手写数字识别-paddle版

Posted 2023-04-09 快乐的GTD吧

平台

https://www.paddlepaddle.org.cn/

环境变量

# 路径
data_dir = \'../data\'
model_dir = \'inference_model\'
base_dir = \'/\'.format(data_dir, model_dir)

# 模型名称
model_name = \'minist\'
model_file = \'/\'.format(base_dir, model_name)
model_dynamic_file = \'//\'.format(data_dir,\'dynamic_model\', model_name)

加载模型，并推理

import paddle
import numpy as np
# 引用 paddle inference 预测库
import paddle.inference as paddle_infer
from paddle.vision.transforms import Normalize


def main():
    # 归一化函数，对[0-255]数据进行归一化，这样好处理
    transform = Normalize(mean=[127.5], std=[127.5], data_format=\'CHW\')
    test_dataset = paddle.vision.datasets.MNIST(mode=\'test\', transform=transform)

    # 设置输入
    fake_input = np.asarray(test_dataset[1][0]).reshape([1,1,28,28])
#     print(fake_input)
    
    # 设置Config
    config = set_config()

    # 创建Predictor
    predictor = paddle_infer.create_predictor(config)

    # 获取输入的名称
    input_names = predictor.get_input_names()
    input_tensor = predictor.get_input_handle(input_names[0])

    # 设置输入
#     fake_input = np.random.randn(1,784).astype("float32")
    input_tensor.copy_from_cpu(fake_input)

    # 运行predictor
    predictor.run()

    # 获取输出
    output_names = predictor.get_output_names()
    output_tensor = predictor.get_output_handle(output_names[0])
    output_data = output_tensor.copy_to_cpu() # numpy.ndarray类型
    print("输出的形状如下: ")
    print(output_data.shape)
    print(output_data.argmax())

def set_config():
    pdmodel_file = \'.pdmodel\'.format(model_file)
    pdiparams_file = \'.pdiparams\'.format(model_file)
    print(\'模型: \'.format(pdmodel_file))
    config = paddle_infer.Config(pdmodel_file, pdiparams_file)
    config.disable_gpu()
    return config

if __name__ == "__main__":
    main()

可视化图片

import paddle
# 可视化图片
from matplotlib import pyplot as plt

test_dataset = paddle.vision.datasets.MNIST(mode=\'test\')
# 从测试集中取出一张图片
img, label = test_dataset[1]
print(img)
plt.imshow(img)
plt.show()

识别手写数字增强版 - pytorch从入门到入道

手写数字识别，神经网络领域的“hello world”例子，通过pytorch一步步构建，通过训练与调整，达到“100%”准确率

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

 

后面我调整了Net类中的两个地方，准确率终于达到100%，这只是在我小批量测试集上的表现而已，而现实中预测是不可能达到100%的，每台机器可能有差异，每次运行的结果会有不同，再次帖出代码

 1 import torch.nn as nn
 2 
 3 
 4 class Net(nn.Module):
 5     def __init__(self):
 6         super(Net, self).__init__()
 7         # 卷积: 1通道输入，6通道输出，卷积核5*5，步长1，前后补2个0
 8         # 激活函数一般用ReLU，后面改良的有LeakyReLU/PReLU
 9         # MaxPool2d池化，一般是2
10         self.conv1 = nn.Sequential(nn.Conv2d(1, 6, 5, 1, 2)
11                                    , nn.PReLU()
12                                    , nn.MaxPool2d(2, 2))
13         self.conv2 = nn.Sequential(nn.Conv2d(6, 16, 5)
14                                    , nn.PReLU()
15                                    , nn.MaxPool2d(2, 2))
16         self.fc1 = nn.Sequential(
17             nn.Linear(16 * 5 * 5, 120),  # 卷积输出16，乘以卷积核5*5
18             # nn.Dropout2d(),  # Dropout接收来自linear的数据，Dropout2d接收来自conv2d的数据
19             nn.PReLU()
20         )
21         self.fc2 = nn.Sequential(
22             nn.Linear(120, 84),
23             nn.Dropout(p=0.2),
24             nn.PReLU()
25         )
26         self.fc3 = nn.Linear(84, 10)  # 输出层节点为10，代表数字0-9
27 
28     # 前向传播
29     def forward(self, x):
30         x = self.conv1(x)
31         x = self.conv2(x)
32         # 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
33         x = x.view(x.size()[0], -1)
34         x = self.fc1(x)
35         x = self.fc2(x)
36         x = self.fc3(x)
37         return x

上面改了两个地方，一个是激活函数ReLU改成了PReLU，正则化Dropout用0.2作为参数，下面是再次运行测试后的结果