前言:
本篇文章基于卷积神经网络CNN,使用PyTorch实现MNIST数据集手写数字识别。
一、PyTorch是什么?
PyTorch 是一个 Torch7 团队开源的 Python 优先的深度学习框架,提供两个高级功能:
- 强大的 GPU 加速 Tensor 计算(类似 numpy)
- 构建基于 tape 的自动升级系统上的深度神经网络
你可以重用你喜欢的 python 包,如 numpy、scipy 和 Cython ,在需要时扩展 PyTorch。
二、程序示例
下面案例可供运行参考
1.引入必要库
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import torchvision import torch from torch.utils.data import DataLoader import torch.nn.functional as F |
2.下载数据集
这里设置download=True,将会自动下载数据集,并存储在./data文件夹。
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train_data = torchvision.datasets.MNIST(root = "./data" ,train = True ,transform = torchvision.transforms.ToTensor(),download = True ) test_data = torchvision.datasets.MNIST(root = "./data" ,train = False ,transform = torchvision.transforms.ToTensor(),download = True ) |
3.加载数据集
batch_size=32表示每一个batch中包含32张手写数字图片,shuffle=True表示打乱测试集(data和target仍一一对应)
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train_loader = DataLoader(train_data,batch_size = 32 ,shuffle = True ) test_loader = DataLoader(test_data,batch_size = 32 ,shuffle = False ) |
4.搭建CNN模型并实例化
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class Net(torch.nn.Module): def __init__( self ): super (Net, self ).__init__() self .con1 = torch.nn.Conv2d( 1 , 10 ,kernel_size = 5 ) self .con2 = torch.nn.Conv2d( 10 , 20 ,kernel_size = 5 ) self .pooling = torch.nn.MaxPool2d( 2 ) self .fc = torch.nn.Linear( 320 , 10 ) def forward( self ,x): batch_size = x.size( 0 ) x = F.relu( self .pooling( self .con1(x))) x = F.relu( self .pooling( self .con2(x))) x = x.view(batch_size, - 1 ) x = self .fc(x) return x #模型实例化 model = Net() |
5.交叉熵损失函数损失函数及SGD算法优化器
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lossfun = torch.nn.CrossEntropyLoss() opt = torch.optim.SGD(model.parameters(),lr = 0.01 ,momentum = 0.5 ) |
6.训练函数
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def train(epoch): running_loss = 0.0 for i,(inputs,targets) in enumerate (train_loader, 0 ): # inputs,targets = inputs.to(device),targets.to(device) opt.zero_grad() outputs = model(inputs) loss = lossfun(outputs,targets) loss.backward() opt.step() running_loss + = loss.item() if i % 300 = = 299 : print ( '[%d,%d] loss:%.3f' % (epoch + 1 ,i + 1 ,running_loss / 300 )) running_loss = 0.0 |
7.测试函数
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def test(): total = 0 correct = 0 with torch.no_grad(): for (inputs,targets) in test_loader: # inputs, targets = inputs.to(device), targets.to(device) outputs = model(inputs) _,predicted = torch. max (outputs.data,dim = 1 ) total + = targets.size( 0 ) correct + = (predicted = = targets). sum ().item() print ( 100 * correct / total) |
8.运行
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if __name__ = = '__main__' : for epoch in range ( 20 ): train(epoch) test() |
三、总结
到此这篇关于PyTorch实现MNIST数据集手写数字识别详情的文章就介绍到这了,更多相关PyTorch MNIST 内容请搜索服务器之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持服务器之家!
原文链接:https://blog.csdn.net/qq_41664447/article/details/126698428