VGG19识别CIFAR10数据集(Pytorch实战)
2024-05-29 17:48:42
文章目录
- 1.导库
- 2.获取数据集
- CIFAR10数据集简单介绍
- 加载数据集
- 3.创建VGG19模型
- 简单查看一下网络结构
- 4.开启训练
- 训练结果
- 训练损失和测试损失关系图
- 训练精度和测试精度关系图
- 5.测试阶段
- 6.查看效果图
1.导库
import time
import osimport numpy as npimport torch
import torch.nn.functional as F
import torch.nn as nnfrom torch.utils.data import Dataset
from torch.utils.data import DataLoader
from torchvision import transformsfrom PIL import Image
import matplotlib.pyplot as pltimport argparseimport numpy as np
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as Data
import matplotlib.pyplot as plt
from PIL import Image
import torch.nn as nn
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
2.获取数据集
CIFAR10数据集简单介绍
CIFAR-10 是一个包含60000张图片的数据集。其中每张照片为32*32的彩色照片，每个像素点包括RGB三个数值，数值范围 0 ~ 255。
所有照片分属10个不同的类别，分别是 ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’, ‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’
其中五万张图片被划分为训练集，剩下的一万张图片属于测试集。
加载数据集
batch_size = 64
train_dataset = datasets.CIFAR10(root='data', train=True, transform=transforms.ToTensor(),download=True)test_dataset = datasets.CIFAR10(root='data', train=False, transform=transforms.ToTensor())train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)
3.创建VGG19模型
import time
import osimport numpy as npimport torch
import torch.nn.functional as F
import torch.nn as nnfrom torch.utils.data import Dataset
from torch.utils.data import DataLoader
from torchvision import transformsfrom PIL import Image
import matplotlib.pyplot as pltclass VGG19(torch.nn.Module):def __init__(self,num_classes):super(VGG19, self).__init__()self.block1 = nn.Sequential(nn.Conv2d(in_channels=3,out_channels=64,kernel_size=(3,3),stride=(1,1),padding=1),nn.BatchNorm2d(num_features=64),nn.ReLU(),nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(1, 1), padding=1),nn.BatchNorm2d(num_features=64),nn.ReLU(),nn.MaxPool2d(kernel_size=(2,2),stride=(2,2)))self.block2 = nn.Sequential(nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3, 3), stride=(1, 1), padding=1),nn.BatchNorm2d(num_features=128),nn.ReLU(),nn.Conv2d(in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(1, 1), padding=1),nn.BatchNorm2d(num_features=128),nn.ReLU(),nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2)))self.block3 = nn.Sequential(nn.Conv2d(in_channels=128, out_channels=256, kernel_size=(3, 3), stride=(1, 1), padding=1),nn.BatchNorm2d(num_features=256),nn.ReLU(),nn.Conv2d(in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), padding=1),nn.BatchNorm2d(num_features=256),nn.ReLU(),nn.Conv2d(in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), padding=1),nn.BatchNorm2d(num_features=256),nn.ReLU(),nn.Conv2d(in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), padding=1),nn.BatchNorm2d(num_features=256),nn.ReLU(),nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2)))self.block4 = nn.Sequential(nn.Conv2d(in_channels=256, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=1),nn.BatchNorm2d(num_features=512),nn.ReLU(),nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=1),nn.BatchNorm2d(num_features=512),nn.ReLU(),nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=1),nn.BatchNorm2d(num_features=512),nn.ReLU(),nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=1),nn.BatchNorm2d(num_features=512),nn.ReLU(),nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2)))self.block5 = nn.Sequential(nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=1),nn.BatchNorm2d(num_features=512),nn.ReLU(),nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=1),nn.BatchNorm2d(num_features=512),nn.ReLU(),nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=1),nn.BatchNorm2d(num_features=512),nn.ReLU(),nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=1),nn.BatchNorm2d(num_features=512),nn.ReLU(),nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2)))self.classifier = nn.Sequential(nn.Linear(512*1*1,4096),nn.ReLU(),nn.Dropout(0.5),nn.Linear(4096,4096),nn.ReLU(),nn.Dropout(0.5),nn.Linear(4096,num_classes))def forward(self,x):x = self.block1(x)x = self.block2(x)x = self.block3(x)x = self.block4(x)x = self.block5(x)logits = self.classifier(x.view(-1,512*1*1))probas = F.softmax(logits,dim = 1)return logits,probas
简单查看一下网络结构
net = VGG19(10)
print(net)
print(net(torch.randn([1,3,32,32])))
VGG19((block1): Sequential((0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU()(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU()(6): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False))(block2): Sequential((0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU()(3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU()(6): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False))(block3): Sequential((0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU()(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU()(6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(8): ReLU()(9): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(10): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(11): ReLU()(12): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False))(block4): Sequential((0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU()(3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU()(6): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(7): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(8): ReLU()(9): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(10): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(11): ReLU()(12): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False))(block5): Sequential((0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU()(3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU()(6): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(7): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(8): ReLU()(9): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(10): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(11): ReLU()(12): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False))(classifier): Sequential((0): Linear(in_features=512, out_features=4096, bias=True)(1): ReLU()(2): Dropout(p=0.5, inplace=False)(3): Linear(in_features=4096, out_features=4096, bias=True)(4): ReLU()(5): Dropout(p=0.5, inplace=False)(6): Linear(in_features=4096, out_features=10, bias=True))
)
(tensor([[ 0.2961, 0.0183, 0.1557, -0.2880, -0.2378, -0.1892, -0.1059, -0.2017,0.2670, 0.4931]], grad_fn=<AddmmBackward>), tensor([[0.1274, 0.0965, 0.1107, 0.0710, 0.0747, 0.0784, 0.0852, 0.0774, 0.1237,0.1551]], grad_fn=<SoftmaxBackward>))
NUM_EPOCHS = 15model = VGG19(num_classes=10)model = model.to(DEVICE)optimizer = torch.optim.Adam(model.parameters(), lr=0.001)4.开启训练
valid_loader = test_loader
