我正在尝试在 Torch 中创建一个自动编码器神经网络，但是当我尝试运行训练阶段时出现错误。
这是我在 data.csv 中的输入数据：
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
这是我的代码
-- 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
这是我的错误：
-- 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
你知道我为什么会出现这个错误吗？