我在尝试使用 Torch 训练一个简单的 XOR 函数测试网络。当我使用 MSECriterion 时它能正常工作，但当我尝试使用 CrossEntropyCriterion 时，它会出现以下错误信息：
/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. at /tmp/luarocks_nn-scm-1-6937/nn/lib/THNN/generic/ClassNLLCriterion.c:31stack 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]: at 0x00406670当我将其分解为 LogSoftMax 和 ClassNLLCriterion 时，我得到了相同的错误信息。代码如下：
dataset={};function dataset:size() return 100 end -- 100 examplesfor i=1,dataset:size() do local input = torch.randn(2); -- normally distributed example in 2d local output = torch.Tensor(2); if input[1]<0 then input[1]=-1 else input[1]=1 end if input[2]<0 then input[2]=-1 else input[2]=1 end if input[1]*input[2]>0 then -- calculate label for XOR function output[2] = 1; else output[1] = 1 end dataset[i] = {input, output}endrequire "nn"mlp = nn.Sequential(); -- make a multi-layer perceptroninputs = 2; outputs = 2; HUs = 20; -- parametersmlp: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 = 0.01trainer: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))回答：
MSE 准则设计用于回归问题。当它用于分类任务时，目标应为独热向量。交叉熵/负对数似然准则专门用于分类；因此，无需将目标类显式表示为向量。在 torch 中，此类准则的目标只是分配类的索引（从 1 到类别数）。
