I0415 15:03:37.603461 27311 solver.cpp:42] Solver scaffolding done.
I0415 15:03:37.603549 27311 solver.cpp:247] Solving AlexNet
I0415 15:03:37.603559 27311 solver.cpp:248] Learning Rate Policy: step
I0415 15:03:37.749981 27311 solver.cpp:214] Iteration 0, loss = 5.45141
I0415 15:03:37.750030 27311 solver.cpp:229]     Train net output #0: loss = 5.45141 (* 1 = 5.45141 loss)
I0415 15:03:37.750048 27311 solver.cpp:489] Iteration 0, lr = 0.001
I0415 15:03:38.316994 27311 solver.cpp:214] Iteration 12, loss = 4.23865
I0415 15:03:38.317054 27311 solver.cpp:229]     Train net output #0: loss = 4.23865 (* 1 = 4.23865 loss)
I0415 15:03:38.317068 27311 solver.cpp:489] Iteration 12, lr = 0.001
I0415 15:03:38.920938 27311 solver.cpp:214] Iteration 24, loss = 2.49914
I0415 15:03:38.921000 27311 solver.cpp:229]     Train net output #0: loss = 2.49914 (* 1 = 2.49914 loss)
I0415 15:03:38.921016 27311 solver.cpp:489] Iteration 24, lr = 0.001
I0415 15:03:39.509793 27311 solver.cpp:214] Iteration 36, loss = 3.76504
I0415 15:03:39.509850 27311 solver.cpp:229]     Train net output #0: loss = 3.76504 (* 1 = 3.76504 loss)
I0415 15:03:39.509861 27311 solver.cpp:489] Iteration 36, lr = 0.001
I0415 15:03:40.080806 27311 solver.cpp:214] Iteration 48, loss = 3.74901
I0415 15:03:40.080862 27311 solver.cpp:229]     Train net output #0: loss = 3.74901 (* 1 = 3.74901 loss)
I0415 15:03:40.080878 27311 solver.cpp:489] Iteration 48, lr = 0.001
I0415 15:03:40.643797 27311 solver.cpp:214] Iteration 60, loss = 2.27091
I0415 15:03:40.643849 27311 solver.cpp:229]     Train net output #0: loss = 2.27091 (* 1 = 2.27091 loss)
I0415 15:03:40.643860 27311 solver.cpp:489] Iteration 60, lr = 0.001
I0415 15:03:41.217475 27311 solver.cpp:214] Iteration 72, loss = 2.67078
I0415 15:03:41.217541 27311 solver.cpp:229]     Train net output #0: loss = 2.67078 (* 1 = 2.67078 loss)
I0415 15:03:41.217561 27311 solver.cpp:489] Iteration 72, lr = 0.001
I0415 15:03:41.793390 27311 solver.cpp:214] Iteration 84, loss = 1.77313
I0415 15:03:41.793452 27311 solver.cpp:229]     Train net output #0: loss = 1.77313 (* 1 = 1.77313 loss)
I0415 15:03:41.793468 27311 solver.cpp:489] Iteration 84, lr = 0.001
I0415 15:03:42.362951 27311 solver.cpp:214] Iteration 96, loss = 3.49406
I0415 15:03:42.363004 27311 solver.cpp:229]     Train net output #0: loss = 3.49406 (* 1 = 3.49406 loss)
I0415 15:03:42.363025 27311 solver.cpp:489] Iteration 96, lr = 0.001
I0415 15:03:42.946568 27311 solver.cpp:214] Iteration 108, loss = 2.81601
I0415 15:03:42.946633 27311 solver.cpp:229]     Train net output #0: loss = 2.81601 (* 1 = 2.81601 loss)
I0415 15:03:42.946651 27311 solver.cpp:489] Iteration 108, lr = 0.001
I0415 15:03:43.524155 27311 solver.cpp:214] Iteration 120, loss = 2.85056
I0415 15:03:43.524247 27311 solver.cpp:229]     Train net output #0: loss = 2.85056 (* 1 = 2.85056 loss)
I0415 15:03:43.524265 27311 solver.cpp:489] Iteration 120, lr = 0.001
I0415 15:03:44.100580 27311 solver.cpp:214] Iteration 132, loss = 3.58945
I0415 15:03:44.100646 27311 solver.cpp:229]     Train net output #0: loss = 3.58945 (* 1 = 3.58945 loss)
I0415 15:03:44.100661 27311 solver.cpp:489] Iteration 132, lr = 0.001
F0415 15:03:44.536542 27311 math_functions.cpp:91] Check failed: error == cudaSuccess (4 vs. 0)  unspecified launch failure
*** Check failure stack trace: ***
    @     0x7f01dbd9ddaa  (unknown)
    @     0x7f01dbd9dce4  (unknown)
    @     0x7f01dbd9d6e6  (unknown)
    @     0x7f01dbda0687  (unknown)
    @     0x7f01dc1bb3f5  caffe::caffe_copy<>()
    @     0x7f01dc230232  caffe::BasePrefetchingDataLayer<>::Forward_gpu()
    @     0x7f01dc1d9d6f  caffe::Net<>::ForwardFromTo()
    @     0x7f01dc1da197  caffe::Net<>::ForwardPrefilled()
    @     0x7f01dc20cbe5  caffe::Solver<>::Step()
    @     0x7f01dc20d52f  caffe::Solver<>::Solve()
    @           0x406428  train()
    @           0x404961  main
    @     0x7f01db2afec5  (unknown)
    @           0x404f0d  (unknown)
    @              (nil)  (unknown)
Aborted
wangxiao@gtx-980:~/Downloads/lstm_caffe_master$

---------------------------------------------------------------------------------------------------------------------------------------

怎么破 ？？？求解答。。。。

well, the only word I want to say is : where amazing happens ???

