（原）torch的训练过程
Posted darkknightzh
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转载请注明出处：
http://www.cnblogs.com/darkknightzh/p/6221622.html
参考网址：
http://ju.outofmemory.cn/entry/284587
https://github.com/torch/nn/blob/master/doc/criterion.md
1. 使用updateParameters
假设已经有了model=setupmodel（自己建立的模型），同时也有自己的训练数据input，实际输出outReal，以及损失函数criterion（参见第二个网址），则使用torch训练过程如下：
1 -- given model, criterion, input, outReal 2 model:training() 3 model:zeroGradParameters() 4 outPredict = model:forward(input) 5 err= criterion:forward(outPredict, outReal) 6 grad_criterion = criterion:backward(outPredict, outReal) 7 model:backward(input, grad_criterion) 8 model:updateParameters(learningRate)
上面第1行假定已知的参数
第2行设置为训练模式
第3行将model中每个模块保存的梯度清零（防止之前的干扰此次迭代）
第4行将输入input通过model，得到预测的输出outPredict
第5行通过损失函数计算在当前参数下模型的预测输出outPredict和实际输出outReal的误差err
第6行通过预测输出outPredict和实际输出outReal计算损失函数的梯度grad_criterion
第7行反向计算model中每个模块的梯度
第8行更新model每个模块的参数
每次迭代时，均需要执行第3行至第8行。
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2. 使用optim
170301更新：
http://x-wei.github.io/learn-torch-6-optim.html
中给出了更方便的方式（是否方便也说不清楚），可以使用torch中的optim来更新参数（直接使用model:updateParameters的话，只能使用最简单的梯度下降算法，optmi中封装了很多算法，梯度下降，adam之类的）。
params_new, fs, ... = optim._method_(feval, params[, config][, state])
其中，param：当前参数向量（1D的tensro），在优化时会被更新
feval：用户自定义的闭包，类似于f, df/dx = feval(x)
config：一个包含算法参数（如learning rate）的table
state：包含状态变量的table
params_new：最小化函数f的新的结果参数（1D的tensor）
fs：a table of f values evaluated during the optimization, fs[#fs] is the optimized function value
注意：由于optmi需要输入数据为1D的tensor，因而需要将模型中的参数变成拉平，通过下面的函数来实现：
params, gradParams = model:getParameters()
params和gradParams均为1D的tensor。
使用上面的方法后，开始得程序可以修改为：
-- given model, criterion, input, outReal, optimState local params, gradParams = model:getParameters() local function feval() return criterion.output, gradParams end for ... model:training() model:zeroGradParameters() outPredict = model:forward(input) err= criterion:forward(outPredict, outReal) grad_criterion = criterion:backward(outPredict, outReal) model:backward(input, grad_criterion) optim.sgd(feval, params, optimState) end
170301更新结束
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3. 使用model:backward注意的问题
170405更新
需要注意的是，model:backward一定要和model:forward对应。
https://github.com/torch/nn/blob/master/doc/module.md中[gradInput] backward(input, gradOutput)写着：
In general this method makes the assumption forward(input) has been called before, with the same input. This is necessary for optimization reasons. If you do not respect this rule, backward() will compute incorrect gradients.
应该是由于backward时，可能会使用forward的某些中间变量，因而backward执行前，必须先执行forward，否则中间变量和backward不对应，导致结果错误。
我这边之前的程序由于最初forward后，保存的是最后一次forward时的中间变量，因而backward时的结果总是不正确（见method5注释的那句）。
只能使用比较坑的方式解决，之前先forward，最终在backward之前，在forward一次，这样能保证结果正确（缺点就是增加了一次计算。。。），代码如method5。
说明：method1为常规的batch的方法。但是该方法对显存要求较高。因而可以使用类似caffe中的iter_size的方式，如method2的方法（和caffe中的iter_size不完全一样）。如果需要使用更多的样本，同时criterion时使用尽可能多的样本，则前两种方法均会出现问题，此时可以使用method3的方法（但是实际上method3有问题，loss收敛的很慢）。method4对method3进行了进一步的改进及测试，如果method4注释那两行，则其收敛正常，但是不注释那两行，则收敛出现问题，和method3收敛类似。method5进行了最终的改进。该程序能正常收敛。同时为了验证forward和backward要对应，将method5中注释的取消注释，同时注释掉上面一行，可以看出，其收敛很慢（和method3,4类似）。下面是各种method前10次的的收敛情况。
程序如下：
