基于CNN神经网络的手写数字识别代码和实验报告

# CNN神经网络实验报告 ## 问题描述 以MNIST手写数据集划分训练集和验证集，将自己手写拍照上传的数字图片作为测试样本，训练卷积神经网络分类器识别0-9的手写数字。 ## 数据集说明 MNIST手写数字数据集包含60000张用于训练的手写数字图片和10000张用于测试的手写数字图片。所有的图片具有相同的尺寸（28x28 pixels），数字均位于图片中央。 数据集链接地址如下： 原训练集各类别分布和示例样本如下图所示：   图 1 训练集图片示例 图 2 训练集样本分布 在本实验中，原数据集中训练集被划分为训练集和验证集，其中验证集占比0.2，训练集占比0.8。原数据集的测试集作为测试集使用，用于评估模型性能。 在上述的分布图中不难发现，原数据集中的训练集基本上是类别均衡的；为了不破坏原数据集的类别均衡性，在划分训练集和验证集时，采用分类别抽样(stratified sampling)划分的方法。  图 3 TensorBoard makegrid看到的输入图片 ## 算法代码 算法概述： 模型架构：resnet18修改了第一个卷积层使其接受 BW 格式图片；修改了fc层使其输出10个类别的概率分布。未使用预先训练的权重。 数据处理：图片转换为 Tensor；按照 imagenet 图片第一个通道的均值和方差标准化处理。 权重更新：采用带动量的随机梯度下降法，初始学习率为 1e-3,动量参数为0.9;学习率每7个epoch缩小为0.1倍 损失计算：softmax + crossentropy loss 其他设置：batch_size=128, epoch_num = 24 使用说明： 训练模块设置了五个命令行参数，用户可自行决定数据文件夹位置，日志存放位置，模型保存位置，模型接受图片大小，训练集验证集的划分比例。 第三方包安装： ```c++ Pytorch Tensorboard Sk-learn Numpy Matplotlib ``` Seaborn(代码中没用上) 导入库 ```python # import necessary packages import torch import torch.nn as nn import torch.optim as optim from torch.optim import lr_scheduler import matplotlib.pyplot as plt import numpy as np from torch.utils.data import Dataset, DataLoader, TensorDataset, Subset import torchvision from torchvision import transforms,utils,models import argparse import seaborn as sns from collections import Counter import time import copy from torch.utils.tensorboard import SummaryWriter from sklearn.metrics import confusion_matrix, accuracy_score ``` 可视化数据模块 ```c++ def dataVisualization(x_train,y_train,x_test,y_test): '''return null visualing first nine images save it as a image then visualize distribution of training set save it as a image ''' plt.figure(figsize=(9, 9)) for i in range(9): plt.subplot(331 + i) plt.imshow(x_train[i], cmap=plt.get_cmap('gray')) plt.savefig('sample.png', bbox_inches='tight', dpi=300) print(f'训练集的样本数：{x_train.shape[0]}，测试集的样本数： {x_test.shape[0]}') print(f'输入图像的大小：{x_train.shape[1]}*{x_train.shape[2]}') y_train = np.array(y_train) label_cnt = Counter(y_train) # 统计每个类别的样本数 print('训练集的图像类别分布：', label_cnt) # 可视化图像类别分布 plt.figure(figsize=(9,9)) plt.pie(x=label_cnt.values(), labels=label_cnt.keys(), autopct='%.2f%%') plt.savefig('label_distribution.png', bbox_inches='tight', dpi=300) ``` Stratified sampling 将原训练集分为训练集和验证集，先用自定义函数选择索引，再通过 torch.utils.Subset函数划分训练集和验证集。 ```c++ def stratified_sampling(ds,k): '''return trainset,validset sampling from ds while maintaining data balance acoss different classes. k is the number of samples in each class in trainset. ''' class_counts = {} train_indices = [] test_indices = [] for i, (data, label) in enumerate(ds): c = label class_counts[c] = class_counts.get(c, 0) + 1 if class_counts[c] <= k: train_indices.append(i) else: test_indices.append(i) print('stratified sampling done') return (train_indices,test_indices) ``` ```python # transform data and generate data loader to create batches raw_trainset = torchvision.datasets.MNIST(args.datasetPath, train=True, download=False, transform=data_transforms['train']) train_indices, valid_indices = stratified_sampling(trainset, 0.8*len(trainset)/10) trainset = Subset(raw_trainset,train_indices) validset = Subset(raw_trainset,valid_indices) train_data_loader = DataLoader(trainset,batch_size=128,shuffle=True,drop_last=False) valid_data_loader = DataLoader(validset,batch_size=128,shuffle=False,drop_last=False) ``` 构建网络模型并检查 ```python def my_model(): model_body = models.resnet18(pretrained=False) num_ftrs = model_body.fc.in_features # Here the size of each output sample is set to 2. # Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)). model_body.fc = nn.Linear(num_ftrs, 10) # modify first conv to accept bw img # weight = copy.deepcopy(model_body.conv1.weight) # new_weight = (torch.sum(weight,dim = 1)/3).unsqueeze(1) model_body.conv1 = nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(2,2), padding=(3, 3), bias=False) # model_body.conv1.weight.data = new_weight return model_body def check_model(trainloader,net): # get some random training images dataiter = iter(trainloader) images, labels = dataiter.next() images = images.to(device) labels = labels.to(device) # create grid of images img_grid = torchvision.utils.make_grid(images) # write to tensorboard writer.add_image('four_mnist_images', img_grid) writer.add_graph(net, images) writer.close() ``` 训练模型 ```python def train_model(model, dataloaders, writer, criterion, optimizer, sched uler, device, args, num_epochs = 25, save = True): '' 'return model load input model as last best model, begin trainning for num_epochs '' ' since = time.time() best_model_wts = copy.deepcopy(model.state_dict()) best_acc = 0.0 best_epoch = 0 for epoch in range(num_epochs): print('Epoch {}/{}'.format(epoch, num_epochs - 1)) print('-' * 10)# Each epoch has a training and validation phase for phase in ['train', 'val']: if phase == 'train': model.train()# Set model to training mode else : model.eval()# Set model to evaluate mode running_loss = 0.0 running_corrects = 0# Iterate over data . count = 0 for inputs, labels in dataloaders[phase]: inputs = inputs.to(device) labels = labels.to(device)# print( inputs.shape)# print(labels.shape)# zero the parameter gradients optimizer.zero_grad()# forward# track history if only in train with torch.set_grad_enabled(phase == 'train'): outputs = model(inputs)# print(f "outputs done:{outputs.shape}") _, preds = torch.max(outputs, 1) loss = criterion(outputs, labels)# backward + optimize only if intraining phase if phase == 'train': loss.backward() optimizer.step()# statistics running_loss += loss.item() * inputs.size( 0) running_corrects += torch.sum(preds == labels.data) count += inputs.size(0) epoch_loss = running_loss / count epoch_acc = running_corrects.double() /