我的机器的GPU有2GB的内存。第一次运行以下代码时没有错误。然而，第二次运行代码时，我遇到了内存错误。作为一个临时解决方案，我唯一能做的就是使用torch.Tensor.float()将数据转换为float32。然而，问题仍然存在，内存占用在进程完成后或进程在运行时被终止后并未释放，机器的RAM也存在这种情况。如何防止Torch中的内存泄漏或释放内存？
require 'nn'require 'image'require 'cunn'require 'paths'collectgarbage(); collectgarbage()if (not paths.filep("cifar10torchsmall.zip")) then os.execute('wget -c https://s3.amazonaws.com/torch7/data/cifar10torchsmall.zip') os.execute('unzip cifar10torchsmall.zip')endtrainset = torch.load('cifar10-train.t7')testset = torch.load('cifar10-test.t7')classes = {'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'}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.function trainset:size() return self.data:size(1) endmean = {} -- store the mean, to normalize the test set in the futurestdv = {} -- store the standard-deviation for the futurefor 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 scalingendtestset.data = testset.data:double() -- convert from Byte tensor to Double tensorfor i=1,3 do -- over each image channel testset.data[{ {}, {i}, {}, {} }]:add(-mean[i]) -- mean subtraction testset.data[{ {}, {i}, {}, {} }]:div(stdv[i]) -- std scalingendtrainset.data = trainset.data:cuda()testset.data = testset.data:cuda()net = nn.Sequential()net:add(nn.SpatialConvolution(3, 6, 5, 5)) -- 3 input image channels, 6 output channels, 5x5 convolution kernelnet: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*5net: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()) net = net:cuda()criterion = nn.ClassNLLCriterion()criterion = criterion:cuda()pred = net:forward(trainset.data)outputEr = criterion:forward(pred, trainset.label:cuda())net:zeroGradParameters()outputGrad = criterion:backward(pred, trainset.label:cuda())collectgarbage()inputGrad = net:backward(trainset.data, outputGrad)附带问题：为什么Torch初始化网络参数时使用双精度，尽管GPU在计算双精度操作时速度较慢，而且几乎所有神经网络应用实际上不需要64位参数值？我如何初始化一个使用float（32位）参数向量的模型？
我找到了附带问题的答案。你可以在代码开始时使用以下命令轻松地将Torch的默认数据类型设置为float：
torch.setdefaulttensortype('torch.FloatTensor')回答：
通过将机器上的CUDA从6.5升级到7.5，我几乎解决了这个问题。现在，大多数情况下，当程序在运行时崩溃时，GPU内存会被释放。然而，有时仍然不会发生这种情况，我不得不重启机器。
此外，为了确保程序在成功运行后清除GPU内存，我会执行以下操作：
net = niltrainset = niltestset = nilpred = nilinputGrad = nilcriterion = nilcollectgarbage()