torch lua

根据上一篇文章分7部分讲解了基于torch识别分类图片的例子，修改了部分代码，识别指定类型的图片分类识别。

1 生成用于训练的t7文件

-- generate training material t7 files require 'io' require 'xlua' require 'image' dataNum = 10 imgSize = 32 aImgs = torch.Tensor(batchSize, 3, imgSize, imgSize):zero():float() label1 = {} for idx = 1,dataNum do im_name = string.format('/home/cgy/torch_bak/image/Chinese flag/d.jpg',idx) --xlua.progress(idx, dataNum) local img1 = image.load(im_name) aImgs[idx] = image.scale(img1, imgSize, imgSize):float() label1[idx] = 1 end print('Save Anchor Images: aImgs.t7: ') --Create the table to save label = torch.Tensor(label1) data_to_write = { data = aImgs, label = label } --Save the table in the /home torch.save('/home/cgy/torch_bak/image/Chinese flag/train_TEST.t7', data_to_write)

2 测试待训练的t7文件

-- load test require 'paths' require 'nn' trainset = torch.load('/home/cgy/torch_bak/image/Chinese flag/train_TEST.t7') trainset.data = trainset.data:double() for idx = 1,10 do --print(trainset.label[idx]) itorch.image(trainset.data[idx]) end

3 设置训练参数，生成模型

require 'paths'; require 'nn'; trainset = torch.load('/home/cgy/torch_bak/image/Chinese flag/train_TEST.t7') setmetatable(trainset, {__index = function(t,i) return {t.data[i],t.label[i]} end} ); trainset.data = trainset.data:double() function trainset:size() return self.data:size(1) end ---Normalize data mean = {} stdv = {} for i=1,3 do mean[i] = trainset.data[{ {}, {i}, {}, {} }]:mean() print('Channel ' .. i .. ', Mean: ' .. mean[i]) trainset.data[{ {}, {i}, {}, {} }]:add(-mean[i]) stdv[i] = trainset.data[{ {}, {i}, {}, {} }]:std() print('Channel ' .. i .. ', Standard Deviation:' .. stdv[i]) trainset.data[{ {}, {i}, {}, {} }]:div(stdv[i]) end --数据的预处理 net = nn.Sequential() --change 1 channel to 3 channels --net:add(nn.SpatialConvolution(1, 6, 5, 5)) net:add(nn.SpatialConvolution(3, 6, 5, 5)) net:add(nn.ReLU()) net:add(nn.SpatialMaxPooling(2,2,2,2)) net:add(nn.SpatialConvolution(6, 16, 5, 5)) net:add(nn.ReLU()) net:add(nn.SpatialMaxPooling(2,2,2,2)) net:add(nn.View(16*5*5)) net:add(nn.Linear(16*5*5, 120)) net:add(nn.ReLU()) net:add(nn.Linear(120, 84)) net:add(nn.ReLU()) net:add(nn.Linear(84, 10)) net:add(nn.LogSoftMax()) criterion = nn.ClassNLLCriterion(); trainer = nn.StochasticGradient(net, criterion) trainer.learningRate = 0.001 trainer.maxIteration = 50 trainer:train(trainset)

4 验证训练结果

for idx = 1,10 do itorch.image(trainset.data[idx]) predicted = net:forward(trainset.data[idx]) print(predicted:exp()) end