深度学习识别CIFAR10：pytorch训练LeNet、AlexNet、VGG19实现及比较（二）
版权声明：本文为博主原创文章，欢迎转载，并请注明出处。联系方式：460356155@qq.com
AlexNet在2012年ImageNet图像分类任务竞赛中获得冠军。网络结构如下图所示：
对CIFAR10，图片是32*32，尺寸远小于227*227，因此对网络结构和参数需做微调：
卷积层1：核大小7*7，步长2，填充2
最后一个max-pool层删除
网络定义代码如下：
class AlexNet(nn.Module):
def __init__(self):
super(AlexNet, self).__init__() self.cnn = nn.Sequential(
# 卷积层1，3通道输入，96个卷积核，核大小7*7，步长2，填充2
# 经过该层图像大小变为32-7+2*2 / 2 +1，15*15
# 经3*3最大池化，2步长，图像变为15-3 / 2 + 1， 7*7
nn.Conv2d(3, 96, 7, 2, 2),
nn.ReLU(inplace=True),
nn.MaxPool2d(3, 2, 0), # 卷积层2，96输入通道，256个卷积核，核大小5*5，步长1，填充2
# 经过该层图像变为7-5+2*2 / 1 + 1，7*7
# 经3*3最大池化，2步长，图像变为7-3 / 2 + 1， 3*3
nn.Conv2d(96, 256, 5, 1, 2),
nn.ReLU(inplace=True),
nn.MaxPool2d(3, 2, 0), # 卷积层3，256输入通道，384个卷积核，核大小3*3，步长1，填充1
# 经过该层图像变为3-3+2*1 / 1 + 1，3*3
nn.Conv2d(256, 384, 3, 1, 1),
nn.ReLU(inplace=True), # 卷积层3，384输入通道，384个卷积核，核大小3*3，步长1，填充1
# 经过该层图像变为3-3+2*1 / 1 + 1，3*3
nn.Conv2d(384, 384, 3, 1, 1),
nn.ReLU(inplace=True), # 卷积层3，384输入通道，256个卷积核，核大小3*3，步长1，填充1
# 经过该层图像变为3-3+2*1 / 1 + 1，3*3
nn.Conv2d(384, 256, 3, 1, 1),
nn.ReLU(inplace=True)
) self.fc = nn.Sequential(
# 256个feature，每个feature 3*3
nn.Linear(256*3*3, 1024),
nn.ReLU(),
nn.Linear(1024, 512),
nn.ReLU(),
nn.Linear(512, 10)
) def forward(self, x):
x = self.cnn(x) # x.size()[0]: batch size
x = x.view(x.size()[0], -1)
x = self.fc(x) return x
其余代码同深度学习识别CIFAR10：pytorch训练LeNet、AlexNet、VGG19实现及比较（一）。运行结果如下：
Files already downloaded and verified
AlexNet(
(cnn): Sequential(
(0): Conv2d(3, 96, kernel_size=(7, 7), stride=(2, 2), padding=(2, 2))
(1): ReLU(inplace)
(2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): Conv2d(96, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): ReLU(inplace)
(5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Conv2d(256, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): ReLU(inplace)
(8): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): ReLU(inplace)
(10): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace)
)
(fc): Sequential(
(0): Linear(in_features=2304, out_features=1024, bias=True)
(1): ReLU()
(2): Linear(in_features=1024, out_features=512, bias=True)
(3): ReLU()
(4): Linear(in_features=512, out_features=10, bias=True)
)
)
Train Epoch: 1 [6400/50000 (13%)] Loss: 2.303003 Acc: 10.000000
Train Epoch: 1 [12800/50000 (26%)] Loss: 2.302847 Acc: 9.000000
Train Epoch: 1 [19200/50000 (38%)] Loss: 2.302748 Acc: 9.000000
Train Epoch: 1 [25600/50000 (51%)] Loss: 2.302349 Acc: 10.000000
Train Epoch: 1 [32000/50000 (64%)] Loss: 2.301069 Acc: 10.000000
Train Epoch: 1 [38400/50000 (77%)] Loss: 2.275476 Acc: 12.000000
Train Epoch: 1 [44800/50000 (90%)] Loss: 2.231073 Acc: 13.000000
