深度学习笔记5torch实现mnist手写数字识别

转自：

http://www.aichengxu.com/view/2464034

本节代码地址：

https://github.com/vic-w/torch-practice/tree/master/mnist

根据个人理解，注释了一些原作者没有注释的代码

luarocks install mnist

require 'torch'require 'nn'require 'optim'--require 'cunn'--require 'cutorch'mnist = require 'mnist'--mnist中包含我们需要的训练数据和测试数据fullset = mnist.traindataset()testset = mnist.testdataset()trainset = {    size = 50000,    data = fullset.data[{{1,50000}}]:double(),    label = fullset.label[{{1,50000}}]}validationset = {    size = 10000,    data = fullset.data[{{50001,60000}}]:double(),    label = fullset.label[{{50001,60000}}]}--数据减去他们的均值trainset.data = trainset.data - trainset.data:mean()validationset.data = validationset.data - validationset.data:mean()model = nn.Sequential()model:add(nn.Reshape(1, 28, 28))model:add(nn.MulConstant(1/256.0*3.2))model:add(nn.SpatialConvolutionMM(1, 20, 5, 5, 1, 1, 0, 0))model:add(nn.SpatialMaxPooling(2, 2 , 2, 2, 0, 0))model:add(nn.SpatialConvolutionMM(20, 50, 5, 5, 1, 1, 0, 0))model:add(nn.SpatialMaxPooling(2, 2 , 2, 2, 0, 0))model:add(nn.Reshape(4*4*50))model:add(nn.Linear(4*4*50, 500))model:add(nn.ReLU())model:add(nn.Linear(500, 10))model:add(nn.LogSoftMax())model = require('weight-init')(model, 'xavier')criterion = nn.ClassNLLCriterion()--model = model:cuda()--criterion = criterion:cuda()--trainset.data = trainset.data:cuda()--trainset.label = trainset.label:cuda()--validationset.data = validationset.data:cuda()--validationset.label = validationset.label:cuda()sgd_params = {   learningRate = 1e-2,   learningRateDecay = 1e-4,   weightDecay = 1e-3,   momentum = 1e-4}--获取模型的初始参数，x是权值，dl_dx是梯度x, dl_dx = model:getParameters()step = function(batch_size)    local current_loss = 0    local count = 0    local shuffle = torch.randperm(trainset.size)--随机下标，用来打乱训练数据    batch_size = batch_size or 200--批处理数目为200    for t = 1,trainset.size,batch_size do        -- setup inputs and targets for this mini-batch        local size = math.min(t + batch_size - 1, trainset.size) - t        local inputs = torch.Tensor(size, 28, 28)--:cuda()        local targets = torch.Tensor(size)--:cuda()        for i = 1,size do            local input = trainset.data[shuffle[i+t]]            local target = trainset.label[shuffle[i+t]]            -- if target == 0 then target = 10 end            inputs[i] = input--将随机打乱位置的数据集加入到训练输入和输出集            targets[i] = target        end        targets:add(1)        local feval = function(x_new)--这个函数是个迭代训练函数            -- reset data            if x ~= x_new then x:copy(x_new) end            dl_dx:zero()--梯度归零，类似这个函数zeroGradParameters()              -- perform mini-batch gradient descent            local loss = criterion:forward(model:forward(inputs), targets)--两次前向计算，一次是带入模型计算输出，一次是用输出和目标输出求误差            model:backward(inputs, criterion:backward(model.output, targets))--两次后向计算，一次是带入误差函数求梯度，一次是用梯度和输入数据更新权值            return loss, dl_dx--返回误差和梯度        end        _, fs = optim.sgd(feval, x, sgd_params)--优化函数        -- fs is a table containing value of the loss function        -- (just 1 value for the SGD optimization)        count = count + 1        current_loss = current_loss + fs[1]    end    -- normalize loss    return current_loss / countendeval = function(dataset, batch_size)    local count = 0    batch_size = batch_size or 200        for i = 1,dataset.size,batch_size do        local size = math.min(i + batch_size - 1, dataset.size) - i        local inputs = dataset.data[{{i,i+size-1}}]--:cuda()        local targets = dataset.label[{{i,i+size-1}}]:long()--:cuda()        local outputs = model:forward(inputs)--用训练好的模型计算输出        local _, indices = torch.max(outputs, 2)        indices:add(-1)        local guessed_right = indices:eq(targets):sum()--计算命中数目        count = count + guessed_right    end    return count / dataset.sizeendmax_iters = 30do    local last_accuracy = 0    local decreasing = 0    local threshold = 1 -- how many deacreasing epochs we allow    for i = 1,max_iters do        local loss = step()        print(string.format('Epoch: %d Current loss: %4f', i, loss))        local accuracy = eval(validationset)        print(string.format('Accuracy on the validation set: %4f', accuracy))        if accuracy < last_accuracy then            if decreasing > threshold then break end            decreasing = decreasing + 1        else            decreasing = 0        end        last_accuracy = accuracy    endendtestset.data = testset.data:double()eval(testset)