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我想从torch7运行一个例子，只是为了遇到这个错误。尝试索引本地'f'（一个零值）Lua/Torch7
[email protected]:~/Downloads/tutorials-master/2_supervised$ luajit doall.lua
==> processing options
==> executing all
==> downloading dataset
==> using regular, full training data
==> loading dataset
==> preprocessing data
==> preprocessing data: colorspace RGB -> YUV
==> preprocessing data: normalize each feature (channel) globally
==> preprocessing data: normalize all three channels locally
==> verify statistics
training data, y-channel, mean: 0.00067706172257129
training data, y-channel, standard deviation: 0.39473240322794
test data, y-channel, mean: -0.0010822884348063
test data, y-channel, standard deviation: 0.38091408093043
training data, u-channel, mean: -0.0048219975630079
training data, u-channel, standard deviation: 0.29768662619471
test data, u-channel, mean: -0.0030795217110624
test data, u-channel, standard deviation: 0.22289780235542
training data, v-channel, mean: 0.0036312269637064
training data, v-channel, standard deviation: 0.25405592463897
test data, v-channel, mean: 0.0033847450016769
test data, v-channel, standard deviation: 0.20362829592977
==> visualizing data
==> define parameters
==> construct model
==> here is the model:
nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> (11) -> (12) -> output]
(1): nn.SpatialConvolutionMM(3 -> 64, 5x5)
(2): nn.Tanh
(3): nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> output]
(1): nn.Square
(2): nn.SpatialAveragePooling(2,2,2,2)
(3): nn.MulConstant
(4): nn.Sqrt
}
(4): nn.SpatialSubtractiveNormalization
(5): nn.SpatialConvolutionMM(64 -> 64, 5x5)
(6): nn.Tanh
(7): nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> output]
(1): nn.Square
(2): nn.SpatialAveragePooling(2,2,2,2)
(3): nn.MulConstant
(4): nn.Sqrt
}
(8): nn.SpatialSubtractiveNormalization
(9): nn.Reshape(1600)
(10): nn.Linear(1600 -> 128)
(11): nn.Tanh
(12): nn.Linear(128 -> 10)
}
==> define loss
==> here is the loss function:
nn.ClassNLLCriterion
==> defining some tools
luajit: /home/sandesh/torch/install/share/lua/5.1/sys/init.lua:38: attempt to index local 'f' (a nil value)
stack traceback:
/home/sandesh/torch/install/share/lua/5.1/sys/init.lua:38: in function 'execute'
/home/sandesh/torch/install/share/lua/5.1/sys/init.lua:71: in function 'uname'
/home/sandesh/torch/install/share/lua/5.1/optim/Logger.lua:38: in function '__init'
/home/sandesh/torch/install/share/lua/5.1/torch/init.lua:91: in function
[C]: in function 'Logger'
4_train.lua:60: in main chunk
[C]: in function 'dofile'
doall.lua:70: in main chunk
[C]: at 0x00406670
我没有改变任何代码在任何LUA文件...
这是4_train.lua文件
----------------------------------------------------------------------
-- This script demonstrates how to define a training procedure,
-- irrespective of the model/loss functions chosen.
--
-- It shows how to:
-- + construct mini-batches on the fly
-- + define a closure to estimate (a noisy) loss
-- function, as well as its derivatives wrt the parameters of the
-- model to be trained
-- + optimize the function, according to several optmization
-- methods: SGD, L-BFGS.
