识别手写数字增强版 - pytorch从入门到入道(一)

1、快速开始

1.1 定义神经网络类，继承torch.nn.Module，文件名为digit_recog.py

 1 import torch.nn as nn
 2 
 3 
 4 class Net(nn.Module):
 5     def __init__(self):
 6         super(Net, self).__init__()
 7         self.conv1 = nn.Sequential(nn.Conv2d(1, 6, 5, 1, 2)
 8                                    , nn.ReLU()
 9                                    , nn.MaxPool2d(2, 2))
10         self.conv2 = nn.Sequential(nn.Conv2d(6, 16, 5)
11                                    , nn.ReLU()
12                                    , nn.MaxPool2d(2, 2))
13         self.fc1 = nn.Sequential(
14             nn.Linear(16 * 5 * 5, 120),
15 # nn.Dropout2d(),
16 nn.ReLU()
17         )
18         self.fc2 = nn.Sequential(
19             nn.Linear(120, 84),
20 nn.Dropout2d(),
21 nn.ReLU()
22         )
23         self.fc3 = nn.Linear(84, 10)
24 
25     # 前向传播
26 def forward(self, x):
27         x = self.conv1(x)
28         x = self.conv2(x)
29         # 线性层的输入输出都是一维数据，所以要把多维度的tensor展平成一维
30 x = x.view(x.size()[0], -1)
31         x = self.fc1(x)
32         x = self.fc2(x)
33         x = self.fc3(x)
34         return x

上面的类定义了一个3层的网络结构，根据问题类型，最后一层是确定的

1.2 开始训练：

import torch
import torchvision as tv
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import os
import copy
import time
from digit_recog import Net
from digit_recog_mydataset import MyDataset


# 读取已保存的模型
def getmodel(pth, net):
    state_filepath = pth
    if os.path.exists(state_filepath):
        # 加载参数
nn_state = torch.load(state_filepath)
        # 加载模型
net.load_state_dict(nn_state)
        # 拷贝一份
return copy.deepcopy(nn_state)
    else:
        return net.state_dict()


# 构建数据集
def getdataset(batch_size):
    # 定义数据预处理方式
transform = transforms.ToTensor()

    # 定义训练数据集
trainset = tv.datasets.MNIST(
        root='./data/',
train=True,
download=True,
transform=transform)

    # 去掉注释，加入自己的数据集
# trainset += MyDataset(os.path.abspath("./data/myimages/"), 'train.txt', transform=transform)

    # 定义训练批处理数据
trainloader = torch.utils.data.DataLoader(
        trainset,
batch_size=batch_size,
shuffle=True,
)

    # 定义测试数据集
testset = tv.datasets.MNIST(
        root='./data/',
train=False,
download=True,
transform=transform)

    # 去掉注释，加入自己的数据集
# testset += MyDataset(os.path.abspath("./data/myimages/"), 'test.txt', transform=transform)

    # 定义测试批处理数据
testloader = torch.utils.data.DataLoader(
        testset,
batch_size=batch_size,
shuffle=False,
)

    return trainloader, testloader


# 训练
def training(device, net, model, dataset_loader, epochs, criterion, optimizer, save_model_path):
    trainloader, testloader = dataset_loader
    # 最佳模型
best_model_wts = model
    # 最好分数
best_acc = 0.0
# 计时
since = time.time()
    for epoch in range(epochs):
        sum_loss = 0.0
# 训练数据集
for i, data in enumerate(trainloader):
            inputs, labels = data
            inputs, labels = inputs.to(device), labels.to(device)
            # 梯度清零，避免带入下一轮累加
optimizer.zero_grad()
            # 神经网络运算
outputs = net(inputs)
            # 损失值
loss = criterion(outputs, labels)
            # 损失值反向传播
loss.backward()
            # 执行优化
optimizer.step()
            # 损失值汇总
sum_loss += loss.item()
            # 每训练完100条数据就显示一下损失值
if i % 100 == 99:
                print('[%d, %d] loss: %.03f'
% (epoch + 1, i + 1, sum_loss / 100))
                sum_loss = 0.0
# 每训练完一轮测试一下准确率
with torch.no_grad():
            correct = 0
total = 0
for data in testloader:
                images, labels = data
                images, labels = images.to(device), labels.to(device)
                outputs = net(images)
                # 取得分最高的
_, predicted = torch.max(outputs.data, 1)
                # print(labels)
                # print(torch.nn.Softmax(dim=1)(outputs.data).detach().numpy()[0])
                # print(torch.nn.functional.normalize(outputs.data).detach().numpy()[0])
total += labels.size(0)
                correct += (predicted == labels).sum()

            print('测试结果:{}/{}'.format(correct, total))
            epoch_acc = correct.double() / total
            print('当前分数:{} 最高分数:{}'.format(epoch_acc, best_acc))
            if epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(net.state_dict())
            print('第%d轮的识别准确率为：%d%%' % (epoch + 1, (100 * correct / total)))

    time_elapsed = time.time() - since
    print('训练完成于 {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
    print('最高分数: {:4f}'.format(best_acc))
    # 保存训练模型
if save_model_path is not None:
        save_state_path = os.path.join('model/', 'net.pth')
        torch.save(best_model_wts, save_state_path)


# 基于cpu还是gpu
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
NET = Net().to(DEVICE)
# 超参数设置
EPOCHS = 8# 训练多少轮
BATCH_SIZE = 64  # 数据集批处理数量 64
LR = 0.001  # 学习率

