Error while running # StochasticGradient: training. Torch, lua

Question

I'm trying to train a feed forward neural network for the first time in torch. Here's my dataset: http://ocw.mit.edu/courses/sloan-school-of-management/15-097-prediction-machine-learning-and-statistics-spring-2012/datasets/transfusion.csv

Here's the code (based, http://mdtux89.github.io/2015/12/11/torch-tutorial.html):

require 'nn'
mlp = nn.Sequential() 
inputSize = 4
hiddenLayer1Size = 4
hiddenLayer2Size = 4

mlp:add(nn.Linear(inputSize,hiddenLayer1Size)) -- row, coulm
mlp:add(nn.Tanh())
mlp:add(nn.Linear(hiddenLayer1Size,hiddenLayer2Size))
mlp:add(nn.Tanh())

nclasses = 1

mlp:add(nn.Linear(hiddenLayer2Size,nclasses))
mlp:add(nn.LogSoftMax())

output = mlp:forward(torch.rand(1,4))
print(output)

-- TRAINING using inbuilt stochastic gradient descent, 2 params: network, criterian fun. --
LRate = 0.1

criterion = nn.ClassNLLCriterion() 
trainer = nn.StochasticGradient(mlp, criterion)
trainer.learningRate = LRate

function string:splitAtCommas()
  local sep, values = ",", {}
  local pattern = string.format("([^%s]+)", sep)
  self:gsub(pattern, function(c) values[#values+1] = c end)
  return values
end

function loadData(dataFile)
  local dataset,i = {},0
  for line in io.lines(dataFile) do
    local values = line:splitAtCommas()
    local y = torch.Tensor(1)
    y[1] = values[#values] -- the target class is the last number in the line
    values[#values] = nil
    local x = torch.Tensor(values) -- the input data is all the other numbers
    dataset[i] = {x, y}
    i = i + 1
  end
  function dataset:size() return (i - 1) end -- the requirement mentioned
  return dataset
end

dataset = loadData("transfusion.csv")

trainer:train(dataset)

Here's the error report:

# StochasticGradient: training  
/Users/drdre/torch/install/share/lua/5.1/nn/THNN.lua:109: Assertion `cur_target >= 0 && cur_target < n_classes' failed.  at /Users/drdre/torch/extra/nn/lib/THNN/generic/ClassNLLCriterion.c:38
stack traceback:
    [C]: in function 'v'
    /Users/drdre/torch/install/share/lua/5.1/nn/THNN.lua:109: in function 'ClassNLLCriterion_updateOutput'
    ...dre/torch/install/share/lua/5.1/nn/ClassNLLCriterion.lua:41: in function 'forward'
    ...re/torch/install/share/lua/5.1/nn/StochasticGradient.lua:35: in function 'f'
    [string "local f = function() return trainer:train(dat..."]:1: in main chunk
    [C]: in function 'xpcall'
    /Users/drdre/torch/install/share/lua/5.1/itorch/main.lua:209: in function </Users/drdre/torch/install/share/lua/5.1/itorch/main.lua:173>
    /Users/drdre/torch/install/share/lua/5.1/lzmq/poller.lua:75: in function 'poll'
    /Users/drdre/torch/install/share/lua/5.1/lzmq/impl/loop.lua:307: in function 'poll'
    /Users/drdre/torch/install/share/lua/5.1/lzmq/impl/loop.lua:325: in function 'sleep_ex'
    /Users/drdre/torch/install/share/lua/5.1/lzmq/impl/loop.lua:370: in function 'start'
    /Users/drdre/torch/install/share/lua/5.1/itorch/main.lua:381: in main chunk
    [C]: in function 'require'
    (command line):1: in main chunk
    [C]: at 0x0105e4cd10

Answer 1

Use nclasses = 2 and y[1] = values[#values] + 1. See the doc:

a desired output y (an integer 1 to n, in this case n = 2 classes)