I’m using CNNs for age and gender detection. As I’m currently training my model, my loss seems to be staying the same and I’m not sure why. Appreciate any help!
class neural_network(nn.Module):
    def __init__(self):
        super().__init__()
            
        self.conv1 = nn.Conv2d(1, 32, 5)
        self.conv2 = nn.Conv2d(32, 64, 5)
        self.conv3 = nn.Conv2d(64, 128, 5)
#         self.conv4 = nn.Conv2d(128, 256, 3)
        self.flatten = nn.Flatten()
        self.output = nn.Linear(512, 2)

    def forward(self, x):
        x = x.view(-1,1,48,48)
        x = F.max_pool2d(F.relu(self.conv1(x)), (2,2))
        x = F.max_pool2d(F.relu(self.conv2(x)), (2,2))
        x = F.max_pool2d(F.relu(self.conv3(x)), (2,2))
#         x = F.max_pool2d(F.relu(self.conv4(x)), (2,2))
        
        x = self.flatten(x)
        x = self.output(x)
        return F.softmax(x, dim=1)

neural_net = neural_network()

import torch.optim as optim

optimizer = optim.Adam(neural_net.parameters(), lr=1e-4, weight_decay = 5e-5)
loss_function = nn.NLLLoss()

EPOCHS = 3
BATCH_SIZE = 10
y = gender_labels
y = y.type(torch.LongTensor)

correct = 0
predictions = []
correct_labels = []
total = 0

for epoch in range(EPOCHS):
    for i in tqdm(range(0, len(X), BATCH_SIZE)):
        
        optimizer.zero_grad()
        train_X = X[i:i+BATCH_SIZE]
        train_y = y[i:i+BATCH_SIZE]
        output = neural_net(train_X)
        loss = loss_function(output, train_y)
        loss.backward()
        optimizer.step()
        
        with torch.no_grad():
            for idx, i in enumerate(output):
                if torch.argmax(i) == train_y[idx]:
                    correct += 1
                predictions.append(torch.argmax(i).tolist())
                correct_labels.append(train_y[idx].tolist())
                total += 1
    print(loss)

print("Accuracy: ", round(correct/total, 3))

And this is my output:
100%|██████████| 2368/2368 [02:53<00:00, 13.67it/s]
  0%|          | 2/2368 [00:00<02:36, 15.08it/s]
tensor(-1.0000, grad_fn=<NllLossBackward>)
100%|██████████| 2368/2368 [03:08<00:00, 12.54it/s]
  0%|          | 2/2368 [00:00<02:52, 13.74it/s]
tensor(-1.0000, grad_fn=<NllLossBackward>)
100%|██████████| 2368/2368 [03:38<00:00, 10.83it/s]
tensor(-1.0000, grad_fn=<NllLossBackward>)
Accuracy:  0.457

Once again, thanks for the help!