num_hiddens, num_outputs = 28 * 28, 256, 10 # 十分类问题self.w_1 = torch.tensor(np.random.normal(0, 0.01, (num_hiddens, num_inputs)), dtype=torch.float32,requires_grad=True)self.b_1 = torch.zeros(num_hiddens, dtype=torch.float32, requires_grad=True)self.w_2 = torch.tensor(np.random.normal(0, 0.01, (num_outputs, num_hiddens)), dtype=torch.float32,requires_grad=True)self.b_2 = torch.zeros(num_outputs, dtype=torch.float32, requires_grad=True)self.params=[self.w_1, self.b_1, self.w_2, self.b_2]self.w = [self.w_1,self.w_2]# 定义模型结构self.input_layer = lambda x: x.view(x.shape[0], -1)self.hidden_layer = lambda x: self.my_relu(torch.matmul(x, self.w_1.t()) + self.b_1)self.output_layer = lambda x: nn.functional.softmax(torch.matmul(x, self.w_2.t()) + self.b_2, dim=1)self.momentum_states = [torch.zeros_like(param) for param in self.params]def my_relu(self, x):return torch.max(input=x, other=torch.tensor(0.0))# 定义前向传播def forward(self, x):x = self.input_layer(x)x = self.hidden_layer(x)x = self.output_layer(x)return xdef my_cross_entropy_loss(y_hat, labels):def log_softmax(y_hat):max_v = torch.max(y_hat, dim=1).values.unsqueeze(dim=1)return y_hat - max_v - torch.log(torch.exp(y_hat-max_v).sum(dim=1).unsqueeze(dim=1))return (-log_softmax(y_hat))[range(len(y_hat)), labels].mean()# nn实现
class MyNet_NN(nn.Module):def __init__(self,dropout=0.0):super(MyNet_NN, self).__init__()# 设置隐藏层和输出层的节点数self.num_inputs, self.num_hiddens, self.num_outputs = 28 * 28, 256, 10 # 十分类问题# 定义模型结构self.input_layer = nn.Flatten()self.hidden_layer = nn.Linear(28*28,256)self.drop = nn.Dropout(dropout)self.output_layer = nn.Linear(256,10)# 使用relu激活函数self.relu = nn.ReLU()# 定义前向传播def forward(self, x):x = self.drop(self.input_layer(x))x = self.drop(self.hidden_layer(x))x = self.relu(x)x = self.output_layer(x)return x
def train_and_test(model=Net(),init_states=None,optimizer=optim.SGD,epochs=10,lr=0.01,L2=False,lambd=0):train_all_loss = [] test_all_loss = [] train_ACC, test_ACC = [], [] begintime = time.time()criterion = CrossEntropyLoss() for epoch in range(epochs):train_l,train_acc_num = 0, 0for data, labels in train_iter:pred = model.forward(data)train_each_loss = criterion(pred, labels) # 若L2为True则表示需要添加L2范数惩罚项if L2 == True:train_each_loss += lambd * l2_penalty(model.w)train_l += train_each_loss.item()train_each_loss.backward() # 反向传播if init_states == None: optimizer(model.params, lr, 128) # 使用小批量随机梯度下降迭代模型参数else:states = init_states(model.params)optimizer(model.params,states,lr=lr)# 梯度清零train_acc_num += (pred.argmax(dim=1)==labels).sum().item()for param in model.params:param.grad.data.zero_()# print(train_each_loss)train_all_loss.append(train_l) # 添加损失值到列表中train_ACC.append(train_acc_num / len(mnist_train)) # 添加准确率到列表中with torch.no_grad():is_train = False test_l, test_acc_num = 0, 0for data, labels in test_iter:pred = model.forward(data)test_each_loss = criterion(pred, labels)test_l += test_each_loss.item()test_acc_num += (pred.argmax(dim=1)==labels).sum().item()test_all_loss.append(test_l)test_ACC.append(test_acc_num / len(mnist_test)) # # 添加准确率到列表中print('epoch: %d\t train loss:%.5f\t test loss:%.5f\t train acc: %.2f\t test acc: %.2f'% (epoch + 1, train_l, test_l, train_ACC[-1],test_ACC[-1]))endtime = time.time()print("%d轮 总用时: %.3f秒" % ( epochs, endtime - begintime))return train_all_loss,test_all_loss,train_ACC,test_ACCdef train_and_test_NN(model=MyNet_NN(),epochs=10,lr=0.01,weight_decay=0.0,optimizer=None):MyModel = modelprint(MyModel)if optimizer == None:optimizer = SGD(MyModel.parameters(), lr=lr,weight_decay=weight_decay) criterion = CrossEntropyLoss() # 损失函数criterion = criterion.to(device)train_all_loss = [] test_all_loss = [] train_ACC, test_ACC = [], []begintime = time.time()for epoch in range(epochs):train_l, train_epoch_count, test_epoch_count = 0, 0, 0for data, labels in train_iter:data, labels = data.to(device), labels.to(device)pred = MyModel(data)train_each_loss = criterion(pred, labels.view(-1)) # 计算每次的损失值optimizer.zero_grad() train_each_loss.backward() optimizer.step() train_l += train_each_loss.item()train_epoch_count += (pred.argmax(dim=1)==labels).sum()train_ACC.append(train_epoch_count/len(mnist_train))train_all_loss.append(train_l) with torch.no_grad():test_loss, test_epoch_count= 0, 0for data, labels in test_iter:data, labels = data.to(device), labels.to(device)pred = MyModel(data)test_each_loss = criterion(pred,labels)test_loss += test_each_loss.item()test_epoch_count += (pred.argmax(dim=1)==labels).sum()test_all_loss.append(test_loss)test_ACC.append(test_epoch_count.cpu()/len(mnist_test))print('epoch: %d\t train loss:%.5f\t test loss:%.5f\t train acc:%5f test acc:%.5f:' % (epoch + 1, train_all_loss[-1], test_all_loss[-1],train_ACC[-1],test_ACC[-1]))endtime = time.time()print("torch.nn实现前馈网络-多分类任务 %d轮 总用时: %.3f秒" % (epochs, endtime - begintime))# 返回训练集和测试集上的 损失值 与 准确率return train_all_loss,test_all_loss,train_ACC,test_ACC
# 手动实现momentum
def init_momentum(params):w1,b1,w2,b2 = torch.zeros(params[0].shape),torch.zeros(params[1].shape),torch.zeros(params[2].shape),torch.zeros(params[3].shape)return (w1,b1,w2,b2)def sgd_momentum(params, states, lr=0.01, momentum=0.9):for p, v in zip(params, states):with torch.no_grad():v[:] = momentum * v - p.gradp[:] += lr*vp.grad.data.zero_()net11 = Net()
trainL11, testL11, trainAcc11, testAcc11 = train_and_test(model=net11,epochs=10,init_states=init_momentum, optimizer=sgd_momentum)
# nn实现Momentum
net12 = MyNet_NN()
net12 = net12.to(device)
momentum_optimizer = optim.SGD(net12.parameters(), lr=0.01, momentum=0.9)
trainL12, testL12, trainAcc12, testAcc12 = train_and_test_NN(model=net12,epochs=10,optimizer=momentum_optimizer)
# 手动实现RMSpropdef init_rmsprop(params):s_w1, s_b1, s_w2, s_b2 = torch.zeros(params[0].shape), torch.zeros(params[1].shape),\torch.zeros(params[2].shape), torch.zeros(params[3].shape)return (s_w1, s_b1, s_w2, s_b2)def rmsprop(params,states,lr=0.01,gamma=0.9):gamma, eps = gamma, 1e-6for p, s in zip(params,states):with torch.no_grad():s[:] = gamma * s + (1 - gamma) * torch.square(p.grad)p[:] -= lr * p.grad / torch.sqrt(s + eps)p.grad.data.zero_()net21= Net()
trainL21, testL21, trainAcc21, testAcc21 = train_and_test(model=net21,epochs=10,init_states=init_rmsprop, optimizer=rmsprop)
# nn实现RMSprop
net22 = MyNet_NN()
net22 = net22.to(device)
