{"action":"created","issue":{"url":"https://api.github.com/repos/yjxiong/caffe/issues/7","labels_url":"https://api.github.com/repos/yjxiong/caffe/issues/7/labels{/name}","comments_url":"https://api.github.com/repos/yjxiong/caffe/issues/7/comments","events_url":"https://api.github.com/repos/yjxiong/caffe/issues/7/events","html_url":"https://github.com/yjxiong/caffe/issues/7","id":104168384,"number":7,"title":"caffe test commond accuracy all zero.","user":{"login":"kli-nlpr","id":13194870,"avatar_url":"https://avatars.githubusercontent.com/u/13194870?v=3","gravatar_id":"","url":"https://api.github.com/users/kli-nlpr","html_url":"https://github.com/kli-nlpr","followers_url":"https://api.github.com/users/kli-nlpr/followers","following_url":"https://api.github.com/users/kli-nlpr/following{/other_user}","gists_url":"https://api.github.com/users/kli-nlpr/gists{/gist_id}","starred_url":"https://api.github.com/users/kli-nlpr/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/kli-nlpr/subscriptions","organizations_url":"https://api.github.com/users/kli-nlpr/orgs","repos_url":"https://api.github.com/users/kli-nlpr/repos","events_url":"https://api.github.com/users/kli-nlpr/events{/privacy}","received_events_url":"https://api.github.com/users/kli-nlpr/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":1,"created_at":"2015-09-01T02:04:30Z","updated_at":"2015-09-01T02:19:09Z","closed_at":null,"body":"Hi, @wanglimin  @yjxiong \r\n\r\nI use caffe test commond to test the temporal model you provided.\r\n```\r\n~/caffe-action/build/install/bin/caffe test -model vgg_16_flow_train_val_fast.prototxt -weights ~/very-deep-two-stream/wanglimin-model/cuhk_action_temporal_vgg_16_split1.caffemodel   -gpu 0\r\n```\r\nBut the accuray all zero. I can't find what's wrong (but the matlab test code works).\r\n```\r\nI0831 20:58:21.425902  5977 caffe.cpp:210] Running for 50 iterations.\r\nI0831 20:58:21.705755  5977 caffe.cpp:234] Batch 0, accuracy = 0\r\nI0831 20:58:21.705826  5977 caffe.cpp:234] Batch 0, loss = 4.56581\r\nI0831 20:58:21.842283  5977 caffe.cpp:234] Batch 1, accuracy = 0\r\nI0831 20:58:21.842308  5977 caffe.cpp:234] Batch 1, loss = 4.57135\r\nI0831 20:58:21.968304  5977 caffe.cpp:234] Batch 2, accuracy = 0\r\nI0831 20:58:21.968328  5977 caffe.cpp:234] Batch 2, loss = 4.56923\r\nI0831 20:58:22.085276  5977 caffe.cpp:234] Batch 3, accuracy = 0\r\nI0831 20:58:22.085300  5977 caffe.cpp:234] Batch 3, loss = 4.56122\r\nI0831 20:58:22.200157  5977 caffe.cpp:234] Batch 4, accuracy = 0.2\r\nI0831 20:58:22.200182  5977 caffe.cpp:234] Batch 4, loss = 4.55828\r\nI0831 20:58:22.312468  5977 caffe.cpp:234] Batch 5, accuracy = 0\r\nI0831 20:58:22.312490  5977 caffe.cpp:234] Batch 5, loss = 4.55105\r\nI0831 20:58:22.422643  5977 caffe.cpp:234] Batch 6, accuracy = 0\r\nI0831 20:58:22.422667  5977 caffe.cpp:234] Batch 6, loss = 4.58159\r\nI0831 20:58:22.533643  5977 caffe.cpp:234] Batch 7, accuracy = 0\r\nI0831 20:58:22.533668  5977 caffe.cpp:234] Batch 7, loss = 4.59634\r\nI0831 20:58:22.646528  5977 caffe.cpp:234] Batch 8, accuracy = 