def compute_accuracy_and_loss(model, data_loader, device):correct_pred, num_examples = 0, 0cross_entropy = 0.for i, (features, targets) in enumerate(data_loader):features = features.to(device)targets = targets.to(device)logits, probas = model(features)cross_entropy += F.cross_entropy(logits, targets).item()_, predicted_labels = torch.max(probas, 1)num_examples += targets.size(0)correct_pred += (predicted_labels == targets).sum()return correct_pred.float()/num_examples * 100, cross_entropy/num_examplesstart_time = time.time()
train_acc_lst, valid_acc_lst = [], []
train_loss_lst, valid_loss_lst = [], []for epoch in range(NUM_EPOCHS):model.train()for batch_idx, (features, targets) in enumerate(train_loader):### PREPARE MINIBATCHfeatures = features.to(DEVICE)targets = targets.to(DEVICE)### FORWARD AND BACK PROPlogits, probas = model(features)cost = F.cross_entropy(logits, targets)optimizer.zero_grad()cost.backward()### UPDATE MODEL PARAMETERSoptimizer.step()### LOGGINGif not batch_idx % 300:print (f'Epoch: {epoch+1:03d}/{NUM_EPOCHS:03d} | 'f'Batch {batch_idx:03d}/{len(train_loader):03d} |' f' Cost: {cost:.4f}')# no need to build the computation graph for backprop when computing accuracymodel.eval()with torch.set_grad_enabled(False):train_acc, train_loss = compute_accuracy_and_loss(model, train_loader, device=DEVICE)valid_acc, valid_loss = compute_accuracy_and_loss(model, valid_loader, device=DEVICE)train_acc_lst.append(train_acc)valid_acc_lst.append(valid_acc)train_loss_lst.append(train_loss)valid_loss_lst.append(valid_loss)print(f'Epoch: {epoch+1:03d}/{NUM_EPOCHS:03d} Train Acc.: {train_acc:.2f}%'f' | Validation Acc.: {valid_acc:.2f}%')elapsed = (time.time() - start_time)/60print(f'Time elapsed: {elapsed:.2f} min')elapsed = (time.time() - start_time)/60
print(f'Total Training Time: {elapsed:.2f} min')训练结果
Epoch: 001/015 | Batch 000/782 | Cost: 2.2996
Epoch: 001/015 | Batch 300/782 | Cost: 1.9033
Epoch: 001/015 | Batch 600/782 | Cost: 1.8213
Epoch: 001/015 Train Acc.: 27.86% | Validation Acc.: 27.72%
Time elapsed: 0.79 min
Epoch: 002/015 | Batch 000/782 | Cost: 1.8215
Epoch: 002/015 | Batch 300/782 | Cost: 1.7313
Epoch: 002/015 | Batch 600/782 | Cost: 1.5908
Epoch: 002/015 Train Acc.: 31.12% | Validation Acc.: 30.99%
Time elapsed: 1.58 min
Epoch: 003/015 | Batch 000/782 | Cost: 1.4785
Epoch: 003/015 | Batch 300/782 | Cost: 1.6117
Epoch: 003/015 | Batch 600/782 | Cost: 1.4389
Epoch: 003/015 Train Acc.: 38.75% | Validation Acc.: 38.88%
Time elapsed: 2.37 min
Epoch: 004/015 | Batch 000/782 | Cost: 1.7106
Epoch: 004/015 | Batch 300/782 | Cost: 1.5534
Epoch: 004/015 | Batch 600/782 | Cost: 1.3664
Epoch: 004/015 Train Acc.: 48.24% | Validation Acc.: 48.28%
Time elapsed: 3.17 min
Epoch: 005/015 | Batch 000/782 | Cost: 1.3960
Epoch: 005/015 | Batch 300/782 | Cost: 1.2528
Epoch: 005/015 | Batch 600/782 | Cost: 1.0285
Epoch: 005/015 Train Acc.: 57.69% | Validation Acc.: 56.56%
Time elapsed: 3.96 min
Epoch: 006/015 | Batch 000/782 | Cost: 1.2201
Epoch: 006/015 | Batch 300/782 | Cost: 1.3973
Epoch: 006/015 | Batch 600/782 | Cost: 0.9932
Epoch: 006/015 Train Acc.: 63.45% | Validation Acc.: 62.00%
Time elapsed: 4.75 min
Epoch: 007/015 | Batch 000/782 | Cost: 1.0917
Epoch: 007/015 | Batch 300/782 | Cost: 0.9945
Epoch: 007/015 | Batch 600/782 | Cost: 0.9642
Epoch: 007/015 Train Acc.: 70.49% | Validation Acc.: 68.02%
Time elapsed: 5.55 min
Epoch: 008/015 | Batch 000/782 | Cost: 0.9970
Epoch: 008/015 | Batch 300/782 | Cost: 1.0228
Epoch: 008/015 | Batch 600/782 | Cost: 0.7637
