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
原文地址：https://www.cnblogs.com/migomiddle/p/11811356.html
时间： 2024-08-04 08:00:09