Fehler beim Ausführen von # StochasticGradient: Training. Torch, lua

Question

Ich versuche, ein Feed-Forward-Netzwerk zum ersten Mal in Fackel zu trainieren. Hier ist meine Daten-Set: http://ocw.mit.edu/courses/sloan-school-of-management/15-097-prediction-machine-learning-and-statistics-spring-2012/datasets/transfusion.csv

Hier ist der Code (basierend, 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) 

Hier ist der Fehlerbericht:

# 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

Verwenden nclasses = 2 und y[1] = values[#values] + 1. Siehe die doc:

eine gewünschte Ausgangs y (eine ganze Zahl 1-n, in diesem Fall n = 2 Klassen)