one epoch spend: 0:00:06.866484
EPOCH:1, ACC:25.06
Train Epoch: 2 [6400/50000 (13%)] Loss: 1.848806 Acc: 25.000000
Train Epoch: 2 [12800/50000 (26%)] Loss: 1.808251 Acc: 27.000000
Train Epoch: 2 [19200/50000 (38%)] Loss: 1.774210 Acc: 29.000000
Train Epoch: 2 [25600/50000 (51%)] Loss: 1.744809 Acc: 31.000000
Train Epoch: 2 [32000/50000 (64%)] Loss: 1.714098 Acc: 32.000000
Train Epoch: 2 [38400/50000 (77%)] Loss: 1.684451 Acc: 34.000000
Train Epoch: 2 [44800/50000 (90%)] Loss: 1.654931 Acc: 35.000000
one epoch spend: 0:00:06.941943
EPOCH:2, ACC:46.64
Train Epoch: 3 [6400/50000 (13%)] Loss: 1.418345 Acc: 45.000000
Train Epoch: 3 [12800/50000 (26%)] Loss: 1.368839 Acc: 47.000000
Train Epoch: 3 [19200/50000 (38%)] Loss: 1.349170 Acc: 48.000000
Train Epoch: 3 [25600/50000 (51%)] Loss: 1.326504 Acc: 49.000000
Train Epoch: 3 [32000/50000 (64%)] Loss: 1.316630 Acc: 50.000000
Train Epoch: 3 [38400/50000 (77%)] Loss: 1.300982 Acc: 51.000000
Train Epoch: 3 [44800/50000 (90%)] Loss: 1.288368 Acc: 52.000000
one epoch spend: 0:00:07.031582
EPOCH:3, ACC:56.72
Train Epoch: 4 [6400/50000 (13%)] Loss: 1.078210 Acc: 60.000000
Train Epoch: 4 [12800/50000 (26%)] Loss: 1.083730 Acc: 60.000000
Train Epoch: 4 [19200/50000 (38%)] Loss: 1.085976 Acc: 60.000000
Train Epoch: 4 [25600/50000 (51%)] Loss: 1.080863 Acc: 61.000000
Train Epoch: 4 [32000/50000 (64%)] Loss: 1.076230 Acc: 61.000000
Train Epoch: 4 [38400/50000 (77%)] Loss: 1.067998 Acc: 61.000000
Train Epoch: 4 [44800/50000 (90%)] Loss: 1.058093 Acc: 62.000000
one epoch spend: 0:00:06.908232
EPOCH:4, ACC:65.4
Train Epoch: 5 [6400/50000 (13%)] Loss: 0.911678 Acc: 67.000000
Train Epoch: 5 [12800/50000 (26%)] Loss: 0.904799 Acc: 67.000000
Train Epoch: 5 [19200/50000 (38%)] Loss: 0.914306 Acc: 67.000000
Train Epoch: 5 [25600/50000 (51%)] Loss: 0.906587 Acc: 67.000000
Train Epoch: 5 [32000/50000 (64%)] Loss: 0.902747 Acc: 67.000000
Train Epoch: 5 [38400/50000 (77%)] Loss: 0.896548 Acc: 68.000000
Train Epoch: 5 [44800/50000 (90%)] Loss: 0.895071 Acc: 68.000000
one epoch spend: 0:00:06.868743
EPOCH:5, ACC:66.47
Train Epoch: 6 [6400/50000 (13%)] Loss: 0.769778 Acc: 72.000000
Train Epoch: 6 [12800/50000 (26%)] Loss: 0.770126 Acc: 73.000000
Train Epoch: 6 [19200/50000 (38%)] Loss: 0.775755 Acc: 72.000000
Train Epoch: 6 [25600/50000 (51%)] Loss: 0.775044 Acc: 72.000000
Train Epoch: 6 [32000/50000 (64%)] Loss: 0.772686 Acc: 72.000000
Train Epoch: 6 [38400/50000 (77%)] Loss: 0.765352 Acc: 73.000000
Train Epoch: 6 [44800/50000 (90%)] Loss: 0.768808 Acc: 73.000000
one epoch spend: 0:00:06.868047
EPOCH:6, ACC:68.26
Train Epoch: 7 [6400/50000 (13%)] Loss: 0.641943 Acc: 77.000000