optim_RMSprop = torch.optim.RMSprop(net22.parameters(), lr=0.01, alpha=0.9, eps=1e-6)
trainL22, testL22, trainAcc22, testAcc22 = train_and_test_NN(model=net22,epochs=10,optimizer=optim_RMSprop)
# 手动实现Adam
def init_adam_states(params):v_w1, v_b1, v_w2, v_b2 = torch.zeros(params[0].shape), torch.zeros(params[1].shape),\torch.zeros(params[2].shape), torch.zeros(params[3].shape)s_w1, s_b1, s_w2, s_b2 = torch.zeros(params[0].shape), torch.zeros(params[1].shape),\torch.zeros(params[2].shape), torch.zeros(params[3].shape)return ((v_w1, s_w1), (v_b1, s_b1),(v_w2, s_w2), (v_b2, s_b2))# 根据Adam算法思想手动实现Adam
Adam_t = 0.01
def Adam(params, states, lr=0.01, t=Adam_t):global Adam_tbeta1, beta2, eps = 0.9, 0.999, 1e-6for p, (v, s) in zip(params, states):with torch.no_grad():v[:] = beta1 * v + (1 - beta1) * p.grads[:] = beta2 * s + (1 - beta2) * (p.grad**2)v_bias_corr = v / (1 - beta1 ** Adam_t)s_bias_corr = s / (1 - beta2 ** Adam_t)p.data -= lr * v_bias_corr / (torch.sqrt(s_bias_corr + eps))p.grad.data.zero_()Adam_t += 1net31 = Net()
trainL31, testL31, trainAcc31, testAcc31 = train_and_test(model=net31,epochs=10,init_states=init_adam_states, optimizer=Adam)
# nn实现adam
net32 = MyNet_NN()
net32 = net32.to(device)optim_Adam = torch.optim.Adam(net32.parameters(), lr=0.01, betas=(0.9,0.999),eps=1e-6)
trainL32, testL32, trainAcc32, testAcc32 = train_and_test_NN(model=net32,epochs=10,optimizer=optim_Adam)
name11= ['RMSprop','Momentum','Adam','手动实现不同的优化器-Loss变化']
train11 = [trainL11,trainL21,trainL31]
test11= [testL11, testL21, testL31]
draw(name11, train11, test11)
name12= ['RMSprop','Momentum','Adam','torch.nn实现不同的优化器-Loss变化']
train12 = [trainL12,trainL22,trainL32]
test12 = [testL12, testL22, testL32]
draw(name12, train12, test12)
二、在多分类任务实验中分别手动实现和用torch.nn实现𝑳𝟐正则化
2.1 任务内容
探究惩罚项的权重对实验结果的影响（可用loss曲线进行展示）
2.2 任务思路及代码
# 定义L2范数惩罚项
def l2_penalty(w):cost = 0for i in range(len(w)):cost += (w[i]**2).sum()return cost / batch_size / 2
# 手动实现
net221 = Net()
trainL221, testL221, trainAcc221, testAcc221 = train_and_test(model=net221,epochs=10,init_states=init_momentum, optimizer=sgd_momentum,lr=0.01,L2=True,lambd=0)net222 = Net()
trainL222, testL222, trainAcc222, testAcc222 = train_and_test(model=net222,epochs=10,init_states=init_momentum, optimizer=sgd_momentum,lr=0.01,L2=True,lambd=2)# 可视化比较
name221 = ['lambd= 0','lambd=2','手动实现不同的惩罚权重lambd-Loss变化']
trains221 = [trainL221,trainL222]
tests221= [testL221,testL222]
draw(name221, trains221, tests221)
## nn实现
net223 = MyNet_NN()
net223 = net223.to(device)
momentum_optimizer = optim.SGD(net223.parameters(), lr=0.01, momentum=0.9)
trainL223, testL223, trainAcc223, testAcc223 = train_and_test_NN(model=net223,epochs=10,optimizer=momentum_optimizer,lr=0.01,weight_decay=0.0)net224 = MyNet_NN()
net224 = net223.to(device)
momentum_optimizer = optim.SGD(net224.parameters(), lr=0.01, momentum=0.9)
trainL224, testL224, trainAcc224, testAcc224 = train_and_test_NN(model=net224,epochs=10,optimizer=momentum_optimizer,lr=0.01,weight_decay=0.01)# 可视化比较
name222 = ['weight_decay=0','weight_decay = 0.01','torch.nn实现不同的惩罚权重lambd-Loss变化']
trains222 = [trainL223,trainL224]
tests222= [testL223,testL224]
draw(name222, trains222, tests222)
三、在多分类任务实验中分别手动实现和用torch.nn实现dropout
3.1 任务内容
探究不同丢弃率对实验结果的影响（可用loss曲线进行展示）
3.2 任务思路及代码
# 为手动模型添加dropout项
class MyNet():def __init__(self,dropout=0.0):# 设置隐藏层和输出层的节点数# global dropoutself.dropout = dropoutprint('dropout: ',self.dropout)self.is_train = Nonenum_inputs, num_hiddens, num_outputs = 28 * 28, 256, 10 # 十分类问题w_1 = torch.tensor(np.random.normal(0, 0.01, (num_hiddens, num_inputs)), dtype=torch.float32,requires_grad=True)b_1 = torch.zeros(num_hiddens, dtype=torch.float32, requires_grad=True)w_2 = torch.tensor(np.random.normal(0, 0.01, (num_outputs, num_hiddens)), dtype=torch.float32,requires_grad=True)b_2 = torch.zeros(num_outputs, dtype=torch.float32, requires_grad=True)self.params = [w_1, b_1, w_2, b_2]self.w = [w_1,w_2]# 定义模型结构self.input_layer = lambda x: x.view(x.shape[0], -1)self.hidden_layer = lambda x: self.my_relu(torch.matmul(x, w_1.t()) + b_1)self.output_layer = lambda x: torch.matmul(x, w_2.t()) + b_2def my_relu(self, x):return torch.max(input=x, other=torch.tensor(0.0))def train(self):self.is_train = Truedef test(self):self.is_test = Falsedef dropout_layer(self, x):dropout =self.dropoutassert 0 <= dropout <= 1 #dropout值必须在0-1之间# dropout==1，所有元素都被丢弃。if dropout == 1:return torch.zeros_like(x)# 在本情况中，所有元素都被保留。if dropout == 0:return xmask = (torch.rand(x.shape) < 1.0 - dropout).float() #rand()返回一个张量，包含了从区间[0, 1)的均匀分布中抽取的一组随机数return mask * x / (1.0 - dropout)# 定义前向传播def forward(self, x):x = self.input_layer(x)if self.is_train: # 如果是训练过程，则需要开启dropout 否则 需要关闭 dropoutx = self.dropout_layer(x) elif self.is_test:x = self.dropout_layer(x)x = self.my_relu(self.hidden_layer(x))x = self.output_layer(x)return xdef train_and_test3(model=MyNet(),init_states=None,optimizer=optim.SGD,epochs=20,lr=0.01,L2=False,lambd=0):train_all_loss = [] test_all_loss = [] train_ACC, test_ACC = [], [] begintime = time.time()criterion = CrossEntropyLoss() # 损失函数model.train() for epoch in range(epochs):train_l,train_acc_num = 0, 0for data, labels in train_iter:pred = model.forward(data)train_each_loss = criterion(pred, labels) # 计算每次的损失值if L2 == True:train_each_loss += lambd * l2_penalty(model.w)train_l += train_each_loss.item()train_each_loss.backward() # 反向传播if init_states == None: optimizer(model.params, lr, 128) # 使用小批量随机梯度下降迭代模型参数else:states = init_states(model.params)optimizer(model.params,states,lr=lr)# 梯度清零train_acc_num += (pred.argmax(dim=1)==labels).sum().item()for param in model.params:param.grad.data.zero_()train_all_loss.append(train_l) train_ACC.append(train_acc_num / len(mnist_train)) # 添加准确率到列表中model.test() with torch.no_grad():is_train = False # 表明当前为测试阶段，不需要dropout参与test_l, test_acc_num = 0, 0for data, labels in test_iter:pred = model.forward(data)test_each_loss = criterion(pred, labels)test_l += test_each_loss.item()test_acc_num += (pred.argmax(dim=1)==labels).sum().item()test_all_loss.append(test_l)test_ACC.append(test_acc_num / len(mnist_test)) # # 添加准确率到列表中print('epoch: %d\t train loss:%.5f\t test loss:%.5f\t train acc: %.2f\t test acc: %.2f'% (epoch + 1, train_l, test_l, train_ACC[-1],test_ACC[-1]))endtime = time.time()print("手动实现dropout, %d轮 总用时: %.3f" % ( epochs, endtime - begintime))return train_all_loss,test_all_loss,train_ACC,test_ACC
# 手动实现dropout
net331 = MyNet(dropout = 0.0)
trainL331, testL331, trainAcc331, testAcc331= train_and_test3(model=net331,epochs=10,init_states=init_momentum, optimizer=sgd_momentum,lr=0.01)net332 = MyNet(dropout = 0.3)
trainL332, testL332, trainAcc332, testAcc332= train_and_test3(model=net332,epochs=10,init_states=init_momentum, optimizer=sgd_momentum,lr=0.01)net333 = MyNet(dropout = 0.5)
trainL333, testL333, trainAcc333, testAcc333= train_and_test3(model=net333,epochs=10,init_states=init_momentum, optimizer=sgd_momentum,lr=0.01)net334 = MyNet(dropout = 0.8)
trainL334, testL334, trainAcc334, testAcc334= train_and_test3(model=net334,epochs=10,init_states=init_momentum, optimizer=sgd_momentum,lr=0.01)
name331 = ['dropout=0','dropout=0.3','dropout=0.5','dropout=0.8','手动实现不同的dropout-Loss变化']
train331 = [trainL331,trainL332,trainL333,trainL334]
test331 = [testL331,testL332,testL333,testL334]
draw(name331, train331, test331)
# nn实现dropout
net341 = MyNet_NN(dropout=0)
net341 = net341.to(device)
momentum_optimizer = optim.SGD(net341.parameters(), lr=0.01, momentum=0.9)
trainL341, testL341, trainAcc341, testAcc341= train_and_test_NN(model=net341,epochs=10,optimizer=momentum_optimizer,lr=0.01)net342 = MyNet_NN(dropout=0.3)
net342 = net342.to(device)
momentum_optimizer = optim.SGD(net342.parameters(), lr=0.01, momentum=0.9)
trainL342, testL342, trainAcc342, testAcc342= train_and_test_NN(model=net342,epochs=10,optimizer=momentum_optimizer,lr=0.01)net343 = MyNet_NN(dropout=0.5)
net343 = net341.to(device)
momentum_optimizer = optim.SGD(net343.parameters(), lr=0.01, momentum=0.9)
trainL343, testL343, trainAcc343, testAcc343= train_and_test_NN(model=net343,epochs=10,optimizer=momentum_optimizer,lr=0.01)net344 = MyNet_NN(dropout=0.8)
net344 = net344.to(device)
momentum_optimizer = optim.SGD(net344.parameters(), lr=0.01, momentum=0.9)
trainL344, testL344, trainAcc344, testAcc344= train_and_test_NN(model=net344,epochs=10,optimizer=momentum_optimizer,lr=0.01)
name332 = ['dropout=0','dropout=0.3','dropout=0.5','dropout=0.8','手动实现不同的dropout-Loss变化']
train332 = [trainL341,trainL342,trainL343,trainL344]
test332 = [testL341,testL342,testL343,testL344]
draw(name332, train332, test332)
四、对多分类任务实验中实现早停机制，并在测试集上测试
4.1 任务内容
选择上述实验中效果最好的组合，手动将训练数据划分为训练集和验证集，实现早停机制， 并在测试集上进行测试。训练集：验证集=8：2，早停轮数为5.
4.2 任务思路及代码
# 构建数据集
import random
index = list(range(len(mnist_train)))
random.shuffle(index)# 按照 训练集和验证集 8：2 的比例分配各自下标
train_index, val_index = index[ : 48000], index[48000 : ]train_dataset, train_labels = mnist_train.data[train_index], mnist_train.targets[train_index]
val_dataset, val_labels = mnist_train.data[val_index], mnist_train.targets[val_index]
print('训练集:', train_dataset.shape, train_labels.shape)
print('验证集:', val_dataset.shape,val_labels.shape)T_dataset = torch.utils.data.TensorDataset(train_dataset,train_labels)
V_dataset = torch.utils.data.TensorDataset(val_dataset,val_labels)
T_dataloader = torch.utils.data.DataLoader(dataset=T_dataset,batch_size=128,shuffle=True)
