Pytorch入门之mnist分类实例
本文实例为大家分享了Pytorch入门之mnist分类的具体代码，供大家参考，具体内容如下
#!/usr/bin/envpython
#-*-coding:utf-8-*-
__author__='denny'
__time__='2017-9-99:03'
importtorch
importtorchvision
fromtorch.autogradimportVariable
importtorch.utils.data.dataloaderasData
train_data=torchvision.datasets.MNIST(
'./mnist',train=True,transform=torchvision.transforms.ToTensor(),download=True
)
test_data=torchvision.datasets.MNIST(
'./mnist',train=False,transform=torchvision.transforms.ToTensor()
)
print("train_data:",train_data.train_data.size())
print("train_labels:",train_data.train_labels.size())
print("test_data:",test_data.test_data.size())
train_loader=Data.DataLoader(dataset=train_data,batch_size=64,shuffle=True)
test_loader=Data.DataLoader(dataset=test_data,batch_size=64)
classNet(torch.nn.Module):
def__init__(self):
super(Net,self).__init__()
self.conv1=torch.nn.Sequential(
torch.nn.Conv2d(1,32,3,1,1),
torch.nn.ReLU(),
torch.nn.MaxPool2d(2))
self.conv2=torch.nn.Sequential(
torch.nn.Conv2d(32,64,3,1,1),
torch.nn.ReLU(),
torch.nn.MaxPool2d(2)
)
self.conv3=torch.nn.Sequential(
torch.nn.Conv2d(64,64,3,1,1),
torch.nn.ReLU(),
torch.nn.MaxPool2d(2)
)
self.dense=torch.nn.Sequential(
torch.nn.Linear(64*3*3,128),
torch.nn.ReLU(),
torch.nn.Linear(128,10)
)
defforward(self,x):
conv1_out=self.conv1(x)
conv2_out=self.conv2(conv1_out)
conv3_out=self.conv3(conv2_out)
res=conv3_out.view(conv3_out.size(0),-1)
out=self.dense(res)
returnout
model=Net()
print(model)
optimizer=torch.optim.Adam(model.parameters())
loss_func=torch.nn.CrossEntropyLoss()
forepochinrange(10):
print('epoch{}'.format(epoch+1))
#training-----------------------------
train_loss=0.
train_acc=0.
forbatch_x,batch_yintrain_loader:
batch_x,batch_y=Variable(batch_x),Variable(batch_y)
out=model(batch_x)
loss=loss_func(out,batch_y)
train_loss+=loss.data[0]
pred=torch.max(out,1)[1]
train_correct=(pred==batch_y).sum()
train_acc+=train_correct.data[0]
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('TrainLoss:{:.6f},Acc:{:.6f}'.format(train_loss/(len(
train_data)),train_acc/(len(train_data))))
#evaluation--------------------------------
model.eval()
eval_loss=0.
eval_acc=0.
forbatch_x,batch_yintest_loader:
batch_x,batch_y=Variable(batch_x,volatile=True),Variable(batch_y,volatile=True)
out=model(batch_x)
loss=loss_func(out,batch_y)
eval_loss+=loss.data[0]
pred=torch.max(out,1)[1]
num_correct=(pred==batch_y).sum()
eval_acc+=num_correct.data[0]
print('TestLoss:{:.6f},Acc:{:.6f}'.format(eval_loss/(len(
test_data)),eval_acc/(len(test_data))))
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持毛票票。