--
-- Clement Farabet
----------------------------------------------------------------------
require 'torch' -- torch
require 'xlua' -- xlua provides useful tools, like progress bars
require 'optim' -- an optimization package, for online and batch methods
----------------------------------------------------------------------
-- parse command line arguments
if not opt then
print '==> processing options'
cmd = torch.CmdLine()
cmd:text()
cmd:text('SVHN Training/Optimization')
cmd:text()
cmd:text('Options:')
cmd:option('-save', 'results', 'subdirectory to save/log experiments in')
cmd:option('-visualize', false, 'visualize input data and weights during training')
cmd:option('-plot', false, 'live plot')
cmd:option('-optimization', 'SGD', 'optimization method: SGD | ASGD | CG | LBFGS')
cmd:option('-learningRate', 1e-3, 'learning rate at t=0')
cmd:option('-batchSize', 1, 'mini-batch size (1 = pure stochastic)')
cmd:option('-weightDecay', 0, 'weight decay (SGD only)')
cmd:option('-momentum', 0, 'momentum (SGD only)')
cmd:option('-t0', 1, 'start averaging at t0 (ASGD only), in nb of epochs')
cmd:option('-maxIter', 2, 'maximum nb of iterations for CG and LBFGS')
cmd:text()
opt = cmd:parse(arg or {})
end
----------------------------------------------------------------------
-- CUDA?
if opt.type == 'cuda' then
model:cuda()
criterion:cuda()
end
----------------------------------------------------------------------
print '==> defining some tools'
-- classes
classes = {'1','2','3','4','5','6','7','8','9','0'}
-- This matrix records the current confusion across classes
confusion = optim.ConfusionMatrix(classes)
-- Log results to files
trainLogger = optim.Logger(paths.concat(opt.save, 'train.log'))
testLogger = optim.Logger(paths.concat(opt.save, 'test.log'))
-- Retrieve parameters and gradients:
-- this extracts and flattens all the trainable parameters of the mode
-- into a 1-dim vector
if model then
parameters,gradParameters = model:getParameters()
end
----------------------------------------------------------------------
print '==> configuring optimizer'
if opt.optimization == 'CG' then
optimState = {
maxIter = opt.maxIter
}
optimMethod = optim.cg
elseif opt.optimization == 'LBFGS' then
optimState = {
learningRate = opt.learningRate,
maxIter = opt.maxIter,
nCorrection = 10
}
optimMethod = optim.lbfgs
elseif opt.optimization == 'SGD' then
optimState = {
learningRate = opt.learningRate,
weightDecay = opt.weightDecay,
momentum = opt.momentum,
learningRateDecay = 1e-7
}
optimMethod = optim.sgd
elseif opt.optimization == 'ASGD' then
optimState = {
eta0 = opt.learningRate,
t0 = trsize * opt.t0
}
optimMethod = optim.asgd
else
error('unknown optimization method')
end
----------------------------------------------------------------------
print '==> defining training procedure'
function train()
-- epoch tracker
epoch = epoch or 1
-- local vars
local time = sys.clock()
-- set model to training mode (for modules that differ in training and testing, like Dropout)
model:training()
-- shuffle at each epoch
shuffle = torch.randperm(trsize)
-- do one epoch
print('==> doing epoch on training data:')
print("==> online epoch # " .. epoch .. ' [batchSize = ' .. opt.batchSize .. ']')
for t = 1,trainData:size(),opt.batchSize do
-- disp progress
xlua.progress(t, trainData:size())
-- create mini batch
local inputs = {}
local targets = {}
for i = t,math.min(t+opt.batchSize-1,trainData:size()) do
-- load new sample
local input = trainData.data[shuffle[i]]
local target = trainData.labels[shuffle[i]]
if opt.type == 'double' then input = input:double()
elseif opt.type == 'cuda' then input = input:cuda() end
table.insert(inputs, input)
table.insert(targets, target)
end
-- create closure to evaluate f(X) and df/dX
local feval = function(x)
-- get new parameters
if x ~= parameters then
parameters:copy(x)
end
-- reset gradients
gradParameters:zero()
-- f is the average of all criterions
local f = 0
-- evaluate function for complete mini batch
for i = 1,#inputs do
-- estimate f
local output = model:forward(inputs[i])
local err = criterion:forward(output, targets[i])
f = f + err
-- estimate df/dW
local df_do = criterion:backward(output, targets[i])
model:backward(inputs[i], df_do)
-- update confusion
confusion:add(output, targets[i])
end
-- normalize gradients and f(X)
gradParameters:div(#inputs)
f = f/#inputs
-- return f and df/dX
return f,gradParameters
end
-- optimize on current mini-batch
if optimMethod == optim.asgd then
_,_,average = optimMethod(feval, parameters, optimState)
else
optimMethod(feval, parameters, optimState)
end
end
-- time taken
time = sys.clock() - time
time = time/trainData:size()
print("\n==> time to learn 1 sample = " .. (time*1000) .. 'ms')
-- print confusion matrix
print(confusion)
-- update logger/plot
trainLogger:add{['% mean class accuracy (train set)'] = confusion.totalValid * 100}
if opt.plot then
trainLogger:style{['% mean class accuracy (train set)'] = '-'}
trainLogger:plot()
end
-- save/log current net
local filename = paths.concat(opt.save, 'model.net')
os.execute('mkdir -p ' .. sys.dirname(filename))
print('==> saving model to '..filename)
torch.save(filename, model)
-- next epoch
confusion:zero()
epoch = epoch + 1
end
这doall.lua
----------------------------------------------------------------------
-- This tutorial shows how to train different models on the street
-- view house number dataset (SVHN),
-- using multiple optimization techniques (SGD, ASGD, CG), and
-- multiple types of models.