0\r\nI0831 20:58:22.646553  5977 caffe.cpp:234] Batch 8, loss = 4.59047\r\nI0831 20:58:22.761013  5977 caffe.cpp:234] Batch 9, accuracy = 0\r\nI0831 20:58:22.761052  5977 caffe.cpp:234] Batch 9, loss = 4.62319\r\nI0831 20:58:22.877241  5977 caffe.cpp:234] Batch 10, accuracy = 0\r\nI0831 20:58:22.877264  5977 caffe.cpp:234] Batch 10, loss = 4.63062\r\nI0831 20:58:22.992969  5977 caffe.cpp:234] Batch 11, accuracy = 0\r\nI0831 20:58:22.992990  5977 caffe.cpp:234] Batch 11, loss = 4.63736\r\nI0831 20:58:23.105216  5977 caffe.cpp:234] Batch 12, accuracy = 0\r\nI0831 20:58:23.105239  5977 caffe.cpp:234] Batch 12, loss = 4.64331\r\nI0831 20:58:23.216982  5977 caffe.cpp:234] Batch 13, accuracy = 0\r\nI0831 20:58:23.217003  5977 caffe.cpp:234] Batch 13, loss = 4.63844\r\nI0831 20:58:23.328269  5977 caffe.cpp:234] Batch 14, accuracy = 0\r\nI0831 20:58:23.328290  5977 caffe.cpp:234] Batch 14, loss = 4.62079\r\nI0831 20:58:23.436784  5977 caffe.cpp:234] Batch 15, accuracy = 0\r\nI0831 20:58:23.436825  5977 caffe.cpp:234] Batch 15, loss = 4.59784\r\nI0831 20:58:23.547108  5977 caffe.cpp:234] Batch 16, accuracy = 0\r\nI0831 20:58:23.547130  5977 caffe.cpp:234] Batch 16, loss = 4.60577\r\nI0831 20:58:23.658761  5977 caffe.cpp:234] Batch 17, accuracy = 0\r\nI0831 20:58:23.658782  5977 caffe.cpp:234] Batch 17, loss = 4.59781\r\nI0831 20:58:23.770740  5977 caffe.cpp:234] Batch 18, accuracy = 0\r\nI0831 20:58:23.770763  5977 caffe.cpp:234] Batch 18, loss = 4.59607\r\nI0831 20:58:23.882455  5977 caffe.cpp:234] Batch 19, accuracy = 0\r\nI0831 20:58:23.882477  5977 caffe.cpp:234] Batch 19, loss = 4.60705\r\nI0831 20:58:23.993892  5977 caffe.cpp:234] Batch 20, accuracy = 0\r\nI0831 20:58:23.993913  5977 caffe.cpp:234] Batch 20, loss = 4.60775\r\nI0831 20:58:24.102294  5977 caffe.cpp:234] Batch 21, accuracy = 0\r\nI0831 20:58:24.102314  5977 caffe.cpp:234] Batch 21, loss = 4.59202\r\nI0831 20:58:24.209605  5977 caffe.cpp:234] Batch 22, accuracy = 0\r\nI0831 20:58:24.209630  5977 caffe.cpp:234] Batch 22, loss = 4.60682\r\nI0831 20:58:24.318430  5977 caffe.cpp:234] Batch 23, accuracy = 0\r\nI0831 20:58:24.318454  5977 caffe.cpp:234] Batch 23, loss = 4.62756\r\nI0831 20:58:24.424371  5977 caffe.cpp:234] Batch 24, accuracy = 0\r\nI0831 20:58:24.424392  5977 caffe.cpp:234] Batch 24, loss = 4.61974\r\nI0831 20:58:24.530743  5977 caffe.cpp:234] Batch 25, accuracy = 0\r\nI0831 20:58:24.530766  5977 caffe.cpp:234] Batch 25, loss = 4.62237\r\nI0831 20:58:24.637039  5977 caffe.cpp:234] Batch 26, accuracy = 0\r\nI0831 20:58:24.637059  5977 caffe.cpp:234] Batch 26, loss = 4.6219\r\nI0831 20:58:24.744755  5977 caffe.cpp:234] Batch 27, accuracy = 0\r\nI0831 20:58:24.744776  5977 caffe.cpp:234] Batch 27, loss = 4.6247\r\nI0831 20:58:24.852677  5977 caffe.cpp:234] Batch 28, accuracy = 0\r\nI0831 20:58:24.852699  5977 caffe.cpp:234] Batch 28, loss = 4.61173\r\nI0831 20:58:24.960541  5977 caffe.cpp:234] Batch 29, accuracy = 0\r\nI0831 20:58:24.960561  5977 caffe.cpp:234] Batch 29, loss = 4.6213\r\nI0831 20:58:25.068442  5977 caffe.cpp:234] Batch 30, accuracy = 0\r\nI0831 20:58:25.068462  5977 caffe.cpp:234] Batch 30, loss = 4.60979\r\nI0831 20:58:25.175941  5977 caffe.cpp:234] Batch 31, accuracy = 0\r\nI0831 20:58:25.175961  5977 caffe.cpp:234] Batch 31, loss = 4.60837\r\nI0831 20:58:25.282083  5977 caffe.cpp:234] Batch 32, accuracy = 0\r\nI0831 20:58:25.282104  5977 caffe.cpp:234] Batch 32, loss = 4.61826\r\nI0831 20:58:25.388100  5977 caffe.cpp:234] Batch 33, accuracy = 0\r\nI0831 20:58:25.388120  5977 caffe.cpp:234] Batch 33, loss = 4.61304\r\nI0831 20:58:25.494299  5977 caffe.cpp:234] Batch 34, accuracy = 0\r\nI0831 20:58:25.494320  5977 caffe.cpp:234] Batch 34, loss = 4.60632\r\nI0831 20:58:25.600524  5977 caffe.cpp:234] Batch 35, accuracy = 0\r\nI0831 20:58:25.600544  5977 caffe.cpp:234] Batch 35, loss = 4.60945\r\nI0831 20:58:25.706799  5977 caffe.cpp:234] Batch 36, accuracy = 0\r\nI0831 20:58:25.706820  5977 caffe.cpp:234] Batch 36, loss = 4.6146\r\nI0831 20:58:25.812988  5977 caffe.cpp:234] Batch 37, accuracy = 0\r\nI0831 20:58:25.813009  5977 caffe.cpp:234] Batch 37, loss = 4.63083\r\nI0831 20:58:25.919095  5977 caffe.cpp:234] Batch 38, accuracy = 0\r\nI0831 20:58:25.919114  5977 caffe.cpp:234] Batch 38, loss = 4.58947\r\nI0831 20:58:26.025218  5977 caffe.cpp:234] Batch 39, accuracy = 0\r\nI0831 20:58:26.025239  5977 caffe.cpp:234] Batch 39, loss = 4.60846\r\nI0831 20:58:26.133335  5977 caffe.cpp:234] Batch 40, accuracy = 0\r\nI0831 20:58:26.133358  5977 caffe.cpp:234] Batch 40, loss = 4.61726\r\nI0831 20:58:26.241446  5977 caffe.cpp:234] Batch 41, accuracy = 0\r\nI0831 20:58:26.241468  5977 caffe.cpp:234] Batch 41, loss = 4.60415\r\nI0831 20:58:26.349599  5977 caffe.cpp:234] Batch 42, accuracy = 0\r\nI0831 20:58:26.349620  5977 caffe.cpp:234] Batch 42, loss = 4.62642\r\nI0831 20:58:26.457484  5977 caffe.cpp:234] Batch 43, accuracy = 0\r\nI0831 20:58:26.457504  5977 caffe.cpp:234] Batch 43, loss = 4.61726\r\nI0831 20:58:26.565490  5977 caffe.cpp:234] Batch 44, accuracy = 0\r\nI0831 20:58:26.565513  5977 caffe.cpp:234] Batch 44, loss = 4.62254\r\nI0831 20:58:26.674124  5977 caffe.cpp:234] Batch 45, accuracy = 0\r\nI0831 20:58:26.674144  5977 caffe.cpp:234] Batch 45, loss = 4.61613\r\nI0831 20:58:26.781986  5977 caffe.cpp:234] Batch 46, accuracy = 0\r\nI0831 20:58:26.782007  5977 caffe.cpp:234] Batch 46, loss = 4.62502\r\nI0831 20:58:26.888824  5977 caffe.cpp:234] Batch 47, accuracy = 0\r\nI0831 20:58:26.888845  5977 caffe.cpp:234] Batch 47, loss = 4.58574\r\nI0831 20:58:26.995069  5977 caffe.cpp:234] Batch 48, accuracy = 0\r\nI0831 20:58:26.995091  5977 caffe.cpp:234] Batch 48, loss = 4.60927\r\nI0831 20:58:27.101228  5977 caffe.cpp:234] Batch 49, accuracy = 0.2\r\nI0831 20:58:27.101250  5977 caffe.cpp:234] Batch 49, loss = 4.60648\r\nI0831 20:58:27.101258  5977 caffe.cpp:239] Loss: 4.60677\r\nI0831 20:58:27.101277  5977 caffe.cpp:251] accuracy = 0.008\r\nI0831 20:58:27.101294  5977 caffe.cpp:251] loss = 4.60677 (* 1 = 4.60677 