# 交叉熵损失函数，通常用于多分类问题上
CRITERION = nn.CrossEntropyLoss()
# 优化器
# OPTIMIZER = optim.SGD(net.parameters(), lr=LR, momentum=0.9)
OPTIMIZER = optim.Adam(NET.parameters(), lr=LR)
MODEL = getmodel(os.path.join('model/', 'net.pth'), NET)
training(DEVICE, NET, MODEL, getdataset(BATCH_SIZE), 1, CRITERION, OPTIMIZER, os.path.join('model/', 'net.pth'))

利用标准的mnist数据集跑出来的识别率能达到99%

2、参与进来

目的是为了识别自己的图片，增加参与感

2.1 打开windows附件中的画图工具，用鼠标画几个数字，然后用截图工具保存下来

2.2 实现自己的数据集：

digit_recog_mydataset.py

from PIL import Image
import torch
import os


# 实现自己的数据集
class MyDataset(torch.utils.data.Dataset):
    def __init__(self, root, datafile, transform=None, target_transform=None):
        super(MyDataset, self).__init__()
        fh = open(os.path.join(root, datafile), 'r')
        datas = []
        for line in fh:
            # 删除本行末尾的字符
line = line.rstrip()
            # 通过指定分隔符对字符串进行拆分，默认为所有的空字符，包括空格、换行、制表符等
words = line.split()
            # words[0]是图片信息，words[1]是标签
datas.append((words[0], int(words[1])))

        self.datas = datas
        self.transform = transform
        self.target_transform = target_transform
        self.root = root

    # 必须实现的方法，用于按照索引读取每个元素的具体内容
def __getitem__(self, index):
        # 获取图片及标签，即上面每行中word[0]和word[1]的信息
img, label = self.datas[index]
        # 打开图片，重设尺寸，转换为灰度图
img = Image.open(os.path.join(self.root, img)).resize((28, 28)).convert('L')

        # 数据预处理
if self.transform is not None:
            img = self.transform(img)
        return img, label

    # 必须实现的方法，返回数据集的长度
def __len__(self):
        return len(self.datas)

2.3 在图片文件夹中新建两个文件，train.txt和test.txt，分别写上训练与测试集的数据，格式如下

训练与测试的数据要严格区分开，否则训练出来的模型会有问题

2.4 加入训练、测试数据集

反注释训练方法中的这两行

# trainset += MyDataset(os.path.abspath("./data/myimages/"), 'train.txt', transform=transform)

# testset += MyDataset(os.path.abspath("./data/myimages/"), 'test.txt', transform=transform)

继续执行训练，这里我训练出来的最高识别率是98%

2.5 测试模型

# -*- coding: utf-8 -*-
# encoding:utf-8

import torch
import numpy as np
from PIL import Image
import os
import matplotlib
import matplotlib.pyplot as plt
import glob
from digit_recog import Net

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = Net().to(device)
# 加载参数
nn_state = torch.load(os.path.join('model/', 'net.pth'))
# 参数加载到指定模型
net.load_state_dict(nn_state)

# 指定默认字体
matplotlib.rcParams['font.sans-serif'] = ['SimHei']
matplotlib.rcParams['font.family'] = 'sans-serif'
# 解决负号'-'显示为方块的问题
matplotlib.rcParams['axes.unicode_minus'] = False

# 要识别的图片
file_list = glob.glob(os.path.join('data/test_image/', '*'))
grid_rows = len(file_list) / 5 + 1

for i, file in enumerate(file_list):
    # 读取图片并重设尺寸
image = Image.open(file).resize((28, 28))
    # 灰度图
gray_image = image.convert('L')
    # 图片数据处理
im_data = np.array(gray_image)
    im_data = torch.from_numpy(im_data).float()
    im_data = im_data.view(1, 1, 28, 28)
    # 神经网络运算
outputs = net(im_data)
    # 取最大预测值
_, pred = torch.max(outputs, 1)
    # print(torch.nn.Softmax(dim=1)(outputs).detach().numpy()[0])
    # print(torch.nn.functional.normalize(outputs).detach().numpy()[0])
    # 显示图片
plt.subplot(grid_rows, 5, i + 1)
    plt.imshow(gray_image)
    plt.title(u"你是{}?".format(pred.item()), fontsize=8)
    plt.axis('off')

    print('[{}]预测数字为: [{}]'.format(file, pred.item()))

plt.show()

可视化结果

 这批图片是经过图片增强后识别的结果，准确率有待提高

3、优化

3.1 更多样本：

收集难度大

3.2 数据增强：

简单地处理一下自己手写的数字图片

# -*- coding: utf-8 -*-
# encoding:utf-8

import torch
import numpy as np
from PIL import Image
import os
import matplotlib
import matplotlib.pyplot as plt
import glob
from scipy.ndimage import filters

class ImageProcceed:
    def __init__(self, image_folder):
        self.image_folder = image_folder

    def save(self, rotate, filter=None, to_gray=True):
        file_list = glob.glob(os.path.join(self.image_folder, '*.png'))
        print(len(file_list))
        for i, file in enumerate(file_list):
            # 读取图片数据
image = Image.open(file)  # .resize((28, 28))
            # 灰度图
if to_gray == True:
                image = image.convert('L')
            # 旋转
image = image.rotate(rotate)
            if filter is not None:
                image = filters.gaussian_filter(image, 0.5)
                image = Image.fromarray(image)
            filename = os.path.basename(file)
            fileext = os.path.splitext(filename)[1]
            savefile = filename.replace(fileext, '-rt{}{}'.format(rotate, fileext))
            print(savefile)
            image.save(os.path.join(self.image_folder, savefile))


ip = ImageProcceed('data/myimages/')
ip.save(20, filter=0.5)

3.3 改变网络大小：

比如把上面的Net类中的3层改为2层

3.4 调参：

改变学习率，训练更多次数等