--
-- This script demonstrates a classical example of training
-- well-known models (convnet, MLP, logistic regression)
-- on a 10-class classification problem.
--
-- It illustrates several points:
-- 1/ description of the model
-- 2/ choice of a loss function (criterion) to minimize
-- 3/ creation of a dataset as a simple Lua table
-- 4/ description of training and test procedures
--
-- Clement Farabet
----------------------------------------------------------------------
require 'torch'
----------------------------------------------------------------------
print '==> processing options'
cmd = torch.CmdLine()
cmd:text()
cmd:text('SVHN Loss Function')
cmd:text()
cmd:text('Options:')
-- global:
cmd:option('-seed', 1, 'fixed input seed for repeatable experiments')
cmd:option('-threads', 2, 'number of threads')
-- data:
cmd:option('-size', 'full', 'how many samples do we load: small | full | extra')
-- model:
cmd:option('-model', 'convnet', 'type of model to construct: linear | mlp | convnet')
-- loss:
cmd:option('-loss', 'nll', 'type of loss function to minimize: nll | mse | margin')
-- training:
cmd:option('-save', 'results', 'subdirectory to save/log experiments in')
cmd:option('-plot', false, 'live plot')
cmd:option('-optimization', 'SGD', 'optimization method: SGD | ASGD | CG | LBFGS')
cmd:option('-learningRate', 1e-3, 'learning rate at t=0')
cmd:option('-batchSize', 1, 'mini-batch size (1 = pure stochastic)')
cmd:option('-weightDecay', 0, 'weight decay (SGD only)')
cmd:option('-momentum', 0, 'momentum (SGD only)')
cmd:option('-t0', 1, 'start averaging at t0 (ASGD only), in nb of epochs')
cmd:option('-maxIter', 2, 'maximum nb of iterations for CG and LBFGS')
cmd:option('-type', 'double', 'type: double | float | cuda')
cmd:text()
opt = cmd:parse(arg or {})
-- nb of threads and fixed seed (for repeatable experiments)
if opt.type == 'float' then
print('==> switching to floats')
torch.setdefaulttensortype('torch.FloatTensor')
elseif opt.type == 'cuda' then
print('==> switching to CUDA')
require 'cunn'
torch.setdefaulttensortype('torch.FloatTensor')
end
torch.setnumthreads(opt.threads)
torch.manualSeed(opt.seed)
----------------------------------------------------------------------
print '==> executing all'
dofile '1_data.lua'
dofile '2_model.lua'
dofile '3_loss.lua'
dofile '4_train.lua'
dofile '5_test.lua'
----------------------------------------------------------------------
print '==> training!'
while true do
train()
test()
end
的git的链接是https://github.com/torch/tutorials/blob/master/2_supervised/4_train.lua
还有我没有使用CUDA，因为我没有一个GPU
这不是一个调试服务！你不能只是粘贴代码和错误消息，并期望别人做你的工作...... – Piglet