loss)\r\n```"},"comment":{"url":"https://api.github.com/repos/yjxiong/caffe/issues/comments/136554031","html_url":"https://github.com/yjxiong/caffe/issues/7#issuecomment-136554031","issue_url":"https://api.github.com/repos/yjxiong/caffe/issues/7","id":136554031,"user":{"login":"wanglimin","id":7599642,"avatar_url":"https://avatars.githubusercontent.com/u/7599642?v=3","gravatar_id":"","url":"https://api.github.com/users/wanglimin","html_url":"https://github.com/wanglimin","followers_url":"https://api.github.com/users/wanglimin/followers","following_url":"https://api.github.com/users/wanglimin/following{/other_user}","gists_url":"https://api.github.com/users/wanglimin/gists{/gist_id}","starred_url":"https://api.github.com/users/wanglimin/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/wanglimin/subscriptions","organizations_url":"https://api.github.com/users/wanglimin/orgs","repos_url":"https://api.github.com/users/wanglimin/repos","events_url":"https://api.github.com/users/wanglimin/events{/privacy}","received_events_url":"https://api.github.com/users/wanglimin/received_events","type":"User","site_admin":false},"created_at":"2015-09-01T02:19:09Z","updated_at":"2015-09-01T02:19:09Z","body":"The name of fully connected layer 8 should be fc8 instead of fc8-1. Please\ncheck.\n\nLimin\n\nOn Tue, Sep 1, 2015 at 10:04 AM, Kai Li <notifications@github.com> wrote:\n\n> Hi, @wanglimin <https://github.com/wanglimin> @yjxiong\n> <https://github.com/yjxiong>\n>\n> I use caffe test commond to test the temporal model you provided.\n>\n> ~/caffe-action/build/install/bin/caffe test -model vgg_16_flow_train_val_fast.prototxt -weights ~/very-deep-two-stream/wanglimin-model/cuhk_action_temporal_vgg_16_split1.caffemodel   -gpu 0\n>\n> But the accuray all zero. I can't find what's wrong (but the matlab test\n> code works).\n>\n> I0831 20:58:21.425902  5977 caffe.cpp:210] Running for 50 iterations.\n> I0831 20:58:21.705755  5977 caffe.cpp:234] Batch 0, accuracy = 0\n> I0831 20:58:21.705826  5977 caffe.cpp:234] Batch 0, loss = 4.56581\n> I0831 20:58:21.842283  5977 caffe.cpp:234] Batch 1, accuracy = 0\n> I0831 20:58:21.842308  5977 caffe.cpp:234] Batch 1, loss = 4.57135\n> I0831 20:58:21.968304  5977 caffe.cpp:234] Batch 2, accuracy = 0\n> I0831 20:58:21.968328  5977 caffe.cpp:234] Batch 2, loss = 4.56923\n> I0831 20:58:22.085276  5977 caffe.cpp:234] Batch 3, accuracy = 0\n> I0831 20:58:22.085300  5977 caffe.cpp:234] Batch 3, loss = 4.56122\n> I0831 20:58:22.200157  5977 caffe.cpp:234] Batch 4, accuracy = 0.2\n> I0831 20:58:22.200182  5977 caffe.cpp:234] Batch 4, loss = 4.55828\n> I0831 20:58:22.312468  5977 caffe.cpp:234] Batch 5, accuracy = 0\n> I0831 20:58:22.312490  5977 caffe.cpp:234] Batch 5, loss = 4.55105\n> I0831 20:58:22.422643  5977 caffe.cpp:234] Batch 6, accuracy = 0\n> I0831 20:58:22.422667  5977 caffe.cpp:234] Batch 6, loss = 4.58159\n> I0831 20:58:22.533643  5977 caffe.cpp:234] Batch 7, accuracy = 0\n> I0831 20:58:22.533668  5977 caffe.cpp:234] Batch 7, loss = 4.59634\n> I0831 20:58:22.646528  5977 caffe.cpp:234] Batch 8, accuracy = 0\n> I0831 20:58:22.646553  5977 caffe.cpp:234] Batch 8, loss = 4.59047\n> I0831 20:58:22.761013  5977 