Epoch: 008/015 Train Acc.: 71.08% | Validation Acc.: 68.62%
Time elapsed: 6.34 min
Epoch: 009/015 | Batch 000/782 | Cost: 0.8868
Epoch: 009/015 | Batch 300/782 | Cost: 0.6673
Epoch: 009/015 | Batch 600/782 | Cost: 0.6563
Epoch: 009/015 Train Acc.: 76.39% | Validation Acc.: 73.18%
Time elapsed: 7.13 min
Epoch: 010/015 | Batch 000/782 | Cost: 0.5915
Epoch: 010/015 | Batch 300/782 | Cost: 0.6803
Epoch: 010/015 | Batch 600/782 | Cost: 0.7547
Epoch: 010/015 Train Acc.: 80.88% | Validation Acc.: 76.85%
Time elapsed: 7.92 min
Epoch: 011/015 | Batch 000/782 | Cost: 0.7416
Epoch: 011/015 | Batch 300/782 | Cost: 0.5792
Epoch: 011/015 | Batch 600/782 | Cost: 0.6585
Epoch: 011/015 Train Acc.: 78.51% | Validation Acc.: 74.52%
Time elapsed: 8.71 min
Epoch: 012/015 | Batch 000/782 | Cost: 0.5723
Epoch: 012/015 | Batch 300/782 | Cost: 0.4954
Epoch: 012/015 | Batch 600/782 | Cost: 0.6602
Epoch: 012/015 Train Acc.: 86.85% | Validation Acc.: 81.15%
Time elapsed: 9.50 min
Epoch: 013/015 | Batch 000/782 | Cost: 0.4229
Epoch: 013/015 | Batch 300/782 | Cost: 0.3894
Epoch: 013/015 | Batch 600/782 | Cost: 0.6071
Epoch: 013/015 Train Acc.: 81.52% | Validation Acc.: 76.20%
Time elapsed: 10.30 min
Epoch: 014/015 | Batch 000/782 | Cost: 0.3842
Epoch: 014/015 | Batch 300/782 | Cost: 0.3581
Epoch: 014/015 | Batch 600/782 | Cost: 0.4606
Epoch: 014/015 Train Acc.: 89.17% | Validation Acc.: 82.13%
Time elapsed: 11.09 min
Epoch: 015/015 | Batch 000/782 | Cost: 0.3284
Epoch: 015/015 | Batch 300/782 | Cost: 0.3410
Epoch: 015/015 | Batch 600/782 | Cost: 0.3267
Epoch: 015/015 Train Acc.: 89.35% | Validation Acc.: 81.93%
Time elapsed: 11.88 min
Total Training Time: 11.88 min
训练损失和测试损失关系图
plt.plot(range(1, NUM_EPOCHS+1), train_loss_lst, label='Training loss')
plt.plot(range(1, NUM_EPOCHS+1), valid_loss_lst, label='Validation loss')
plt.legend(loc='upper right')
plt.ylabel('Cross entropy')
plt.xlabel('Epoch')
plt.show()
训练精度和测试精度关系图
plt.plot(range(1, NUM_EPOCHS+1), train_acc_lst, label='Training accuracy')
plt.plot(range(1, NUM_EPOCHS+1), valid_acc_lst, label='Validation accuracy')
plt.legend(loc='upper left')
plt.ylabel('Cross entropy')
plt.xlabel('Epoch')
plt.show()
5.测试阶段
model.eval()
with torch.set_grad_enabled(False): # save memory during inferencetest_acc, test_loss = compute_accuracy_and_loss(model, test_loader, DEVICE)print(f'Test accuracy: {test_acc:.2f}%')Test accuracy: 81.93%
6.查看效果图
from PIL import Image
import matplotlib.pyplot as plt
for features, targets in train_loader:break
#预测环节
_, predictions = model.forward(features[:8].to(DEVICE))
predictions = torch.argmax(predictions, dim=1)
print(predictions)features = features[:7]
fig = plt.figure()
tname = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
# print(features[i].size())
for i in range(6):plt.subplot(2,3,i+1)plt.tight_layout()tmp = features[i]plt.imshow(np.transpose(tmp, (1, 2, 0)))plt.title("Actual value: {}".format(tname[targets[i]])+'\n'+"Prediction value: {}".format(tname[predictions[i]]),size = 10)# plt.title("Prediction value: {}".format(tname[targets[i]]))
plt.show()
VGG19识别CIFAR10数据集(Pytorch实战)相关推荐
- ResNet18识别CelebA数据集(Pytorch实战)
文章目录 1.导库 2.获取数据集 自定义数据集 简单查看 创建数据集 3.创建Resnet18模型 简单查看一下网络结构 4.开启训练 训练结果 训练损失和测试损失关系图 训练精度和测试精度关系图 ...
- cifar10数据集测试有多少张图_pytorch VGG11识别cifar10数据集(训练+预测单张输入图片操作)...
首先这是VGG的结构图,VGG11则是红色框里的结构,共分五个block,如红框中的VGG11第一个block就是一个conv3-64卷积层: 一,写VGG代码时,首先定义一个 vgg_block(n ...
