卷积神经网络效果

构建卷积神经网络

  • 卷积网络中的输入和层与传统神经网络有些区别，需重新设计，训练模块基本一致
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import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchvision import datasets,transforms 
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

首先读取数据

  • 分别构建训练集和测试集（验证集）
  • DataLoader来迭代取数据
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# 定义超参数 
input_size = 28  #图像的总尺寸28*28
num_classes = 10  #标签的种类数
num_epochs = 3  #训练的总循环周期
batch_size = 64  #一个撮（批次）的大小，64张图片

# 训练集
train_dataset = datasets.MNIST(root='./data',  
                            train=True,   
                            transform=transforms.ToTensor(),  
                            download=True) 

# 测试集
test_dataset = datasets.MNIST(root='./data', 
                           train=False, 
                           transform=transforms.ToTensor())

# 构建batch数据
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, 
                                           batch_size=batch_size, 
                                           shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, 
                                           batch_size=batch_size, 
                                           shuffle=True)

卷积网络模块构建

  • 一般卷积层，relu层，池化层可以写成一个套餐
  • 注意卷积最后结果还是一个特征图，需要把图转换成向量才能做分类或者回归任务
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class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Sequential(         # 输入大小 (1, 28, 28)
            nn.Conv2d(
                in_channels=1,              # 灰度图
                out_channels=16,            # 要得到几多少个特征图
                kernel_size=5,              # 卷积核大小
                stride=1,                   # 步长
                padding=2,                  # 如果希望卷积后大小跟原来一样，需要设置padding=(kernel_size-1)/2 if stride=1
            ),                              # 输出的特征图为 (16, 28, 28)
            nn.ReLU(),                      # relu层
            nn.MaxPool2d(kernel_size=2),    # 进行池化操作（2x2 区域）, 输出结果为： (16, 14, 14)
        )
        self.conv2 = nn.Sequential(         # 下一个套餐的输入 (16, 14, 14)
            nn.Conv2d(16, 32, 5, 1, 2),     # 输出 (32, 14, 14)
            nn.ReLU(),                      # relu层
            nn.MaxPool2d(2),                # 输出 (32, 7, 7)
        )
        self.out = nn.Linear(32 * 7 * 7, 10)   # 全连接层得到的结果

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.view(x.size(0), -1)           # flatten操作，结果为：(batch_size, 32 * 7 * 7)
        output = self.out(x)
        return output

准确率作为评估标准

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def accuracy(predictions, labels):
    pred = torch.max(predictions.data, 1)[1] 
    rights = pred.eq(labels.data.view_as(pred)).sum() 
    return rights, len(labels)

训练网络模型

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# 实例化
net = CNN() 
#损失函数
criterion = nn.CrossEntropyLoss() 
#优化器
optimizer = optim.Adam(net.parameters(), lr=0.001) #定义优化器，普通的随机梯度下降算法

#开始训练循环
for epoch in range(num_epochs):
    #当前epoch的结果保存下来
    train_rights = [] 
    
    for batch_idx, (data, target) in enumerate(train_loader):  #针对容器中的每一个批进行循环
        net.train()                             
        output = net(data) 
        loss = criterion(output, target) 
        optimizer.zero_grad() 
        loss.backward() 
        optimizer.step() 
        right = accuracy(output, target) 
        train_rights.append(right) 

    
        if batch_idx % 100 == 0: 
            
            net.eval() 
            val_rights = [] 
            
            for (data, target) in test_loader:
                output = net(data) 
                right = accuracy(output, target) 
                val_rights.append(right)
                
            #准确率计算
            train_r = (sum([tup[0] for tup in train_rights]), sum([tup[1] for tup in train_rights]))
            val_r = (sum([tup[0] for tup in val_rights]), sum([tup[1] for tup in val_rights]))