1 require \'torch\' 2 require \'nn\' 3 require \'optim\' 4 require \'cunn\' 5 require \'cutorch\' 6 local mnist = require \'mnist\' 7 8 local fullset = mnist.traindataset() 9 local testset = mnist.testdataset() 10 11 local trainset = { 12 size = 50000, 13 data = fullset.data[{{1,50000}}]:double(), 14 label = fullset.label[{{1,50000}}] 15 } 16 trainset.data = trainset.data - trainset.data:mean() 17 trainset.data = trainset.data:cuda() 18 trainset.label = trainset.label:cuda() 19 20 local validationset = { 21 size = 10000, 22 data = fullset.data[{{50001,60000}}]:double(), 23 label = fullset.label[{{50001,60000}}] 24 } 25 validationset.data = validationset.data - validationset.data:mean() 26 validationset.data = validationset.data:cuda() 27 validationset.label = validationset.label:cuda() 28 29 local model = nn.Sequential() 30 model:add(nn.Reshape(1, 28, 28)) 31 model:add(nn.MulConstant(1/256.0*3.2)) 32 model:add(nn.SpatialConvolutionMM(1, 20, 5, 5, 1, 1, 0, 0)) 33 model:add(nn.SpatialMaxPooling(2, 2 , 2, 2, 0, 0)) 34 model:add(nn.SpatialConvolutionMM(20, 50, 5, 5, 1, 1, 0, 0)) 35 model:add(nn.SpatialMaxPooling(2, 2 , 2, 2, 0, 0)) 36 model:add(nn.Reshape(4*4*50)) 37 model:add(nn.Linear(4*4*50, 500)) 38 model:add(nn.ReLU()) 39 model:add(nn.Linear(500, 10)) 40 model:add(nn.LogSoftMax()) 41 42 model = require(\'weight-init\')(model, \'xavier\') 43 model = model:cuda() 44 45 x, dl_dx = model:getParameters() 46 47 local criterion = nn.ClassNLLCriterion():cuda() 48 49 local sgd_params = { 50 learningRate = 1e-2, 51 learningRateDecay = 1e-4, 52 weightDecay = 1e-3, 53 momentum = 1e-4 54 } 55 56 local training = function(batchSize) 57 local current_loss = 0 58 local count = 0 59 local shuffle = torch.randperm(trainset.size) 60 batchSize = batchSize or 200 61 for t = 0, trainset.size-1, batchSize do 62 -- setup inputs and targets for batch iteration 63 local size = math.min(t + batchSize, trainset.size) - t 64 local inputs = torch.Tensor(size, 28, 28):cuda() 65 local targets = torch.Tensor(size):cuda() 66 for i = 1, size do 67 inputs[i] = trainset.data[shuffle[i+t]] 68 targets[i] = trainset.label[shuffle[i+t]] + 1 69 end 70 71 local feval = function(x_new) 72 local miniBatchSize = 20 73 if x ~= x_new then x:copy(x_new) end -- reset data 74 dl_dx:zero() 75 76 --[[ ------------------ method 1 original batch 77 local outputs = model:forward(inputs) 78 local loss = criterion:forward(outputs, targets) 79 local gradInput = criterion:backward(outputs, targets) 80 model:backward(inputs, gradInput) 81 --]] 82 83 --[[ ------------------ method 2 iter-size with batch 84 local loss = 0 85 for idx = 1, batchSize, miniBatchSize do 86 local outputs = model:forward(inputs[{{idx, idx + miniBatchSize - 1}}]) 87 loss = loss + criterion:forward(outputs, targets[{{idx, idx + miniBatchSize - 1}}]) 88 local gradInput = criterion:backward(outputs, targets[{{idx, idx + miniBatchSize - 1}}]) 89 model:backward(inputs[{{idx, idx + miniBatchSize - 1}}], gradInput) 90 end 91 dl_dx:mul(1.0 * miniBatchSize / batchSize) 92 loss = loss * miniBatchSize / batchSize 93 --]] 94 95 --[[ ------------------ method 3 mini-batch in batch 96 local outputs = torch.Tensor(batchSize, 10):zero():cuda() 97 for idx = 1, batchSize, miniBatchSize do 98 outputs[{{idx, idx + miniBatchSize - 1}}]:copy(model:forward(inputs[{{idx, idx + miniBatchSize - 1}}])) 99 end 100 local loss = 0 101 for idx = 1, batchSize, miniBatchSize do 102 loss = loss + criterion:forward(outputs[{{idx, idx + miniBatchSize - 1}}], 103 targets[{{idx, idx + miniBatchSize - 1}}]) 104 end 105 local gradInput = torch.Tensor(batchSize, 10):zero():cuda() 106 for idx = 1, batchSize, miniBatchSize do 107 gradInput[{{idx, idx + miniBatchSize - 1}}]:copy(criterion:backward( 108 outputs[{{idx, idx + miniBatchSize - 1}}], targets[{{idx, idx + miniBatchSize - 1}}])) 109 end 110 for idx = 1, batchSize, miniBatchSize do 111 model:backward(inputs[{{idx, idx + miniBatchSize - 1}}], gradInput[{{idx, idx + miniBatchSize - 1}}]) 112 end 113 dl_dx:mul( 1.0 * miniBatchSize / batchSize) 114 loss = loss * miniBatchSize / batchSize 115 --]] 116 117 --[[ ------------------ method 4 mini-batch in batch 118 local outputs = torch.Tensor(batchSize, 10):zero():cuda() 119 local loss = 0 120 local gradInput = torch.Tensor(batchSize, 10):zero():cuda() 121 for idx = 1, batchSize, miniBatchSize do 122 outputs[{{idx, idx + miniBatchSize - 1}}]:copy(model:forward(inputs[{{idx, idx + miniBatchSize - 1}}])) 123 loss = loss + criterion:forward(outputs[{{idx, idx + miniBatchSize - 1}}], 124 targets[{{idx, idx + miniBatchSize - 1}}]) 125 gradInput[{{idx, idx + miniBatchSize - 1}}]:copy(criterion:backward( 126 outputs[{{idx, idx + miniBatchSize - 1}}], targets[{{idx, idx + miniBatchSize - 1}}])) 127 -- end 128 -- for idx = 1, batchSize, miniBatchSize do 129 model:backward(inputs[{{idx, idx + miniBatchSize - 1}}], gradInput[{{idx, idx + miniBatchSize - 1}}]) 130 end 131 132 dl_dx:mul( 1.0 * miniBatchSize / batchSize) 133 loss = loss * miniBatchSize / batchSize 134 --]] 135 136 137 ---[[ ------------------ method 5 mini-batch in batch 138 local loss = 0 139 local gradInput = torch.Tensor(batchSize, 10):zero():cuda() 140 141 for idx = 1, batchSize, miniBatchSize do 142 local outputs = model:forward(inputs[{{idx, idx + miniBatchSize - 1}}]) 143 loss = loss + criterion:forward(outputs, targets[{{idx, idx + miniBatchSize - 1}}]) 144 gradInput[{{idx, idx + miniBatchSize - 1}}]:copy(criterion:backward(outputs, targets[{{idx, idx + miniBatchSize - 1}}])) 145 end 146 147 for idx = 1, batchSize, miniBatchSize do 148 model:forward(inputs[{{idx, idx + miniBatchSize - 1}}]) 149 --model:forward(inputs[{{batchSize - miniBatchSize + 1, batchSize}}]) 150 model:backward(inputs[{{idx, idx + miniBatchSize - 1}}], gradInput[{{idx, idx + miniBatchSize - 1}}]) 151 end 152 153 dl_dx:mul( 1.0 * miniBatchSize / batchSize) 154 loss = loss * miniBatchSize / batchSize 155 --]] 156 157 return loss, dl_dx 158 end 159 160 _, fs = optim.sgd(feval, x, sgd_params) 161 162 count = count + 1 163 current_loss = current_loss + fs[1] 164 end 165 166 return current_loss / count -- normalize loss 167 end 168 169 local eval = function(dataset, batchSize) 170 local count = 0 171 batchSize = batchSize or 200 172 173 for i = 1, dataset.size, batchSize do 174 local size = math.min(i + batchSize - 1, dataset.size) - i 175 local inputs = dataset.data[{{i,i+size-1}}]:cuda() 176 local targets = dataset.label[{{i,i+size-1}}] 177 local outputs = model:forward(inputs) 178 local _, indices = torch.max(outputs, 2) 179 indices:add(-1) 180 indices = indices:cuda() 181 local guessed_right = indices:eq(targets):sum() 182 count = count + guessed_right 183 end 184 185 return count / dataset.size 186 end 187 188 189 local max_iters = 50 190 local last_accuracy = 0 191 local decreasing = 0 192 local threshold = 1 -- how many deacreasing epochs we allow 193 for i = 1,max_iters do 194 -- timer = torch.Timer() 195 196 model:training() 197 local loss = training() 198 199 model:evaluate() 200 local accuracy = eval(validationset) 201 print(string.format(\'Epoch: %d Current loss: %4f; validation set accu: %4f\', i, loss, accuracy)) 202 if accuracy < last_accuracy then 203 if decreasing > threshold then break end 204 decreasing = decreasing + 1 205 else 206 decreasing = 0 207 end 208 last_accuracy = accuracy 209 210 --print(\' Time elapsed: \' .. i .. \'iter: \' .. timer:time().real .. \' seconds\') 211 end 212 213 testset.data = testset.data:double() 214 eval(testset)
