I built a cnn module according to the 60-minutes tutorial:
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        self.conv1=nn.Conv2d(3,6,5)
        self.pool1=nn.MaxPool2d(2,2)
        self.conv2=nn.Conv2d(6,16,5)
        self.pool2=nn.MaxPool2d(2,2)
        self.fc1=nn.Linear(16*5*5,200)
        self.fc2=nn.Linear(200,120)
        self.fc3=nn.Linear(120,80)
    
    def forward(self,x):
        x=self.pool1(F.relu(self.conv1(x)))
        x=self.pool2(F.relu(self.conv2(x)))
        x=x.view(-1,16*5*5)
        x=F.relu(self.fc1(x))
        x=F.relu(self.fc2(x))
        x=self.fc3(x)
        return x
net=Net()
print(net)

Then I trained it:
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable

criterion=nn.CrossEntropyLoss()
optimizer=optim.SGD(net.parameters(),lr=0.001,momentum=0.9)
for epoch in range(2):
    runningLoss=0.0
    
    for i,inputs in enumerate(imageTensor):
        inputs=inputs.float()
        inputs=Variable(inputs)
        labels=classIdList[i:i+4]
        labels=np.array(labels)
        labels=torch.from_numpy(labels)
        labels=Variable(labels)
        
        optimizer.zero_grad()
        outputs=net(inputs)
        loss=criterion(outputs,labels)
        loss.backward()
        optimizer.step()
        
        runningLoss+=loss.data[0]
        if i%200==199:
            print('[%d,%5d] %.3f'%(epoch+1,i+1,runningLoss/200))
            runningLoss=0.0
print('Finished Training!')

But the loss rate didn’t go down as expected:

Then I test the module on validation images:
from torch.autograd import Variable
correct=0
total=0
for i,image in enumerate(imageTensor):
    image=image.float()
    image=image.unsqueeze(0)
    image=Variable(image)
    label=classIdList[i]
    output=net(image)
    _,predicted=torch.max(output.data,1)
    predicted=predicted.numpy()[0]
    print('predicted:',predicted)
    print('label:',label)
    correct+=(predicted==label)
    total+=1
print(correct,total)
print('Accuracy on validation is %.3f'%(correct/total))

But the module always gives the same label 19 for different image:

Any help will be appreciated!