Im trying to train a simple model on CIFAR10 and im using the Pytorch tutorial with adding just more layers. can someone please help me and tell me why my loss is constant…am i doing something wrong?
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms

#loadind CIFAR10
transform = transforms.Compose(
        [transforms.ToTensor(),
         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)

trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=1,
                                         shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')


# defining Network:


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        
        self.conv11 = nn.Conv2d(3, 64, 3, padding = 1 )
        self.pool1 = nn.AvgPool2d(2, 2)

        self.conv21 = nn.Conv2d(64, 64*2, 3, padding = 1 )
        self.pool2 = nn.AvgPool2d(2, 2)
        
        self.conv31 = nn.Conv2d(64*2, 64*4, 3, padding = 1 )
        self.pool3 = nn.AvgPool2d(2, 2)
        
        self.conv41 = nn.Conv2d(64*4, 64*8, 3, padding = 1 )
        self.pool4 = nn.AvgPool2d(2, 2)

        self.conv51 = nn.Conv2d(64*8, 64*16, 1)
        self.conv52 = nn.Conv2d(64*16, 10, 1)
        self.pool5 = nn.AvgPool2d(2, 2)
        
    def forward(self, x):
        
        x = F.relu(self.conv11(x))
        x = self.pool1(x)

        x = F.relu(self.conv21(x))
        x = self.pool2(x)
        
        x = F.relu(self.conv31(x))
        x = self.pool3(x)

        x = F.relu(self.conv41(x))
        x = self.pool4(x)
        
        x = F.relu(self.conv51(x))
        x = F.relu(self.conv52(x))
        
        x = self.pool5(x)
        
        x = x.view(-1, 10)
        
        return x


net = Net()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
net.to(device)

# =============================================================================
# 3. Define a Loss function and optimizer
# =============================================================================
import torch.optim as optim
import torch.nn as nn
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)
# =============================================================================
# 4. Train the network
# =============================================================================
  
for epoch in range(30):  # loop over the dataset multiple times
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):

        # get the inputs
        inputs, labels = data     
        inputs, labels = inputs.to(device), labels.to(device)

        optimizer.zero_grad()
        # forward + backward + optimize
        outputs = net(inputs)      
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0
            
    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            images, labels = images.to(device), labels.to(device)
            outputs = net(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    
    print('Accuracy of the network on the 10000 test images: %d %%' % (
        100 * correct / total))
    

The output is so weird:
Files already downloaded and verified
Files already downloaded and verified
cuda:0

[1,  2000] loss: 2.303
[1,  4000] loss: 2.303
[1,  6000] loss: 2.303
[1,  8000] loss: 2.303
[1, 10000] loss: 2.303