machine learning - Torch CrossEntropyCriterion error -


i'm trying train simple test network on xor function in torch. works when use msecriterion, when try crossentropycriterion fails following error message:

/home/a/torch/install/bin/luajit: /home/a/torch/install/share/lua/5.1/nn/thnn.lua:699: assertion `cur_target >= 0 && cur_target < n_classes' failed.  @ /tmp/luarocks_nn-scm-1-6937/nn/lib/thnn/generic/classnllcriterion.c:31 stack traceback:     [c]: in function 'v'     /home/a/torch/install/share/lua/5.1/nn/thnn.lua:699: in function 'classnllcriterion_updateoutput'     ...e/a/torch/install/share/lua/5.1/nn/classnllcriterion.lua:41: in function 'updateoutput'     ...torch/install/share/lua/5.1/nn/crossentropycriterion.lua:13: in function 'forward'     .../a/torch/install/share/lua/5.1/nn/stochasticgradient.lua:35: in function 'train'     a.lua:34: in main chunk     [c]: in function 'dofile'     /home/a/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:145: in main chunk     [c]: @ 0x00406670

i same error message when decomposing logsoftmax , classnllcriterion. code is:

dataset={}; function dataset:size() return 100 end -- 100 examples i=1,dataset:size()   local input = torch.randn(2);     -- distributed example in 2d   local output = torch.tensor(2);   if input[1]<0       input[1]=-1   else       input[1]=1   end   if input[2]<0       input[2]=-1   else       input[2]=1   end   if input[1]*input[2]>0     -- calculate label xor function     output[2] = 1;   else     output[1] = 1   end   dataset[i] = {input, output} end  require "nn" mlp = nn.sequential();  -- make multi-layer perceptron inputs = 2; outputs = 2; hus = 20; -- parameters mlp:add(nn.linear(inputs, hus)) mlp:add(nn.tanh()) mlp:add(nn.linear(hus, outputs))  criterion = nn.crossentropycriterion() trainer = nn.stochasticgradient(mlp, criterion) trainer.learningrate = 0.01 trainer:train(dataset)  x = torch.tensor(2) x[1] =  1; x[2] =  1; print(mlp:forward(x)) x[1] =  1; x[2] = -1; print(mlp:forward(x)) x[1] = -1; x[2] =  1; print(mlp:forward(x)) x[1] = -1; x[2] = -1; print(mlp:forward(x))

mse criterion designed regression problems. when it's used classification tasks, targets should one-hot vectors. cross entropy / negative log likelihood criteria used exclusively classification; therefore, there's no need explicitly represent target class vector. in torch target such criteria index of assigned class (1 number of classes).


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