caffe.cpp:234] Batch 9, accuracy = 0\n> I0831 20:58:22.761052  5977 caffe.cpp:234] Batch 9, loss = 4.62319\n> I0831 20:58:22.877241  5977 caffe.cpp:234] Batch 10, accuracy = 0\n> I0831 20:58:22.877264  5977 caffe.cpp:234] Batch 10, loss = 4.63062\n> I0831 20:58:22.992969  5977 caffe.cpp:234] Batch 11, accuracy = 0\n> I0831 20:58:22.992990  5977 caffe.cpp:234] Batch 11, loss = 4.63736\n> I0831 20:58:23.105216  5977 caffe.cpp:234] Batch 12, accuracy = 0\n> I0831 20:58:23.105239  5977 caffe.cpp:234] Batch 12, loss = 4.64331\n> I0831 20:58:23.216982  5977 caffe.cpp:234] Batch 13, accuracy = 0\n> I0831 20:58:23.217003  5977 caffe.cpp:234] Batch 13, loss = 4.63844\n> I0831 20:58:23.328269  5977 caffe.cpp:234] Batch 14, accuracy = 0\n> I0831 20:58:23.328290  5977 caffe.cpp:234] Batch 14, loss = 4.62079\n> I0831 20:58:23.436784  5977 caffe.cpp:234] Batch 15, accuracy = 0\n> I0831 20:58:23.436825  5977 caffe.cpp:234] Batch 15, loss = 4.59784\n> I0831 20:58:23.547108  5977 caffe.cpp:234] Batch 16, accuracy = 0\n> I0831 20:58:23.547130  5977 caffe.cpp:234] Batch 16, loss = 4.60577\n> I0831 20:58:23.658761  5977 caffe.cpp:234] Batch 17, accuracy = 0\n> I0831 20:58:23.658782  5977 caffe.cpp:234] Batch 17, loss = 4.59781\n> I0831 20:58:23.770740  5977 caffe.cpp:234] Batch 18, accuracy = 0\n> I0831 20:58:23.770763  5977 caffe.cpp:234] Batch 18, loss = 4.59607\n> I0831 20:58:23.882455  5977 caffe.cpp:234] Batch 19, accuracy = 0\n> I0831 20:58:23.882477  5977 caffe.cpp:234] Batch 19, loss = 4.60705\n> I0831 20:58:23.993892  5977 caffe.cpp:234] Batch 20, accuracy = 0\n> I0831 20:58:23.993913  5977 caffe.cpp:234] Batch 20, loss = 4.60775\n> I0831 20:58:24.102294  5977 caffe.cpp:234] Batch 21, accuracy = 0\n> I0831 20:58:24.102314  5977 caffe.cpp:234] Batch 21, loss = 4.59202\n> I0831 20:58:24.209605  5977 caffe.cpp:234] Batch 22, accuracy = 0\n> I0831 20:58:24.209630  5977 caffe.cpp:234] Batch 22, loss = 4.60682\n> I0831 20:58:24.318430  5977 caffe.cpp:234] Batch 23, accuracy = 0\n> I0831 20:58:24.318454  5977 caffe.cpp:234] Batch 23, loss = 4.62756\n> I0831 20:58:24.424371  5977 caffe.cpp:234] Batch 24, accuracy = 0\n> I0831 20:58:24.424392  5977 caffe.cpp:234] Batch 24, loss = 4.61974\n> I0831 20:58:24.530743  5977 caffe.cpp:234] Batch 25, accuracy = 0\n> I0831 20:58:24.530766  5977 caffe.cpp:234] Batch 25, loss = 4.62237\n> I0831 20:58:24.637039  5977 caffe.cpp:234] Batch 26, accuracy = 0\n> I0831 20:58:24.637059  5977 caffe.cpp:234] Batch 26, loss = 4.6219\n> I0831 20:58:24.744755  5977 caffe.cpp:234] Batch 27, accuracy = 0\n> I0831 20:58:24.744776  5977 caffe.cpp:234] Batch 27, loss = 4.6247\n> I0831 20:58:24.852677  5977 caffe.cpp:234] Batch 28, accuracy = 0\n> I0831 20:58:24.852699  5977 caffe.cpp:234] Batch 28, loss = 4.61173\n> I0831 20:58:24.960541  5977 caffe.cpp:234] Batch 29, accuracy = 0\n> I0831 20:58:24.960561  5977 caffe.cpp:234] Batch 29, loss = 4.6213\n> I0831 20:58:25.068442  5977 caffe.cpp:234] Batch 30, accuracy = 0\n> I0831 20:58:25.068462  5977 caffe.cpp:234] Batch 30, loss = 4.60979\n> I0831 20:58:25.175941  