hughperkins
commented
7 years ago i dont remember this symbol. might not be implemented. can you grep through the cltorch sourcecode, and see if it exists? (grep -r stdall *)
On 10 April 2017 07:10:51 CEST, TylerLindell notifications@github.com wrote:
i'm getting the following error when using
trainset.data[{ {}, {i}, {}, {} }]:div(stdv[i]) -- std scalingdyld: lazy symbol binding failed: Symbol not found: _THClTensor_stdall Referenced from: ~/torch-cl/install/lib/lua/5.1/libcltorch.so Expected in: flat namespace dyld: Symbol not found: _THClTensor_stdall Referenced from: ~/torch-cl/install/lib/lua/5.1/libcltorch.so Expected in: flat namespace Trace/BPT trap: 5the code i'm using is here:
--///////////////////////////////////////////////////////////////////////////// require 'torch' require 'nn' --///////////////////////////////////////////////////////////////////////////// require 'cltorch' require 'clnn' -- require 'cunn'; --///////////////////////////////////////////////////////////////////////////// require 'paths' if (not paths.filep("cifar10torchsmall.zip")) then os.execute('wget -c https://s3.amazonaws.com/torch7/data/cifar10torchsmall.zip') os.execute('unzip cifar10torchsmall.zip') end trainset = torch.load('cifar10-train.t7') testset = torch.load('cifar10-test.t7') classes = {'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'} --///////////////////////////////////////////////////////////////////////////// print(trainset) print(#trainset.data) --///////////////////////////////////////////////////////////////////////////// -- itorch.image(trainset.data[100]) -- display the 100-th image in dataset print(classes[trainset.label[100]]) --///////////////////////////////////////////////////////////////////////////// -- ignore setmetatable for now, it is a feature beyond the scope of this tutorial. It sets the index operator. setmetatable(trainset, {__index = function(t, i) return {t.data[i], t.label[i]} end} ); trainset.data = trainset.data:double() -- convert the data from a ByteTensor to a DoubleTensor. trainset.data = trainset.data:cl() trainset.label = trainset.label:cl() -- trainset.data = trainset.data:cuda() -- trainset.label = trainset.label:cuda() function trainset:size() return self.data:size(1) end --///////////////////////////////////////////////////////////////////////////// print(trainset:size()) -- just to test --///////////////////////////////////////////////////////////////////////////// print(trainset[33]) -- load sample number 33. -- itorch.image(trainset[33][1]) --///////////////////////////////////////////////////////////////////////////// redChannel = trainset.data[{ {}, {1}, {}, {} }] -- this picks {all images, 1st channel, all vertical pixels, all horizontal pixels} --///////////////////////////////////////////////////////////////////////////// print(#redChannel) --///////////////////////////////////////////////////////////////////////////// mean = {} -- store the mean, to normalize the test set in the future stdv = {} -- store the standard-deviation for the future for i=1,3 do -- over each image channel mean[i] = trainset.data[{ {}, {i}, {}, {} }]:mean() -- mean estimation print('Channel ' .. i .. ', Mean: ' .. mean[i]) trainset.data[{ {}, {i}, {}, {} }]:add(-mean[i]) -- mean subtraction stdv[i] = trainset.data[{ {}, {i}, {}, {} }]:std() -- std estimation print('Channel ' .. i .. ', Standard Deviation: ' .. stdv[i]) trainset.data[{ {}, {i}, {}, {} }]:div(stdv[i]) -- std scaling end --///////////////////////////////////////////////////////////////////////////// net = nn.Sequential() net = net:cl() -- net = net:cuda() net:add(nn.SpatialConvolution(3, 6, 5, 5)) -- 3 input image channels, 6 output channels, 5x5 convolution kernel net:add(nn.ReLU()) -- non-linearity net:add(nn.SpatialMaxPooling(2,2,2,2)) -- A max-pooling operation that looks at 2x2 windows and finds the max. net:add(nn.SpatialConvolution(6, 16, 5, 5)) net:add(nn.ReLU()) -- non-linearity net:add(nn.SpatialMaxPooling(2,2,2,2)) net:add(nn.View(16*5*5)) -- reshapes from a 3D tensor of 16x5x5 into 1D tensor of 16*5*5 net:add(nn.Linear(16*5*5, 120)) -- fully connected layer (matrix multiplication between input and weights) net:add(nn.ReLU()) -- non-linearity net:add(nn.Linear(120, 84)) net:add(nn.ReLU()) -- non-linearity net:add(nn.Linear(84, 10)) -- 10 is the number of outputs of the network (in this case, 10 digits) net:add(nn.LogSoftMax()) -- converts the output to a log-probability. Useful for classification problems --///////////////////////////////////////////////////////////////////////////// criterion = nn.ClassNLLCriterion() criterion = criterion:cl() -- criterion = criterion:cuda() --///////////////////////////////////////////////////////////////////////////// trainer = nn.StochasticGradient(net, criterion) trainer.learningRate = 0.001 trainer.maxIteration = 5 -- just do 5 epochs of training. --///////////////////////////////////////////////////////////////////////////// trainer:train(trainset) --///////////////////////////////////////////////////////////////////////////// print(classes[testset.label[100]]) -- itorch.image(testset.data[100]) --///////////////////////////////////////////////////////////////////////////// testset.data = testset.data:double() -- convert from Byte tensor to Double tensor for i=1,3 do -- over each image channel testset.data[{ {}, {i}, {}, {} }]:add(-mean[i]) -- mean subtraction testset.data[{ {}, {i}, {}, {} }]:div(stdv[i]) -- std scaling end --///////////////////////////////////////////////////////////////////////////// -- for fun, print the mean and standard-deviation of example-100 horse = testset.data[100] print(horse:mean(), horse:std()) --///////////////////////////////////////////////////////////////////////////// print(classes[testset.label[100]]) -- itorch.image(testset.data[100]) predicted = net:forward(testset.data[100]) --///////////////////////////////////////////////////////////////////////////// -- the output of the network is Log-Probabilities. To convert them to probabilities, you have to take e^x print(predicted:exp()) --///////////////////////////////////////////////////////////////////////////// for i=1,predicted:size(1) do print(classes[i], predicted[i]) end --///////////////////////////////////////////////////////////////////////////// correct = 0 for i=1,10000 do local groundtruth = testset.label[i] local prediction = net:forward(testset.data[i]) local confidences, indices = torch.sort(prediction, true) -- true means sort in descending order if groundtruth == indices[1] then correct = correct + 1 end end --///////////////////////////////////////////////////////////////////////////// print(correct, 100*correct/10000 .. ' % ') --///////////////////////////////////////////////////////////////////////////// class_performance = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0} for i=1,10000 do local groundtruth = testset.label[i] local prediction = net:forward(testset.data[i]) local confidences, indices = torch.sort(prediction, true) -- true means sort in descending order if groundtruth == indices[1] then class_performance[groundtruth] = class_performance[groundtruth] + 1 end end --///////////////////////////////////////////////////////////////////////////// for i=1,#classes do print(classes[i], 100*class_performance[i]/1000 .. ' %') end-- You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub: https://github.com/hughperkins/distro-cl/issues/27
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tylerlindell
alex3s
i'm getting the following error when using
trainset.data[{ {}, {i}, {}, {} }]:div(stdv[i]) -- std scalingthe code i'm using is here: