深度学习与Pytorch入门实战（八）数据集划分&正则化方法

笔记摘抄

1. 训练集&验证集&测试集

• 训练集：训练数据

• 验证集：验证不同算法（比如，利用网格搜索对超参数进行调整等），检验哪种更有效

• 测试集：正确评估分类器的性能

• 正常流程：

  • 验证集会记录每个时间戳的参数

  • 在加载test数据前会加载那个最好的参数，再来评估。

  • 比方说训练完6000个epoch后，发现在第3520个epoch的validation表现最好，测试时会加载第3520个epoch的参数。

import  torch
import  torch.nn as nn
import  torch.nn.functional as F
import  torch.optim as optim
from    torchvision import datasets, transforms

# 超参数
batch_size=200
learning_rate=0.01
epochs=10

# 获取训练数据
train_db = datasets.MNIST('../data', train=True, download=True,  # train=True则得到的是训练集
                   transform=transforms.Compose([                 # transform进行数据预处理
                       transforms.ToTensor(),                     # 转成Tensor类型的数据
                       transforms.Normalize((0.1307,), (0.3081,)) # 进行数据标准化(减去均值除以方差)
                   ]))

# DataLoader把训练数据分成多个小组，此函数每次抛出一组数据。直至把所有的数据都抛出。就是做一个数据的初始化
train_loader = torch.utils.data.DataLoader(train_db, batch_size=batch_size, shuffle=True)


# 获取测试数据
test_db = datasets.MNIST('../data', train=False,
                   transform=transforms.Compose([
                        transforms.ToTensor(),
                        transforms.Normalize((0.1307,), (0.3081,))
                   ]))

test_loader = torch.utils.data.DataLoader(test_db, batch_size=batch_size, shuffle=True)


#将训练集拆分成训练集和验证集
print('train:', len(train_db), 'dev:', len(test_db))                         # train: 60000 dev: 10000
train_db, val_db = torch.utils.data.random_split(train_db, [50000, 10000])
print('db1:', len(train_db), 'db2:', len(val_db))                             # db1: 50000 db2: 10000

train_loader = torch.utils.data.DataLoader(train_db, batch_size=batch_size, shuffle=True)
val_loader = torch.utils.data.DataLoader(val_db, batch_size=batch_size, shuffle=True)


class MLP(nn.Module):

    def __init__(self):
        super(MLP, self).__init__()

        self.model = nn.Sequential(         #定义网络的每一层,
            nn.Linear(784, 200),
            nn.ReLU(inplace=True),
            nn.Linear(200, 200),
            nn.ReLU(inplace=True),
            nn.Linear(200, 10),
            nn.ReLU(inplace=True),
        )

    def forward(self, x):
        x = self.model(x)
        return x


net = MLP()
#定义sgd优化器,指明优化参数、学习率，net.parameters()得到这个类所定义的网络的参数[[w1,b1,w2,b2,...]
optimizer = optim.SGD(net.parameters(), lr=learning_rate)
criteon = nn.CrossEntropyLoss()


for epoch in range(epochs):

    for batch_idx, (data, target) in enumerate(train_loader):
        data = data.view(-1, 28*28)          # 将二维的图片数据摊平[样本数,784]

        logits = net(data)                   # 前向传播
        loss = criteon(logits, target)       # nn.CrossEntropyLoss()自带Softmax

        optimizer.zero_grad()                # 梯度信息清空
        loss.backward()                      # 反向传播获取梯度
        optimizer.step()                     # 优化器更新

        if batch_idx % 100 == 0:             # 每100个batch输出一次信息
            print('Train Epoch: {} [{}/{} ({:.0f}%)]	Loss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                       100. * batch_idx / len(train_loader), loss.item()))

    #验证集用来检测训练是否过拟合
    val_loss = 0
    correct = 0
    for data, target in val_loader:
        data = data.view(-1, 28 * 28)
        logits = net(data)                          # 前向传播
        val_loss += criteon(logits, target).item()  # 代价函数

        pred = logits.data.max(dim=1)[1]
        correct += pred.eq(target.data).sum()

    val_loss /= len(val_loader.dataset)
    print('
VAL set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)
'.format(
        val_loss, correct, len(val_loader.dataset),
        100. * correct / len(val_loader.dataset)))



#测试集用来评估
test_loss = 0
correct = 0                                         # correct记录正确分类的样本数
for data, target in test_loader:
    data = data.view(-1, 28 * 28)
    logits = net(data)
    test_loss += criteon(logits, target).item()     # 其实就是criteon(logits, target)的值，标量

    pred = logits.data.max(dim=1)[1]                # 也可以写成pred=logits.argmax(dim=1)
    correct += pred.eq(target.data).sum()

test_loss /= len(test_loader.dataset)
print('
Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)
'.format(
    test_loss, correct, len(test_loader.dataset),
    100. * correct / len(test_loader.dataset)))

view result

train: 60000 dev: 10000
db1: 50000 db2: 10000
Train Epoch: 0 [0/50000 (0%)]	Loss: 2.301233
Train Epoch: 0 [20000/50000 (40%)]	Loss: 2.121324
Train Epoch: 0 [40000/50000 (80%)]	Loss: 1.711887

VAL set: Average loss: 0.0071, Accuracy: 6522/10000 (65%)

Train Epoch: 1 [0/50000 (0%)]	Loss: 1.394418
Train Epoch: 1 [20000/50000 (40%)]	Loss: 0.941196
Train Epoch: 1 [40000/50000 (80%)]	Loss: 0.618081

VAL set: Average loss: 0.0027, Accuracy: 8606/10000 (86%)

Train Epoch: 2 [0/50000 (0%)]	Loss: 0.451805
Train Epoch: 2 [20000/50000 (40%)]	Loss: 0.463975
Train Epoch: 2 [40000/50000 (80%)]	Loss: 0.389160

VAL set: Average loss: 0.0020, Accuracy: 8914/10000 (89%)

Train Epoch: 3 [0/50000 (0%)]	Loss: 0.358770
Train Epoch: 3 [20000/50000 (40%)]	Loss: 0.348269
Train Epoch: 3 [40000/50000 (80%)]	Loss: 0.315913

VAL set: Average loss: 0.0018, Accuracy: 9030/10000 (90%)

Train Epoch: 4 [0/50000 (0%)]	Loss: 0.314491
Train Epoch: 4 [20000/50000 (40%)]	Loss: 0.347182
Train Epoch: 4 [40000/50000 (80%)]	Loss: 0.208284

VAL set: Average loss: 0.0016, Accuracy: 9091/10000 (91%)

Train Epoch: 5 [0/50000 (0%)]	Loss: 0.306007
Train Epoch: 5 [20000/50000 (40%)]	Loss: 0.234249
Train Epoch: 5 [40000/50000 (80%)]	Loss: 0.253510

VAL set: Average loss: 0.0015, Accuracy: 9160/10000 (92%)

Train Epoch: 6 [0/50000 (0%)]	Loss: 0.307625
Train Epoch: 6 [20000/50000 (40%)]	Loss: 0.311399
Train Epoch: 6 [40000/50000 (80%)]	Loss: 0.332431

VAL set: Average loss: 0.0014, Accuracy: 9218/10000 (92%)

Train Epoch: 7 [0/50000 (0%)]	Loss: 0.354180
Train Epoch: 7 [20000/50000 (40%)]	Loss: 0.227610
Train Epoch: 7 [40000/50000 (80%)]	Loss: 0.374276

VAL set: Average loss: 0.0014, Accuracy: 9224/10000 (92%)

Train Epoch: 8 [0/50000 (0%)]	Loss: 0.199506
Train Epoch: 8 [20000/50000 (40%)]	Loss: 0.288594
Train Epoch: 8 [40000/50000 (80%)]	Loss: 0.371002

VAL set: Average loss: 0.0013, Accuracy: 9270/10000 (93%)

Train Epoch: 9 [0/50000 (0%)]	Loss: 0.199139
Train Epoch: 9 [20000/50000 (40%)]	Loss: 0.180454
Train Epoch: 9 [40000/50000 (80%)]	Loss: 0.251302

VAL set: Average loss: 0.0012, Accuracy: 9320/10000 (93%)


Test set: Average loss: 0.0012, Accuracy: 9347/10000 (93%)

2. 正则化

正则化可以解决过拟合问题。

2.1 L2范数(更常用)

• 在定义优化器的时候设定weigth_decay，即L2范数前面的 (lambda) 参数。

optimizer = torch.optim.SGD(net.parameters(), lr=learning_rate, weight_decay=0.01)

2.2 L1范数(不咋用)

Pytorch没有直接可以调用的方法，实现如下：

3. 动量（Momentum）

• 使用 args.momentum

optimizer = torch.optim.SGD(model.parameters(), args=lr,
                            momentum=args.momentum, 
                            weight_decay=args.weight_decay)

• 使用adam优化器

# 定义Adam优化器,指明优化目标是x,学习率是1e-3
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)       

4. 学习率衰减

• torch.optim.lr_scheduler 中提供了基于多种epoch数目 调整学习率的方法。

4.1 ReduceLROnPlateau

• torch.optim.lr_scheduler.ReduceLROnPlateau：基于 测量指标 对学习率进行动态的下降

torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', 
                                          factor=0.1, patience=10, verbose=False, 
                                          threshold=0.0001, threshold_mode='rel', 
                                          cooldown=0, min_lr=0, eps=1e-08)

• 训练过程中，optimizer会把 learning rate 交给scheduler管理

  • 当指标（比如loss）连续patience次数还没有改进时，需要降低学习率，factor为每次下降的比例。

• scheduler.step(loss_val) 每调用一次就会监听一次 loss_val。

4.2 StepLR

• torch.optim.lr_scheduler.StepLR：基于epoch

torch.optim.lr_scheduler.StepLR(optimizer, step_size, gamma=0.1, last_epoch=-1)

• 当epoch每过stop_size时，学习率都变为初始学习率的 gamma 倍。

5. 提前停止（防止overfitting）

• 基于经验值

6. Dropout随机失活

• 遍历每一层，设置消除神经网络中的节点概率，得到精简后的一个样本。

• torch.nn.Dropout(p=dropout_prob)

• p 表示的是 删除节点数 的比例（Tip：tensorflow中keep_prob表示保留节点数的比例，不要混淆）

• 测试阶段无需使用dropout

  • 所以在train之前执行 net_dropped.train() 相当于启用dropout

  • 测试之前执行 net_dropped.eval() 相当于不启用dropout。