Hi to all,

I am a beginner and trying to learn PyTorch, I am facing that my training overfitting and I have tried once the L2 by applying it with optimizer SGD weight_decay = 0.4 and the I used Dropout with my model and I still face the accuracy too bad I don’t know why its bad.

Once I found that should apply the weights of each class. can anyone help me for that.?

Note I have 6 classes which are (0, 1, 2, 3, 6, 7) and

class 0: 14626 samples

class 1: 13010 samples

class 2: 15902 samples

class 3: 13337 samples

class 6: 15079 samples

class 7: 16403 samples

how can I apply the weights?

this is my training code

```
model = Model()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum = 0.9, weight_decay = 0.4)
criterion = torch.nn.CrossEntropyLoss()
num_epochs = 25
model.train()
for epoch in range(num_epochs):
correct = 0
for inputs,labels in train_loader:
inputs = inputs.float()
labels = labels.long()
# Feed Forward
outputs = model(inputs)
# Loss Calculation
loss_train = criterion(outputs, labels)
L1_lambda = 0.5
for name, param,*_ in model.parameters():# Applying for L1 Regularization
if 'weight' in name:
L1_1 = Variable(param, requires_grad=True)
L1_2 = torch.norm(L1_1, 6)
L1_3 = L1_lambda * L1_2
loss_train = loss_train + L1_3
# Clear the gradient buffer (we don't want to accumulate gradients)
optimizer.zero_grad()
# Backpropagation
loss_train.backward()
# Weight Update: w <-- w - lr * gradient
optimizer.step()
#Accuracy
_, predicted = torch.max(outputs, 1)
labels1 = labels.long()
correct += (predicted == labels1).float().sum()
accuracy = (100 * correct / len(train_dataset))
# Print statistics
print("Epoch {}/{}, Loss: {:.3f}, Accuracy: {:.3f}".format(epoch+1,num_epochs, loss_train, accuracy))
```

Thanks in advance.