{"action":"closed","issue":{"url":"https://api.github.com/repos/dmlc/cxxnet/issues/244","repository_url":"https://api.github.com/repos/dmlc/cxxnet","labels_url":"https://api.github.com/repos/dmlc/cxxnet/issues/244/labels{/name}","comments_url":"https://api.github.com/repos/dmlc/cxxnet/issues/244/comments","events_url":"https://api.github.com/repos/dmlc/cxxnet/issues/244/events","html_url":"https://github.com/dmlc/cxxnet/issues/244","id":133540200,"number":244,"title":"cifar10 CNN训练问题","user":{"login":"supengyu","id":13342629,"avatar_url":"https://avatars.githubusercontent.com/u/13342629?v=3","gravatar_id":"","url":"https://api.github.com/users/supengyu","html_url":"https://github.com/supengyu","followers_url":"https://api.github.com/users/supengyu/followers","following_url":"https://api.github.com/users/supengyu/following{/other_user}","gists_url":"https://api.github.com/users/supengyu/gists{/gist_id}","starred_url":"https://api.github.com/users/supengyu/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/supengyu/subscriptions","organizations_url":"https://api.github.com/users/supengyu/orgs","repos_url":"https://api.github.com/users/supengyu/repos","events_url":"https://api.github.com/users/supengyu/events{/privacy}","received_events_url":"https://api.github.com/users/supengyu/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"milestone":null,"comments":1,"created_at":"2016-02-14T13:44:24Z","updated_at":"2016-02-15T03:27:25Z","closed_at":"2016-02-15T03:27:25Z","body":"在cxxnet.exe程序训练cifar10图片库是遇到问题，程序异常退出，如下所示：\r\n\r\nE:\\deepLearning\\cxxnet-master\\bin>E:\\deepLearning\\cxxnet-master\\bin\\cxxnet.exe E\r\n:\\deepLearning\\cxxnet-master\\bin\\ImageNet\\ImageNet.conf\r\nUse CUDA Device 0: GeForce GTX 980 Ti\r\nfinish initialization with 1 devices\r\nInitializing layer: conv1\r\nInitializing layer: relu1\r\nInitializing layer: pool1\r\nInitializing layer: lrn1\r\nInitializing layer: conv2\r\nInitializing layer: relu2\r\nInitializing layer: pool2\r\nInitializing layer: lrn2\r\nInitializing layer: conv3\r\nInitializing layer: relu3\r\nInitializing layer: pool3\r\nInitializing layer: flatten1\r\nInitializing layer: fc4\r\nInitializing layer: softmax1\r\nLossLayer is an self-loop Layer代码跟踪发现：在LoadModel函数中执行this->FreeSpace()时意外退出了，不知道是什么问题，\r\n\r\n\r\n\r\n\r\n配置文件imageNet.conf如下：\r\n# Configuration for ImageNet  \r\n# Acknowledgement:\r\n#  Ref: http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf\r\n#  The scheduling parameters is adapted from Caffe(http://caffe.berkeleyvision.org/)\r\n\r\ndata = train\r\niter = imgrec\r\n#  image_list = \"../../NameList.train\"\r\n  image_rec  = \"E:/deepLearning/cxxnet-master/bin/data/cifar10train.bin\"\r\n  image_root = \"E:/deepLearning/cifar-10/cifar-10-py-colmajor/batch_1/\"\r\n  image_mean = \"E:/deepLearning/cxxnet-master/bin/models/image_net_mean.bin\"\r\n  rand_crop=1\r\n  rand_mirror=1\r\niter = threadbuffer\r\niter = end\r\n\r\neval = test\r\niter = imgrec\r\n#  image_list = \"../../NameList.test\"\r\n  image_rec = \"E:/deepLearning/cxxnet-master/bin/data/cifar10test.bin\"\r\n  image_root = \"E:/deepLearning/cifar-10/cifar-10-py-colmajor/test_batch/\"\r\n  image_mean = \"E:/deepLearning/cxxnet-master/bin/models/image_net_mean.bin\"\r\n# no random crop and mirror in test\r\niter = end\r\n\r\nnetconfig=start\r\nlayer[0->1] = conv:conv1\r\n  kernel_size = 5\r\n  stride = 1\r\n  nchannel = 32\r\nlayer[1->2] = relu:relu1\r\nlayer[2->3] = max_pooling:pool1\r\n  kernel_size = 3\r\n  stride = 2\r\nlayer[3->4] = lrn:lrn1\r\n  local_size = 5\r\n  alpha = 0.0001\r\n  beta = 0.75\r\n  knorm = 1\r\n###############\r\nlayer[4->5] = conv:conv2\r\n  ngroup = 1\r\n  nchannel = 32\r\n  kernel_size = 5\r\n  pad = 1\r\nlayer[5->6] = relu:relu2\r\nlayer[6->7] = max_pooling:pool2\r\n  kernel_size = 3\r\n  stride = 2\r\nlayer[7->8] = lrn:lrn2\r\n  local_size = 5\r\n  alpha = 0.0001\r\n  beta = 0.75\r\n  knorm = 1\r\n#############\r\nlayer[8->9] = conv:conv3\r\n  nchannel = 64\r\n  kernel_size = 3\r\n  pad = 1\r\nlayer[9->10]= relu:relu3\r\nlayer[10->11] = max_pooling:pool3\r\n kernel_size = 3\r\n  stride = 2\r\nlayer[11->12] = flatten:flatten1\r\nlayer[12->13] = fullc:fc4\r\n  nhidden = 512\r\n  init_sigma = 0.005\r\n  init_bias = 1.0\r\nlayer[13->14] = softmax:softmax1\r\n\r\nnetconfig=end\r\n\r\n# evaluation metric\r\nmetric = error\r\nmetric = rec@1\r\nmetric = rec@5\r\n\r\nmax_round = 45\r\nnum_round = 45\r\n\r\n# input shape not including batch\r\ninput_shape = 3,32,32\r\n\r\nbatch_size = 128\r\n\r\n# global parameters in any sectiion outside netconfig, and iter\r\nmomentum = 0.9\r\nwmat:lr  = 0.01\r\nwmat:wd  = 0.0005\r\n\r\nbias:wd  = 0.000\r\nbias:lr  = 0.02\r\n\r\n# all the learning rate schedule starts with lr\r\nlr:schedule = expdecay\r\nlr:gamma = 0.1\r\nlr:step = 100000\r\n\r\nsave_model=1\r\nmodel_dir=models\r\n\r\n# random config\r\nrandom_type = xavier\r\n\r\n\r\n# new line\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n"}}