Pytorch框架下的Kaggle貓狗識別

kaggle貓狗識別，訓練集為25000張打好標籤的貓狗，測試集為劃分出來的5000張貓和狗的**。

使用的網路為le-net，其結構圖如下

**如下:

import

torch
import
numpy
import
matplotlib.pylab as plt
from torch.autograd import
variable
from torchvision import
transforms,datasets
import
cv2import
torch.nn as nn
from tensorboardx import
summarywriter
print
(torch.cuda.is_**ailable())
print
(torch.cuda.current_device())
print
(torch.cuda.get_device_capability(),torch.cuda.get_device_name())
image_transform =transforms.compose([
transforms.resize(84),
transforms.centercrop(84),
transforms.totensor(),
transforms.normalize([0.5,0.5,0.5],[0.5,0.5,0.5])
])train_dataset = datasets.imagefolder(root =r'
d:\python\project\venv\include\kaggle資料集\train/
',transform=image_transform)
train_loader = torch.utils.data.dataloader(train_dataset,4,true)
test_dataset = datasets.imagefolder(root = r'
d:\python\project\venv\include\kaggle資料集\selftest/
',transform=image_transform)
test_loader = torch.utils.data.dataloader(test_dataset,4,true)
use_dataset = datasets.imagefolder(root = r'
d:\python\project\venv\include\kaggle資料集\use/
',transform=image_transform)
use_loader = torch.utils.data.dataloader(use_dataset,1,true)
class
net(nn.module):
def__init__
(self):
super(net, self).
__init__
() self.conv1 = nn.sequential(nn.conv2d(3,6,5),nn.relu(),nn.maxpool2d(2,2))
self.conv2 = nn.sequential(nn.conv2d(6,16,5),nn.relu(),nn.maxpool2d(2,2))
self.fc1 = nn.sequential(nn.linear(16*18*18,1024),nn.relu())
self.fc2 = nn.sequential(nn.linear(1024,512),nn.relu(),nn.linear(512,2))
defforward(self,input):
x =self.conv1(input)
x =self.conv2(x)
x = x.view(-1,16*18*18)
x =self.fc1(x)
x =self.fc2(x)
x =torch.sigmoid(x)
return
xnet =net()
net.cuda()
net.load_state_dict(torch.load(r
'd:\python\project\venv\include\nural_net/torch_net10(74%)'))
writer = summarywriter('
figure')
opti = torch.optim.sgd(net.parameters(),lr = 0.0005,momentum=0.9)
loss_fun =nn.crossentropyloss()
stepp =0
for epoch in range(5):
run_loss = 0.0
for i,data in
enumerate(train_loader,0):
images,labels =data
images,labels =images.cuda(),labels.cuda()
images,labels =variable(images),variable(labels)
out =net(images)
loss =loss_fun(out,labels)
opti.zero_grad()
loss.backward()
opti.step()
print
(i) 
print
(loss.item())
run_loss +=loss.item()
if i % 10 == 9:
stepp += 10writer.add_scalar(
'train_loss
',loss.item(),stepp)
if i%2000 == 1999:
print('
[%d %d]: %.4f
'%(epoch+1,i+1,run_loss))
run_loss = 0.0
print('
finish train')
torch.s**e(net.state_dict(),r
'd:\python\project\venv\include\nural_net/torch_net0')
print('
s**e finish')
net.eval()
correct = 0.0total =0
for data in
test_loader:
images, labels =data
images, labels =images.cuda(), labels.cuda()
images, labels =variable(images), variable(labels)
out =net(images)
_,prediction = torch.max(out.data,1)
print('
prediction 
',prediction)
print('
labels 
',labels)
total +=labels.size(0)
correct += ((prediction ==labels).data).sum()
print('
the acc is %.4f
'%(correct*100/total))

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