Update annotation of layers.py and faq #4329
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lcy-seso
requested changes
Sep 25, 2017
doc/faq/index_cn.rst
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| @@ -440,7 +440,6 @@ PaddlePaddle保存的模型参数文件内容由16字节头信息和网络参数 | |||
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| * :code:`paddle.layer.lstmemory`、:code:`paddle.layer.grumemory`、:code:`paddle.layer.recurrent` 不是通过一般的方式来实现对输出的激活，所以不能采用第一种方式在这几个layer里设置 :code:`drop_rate` 来使用dropout。若要对这几个layer使用dropout，可采用第二种方式，即使用 :code:`paddle.layer.dropout`。 | |||
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| 22. 如何设置learning_rate_schedule | |||
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- 如何设置学习率退火（learning rate annealing ）
- please make the underlying "------" equal to or longer than the title.
doc/faq/index_cn.rst
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| 其中，num_samples_processed为当前已处理的样本总数。 | ||
| * "manual" | ||
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| 这是一种按已训练样本数分段取值的learning_rate_schedule。使用该learning_rate_schedule时，用户通过参数 :code:`learning_rate_args` 设置学习率衰减因子分段函数，当前的学习率为所设置 :code:`learning_rate` 与当前的衰减因子的乘积。以使用Adam算法为例，代码如下： |
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- 这是一种按已训练样本数分段取值的学习率退火方法，用户通过参数……
doc/faq/index_cn.rst
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| * "pass_manual" | ||
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| 这是一种按已训练pass数分段取值的learning_rate_schedule。使用该learning_rate_schedule时，用户通过参数 :code:`learning_rate_args` 设置学习率衰减因子分段函数，当前的学习率为所设置 :code:`learning_rate` 与当前的衰减因子的乘积。以使用Adam算法为例，代码如下： |
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- 这是一种按已训练pass数分段取值的学习率退火方法，用户通过参数 ……
lcy-seso
approved these changes
Sep 25, 2017
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