Train Epoch: 7 [12800/50000 (26%)] Loss: 0.643955 Acc: 77.000000
Train Epoch: 7 [19200/50000 (38%)] Loss: 0.642063 Acc: 77.000000
Train Epoch: 7 [25600/50000 (51%)] Loss: 0.647976 Acc: 77.000000
Train Epoch: 7 [32000/50000 (64%)] Loss: 0.648042 Acc: 77.000000
Train Epoch: 7 [38400/50000 (77%)] Loss: 0.652435 Acc: 77.000000
Train Epoch: 7 [44800/50000 (90%)] Loss: 0.655997 Acc: 77.000000
one epoch spend: 0:00:06.962986
EPOCH:7, ACC:72.21
Train Epoch: 8 [6400/50000 (13%)] Loss: 0.541914 Acc: 80.000000
Train Epoch: 8 [12800/50000 (26%)] Loss: 0.543631 Acc: 81.000000
Train Epoch: 8 [19200/50000 (38%)] Loss: 0.551045 Acc: 80.000000
Train Epoch: 8 [25600/50000 (51%)] Loss: 0.551447 Acc: 80.000000
Train Epoch: 8 [32000/50000 (64%)] Loss: 0.554876 Acc: 80.000000
Train Epoch: 8 [38400/50000 (77%)] Loss: 0.560712 Acc: 80.000000
Train Epoch: 8 [44800/50000 (90%)] Loss: 0.561110 Acc: 80.000000
one epoch spend: 0:00:07.025618
EPOCH:8, ACC:74.15
Train Epoch: 9 [6400/50000 (13%)] Loss: 0.452407 Acc: 84.000000
Train Epoch: 9 [12800/50000 (26%)] Loss: 0.462235 Acc: 83.000000
Train Epoch: 9 [19200/50000 (38%)] Loss: 0.476642 Acc: 83.000000
Train Epoch: 9 [25600/50000 (51%)] Loss: 0.478906 Acc: 83.000000
Train Epoch: 9 [32000/50000 (64%)] Loss: 0.476015 Acc: 83.000000
Train Epoch: 9 [38400/50000 (77%)] Loss: 0.477935 Acc: 83.000000
Train Epoch: 9 [44800/50000 (90%)] Loss: 0.480251 Acc: 83.000000
one epoch spend: 0:00:06.840690
EPOCH:9, ACC:74.49
Train Epoch: 10 [6400/50000 (13%)] Loss: 0.383466 Acc: 87.000000
Train Epoch: 10 [12800/50000 (26%)] Loss: 0.376466 Acc: 87.000000
Train Epoch: 10 [19200/50000 (38%)] Loss: 0.386534 Acc: 86.000000
Train Epoch: 10 [25600/50000 (51%)] Loss: 0.394657 Acc: 86.000000
Train Epoch: 10 [32000/50000 (64%)] Loss: 0.394315 Acc: 86.000000
Train Epoch: 10 [38400/50000 (77%)] Loss: 0.395472 Acc: 86.000000
Train Epoch: 10 [44800/50000 (90%)] Loss: 0.399573 Acc: 86.000000
one epoch spend: 0:00:06.866040
EPOCH:10, ACC:73.13
Train Epoch: 11 [6400/50000 (13%)] Loss: 0.297959 Acc: 89.000000
Train Epoch: 11 [12800/50000 (26%)] Loss: 0.305871 Acc: 89.000000
Train Epoch: 11 [19200/50000 (38%)] Loss: 0.315880 Acc: 89.000000
Train Epoch: 11 [25600/50000 (51%)] Loss: 0.322634 Acc: 88.000000
Train Epoch: 11 [32000/50000 (64%)] Loss: 0.326418 Acc: 88.000000
Train Epoch: 11 [38400/50000 (77%)] Loss: 0.333330 Acc: 88.000000
Train Epoch: 11 [44800/50000 (90%)] Loss: 0.337955 Acc: 88.000000
one epoch spend: 0:00:06.884786
EPOCH:11, ACC:73.79
Train Epoch: 12 [6400/50000 (13%)] Loss: 0.242202 Acc: 91.000000
Train Epoch: 12 [12800/50000 (26%)] Loss: 0.250616 Acc: 91.000000
Train Epoch: 12 [19200/50000 (38%)] Loss: 0.265347 Acc: 90.000000
