我正在尝试在torch7中实现神经网络示例。我的数据以这种格式[19 cols x 10000行]存储在文本文件中：
11 38 20 44 11 38 21 44 29 42 30 44 34 38 6 34 45 42 1
11 38 20 44 11 38 27 44 31 42 18 44 34 38 6 34 45 42 2
6 42 20 44 11 38 21 44 29 42 30 44 34 38 6 34 45 42 3
...
34 40 20 44 11 38 21 44 29 38 30 38 34 45 38 0 0 0 100
...
在最后一列中有标签[100个标签]。
使用此代码：
require 'nn'
-- ======================================= --
-- Start loading data
-- ======================================= --
print '[INFO] Loading data..'
dataset = {}
function dataset:size() return 10000 end
local lin = 1
train_file = 'train_10000.t7'
local file = io.open(train_file)
if file then
for line in file:lines() do
local input = torch.Tensor(18);
local output = torch.Tensor(1);
local X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, Y = unpack(line:split(" "))
input = {X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18}
output = Y
dataset[lin] = {input, output}
lin = lin +1
end
end
-- ======================================= --
-- Create NN
-- ======================================= --
print '[INFO] Creating NN..'
mlp = nn.Sequential(); -- make a multi-layer perceptron
inputs = 18; outputs = 1; HUs = 25; -- parameters
mlp:add(nn.Linear(inputs, HUs))
mlp:add(nn.Tanh())
mlp:add(nn.Linear(HUs, outputs))
-- ======================================= --
-- MSE and Training
-- ======================================= --
print '[INFO] MSE and train NN..'
criterion = nn.MSECriterion()
trainer = nn.StochasticGradient(mlp, criterion)
trainer.learningRate = 0.01
trainer:train(dataset)
我收到此错误消息：
# StochasticGradient: training
/home/yosaikan/torch/install/share/lua/5.1/nn/Linear.lua:34: attempt to call method 'dim' (a nil value)
stack traceback:
/home/yosaikan/torch/install/share/lua/5.1/nn/Linear.lua:34: in function 'updateOutput'
...e/yosaikan/torch/install/share/lua/5.1/nn/Sequential.lua:25: in function 'forward'
...an/torch/install/share/lua/5.1/nn/StochasticGradient.lua:35: in function 'train'
iparseSchemeConversion.lua:45: in main chunk
[C]: in function 'f'
[string "local f = function() return dofile 'iparseSch..."]:1: in main chunk
[C]: in function 'xpcall'
/home/yosaikan/torch/install/share/lua/5.1/itorch/main.lua:174: in function </home/yosaikan/torch/install/share/lua/5.1/itorch/main.lua:140>
/home/yosaikan/torch/install/share/lua/5.1/lzmq/poller.lua:75: in function 'poll'
.../yosaikan/torch/install/share/lua/5.1/lzmq/impl/loop.lua:307: in function 'poll'
.../yosaikan/torch/install/share/lua/5.1/lzmq/impl/loop.lua:325: in function 'sleep_ex'
.../yosaikan/torch/install/share/lua/5.1/lzmq/impl/loop.lua:370: in function 'start'
/home/yosaikan/torch/install/share/lua/5.1/itorch/main.lua:341: in main chunk
[C]: in function 'require'
(command line):1: in main chunk
[C]: at 0x00405980
你能帮我么 ？
谢谢。
我收到此错误消息[...]您能帮我吗？
在您的数据集中input，output应该为Tensor-s（这input是一个普通的Lua表，这就是为什么您会收到此错误的原因，即没有dim方法）。
为了简化数据加载，我建议您使用csv解析器，例如，可以使用csv2tensor将数据加载到Tensor。
首先，请确保将标头（作为第一行）添加到文件中，如下所示：
x001,x002,x003,x004,x005,x006,x007,x008,x009,x010,x011,x012,x013,x014,x015,x016,x017,x018,label
然后按以下方式加载数据：
local csv2tensor = require 'csv2tensor'
local inputs = csv2tensor.load("data.csv", {exclude={"label"}})
local labels = csv2tensor.load("data.csv", {include={"label"}})
local dataset = {}
for i=1,inputs:size(1) do
dataset[i] = {inputs[i], torch.Tensor{labels[i]}}
end
dataset.size = function(self)
return inputs:size(1)
end
并使用此数据集进行训练：
-- ...
trainer:train(dataset)
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