weight-init.lua
1 -- 2 -- Different weight initialization methods 3 -- 4 -- > model = require(\'weight-init\')(model, \'heuristic\') 5 -- 6 require("nn") 7 8 9 -- "Efficient backprop" 10 -- Yann Lecun, 1998 11 local function w_init_heuristic(fan_in, fan_out) 12 return math.sqrt(1/(3*fan_in)) 13 end 14 15 -- "Understanding the difficulty of training deep feedforward neural networks" 16 -- Xavier Glorot, 2010 17 local function w_init_xavier(fan_in, fan_out) 18 return math.sqrt(2/(fan_in + fan_out)) 19 end 20 21 -- "Understanding the difficulty of training deep feedforward neural networks" 22 -- Xavier Glorot, 2010 23 local function w_init_xavier_caffe(fan_in, fan_out) 24 return math.sqrt(1/fan_in) 25 end 26 27 -- "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification" 28 -- Kaiming He, 2015 29 local function w_init_kaiming(fan_in, fan_out) 30 return math.sqrt(4/(fan_in + fan_out)) 31 end 32 33 local function w_init(net, arg) 34 -- choose initialization method 35 local method = nil 36 if arg == \'heuristic\' then method = w_init_heuristic 37 elseif arg == \'xavier\' then method = w_init_xavier 38 elseif arg == \'xavier_caffe\' then method = w_init_xavier_caffe 39 elseif arg == \'kaiming\' then method = w_init_kaiming 40 else 41 assert(false) 42 end 43 44 -- loop over all convolutional modules 45 for i = 1, #net.modules do 46 local m = net.modules[i] 47 if m.__typename == \'nn.SpatialConvolution\' then 48 m:reset(method(m.nInputPlane*m.kH*m.kW, m.nOutputPlane*m.kH*m.kW)) 49 elseif m.__typename == \'nn.SpatialConvolutionMM\' then 50 m:reset(method(m.nInputPlane*m.kH*m.kW, m.nOutputPlane*m.kH*m.kW)) 51 elseif m.__typename == \'cudnn.SpatialConvolution\' then 52 m:reset(method(m.nInputPlane*m.kH*m.kW, m.nOutputPlane*m.kH*m.kW)) 53 elseif m.__typename == \'nn.LateralConvolution\' then 54 m:reset(method(m.nInputPlane*1*1, m.nOutputPlane*1*1)) 55 elseif m.__typename == \'nn.VerticalConvolution\' then 56 m:reset(method(1*m.kH*m.kW, 1*m.kH*m.kW)) 57 elseif m.__typename == \'nn.HorizontalConvolution\' then 58 m:reset(method(1*m.kH*m.kW, 1*m.kH*m.kW)) 59 elseif m.__typename == \'nn.Linear\' then 60 m:reset(method(m.weight:size(2), m.weight:size(1))) 61 elseif m.__typename == \'nn.TemporalConvolution\' then 62 m:reset(method(m.weight:size(2), m.weight:size(1))) 63 end 64 65 if m.bias then 66 m.bias:zero() 67 end 68 end 69 return net 70 end 71 72 return w_init
Method 1 Epoch: 1 Current loss: 0.616950; validation set accu: 0.920900 Epoch: 2 Current loss: 0.228665; validation set accu: 0.942400 Epoch: 3 Current loss: 0.168047; validation set accu: 0.957900 Epoch: 4 Current loss: 0.134796; validation set accu: 0.961800 Epoch: 5 Current loss: 0.113071; validation set accu: 0.966200 Epoch: 6 Current loss: 0.098782; validation set accu: 0.968800 Epoch: 7 Current loss: 0.088252; validation set accu: 0.970000 Epoch: 8 Current loss: 0.080225; validation set accu: 0.971200 Epoch: 9 Current loss: 0.073702; validation set accu: 0.972200 Epoch: 10 Current loss: 0.068171; validation set accu: 0.972400 method 2 Epoch: 1 Current loss: 0.624633; validation set accu: 0.922200 Epoch: 2 Current loss: 0.238459; validation set accu: 0.945200 Epoch: 3 Current loss: 0.174089; validation set accu: 0.959000 Epoch: 4 Current loss: 0.140234; validation set accu: 0.963800 Epoch: 5 Current loss: 0.116498; validation set accu: 0.968000 Epoch: 6 Current loss: 0.101376; validation set