Train Epoch: 12 [25600/50000 (51%)] Loss: 0.271456 Acc: 90.000000
Train Epoch: 12 [32000/50000 (64%)] Loss: 0.273988 Acc: 90.000000
Train Epoch: 12 [38400/50000 (77%)] Loss: 0.280836 Acc: 90.000000
Train Epoch: 12 [44800/50000 (90%)] Loss: 0.281419 Acc: 90.000000
one epoch spend: 0:00:06.906915
EPOCH:12, ACC:75.89
Train Epoch: 13 [6400/50000 (13%)] Loss: 0.228122 Acc: 92.000000
Train Epoch: 13 [12800/50000 (26%)] Loss: 0.228350 Acc: 92.000000
Train Epoch: 13 [19200/50000 (38%)] Loss: 0.227151 Acc: 92.000000
Train Epoch: 13 [25600/50000 (51%)] Loss: 0.228918 Acc: 92.000000
Train Epoch: 13 [32000/50000 (64%)] Loss: 0.232642 Acc: 91.000000
Train Epoch: 13 [38400/50000 (77%)] Loss: 0.237782 Acc: 91.000000
Train Epoch: 13 [44800/50000 (90%)] Loss: 0.242339 Acc: 91.000000
one epoch spend: 0:00:06.869576
EPOCH:13, ACC:74.39
Train Epoch: 14 [6400/50000 (13%)] Loss: 0.179683 Acc: 93.000000
Train Epoch: 14 [12800/50000 (26%)] Loss: 0.182840 Acc: 93.000000
Train Epoch: 14 [19200/50000 (38%)] Loss: 0.182861 Acc: 93.000000
Train Epoch: 14 [25600/50000 (51%)] Loss: 0.189549 Acc: 93.000000
Train Epoch: 14 [32000/50000 (64%)] Loss: 0.193639 Acc: 93.000000
Train Epoch: 14 [38400/50000 (77%)] Loss: 0.196073 Acc: 93.000000
Train Epoch: 14 [44800/50000 (90%)] Loss: 0.198425 Acc: 93.000000
one epoch spend: 0:00:06.927269
EPOCH:14, ACC:75.63
Train Epoch: 15 [6400/50000 (13%)] Loss: 0.123262 Acc: 95.000000
Train Epoch: 15 [12800/50000 (26%)] Loss: 0.136458 Acc: 95.000000
Train Epoch: 15 [19200/50000 (38%)] Loss: 0.141503 Acc: 95.000000
Train Epoch: 15 [25600/50000 (51%)] Loss: 0.147542 Acc: 94.000000
Train Epoch: 15 [32000/50000 (64%)] Loss: 0.149795 Acc: 94.000000
Train Epoch: 15 [38400/50000 (77%)] Loss: 0.154987 Acc: 94.000000
Train Epoch: 15 [44800/50000 (90%)] Loss: 0.157952 Acc: 94.000000
one epoch spend: 0:00:07.015382
EPOCH:15, ACC:74.6
Train Epoch: 16 [6400/50000 (13%)] Loss: 0.144001 Acc: 94.000000
Train Epoch: 16 [12800/50000 (26%)] Loss: 0.141813 Acc: 94.000000
Train Epoch: 16 [19200/50000 (38%)] Loss: 0.139413 Acc: 95.000000
Train Epoch: 16 [25600/50000 (51%)] Loss: 0.136546 Acc: 95.000000
Train Epoch: 16 [32000/50000 (64%)] Loss: 0.138039 Acc: 95.000000
Train Epoch: 16 [38400/50000 (77%)] Loss: 0.139393 Acc: 95.000000
Train Epoch: 16 [44800/50000 (90%)] Loss: 0.142776 Acc: 95.000000
one epoch spend: 0:00:06.883968
EPOCH:16, ACC:75.54
Train Epoch: 17 [6400/50000 (13%)] Loss: 0.080704 Acc: 97.000000
Train Epoch: 17 [12800/50000 (26%)] Loss: 0.098754 Acc: 96.000000
Train Epoch: 17 [19200/50000 (38%)] Loss: 0.104385 Acc: 96.000000
Train Epoch: 17 [25600/50000 (51%)] Loss: 0.107634 Acc: 96.000000
