pytorch实现mnist分类的示例讲解
torchvision包包含了目前流行的数据集，模型结构和常用的图片转换工具。
torchvision.datasets中包含了以下数据集
MNIST
COCO（用于图像标注和目标检测）(CaptioningandDetection)
LSUNClassification
ImageFolder
Imagenet-12
CIFAR10andCIFAR100
STL10
torchvision.models
torchvision.models模块的子模块中包含以下模型结构。
AlexNet
VGG
ResNet
SqueezeNet
DenseNetYoucanconstructamodelwithrandomweightsbycallingitsconstructor:
pytorchtorchvisiontransform
对PIL.Image进行变换
from__future__importprint_function
importargparse#Python命令行解析工具
importtorch
importtorch.nnasnn
importtorch.nn.functionalasF
importtorch.optimasoptim
fromtorchvisionimportdatasets,transforms
classNet(nn.Module):
def__init__(self):
super(Net,self).__init__()
self.conv1=nn.Conv2d(1,10,kernel_size=5)
self.conv2=nn.Conv2d(10,20,kernel_size=5)
self.conv2_drop=nn.Dropout2d()
self.fc1=nn.Linear(320,50)
self.fc2=nn.Linear(50,10)
defforward(self,x):
x=F.relu(F.max_pool2d(self.conv1(x),2))
x=F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)),2))
x=x.view(-1,320)
x=F.relu(self.fc1(x))
x=F.dropout(x,training=self.training)
x=self.fc2(x)
returnF.log_softmax(x,dim=1)
deftrain(args,model,device,train_loader,optimizer,epoch):
model.train()
forbatch_idx,(data,target)inenumerate(train_loader):
data,target=data.to(device),target.to(device)
optimizer.zero_grad()
output=model(data)
loss=F.nll_loss(output,target)
loss.backward()
optimizer.step()
ifbatch_idx%args.log_interval==0:
print('TrainEpoch:{}[{}/{}({:.0f}%)]\tLoss:{:.6f}'.format(
epoch,batch_idx*len(data),len(train_loader.dataset),
100.*batch_idx/len(train_loader),loss.item()))
deftest(args,model,device,test_loader):
model.eval()
test_loss=0
correct=0
withtorch.no_grad():
fordata,targetintest_loader:
data,target=data.to(device),target.to(device)
output=model(data)
test_loss+=F.nll_loss(output,target,size_average=False).item()#sumupbatchloss
pred=output.max(1,keepdim=True)[1]#gettheindexofthemaxlog-probability
correct+=pred.eq(target.view_as(pred)).sum().item()
test_loss/=len(test_loader.dataset)
print('\nTestset:Averageloss:{:.4f},Accuracy:{}/{}({:.0f}%)\n'.format(
test_loss,correct,len(test_loader.dataset),
100.*correct/len(test_loader.dataset)))
defmain():
#Trainingsettings
parser=argparse.ArgumentParser(description='PyTorchMNISTExample')
parser.add_argument('--batch-size',type=int,default=64,metavar='N',
help='inputbatchsizefortraining(default:64)')
parser.add_argument('--test-batch-size',type=int,default=1000,metavar='N',
help='inputbatchsizefortesting(default:1000)')
parser.add_argument('--epochs',type=int,default=10,metavar='N',
help='numberofepochstotrain(default:10)')
parser.add_argument('--lr',type=float,default=0.01,metavar='LR',
help='learningrate(default:0.01)')
parser.add_argument('--momentum',type=float,default=0.5,metavar='M',
help='SGDmomentum(default:0.5)')
parser.add_argument('--no-cuda',action='store_true',default=False,
help='disablesCUDAtraining')
parser.add_argument('--seed',type=int,default=1,metavar='S',
help='randomseed(default:1)')
parser.add_argument('--log-interval',type=int,default=10,metavar='N',
help='howmanybatchestowaitbeforeloggingtrainingstatus')
args=parser.parse_args()
use_cuda=notargs.no_cudaandtorch.cuda.is_available()
torch.manual_seed(args.seed)
device=torch.device("cuda"ifuse_cudaelse"cpu")
kwargs={'num_workers':1,'pin_memory':True}ifuse_cudaelse{}
train_loader=torch.utils.data.DataLoader(
datasets.MNIST('../data',train=True,download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,),(0.3081,))
])),
batch_size=args.batch_size,shuffle=True,**kwargs)
test_loader=torch.utils.data.DataLoader(
datasets.MNIST('../data',train=False,transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,),(0.3081,))
])),
batch_size=args.test_batch_size,shuffle=True,**kwargs)
model=Net().to(device)
optimizer=optim.SGD(model.parameters(),lr=args.lr,momentum=args.momentum)
forepochinrange(1,args.epochs+1):
train(args,model,device,train_loader,optimizer,epoch)
test(args,model,device,test_loader)
if__name__=='__main__':
main()
以上这篇pytorch实现mnist分类的示例讲解就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持毛票票。
声明：本文内容来源于网络，版权归原作者所有，内容由互联网用户自发贡献自行上传，本网站不拥有所有权，未作人工编辑处理，也不承担相关法律责任。如果您发现有涉嫌版权的内容，欢迎发送邮件至：czq8825#qq.com（发邮件时，请将#更换为@）进行举报，并提供相关证据，一经查实，本站将立刻删除涉嫌侵权内容。