From 4616a8c8bb6a11c25d4cd94b05f9b640c4aa0d8f Mon Sep 17 00:00:00 2001 From: Travis CI Date: Tue, 14 Mar 2017 09:53:35 +0000 Subject: [PATCH] Deploy to GitHub Pages: 95b4e397dc6a81c008eb544a1d9d9576ce7dc7c2 --- develop/doc_cn/_sources/faq/index_cn.rst.txt | 13 +++++++++++++ develop/doc_cn/faq/index_cn.html | 13 +++++++++++++ develop/doc_cn/searchindex.js | 2 +- 3 files changed, 27 insertions(+), 1 deletion(-) diff --git a/develop/doc_cn/_sources/faq/index_cn.rst.txt b/develop/doc_cn/_sources/faq/index_cn.rst.txt index 6d5367177da..df5e1722522 100644 --- a/develop/doc_cn/_sources/faq/index_cn.rst.txt +++ b/develop/doc_cn/_sources/faq/index_cn.rst.txt @@ -286,3 +286,16 @@ PaddlePaddle的参数使用名字 :code:`name` 作为参数的ID，相同名字 .. code-block:: bash paddle train --use_gpu=true --trainer_count=2 --gpu_id=2 + + +12. 训练过程中出现 :code:`Floating point exception`, 训练因此退出怎么办? +------------------------------------------------------------------------ + +Paddle二进制在运行时捕获了浮点数异常，只要出现浮点数异常(即训练过程中出现NaN或者Inf)，立刻退出。浮点异常通常的原因是浮点数溢出、除零等问题。 +主要原因包括两个方面: + +* 训练过程中参数或者训练过程中的梯度尺度过大，导致参数累加，乘除等时候，导致了浮点数溢出。 +* 模型一直不收敛，发散到了一个数值特别大的地方。 +* 训练数据有问题，导致参数收敛到了一些奇异的情况。或者输入数据尺度过大，有些特征的取值达到数百万，这时进行矩阵乘法运算就可能导致浮点数溢出。 + +主要的解决办法是减小学习律或者对数据进行归一化处理。 diff --git a/develop/doc_cn/faq/index_cn.html b/develop/doc_cn/faq/index_cn.html index fc0f542d556..ff6c043249a 100644 --- a/develop/doc_cn/faq/index_cn.html +++ b/develop/doc_cn/faq/index_cn.html @@ -203,6 +203,7 @@
  • 10. CMake源码编译, 找到的PythonLibs和PythonInterp版本不一致
  • 10. A protocol message was rejected because it was too big
  • 11. 如何指定GPU设备
    • +
  • 12. 训练过程中出现 Floating point exception, 训练因此退出怎么办?
    • @@ -260,6 +261,7 @@
  • 10. CMake源码编译, 找到的PythonLibs和PythonInterp版本不一致
  • 10. A protocol message was rejected because it was too big
  • 11. 如何指定GPU设备
    • +
  • 12. 训练过程中出现 Floating point exception, 训练因此退出怎么办?
    • @@ -561,6 +563,17 @@ F1205 14:59:50.295174 14703 Traine +
    +

    12. 训练过程中出现 Floating point exception, 训练因此退出怎么办?

    +

    Paddle二进制在运行时捕获了浮点数异常，只要出现浮点数异常(即训练过程中出现NaN或者Inf)，立刻退出。浮点异常通常的原因是浮点数溢出、除零等问题。 +主要原因包括两个方面:

    + +

    主要的解决办法是减小学习律或者对数据进行归一化处理。

    +
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