Train Epoch: 17 [32000/50000 (64%)] Loss: 0.112148 Acc: 96.000000
Train Epoch: 17 [38400/50000 (77%)] Loss: 0.113687 Acc: 96.000000
Train Epoch: 17 [44800/50000 (90%)] Loss: 0.114508 Acc: 96.000000
one epoch spend: 0:00:06.905244
EPOCH:17, ACC:74.9
Train Epoch: 18 [6400/50000 (13%)] Loss: 0.085284 Acc: 97.000000
Train Epoch: 18 [12800/50000 (26%)] Loss: 0.087985 Acc: 97.000000
Train Epoch: 18 [19200/50000 (38%)] Loss: 0.096691 Acc: 96.000000
Train Epoch: 18 [25600/50000 (51%)] Loss: 0.102257 Acc: 96.000000
Train Epoch: 18 [32000/50000 (64%)] Loss: 0.103708 Acc: 96.000000
Train Epoch: 18 [38400/50000 (77%)] Loss: 0.103074 Acc: 96.000000
Train Epoch: 18 [44800/50000 (90%)] Loss: 0.106078 Acc: 96.000000
one epoch spend: 0:00:06.909887
EPOCH:18, ACC:74.86
Train Epoch: 19 [6400/50000 (13%)] Loss: 0.074644 Acc: 97.000000
Train Epoch: 19 [12800/50000 (26%)] Loss: 0.072871 Acc: 97.000000
Train Epoch: 19 [19200/50000 (38%)] Loss: 0.075573 Acc: 97.000000
Train Epoch: 19 [25600/50000 (51%)] Loss: 0.079646 Acc: 97.000000
Train Epoch: 19 [32000/50000 (64%)] Loss: 0.081056 Acc: 97.000000
Train Epoch: 19 [38400/50000 (77%)] Loss: 0.084256 Acc: 97.000000
Train Epoch: 19 [44800/50000 (90%)] Loss: 0.086415 Acc: 97.000000
one epoch spend: 0:00:07.215059
EPOCH:19, ACC:75.69
Train Epoch: 20 [6400/50000 (13%)] Loss: 0.062469 Acc: 97.000000
Train Epoch: 20 [12800/50000 (26%)] Loss: 0.061595 Acc: 97.000000
Train Epoch: 20 [19200/50000 (38%)] Loss: 0.062788 Acc: 97.000000
Train Epoch: 20 [25600/50000 (51%)] Loss: 0.065734 Acc: 97.000000
Train Epoch: 20 [32000/50000 (64%)] Loss: 0.067006 Acc: 97.000000
Train Epoch: 20 [38400/50000 (77%)] Loss: 0.066818 Acc: 97.000000
Train Epoch: 20 [44800/50000 (90%)] Loss: 0.068419 Acc: 97.000000
one epoch spend: 0:00:07.187726
EPOCH:20, ACC:74.23
CIFAR10 pytorch LeNet Train: EPOCH:20, BATCH_SZ:64, LR:0.01, ACC:75.89
train spend time: 0:02:30.334005
Process finished with exit code 0
准确率达到75%，对比LeNet-5的63%，有大幅提升。
深度学习识别CIFAR10：pytorch训练LeNet、AlexNet、VGG19实现及比较（二）的更多相关文章
- 深度学习识别CIFAR10：pytorch训练LeNet、AlexNet、VGG19实现及比较（三）
版权声明:本文为博主原创文章,欢迎转载,并请注明出处.联系方式:460356155@qq.com VGGNet在2014年ImageNet图像分类任务竞赛中有出色的表现.网络结构如下图所示: 同样的, ...
- 深度学习识别CIFAR10：pytorch训练LeNet、AlexNet、VGG19实现及比较（一）
版权声明:本文为博主原创文章,欢迎转载,并请注明出处.联系方式:460356155@qq.com 前面几篇文章介绍了MINIST,对这种简单图片的识别,LeNet-5可以达到99%的识别率. CIFA ...
- MINIST深度学习识别：python全连接神经网络和pytorch LeNet CNN网络训练实现及比较（三）
版权声明:本文为博主原创文章,欢迎转载,并请注明出处.联系方式:460356155@qq.com 在前两篇文章MINIST深度学习识别:python全连接神经网络和pytorch LeNet CNN网 ...
- pytorch识别CIFAR10：训练ResNet-34（准确率80%）
版权声明:本文为博主原创文章,欢迎转载,并请注明出处.联系方式:460356155@qq.com CNN的层数越多,能够提取到的特征越丰富,但是简单地增加卷积层数,训练时会导致梯度弥散或梯度爆炸. 何 ...