- cifar10数据集pytorch
提示:文章写完后,目录可以自动生成,如何生成可参考右边的帮助文档 文章目录 cifar10数据集 导入库 导入数据集 定义网络 标定义损失函数和优化器 训练结果 采用其他网络进行优化 LeNet VG ...
- vs2017\vs2019 VGG19处理cifar-10数据集的TensorFlow实现
这是针对于博客vs2017安装和使用教程(详细)和vs2019安装和使用教程(详细)的VGG19-CIFAR10项目新建示例 目录 一.代码(附有重要的注释) 二.项目结构 三.VGG简介 四.程序执 ...
- 我用 PyTorch 复现了 LeNet-5 神经网络（CIFAR10 数据集篇）！
大家好,我是红色石头! 在上两篇文章: 这可能是神经网络 LeNet-5 最详细的解释了! 我用 PyTorch 复现了 LeNet-5 神经网络(MNIST 手写数据集篇)! 详细介绍了卷积神经网络 ...
- 【深度学习】我用 PyTorch 复现了 LeNet-5 神经网络（CIFAR10 数据集篇）！
今天我们将使用 Pytorch 来继续实现 LeNet-5 模型,并用它来解决 CIFAR10 数据集的识别. 正文开始! 二.使用LeNet-5网络结构创建CIFAR-10识别分类器 LeNet-5 ...
- 【Pytorch神经网络实战案例】28 GitSet模型进行步态与身份识别(CASIA-B数据集)
1 CASIA-B数据集 本例使用的是预处理后的CASIA-B数据集, 数据集下载网址如下. http://www.cbsr.ia.ac.cn/china/Gait%20Databases%20cH. ...
- Pytorch实战1：LeNet手写数字识别 (MNIST数据集)
版权说明:此文章为本人原创内容,转载请注明出处,谢谢合作! Pytorch实战1:LeNet手写数字识别 (MNIST数据集) 实验环境: Pytorch 0.4.0 torchvision 0.2. ...
- 【Pytorch实战4】基于CIFAR10数据集训练一个分类器
参考资料: <深度学习之pytorch实战计算机视觉> Pytorch官方教程 Pytorch中文文档 先是数据的导入与预览. import torch import torchvisio ...
- （！详解 Pytorch实战：①）kaggle猫狗数据集二分类：加载（集成/自定义）数据集
这系列的文章是我对Pytorch入门之后的一个总结,特别是对数据集生成加载这一块加强学习 另外,这里有一些比较常用的数据集,大家可以进行下载: 需要注意的是,本篇文章使用的PyTorch的版本是v0. ...
最新文章
- mac使用的快捷方式
- springboot pom文件指定jdk_Spring Boot 入门
- GM6 pageset - Cache get scenario /ui2/cl_pfcg_utils
- mysql系列:加深对脏读、脏写、可重复读、幻读的理解
- c语言程序装萝卜,萝卜花园练习win7系统安装SkyDrive的图文步骤
- mysql 数据库复制软件_mysql 快速复制数据库
- 编写一个函数把华氏温度转换为摄氏温度
- 与神经网络相比，你对P图一无所知
- idea 热部署_Spring Boot Idea中热部署（自动刷新）
- Open3d之网格（Mesh）操作
- sqoop导出数据时：ERROR tool.ExportTool: Error during export: Export job failed!解决
- 使用Adobe acrobat压缩pdf大小
- Linux不同发型版本(Debian REHL)与软件包管理器(RPM dpkg)的概念
- JavaC++题解与拓展——leetcode953.验证外星语【么的新知识】
- Gephi可视化人物关系图
- 小熊派移植 TencentOS-tiny+M26/EC20+MQTT 对接腾讯云平台IoThub
- 天津大学仁爱学院计算机科学与技术学费,天津大学仁爱学院计算机科学与技术专业2016年在山西理科高考录取最低分数线...
- 学了 Python 能用来做什么？
- python中的列表
- CentOS 搭建 Vsftpd 服务，一次性成功，收藏了！