I have a dataset of 21 input features and 1 class label ranging from 0-3.
I’m quite new to PyTorch and I simply don’t see how I can have multiple fully-connected layers with the OH-Encoded target list at the end. Here is what I have so far, after a day of experimentation -
class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()
        self.linear_relu_stack = nn.Sequential(
            nn.Linear(21, 42),
            nn.ReLU(),
            nn.Linear(42, 21),
            nn.ReLU(),
            nn.Linear(21, 10),
            nn.ReLU(),            
            nn.Linear(10, 1),
            nn.ReLU()
        )

    def forward(self, x):
        logits = self.linear_relu_stack(x)
        return logits

If nothing else, I was planning on normalizing the target column as well, but of course that is a very crude idea. I would like to know how I can use one-hot encoded labels as my target.
I’m using the Quickstart tutorial 1 as a reference.