Using Torch's ClassNLLCriterion - neural-network

Question

I am currently using Torch and just trying to get a simple neural network program running. Each one of my inputs has 3 attributes and the output is supposed to be a classification between the numbers 1 and 7. I've extracted my data from a CSV file and have put it into 2 Tensors (1 with the inputs and 1 with the outputs). The data is in this format.
**Data**
1914 1993 2386
1909 1990 2300
.....
1912 1989 2200
[torch.DoubleTensor of size 99999x3]
**Class**
1
1
2
.....
7
[torch.DoubleTensor of size 99999]
For the model I'm using to train the network, I simply have
model = nn.Sequential()
model:add(nn.Linear(3, 7))
model:add(nn.LogSoftMax())
criterion = nn.ClassNLLCriterion()
And this is the code I have to train the network
for int i = 1, 10 do
prediction = model:forward(data)
loss = criterion:forward(prediction, class)
model:zeroGradParameters()
grad = criterion:backward(prediction, class)
model:backward(data, grad)
model:updateParameters(.1)
end
In my test data tensor, I have formatted it in the same way as I formatted the test data (Tensor of 99999x3). I want the program to give me a prediction of what the classification would be when I run this line.
print (model:forward(test_data))
However, I am getting negative numbers (which shouldn't happen with ClassNLLCriterion?) and the sums of the probabilities are not adding to 0. My doubt is that I have either not formatted the data correctly or that I wasn't able to perform the training process correctly. If anyone could help me figure out what the issue is, I'd be very grateful.
Thank you!

Answer 1

The reason why you cannot see the prediction yields on the layer model:add(nn.LogSoftMax()) which implements the log function, that's why you have negative values (they are not probabilities). As an example, to get the probabilities back you should do:
model = nn.Sequential()
model:add(nn.Linear(3, 7));
model:add(nn.LogSoftMax());
criterion = nn.ClassNLLCriterion();
data = torch.Tensor{1914, 1993 , 2386}
print (model:forward(data):exp())
>> 0.0000
0.0000
1.0000
0.0000
0.0000
0.0000
0.0000 [torch.DoubleTensor of size 7]
Sorry for the late answer.

Answer 2

Here is what I currently use which maybe the wrong way of using classnllcriterion but atleast it will get you somewhere to understanding it.
Make the targets to be either
(7,1,1,1,1,1,1) <--First class representation
.......
(1,1,1,1,1,1,7) <--Last class representation
or
(1,1,1,1,1,1,1) <--First class representation
.......
(7,7,7,7,7,7,7) <--Last class representation
I figured it's much easier to train the last representation as targets, but I have a feeling we should use the first one instead.
EDIT: I've just found out that classnllcriterion only accepts a scalars as targets, hence using the above is wrong!
You should instead use either
1 .. 7 as target values, either just 1 or just 7.
That's