I restart my pc and run the code again and it worked ......

caffe 训练时，出现错误：Check failed: error == cudaSuccess (4 vs. 0) unspecified launch failure的更多相关文章

  1. caffe运行错误： im2col.cu:61] Check failed: error == cudaSuccess (8 vs. 0) invalid device function

    错误: im2col.cu:61] Check failed: error == cudaSuccess (8 vs. 0)  invalid device function 原因:由于Makefil ...

  2. 配置SSD-caffe测试时出现“Check failed: error == cudaSuccess (10 vs. 0) invalid device ordinal”解决方案

    这是由于GPU数量不匹配造成的,如果训练自己的数据,那么我们只需要将solver.prototxt文件中的device_id项改为自己的GPU块数,一块就是0,两块就是1,以此类推. 但是SSD配置时 ...

  3. 【CUDA开发】 Check failed: error == cudaSuccess (8 vs. 0) invalid device function

    最近在复现R-CNN一系列的实验时,配置代码环境真是花费了不少时间.由于对MATLAB不熟悉,实验采用的都是github上rbg大神的Python版本.在配置Faster R-CNN时,编译没有问题, ...

  4. Caffe 分类问题 Check failed: error == cudaSuccess (2 vs. 0) out of memory

    如果图片过大,需要适当缩小batch_size的值,否则使用GPU时可能超出其缓存大小而报错

  5. Check failed: status == CUBLAS_STATUS_SUCCESS (11 vs. 0) CUBLAS_STATUS_MAPPING_ERROR

    I0930 21:23:15.115576 30918 solver.cpp:281] Learning Rate Policy: multistepF0930 21:23:17.263314 310 ...

  6. check failed status == cudnn_status_success (4 vs. 0) cudnn_status_internal_error

    Check failed: error == cudaSuccess (30 vs. 0) unknown error  这个有可能是显存不足造成的,或者网络参数不对造成的 check failed ...

  7. 目标检测faster rcnn error == cudaSuccess (2 vs. 0) out of memory

    想尝试 更深更强的网络,或者自己写了一个费显存的层,发现1080 ti的11G显存不够用了,老师报显存不够怎么办? Check failed: error == cudaSuccess (2 vs. ...

  8. 发布到远程存储库时遇到错误: Git failed with a fatal error.

    正在推送 master发布到远程存储库时遇到错误: Git failed with a fatal error.Authentication failed for 'http://1212121xxx ...

  9. windows7下解决caffe check failed registry.count(type) == 1(0 vs. 1) unknown layer type问题

    在Windows7下调用vs2013生成的Caffe静态库时经常会提示Check failed: registry.count(type) == 1 (0 vs. 1) Unknown layer t ...

随机推荐

  1. windows azure中国 里面建立一个虚拟机，与虚拟机建立通信 里面部署IIS，外网访问

    在windows azure中国 里面建立一个虚拟机,里面部署IIS,外网不能访问么? 外网访问的地址是给的那个DNS地址 ,比如我的是 DNS 名称 urbanairserver.cloudapp. ...

  2. sidePagination: "server"和responseHandler: responseHandler

    bootstrapTable()中有两个属性 一个是sidePagination,表示服务器分页,responseHandler:responseHandler 表示回应操作的rows和total 两 ...

  3. PAT 06-1 简单计算器

    想看一般简单计算器实现的看客不好意思了,这不是你想要点东西,此处题设为“只能进行加减乘除”.“都是整数”.”优先级相同“和"从左到右".此题来自PAT(http://www.pat ...

  4. fwrite错误

    使用fwrite出错 f:\dd\vctools\crt_bld\self_x86\crt\srt\write.cline:69expression:_osfile(fh)&FOPEN 使用w ...

  5. 【转】FFMPEG 库移植到 VC 需要的步骤

    原文:http://blog.csdn.net/leixiaohua1020/article/details/12747899 在VC下使用FFMPEG编译好的库,不仅仅是把.h,.lib,.dll拷 ...

  6. HDOJ-三部曲-1002-Radar Installation

    Radar Installation Time Limit : 2000/1000ms (Java/Other)   Memory Limit : 20000/10000K (Java/Other) ...

  7. 后序/中序--->前序

    preOrder 5 3 2 4 8 6 9   midOrder 2 3 4 5 6 8 9  postOrder 2 4 3 6 9 8 5 #include <iostream> # ...

  8. Angularjs相关理论

    1.AngularJS的工作流程: (1)浏览器载入HTML,然后把它解析成DOM (2)浏览器载入angularjs脚本 (3)AngularJS等到DOMContentLoaded事件触发 (4) ...

  9. Helo command rejected: need fully-qualified hostname

    Helo command rejected: need fully-qualified hostname问题 是由于postfix的配置文件(main.cf)有问题.其中有一个smtpd_sasl_l ...

  10. Magento在IE下登陆不了后台，在Firefox下正常

    目前的解决办法如下: 方法一,用FF登陆后台,在 System—Configuration-Web-Session Cookie management....timeout 改为:86400 方法二: ...