accu: 0.968800 Epoch: 7 Current loss: 0.089484; validation set accu: 0.972600 Epoch: 8 Current loss: 0.080812; validation set accu: 0.973000 Epoch: 9 Current loss: 0.073929; validation set accu: 0.975100 Epoch: 10 Current loss: 0.068330; validation set accu: 0.975400 method 3 Epoch: 1 Current loss: 2.202240; validation set accu: 0.548500 Epoch: 2 Current loss: 2.049710; validation set accu: 0.669300 Epoch: 3 Current loss: 1.993560; validation set accu: 0.728900 Epoch: 4 Current loss: 1.959818; validation set accu: 0.774500 Epoch: 5 Current loss: 1.945992; validation set accu: 0.757600 Epoch: 6 Current loss: 1.930599; validation set accu: 0.809600 Epoch: 7 Current loss: 1.911803; validation set accu: 0.837200 Epoch: 8 Current loss: 1.904754; validation set accu: 0.842100 Epoch: 9 Current loss: 1.903705; validation set accu: 0.846400 Epoch: 10 Current loss: 1.903911; validation set accu: 0.848100 method 4 Epoch: 1 Current loss: 0.624240; validation set accu: 0.924900 Epoch: 2 Current loss: 0.213469; validation set accu: 0.948500 Epoch: 3 Current loss: 0.156797; validation set accu: 0.959800 Epoch: 4 Current loss: 0.126438; validation set accu: 0.963900 Epoch: 5 Current loss: 0.106664; validation set accu: 0.965900 Epoch: 6 Current loss: 0.094166; validation set accu: 0.967200 Epoch: 7 Current loss: 0.084848; validation set accu: 0.971200 Epoch: 8 Current loss: 0.077244; validation set accu: 0.971800 Epoch: 9 Current loss: 0.071417; validation set accu: 0.973300 Epoch: 10 Current loss: 0.065737; validation set accu: 0.971600 取消注释 Epoch: 1 Current loss: 2.178319; validation set accu: 0.542200 Epoch: 2 Current loss: 2.031493; validation set accu: 0.648700 Epoch: 3 Current loss: 1.982282; validation set accu: 0.703700 Epoch: 4 Current loss: 1.956709; validation set accu: 0.762700 Epoch: 5 Current loss: 1.927590; validation set accu: 0.808100 Epoch: 6 Current loss: 1.924535; validation set accu: 0.817200 Epoch: 7 Current loss: 1.911364; validation set accu: 0.820100 Epoch: 8 Current loss: 1.898206; validation set accu: 0.855400 Epoch: 9 Current loss: 1.885394; validation set accu: 0.836500 Epoch: 10 Current loss: 1.880787; validation set accu: 0.870200 method 5 Epoch: 1 Current loss: 0.619814; validation set accu: 0.924300 Epoch: 2 Current loss: 0.232870; validation set accu: 0.948800 Epoch: 3 Current loss: 0.172606; validation set accu: 0.954900 Epoch: 4 Current loss: 0.137763; validation set accu: 0.961800 Epoch: 5 Current loss: 0.116268; validation set accu: 0.967700 Epoch: 6 Current loss: 0.101985; validation set accu: 0.968800 Epoch: 7 Current loss: 0.091154; validation set accu: 0.970900 Epoch: 8 Current loss: 0.083219; validation set accu: 0.972700 Epoch: 9 Current loss: 0.074921; validation set accu: 0.972800 Epoch: 10 Current loss: 0.070208; validation set accu: 0.972800 取消注释，同时注释上面一行 Epoch: 1 Current loss: 2.161032; validation set accu: 0.497500 Epoch: 2 Current loss: 2.027255; validation set accu: 0.690900 Epoch: 3 Current loss: 1.972939; validation set accu: 0.767600 Epoch: 4 Current loss: 1.940982; validation set accu: 0.766000 Epoch: 5 Current loss: 1.933135; validation set accu: 0.812800 Epoch: 6 Current loss: 1.913039; validation set accu: 0.799300 Epoch: 7 Current loss: 1.896871; validation set accu: 0.848800 Epoch: 8 Current loss: 1.899655; validation set accu: 0.854400 Epoch: 9 Current loss: 1.889465; validation set accu: 0.845700 Epoch: 10 Current loss: 1.878703; validation set accu: 0.846400
170301更新结束
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