- 深度学习面试题12：LeNet(手写数字识别)
目录 神经网络的卷积.池化.拉伸 LeNet网络结构 LeNet在MNIST数据集上应用 参考资料 LeNet是卷积神经网络的祖师爷LeCun在1998年提出,用于解决手写数字识别的视觉任务.自那时起 ...
- pytorch识别CIFAR10：训练ResNet-34（自定义transform，动态调整学习率，准确率提升到94.33%）
版权声明:本文为博主原创文章,欢迎转载,并请注明出处.联系方式:460356155@qq.com 前面通过数据增强,ResNet-34残差网络识别CIFAR10,准确率达到了92.6. 这里对训练过程 ...
- pytorch识别CIFAR10：训练ResNet-34（数据增强，准确率提升到92.6%）
版权声明:本文为博主原创文章,欢迎转载,并请注明出处.联系方式:460356155@qq.com 在前一篇中的ResNet-34残差网络,经过减小卷积核训练准确率提升到85%. 这里对训练数据集做数据 ...
- pytorch识别CIFAR10：训练ResNet-34（微调网络，准确率提升到85%）
版权声明:本文为博主原创文章,欢迎转载,并请注明出处.联系方式:460356155@qq.com 在前一篇中的ResNet-34残差网络,经过训练准确率只达到80%. 这里对网络做点小修改,在最开始的 ...
- windows10环境下安装深度学习环境anaconda+pytorch+CUDA+cuDDN
步骤零:安装anaconda.opencv.pytorch(这些不详细说明).复制运行代码,如果没有报错,说明已经可以了.不过大概率不行,我的会报错提示AssertionError: Torch no ...
随机推荐
- lambda和匿名内部类使用外部变量为什么要语义final?
今天群里讨论java的lambda实现. 后来不断衍生谈到了为什么lambda和匿名内部类只能使用语义final的外部变量. 最开始以为是java的lambda实现问题,编译期魔法会把外部引用作为参数 ...
- Java中实现多线程的四种方式
Java多线程实现方式主要有四种:继承Thread类.实现Runnable接口.实现Callable接口通过FutureTask包装器来创建Thread线程.使用ExecutorService.Cal ...
- sql学习笔记（三）—— 联表查询
上篇写了一些sql查询的知识,这篇接着写一下有关联表查询的知识. 既然是联表查询,那肯定得多个表啊,所以,我们先创建一个教师表,表名为 teacher,并且向表中插入数据. 准备工作: 创建表语句: ...
- Java Calendar类使用总结
平时在浏览一些网站时,有些网站会显示出当前时间,如现在是xx年xx月xx日 xx时xx分xx秒,在实际的开发过程中,也会涉及到日期和时间的计算,Java中提供了一个专门的类Calendar来处理日期与 ...
- 我的Python之旅第六天--面向对象初识
一.概念 类:是具有相同属性的技能的一类实物 对象:实例化的一个类,是类的具体体现 class Person: '''内容''' animal='高级动物' soul='有思想' #animal,so ...
- APP 技术支持
APP使用过程中,有任何问题,可以在此博客下方留言. 或者,发送邮件到邮箱:nbglsoft@163.com 反馈的任何问题,我们将在2个工作日内进行响应. 感谢大家的支持!
- 四款让你绝对上瘾的手机APP 用一次就会爱不释手
如今我们出门在外,无时无刻不都在使用手机,在外游玩吃饭.乘地铁公交.购物逛街等,只要有手机不需要现金就可以完成这些事情,手机功能我们每天都在使用着,不用多说,大家都知道手机的重要性. 下面就是分享福利 ...
- 46.Odoo产品分析 (五) – 定制板块(2) – 为业务自定义odoo(1)
查看Odoo产品分析系列--目录 在这一章节中,将学习到如何设置"开发者模式"以及备份数据库:然后学习如何添加字段到数据库并在表单和视图中显示. 1 了解odoo的构架 每一个应用 ...
- iOS---------获取当前年份
NSDate * senddate=[NSDate date]; NSDateFormatter *dateformatter=[[NSDateFormatter alloc] init]; [d ...
- MySQL向数据库表的某字段追加数据
使用CONCAT()函数 mysql向表中某字段后追加一段字符串(field为字段名): update table_name set field=CONCAT(field,'str',) mysql ...