V_dataloader = torch.utils.data.DataLoader(dataset=V_dataset,batch_size=128,shuffle=True)
print('T_dataset',len(T_dataset),'T_dataloader batch_size: 128')
print('V_dataset',len(V_dataset),'V_dataloader batch_size: 128')def train_and_test_4(model=MyNet(0.0),epochs=10,lr=0.01,weight_decay=0.0):print(model)# 优化函数, 默认情况下weight_decay为0 通过更改weight_decay的值可以实现L2正则化。optimizer = torch.optim.Adam(model.parameters(), lr=0.01, betas=(0.9,0.999),eps=1e-6)criterion = CrossEntropyLoss() # 损失函数train_all_loss = [] # 记录训练集上得loss变化val_all_loss = [] # 记录测试集上的loss变化train_ACC, val_ACC = [], []begintime = time.time()flag_stop = 0for epoch in range(1000):train_l, train_epoch_count, val_epoch_count = 0, 0, 0for data, labels in T_dataloader:data, labels = data.to(torch.float32).to(device), labels.to(device)pred = model(data)train_each_loss = criterion(pred, labels.view(-1)) # 计算每次的损失值optimizer.zero_grad() # 梯度清零train_each_loss.backward() # 反向传播optimizer.step() # 梯度更新train_l += train_each_loss.item()train_epoch_count += (pred.argmax(dim=1)==labels).sum()train_ACC.append(train_epoch_count/len(train_dataset))train_all_loss.append(train_l) # 添加损失值到列表中with torch.no_grad():val_loss, val_epoch_count= 0, 0for data, labels in V_dataloader:data, labels = data.to(torch.float32).to(device), labels.to(device)pred = model(data)val_each_loss = criterion(pred,labels)val_loss += val_each_loss.item()val_epoch_count += (pred.argmax(dim=1)==labels).sum()val_all_loss.append(val_loss)val_ACC.append(val_epoch_count / len(val_dataset))# 实现早停机制# 若连续五次验证集的损失值连续增大，则停止运行，否则继续运行，if epoch > 5 and val_all_loss[-1] > val_all_loss[-2]:flag_stop += 1if flag_stop == 5 or epoch > 35:print('停止运行，防止过拟合')breakelse:flag_stop = 0if epoch == 0 or (epoch + 1) % 4 == 0:print('epoch: %d | train loss:%.5f | val loss:%.5f | train acc:%5f val acc:%.5f:' % (epoch + 1, train_all_loss[-1], val_all_loss[-1],train_ACC[-1],val_ACC[-1]))endtime = time.time()print("torch.nn实现前馈网络-多分类任务 %d轮 总用时: %.3fs" % (epochs, endtime - begintime))# 返回训练集和测试集上的 损失值 与 准确率return train_all_loss,val_all_loss,train_ACC,val_ACCnet4 = MyNet_NN(dropout=0.5)
net4 = net4.to(device)
trainL4, testL4, trainAcc4, testAcc4= train_and_test_4(model=net4,epochs = 10000,lr=0.1)
draw(['', '早停机制'], [trainL4], [testL4])
实验总结
实验中我们通过两种方式构建了前馈神经网络，一种是手动搭建，另一种是利用PyTorch中的torch.nn模块进行构建。在这两种网络结构的基础上，分别引入了dropout层，以有效地防止模型的过拟合现象。
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首先，在优化器的选择上，我们尝试了不同的优化函数，并对它们在模型训练中的效果进行了比较。不同的优化器具有不同的优点，通过对比它们的性能，我们可以更好地选择适合具体任务的优化器，进一步提升模型的性能。
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其次，我们引入了惩罚权重的概念，通过增加惩罚项来约束模型的复杂度。实验结果表明，适度增加惩罚权重可以在一定程度上增大模型输出的损失，但同时也达到了防止过拟合的效果。这进一步证实了模型复杂度与过拟合之间存在一定的权衡关系。
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通过实验我们观察到，适当设置dropout的概率可以显著减轻模型的过拟合问题。dropout通过在训练过程中随机丢弃一部分神经元的输出，有效降低了模型对于训练数据的过度依赖，提高了模型的泛化能力，从而在测试集上表现更为鲁棒。
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最后，为了进一步提高模型的训练效果，我们引入了早停机制。该机制通过监测在验证集上的测试误差，在发现测试误差上升的情况下停止训练，以防止网络过拟合。早停机制在一定程度上能够避免模型在训练过程中过分拟合训练数据，从而提高了模型的泛化性能。
通过以上实验，我们综合考虑了dropout、惩罚权重、不同优化器以及早停机制等因素，为构建更稳健、泛化能力强的前馈神经网络提供了有益的经验和指导。这些技术手段的灵活运用可以在实际任务中更好地平衡模型的性能和泛化能力。
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