            print('当前epoch: {} [{}/{} ({:.0f}%)]\t损失: {:.6f}\t训练集准确率: {:.2f}%\t测试集正确率: {:.2f}%'.format(
                epoch, batch_idx * batch_size, len(train_loader.dataset),
                100. * batch_idx / len(train_loader), 
                loss.data, 
                100. * train_r[0].numpy() / train_r[1], 
                100. * val_r[0].numpy() / val_r[1]))
当前epoch: 0 [0/60000 (0%)]    损失: 2.298275    训练集准确率: 18.75%    测试集正确率: 16.69%
当前epoch: 0 [6400/60000 (11%)]    损失: 0.366936    训练集准确率: 77.09%    测试集正确率: 91.76%
当前epoch: 0 [12800/60000 (21%)]    损失: 0.197412    训练集准确率: 85.04%    测试集正确率: 95.32%
当前epoch: 0 [19200/60000 (32%)]    损失: 0.065437    训练集准确率: 88.57%    测试集正确率: 95.96%
当前epoch: 0 [25600/60000 (43%)]    损失: 0.245751    训练集准确率: 90.43%    测试集正确率: 97.05%
当前epoch: 0 [32000/60000 (53%)]    损失: 0.116508    训练集准确率: 91.65%    测试集正确率: 97.33%
当前epoch: 0 [38400/60000 (64%)]    损失: 0.106026    训练集准确率: 92.51%    测试集正确率: 97.47%
当前epoch: 0 [44800/60000 (75%)]    损失: 0.024781    训练集准确率: 93.20%    测试集正确率: 97.79%
当前epoch: 0 [51200/60000 (85%)]    损失: 0.040254    训练集准确率: 93.77%    测试集正确率: 97.44%
当前epoch: 0 [57600/60000 (96%)]    损失: 0.013604    训练集准确率: 94.19%    测试集正确率: 97.57%
当前epoch: 1 [0/60000 (0%)]    损失: 0.038379    训练集准确率: 100.00%    测试集正确率: 97.90%
当前epoch: 1 [6400/60000 (11%)]    损失: 0.091921    训练集准确率: 97.94%    测试集正确率: 98.26%
当前epoch: 1 [12800/60000 (21%)]    损失: 0.082685    训练集准确率: 97.88%    测试集正确率: 98.12%
当前epoch: 1 [19200/60000 (32%)]    损失: 0.030613    训练集准确率: 97.95%    测试集正确率: 98.53%
当前epoch: 1 [25600/60000 (43%)]    损失: 0.098491    训练集准确率: 97.96%    测试集正确率: 98.30%
当前epoch: 1 [32000/60000 (53%)]    损失: 0.078065    训练集准确率: 97.97%    测试集正确率: 98.50%
当前epoch: 1 [38400/60000 (64%)]    损失: 0.013370    训练集准确率: 98.02%    测试集正确率: 98.55%
当前epoch: 1 [44800/60000 (75%)]    损失: 0.065581    训练集准确率: 98.09%    测试集正确率: 98.65%
当前epoch: 1 [51200/60000 (85%)]    损失: 0.077535    训练集准确率: 98.12%    测试集正确率: 98.23%
当前epoch: 1 [57600/60000 (96%)]    损失: 0.007826    训练集准确率: 98.16%    测试集正确率: 98.65%
当前epoch: 2 [0/60000 (0%)]    损失: 0.170131    训练集准确率: 98.44%    测试集正确率: 98.57%
当前epoch: 2 [6400/60000 (11%)]    损失: 0.046841    训练集准确率: 98.64%    测试集正确率: 98.40%
当前epoch: 2 [12800/60000 (21%)]    损失: 0.095354    训练集准确率: 98.50%    测试集正确率: 98.58%
当前epoch: 2 [19200/60000 (32%)]    损失: 0.009594    训练集准确率: 98.58%    测试集正确率: 98.68%
当前epoch: 2 [25600/60000 (43%)]    损失: 0.017973    训练集准确率: 98.62%    测试集正确率: 98.82%
当前epoch: 2 [32000/60000 (53%)]    损失: 0.045781    训练集准确率: 98.66%    测试集正确率: 98.63%
当前epoch: 2 [38400/60000 (64%)]    损失: 0.056535    训练集准确率: 98.65%    测试集正确率: 98.94%
当前epoch: 2 [44800/60000 (75%)]    损失: 0.014779    训练集准确率: 98.66%    测试集正确率: 98.97%
当前epoch: 2 [51200/60000 (85%)]    损失: 0.010532    训练集准确率: 98.71%    测试集正确率: 98.50%
当前epoch: 2 [57600/60000 (96%)]    损失: 0.076463    训练集准确率: 98.68%    测试集正确率: 98.79%

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卷积神经网络效果
https://zhangfuli.github.io/2020/09/03/卷积神经网络效果/
作者
张富利
发布于
2020年9月3日
许可协议