Факел, ошибка в обучении StochasticGradient

Question

Я пытаюсь создать нейронную сеть autoencoder в Torch, но у меня возникла ошибка, когда я пытаюсь запустить фазу обучения.

Вот мои входные данные в data.csv:

chrName-chrStart-chrEnd,gm12878,h1-hesc,hela-s3,hepg2,huvec,k562 
    chr21-10861860-10862010,0.6904761905,0,0,1.2555555556,0.25,1.05 
    chr21-10896160-10896310,0.35,2.4934782609,0.8181818182,1.05,0.75,1.9611111111 
    chr21-10936080-10936230,1.525,0.2,0.525,1.0823529412,0.7409090909,0.85 
    chr21-10972180-10972330,0.7333333333,1,0.67,1.7227272727,0.9571428571,0.8823529412 
    chr21-11059120-11059270,3.7268292683,2.9347826087,1.3833333333,3.328358209,4.35,2.31 
    chr21-11101660-11101810,1.78,5.328125,1.05,0,2.9916666667,1.25 
    chr21-11181780-11181930,2.9542857143,4.3173913043,0.9666666667,2.65,4.772972973,1.01 
    chr21-14911600-14911750,0.7882352941,0,0,0,0.3,1.25 
    chr21-14973280-14973430,0.3,0.65,0,0.4,0.1,0 

Вот мой код

-- see if the file exists 
function file_exists(file) 
    local f = io.open(file, "rb") 
    if f then f:close() end 
    return f ~= nil 
end 

-- get all lines from a file, returns an empty 
-- list/table if the file does not exist 
function lines_from(file) 
    if not file_exists(file) then return {} end 
    lines = {} 
    for line in io.lines(file) do 
    lines[#lines + 1] = line 
    end 
    return lines 
end 


-- Torch program that creates a simple AutoEncoder-style neural network, where output=input 
print('\n\n @ @ @ @ @ @ START @ @ @ @ @ @ @ '); 
print('Torch program that creates a simple AutoEncoder-style neural network, where output=input'); 

require 'os'; 
require 'lfs'; 

file = 'data.csv'; 

print('selected file: ' .. file); 

chromRegions = {} 
cellTypes = {} 
dnaseIsignals = {} 
cr = 0 


chromRegion = {}; 
gm12878 = {}; 
h1hesc = {}; 
helas3 = {}; 
hepg2 = {}; 
huvec = {}; 
k562 = {}; 
dataset = {}; 

i = 0; 
CellTypesNumber = 6 

print('\n-- dataset building --'); 
-- data building 

local file1 = io.open(file) 
for line in file1:lines() do 
    if i >= 1 then 
     chromRegion[i-1], gm12878[i-1], h1hesc[i-1], helas3[i-1], hepg2[i-1], huvec[i-1], k562[i-1] = unpack(line:split(",")) 
    profile = torch.Tensor(CellTypesNumber); 
    profile = {tonumber(gm12878[i-1]), tonumber(h1hesc[i-1]), tonumber(helas3[i-1]), tonumber(hepg2[i-1]), tonumber(huvec[i-1]), tonumber(k562[i-1])} 
    dnaseIsignals[i-1] = profile; 
    profile = null; 
    end 
i = i + 1 
end 

chromRegionsNumber = #chromRegion+1 
print('chromRegionsNumber '..chromRegionsNumber); 

inputs=chromRegionsNumber; 
outputs=inputs; -- 

print(dnaseIsignals[0]) 
print(dnaseIsignals[chromRegionsNumber-1]) 

for i=1,chromRegionsNumber do -- 15 times 

    dataset[i] = {dnaseIsignals[i-1], dnaseIsignals[i-1]} 

    print(dataset[i]); 
end 
function dataset:size() return chromRegionsNumber end -- input dataset hgas 
function dataset:dim() return chromRegionsNumber end -- input dataset hgas 


HUs = 5; 

-- Creation of the neural network 
print('-- creation of the neural network --'); 
require "nn" 
mlp=nn.Sequential(); -- make a multi-layer perceptron 
mlp:add(nn.Linear(inputs,HUs)) -- adds a (input x HUs) layer to the network 
mlp:add(nn.Sigmoid()); 
mlp:add(nn.Linear(HUs,outputs)) -- adds a (HUs x outputs) layer to the network 
mlp:add(nn.Sigmoid()); -- Sigmoid also in output, Peter says 

-- Training 
print('-- training --'); 
criterion = nn.MSECriterion() 
trainer = nn.StochasticGradient(mlp, criterion) 
trainer.learningRate = 0.01 
trainer:train(dataset) --> THE PROBLEMS START HERE! :-(

-- Testing 
print('-- testing --'); 
geneProfileChosen = 0; 
x=torch.Tensor(inputs); -- create a test example Tensor. Test element: first gene 

И вот моя ошибка:

-- training -- 
# StochasticGradient: training 
/home/david/torch/install/bin/luajit: /home/david/torch/install/share/lua/5.1/nn/Linear.lua:34: attempt to call method 'dim' (a nil value) 
stack traceback: 
    /home/david/torch/install/share/lua/5.1/nn/Linear.lua:34: in function 'updateOutput' 
    /home/david/torch/install/share/lua/5.1/nn/Sequential.lua:25: in function 'forward' 
    ...id/torch/install/share/lua/5.1/nn/StochasticGradient.lua:35: in function 'train' 
    autoencoder_chromatin_interactions.lua:103: in main chunk 
    [C]: in function 'dofile' 
    ...avid/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:131: in main chunk 
    [C]: at 0x00405330 

Есть ли у вас какие-либо идеи о том, почему я «Получите эти ошибки?

Answer 1

dataset[i] = {dnaseIsignals[i-1], dnaseIsignals[i-1]} 

Это неправильно. То, что вы хотите дать в качестве входных данных для нейронной сети, - это torch.Tensor, но в этом случае вы инициализируете элементы набора данных как таблицы. Преобразуйте свой вход в тензор, и вы должны быть хорошими.