What are the in_features and out_features supposed to be?
torch.nn.Linear(in_features, out_features, bias=True, device=None, dtype=None)
I have a dataset of [914,19] shape. should my in_features be 914? And I want to predict 5 different values so should my output feature be 5?
class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()
        self.linear1 = nn.Linear(914,512)
        self.linear2 = nn.Linear(512,512)
        self.linear3 = nn.Linear(512,512)
        self.linear4 = nn.Linear(512,5)
        

    def forward(self, x):
        x = F.relu(self.linear1(x))
        x = F.relu(self.linear2(x))
        x = F.relu(self.linear3(x))
        x = self.linear4(x)
        
        return x
NeuralNet = NeuralNetwork()
print(NeuralNet)


Your input data is shaped (914, 19), assuming 914 refers to your batch size here, then the in_features corresponds to 19. This can be read as a tensor containing 914 19-feature-long input vectors.
In this case, the in_features of linear1 would be set to 19.