5977 caffe.cpp:234] Batch 31, accuracy = 0\n> I0831 20:58:25.175961  5977 caffe.cpp:234] Batch 31, loss = 4.60837\n> I0831 20:58:25.282083  5977 caffe.cpp:234] Batch 32, accuracy = 0\n> I0831 20:58:25.282104  5977 caffe.cpp:234] Batch 32, loss = 4.61826\n> I0831 20:58:25.388100  5977 caffe.cpp:234] Batch 33, accuracy = 0\n> I0831 20:58:25.388120  5977 caffe.cpp:234] Batch 33, loss = 4.61304\n> I0831 20:58:25.494299  5977 caffe.cpp:234] Batch 34, accuracy = 0\n> I0831 20:58:25.494320  5977 caffe.cpp:234] Batch 34, loss = 4.60632\n> I0831 20:58:25.600524  5977 caffe.cpp:234] Batch 35, accuracy = 0\n> I0831 20:58:25.600544  5977 caffe.cpp:234] Batch 35, loss = 4.60945\n> I0831 20:58:25.706799  5977 caffe.cpp:234] Batch 36, accuracy = 0\n> I0831 20:58:25.706820  5977 caffe.cpp:234] Batch 36, loss = 4.6146\n> I0831 20:58:25.812988  5977 caffe.cpp:234] Batch 37, accuracy = 0\n> I0831 20:58:25.813009  5977 caffe.cpp:234] Batch 37, loss = 4.63083\n> I0831 20:58:25.919095  5977 caffe.cpp:234] Batch 38, accuracy = 0\n> I0831 20:58:25.919114  5977 caffe.cpp:234] Batch 38, loss = 4.58947\n> I0831 20:58:26.025218  5977 caffe.cpp:234] Batch 39, accuracy = 0\n> I0831 20:58:26.025239  5977 caffe.cpp:234] Batch 39, loss = 4.60846\n> I0831 20:58:26.133335  5977 caffe.cpp:234] Batch 40, accuracy = 0\n> I0831 20:58:26.133358  5977 caffe.cpp:234] Batch 40, loss = 4.61726\n> I0831 20:58:26.241446  5977 caffe.cpp:234] Batch 41, accuracy = 0\n> I0831 20:58:26.241468  5977 caffe.cpp:234] Batch 41, loss = 4.60415\n> I0831 20:58:26.349599  5977 caffe.cpp:234] Batch 42, accuracy = 0\n> I0831 20:58:26.349620  5977 caffe.cpp:234] Batch 42, loss = 4.62642\n> I0831 20:58:26.457484  5977 caffe.cpp:234] Batch 43, accuracy = 0\n> I0831 20:58:26.457504  5977 caffe.cpp:234] Batch 43, loss = 4.61726\n> I0831 20:58:26.565490  5977 caffe.cpp:234] Batch 44, accuracy = 0\n> I0831 20:58:26.565513  5977 caffe.cpp:234] Batch 44, loss = 4.62254\n> I0831 20:58:26.674124  5977 caffe.cpp:234] Batch 45, accuracy = 0\n> I0831 20:58:26.674144  5977 caffe.cpp:234] Batch 45, loss = 4.61613\n> I0831 20:58:26.781986  5977 caffe.cpp:234] Batch 46, accuracy = 0\n> I0831 20:58:26.782007  5977 caffe.cpp:234] Batch 46, loss = 4.62502\n> I0831 20:58:26.888824  5977 caffe.cpp:234] Batch 47, accuracy = 0\n> I0831 20:58:26.888845  5977 caffe.cpp:234] Batch 47, loss = 4.58574\n> I0831 20:58:26.995069  5977 caffe.cpp:234] Batch 48, accuracy = 0\n> I0831 20:58:26.995091  5977 caffe.cpp:234] Batch 48, loss = 4.60927\n> I0831 20:58:27.101228  5977 caffe.cpp:234] Batch 49, accuracy = 0.2\n> I0831 20:58:27.101250  5977 caffe.cpp:234] Batch 49, loss = 4.60648\n> I0831 20:58:27.101258  5977 caffe.cpp:239] Loss: 4.60677\n> I0831 20:58:27.101277  5977 caffe.cpp:251] accuracy = 0.008\n> I0831 20:58:27.101294  5977 caffe.cpp:251] loss = 4.60677 (* 1 = 4.60677 loss)\n>\n> —\n> Reply to this email directly or view it on GitHub\n> <https://github.com/yjxiong/caffe/issues/7>.\n>\n\n\n\n-- \nLimin Wang\nPhD, The Chinese University of Hong Kong\nHomepage: http://wanglimin.github.io/\n"}}