Hello,
When running multi-class classification, I have to set the number of classes as n+1, as setting the number of classes as n returns error.
I attempted to modify CrossEntropyLoss as per instructions on this post 4, but I must have done something wrong.
I also read this post 1 , but I could not find out a solution.
I have 10 variables split as (X_train,X_test) and 1 target variable split as (Y_train, Y_test). My target/label is a 3 class (1,2,3)
Here’s the code:
class NeuralNet(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(NeuralNet, self).__init__()
        self.layer1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.layer2 = nn.Linear(hidden_size, num_classes)
        
    def forward(self, x):
        out = self.layer1(x)
        out = self.relu(out)
        out = self.layer2(out)
        return out

tr_latent_X = data_utils.TensorDataset(train_x, train_y)
te_latent_X = data_utils.TensorDataset(test_x, test_y)

x_train_label = Y_train
x_test_label = y_test

train_loader_X = torch.utils.data.DataLoader(dataset=tr_latent_X,
                                             batch_size=batch_size,
                                             shuffle=False)

test_loader_X = torch.utils.data.DataLoader(dataset=te_latent_X,
                                             batch_size=batch_size,
                                             shuffle=False)

If I set the number of dimensions as 3(instead of 4)  here later I obtain the error:
vae = NeuralNet(10,2,3)


loss_function = nn.CrossEntropyLoss(reduction='mean',ignore_index=-1)

optimizer = torch.optim.SGD(vae.parameters(), lr = 0.01)

def train(epoch):
    vae.train()

    train_loss = 0

    for batch_idx, (data,label) in enumerate(train_loader_X):
        
        optimizer.zero_grad()

        out = vae(data)
        loss = loss_function(out, label)

        loss.backward()
        train_loss += loss.item()
        optimizer.step()

        if batch_idx % 5000 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader_X.dataset),
                100. * batch_idx / len(train_loader_X), loss.item() / len(data)))
    print('====> Epoch: {} Average loss: {:.4f}'.format(epoch, train_loss / len(train_loader_X.dataset)))

for epoch in range(1, 10):
    train(epoch)

This is the error I obtained (last part of the long sequenece)
4 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
   1836                          .format(input.size(0), target.size(0)))
   1837     if dim == 2:
-> 1838         ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
   1839     elif dim == 4:
   1840         ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)

RuntimeError: Assertion `cur_target >= 0 && cur_target < n_classes' failed.  at /pytorch/aten/src/THNN/generic/ClassNLLCriterion.c:97

If instead of setting 3 classes, I set 4, then it runs well:
vae = NeuralNet(10,2,4)
loss_function = nn.CrossEntropyLoss(reduction='mean',ignore_index=-1)
optimizer = torch.optim.SGD(vae.parameters(), lr = 0.01)

If anyone could shed some light on this, I would be very grateful.
Sincerely,