From 615e6d912d8bd1058d52372c89c357c150245e9d Mon Sep 17 00:00:00 2001 From: Travis CI Date: Mon, 25 Sep 2017 05:19:02 +0000 Subject: [PATCH] Deploy to GitHub Pages: 14a7399d22d26de242196cf0085081780a21a918 --- develop/doc_cn/_sources/faq/index_cn.rst.txt | 123 ++++++++++- develop/doc_cn/faq/index_cn.html | 211 ++++++++++++++----- develop/doc_cn/searchindex.js | 2 +- 3 files changed, 284 insertions(+), 52 deletions(-) diff --git a/develop/doc_cn/_sources/faq/index_cn.rst.txt b/develop/doc_cn/_sources/faq/index_cn.rst.txt index acbf4c87ae..b3ecfba791 100644 --- a/develop/doc_cn/_sources/faq/index_cn.rst.txt +++ b/develop/doc_cn/_sources/faq/index_cn.rst.txt @@ -390,4 +390,125 @@ PaddlePaddle保存的模型参数文件内容由16字节头信息和网络参数 * 如果发现最早的报错就是网络通信的问题，很有可能是非独占方式执行导致的端口冲突，可以联系OP，看当前MPI集群是否支持resource=full参数提交，如果支持增加此参数提交，并更换job 端口。 -* 如果当前MPI集群并不支持任务独占模式，可以联系OP是否可以更换集群或升级当前集群。 \ No newline at end of file +* 如果当前MPI集群并不支持任务独占模式，可以联系OP是否可以更换集群或升级当前集群。 + +19. PaddlePaddle如何输出多个层 +------------------------------ + +* 将需要输出的层作为 :code:`paddle.inference.Inference()` 接口的 :code:`output_layer` 参数输入，代码如下： + +.. code-block:: python + + inferer = paddle.inference.Inference(output_layer=[layer1, layer2], parameters=parameters) + +* 指定要输出的字段进行输出。以输出 :code:`value` 字段为例，代码如下： + +.. code-block:: python + + out = inferer.infer(input=data_batch, flatten_result=False, field=["value"]) + +这里设置 :code:`flatten_result=False`，得到的输出结果是元素个数等于输出字段数的 :code:`list`，该 :code:`list` 的每个元素是由所有输出层相应字段结果组成的 :code:`list`，每个字段结果的类型是 :code:`numpy.array`。:code:`flatten_result` 的默认值为 :code:`True`，该情况下，PaddlePaddle会分别对每个字段将所有输出层的结果按行进行拼接，如果各输出层该字段 :code:`numpy.array` 结果的相应维数不匹配，程序将不能正常运行。 + +20. :code:`paddle.layer.memory` 的参数 :code:`name` 如何使用 +------------------------------------------------------------- + +* :code:`paddle.layer.memory` 用于获取特定layer上一时间步的输出，该layer是通过参数 :code:`name` 指定，即，:code:`paddle.layer.memory` 会关联参数 :code:`name` 取值相同的layer，并将该layer上一时间步的输出作为自身当前时间步的输出。 + +* PaddlePaddle的所有layer都有唯一的name，用户通过参数 :code:`name` 设定，当用户没有显式设定时，PaddlePaddle会自动设定。而 :code:`paddle.layer.memory` 不是真正的layer，其name由参数 :code:`memory_name` 设定，当用户没有显式设定时，PaddlePaddle会自动设定。:code:`paddle.layer.memory` 的参数 :code:`name` 用于指定其要关联的layer，需要用户显式设定。 + +21. dropout 使用 +----------------- + +* 在PaddlePaddle中使用dropout有两种方式 + + * 在相应layer的 :code:`layer_atter` 设置 :code:`drop_rate`，以 :code:`paddle.layer.fc` 为例，代码如下： + + .. code-block:: python + + fc = paddle.layer.fc(input=input, layer_attr=paddle.attr.ExtraLayerAttribute(drop_rate=0.5)) + + * 使用 :code:`paddle.layer.dropout`，以 :code:`paddle.layer.fc` 为例，代码如下： + + .. code-block:: python + + fc = paddle.layer.fc(input=input) + drop_fc = paddle.layer.dropout(input=fc, dropout_rate=0.5) + +* :code:`paddle.layer.dropout` 实际上使用了 :code:`paddle.layer.add_to`，并在该layer里采用第一种方式设置 :code:`drop_rate` 来使用dropout的。这种方式对内存消耗较大。 + +* PaddlePaddle在激活函数里实现dropout，而不是在layer里实现。 + +* :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`。 + +22. 如何设置学习率退火（learning rate annealing） +------------------------------------------------ + +在相应的优化算法里设置learning_rate_schedule及相关参数，以使用Adam算法为例，代码如下： + +.. code-block:: python + + optimizer = paddle.optimizer.Adam( + learning_rate=1e-3, + learning_rate_decay_a=0.5, + learning_rate_decay_b=0.75, + learning_rate_schedule="poly",) + +PaddlePaddle目前支持8种learning_rate_schedule，这8种learning_rate_schedule及其对应学习率计算方式如下： + +* "constant" + + lr = learning_rate + +* "poly" + + lr = learning_rate * pow(1 + learning_rate_decay_a * num_samples_processed, -learning_rate_decay_b) + + 其中，num_samples_processed为已训练样本数，下同。 + +* "caffe_poly" + + lr = learning_rate * pow(1.0 - num_samples_processed / learning_rate_decay_a, learning_rate_decay_b) + +* "exp" + + lr = learning_rate * pow(learning_rate_decay_a, num_samples_processed / learning_rate_decay_b) + +* "discexp" + + lr = learning_rate * pow(learning_rate_decay_a, floor(num_samples_processed / learning_rate_decay_b)) + +* "linear" + + lr = max(learning_rate - learning_rate_decay_a * num_samples_processed, learning_rate_decay_b) + +* "manual" + + 这是一种按已训练样本数分段取值的学习率退火方法。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： + + .. code-block:: python + + optimizer = paddle.optimizer.Adam( + learning_rate=1e-3, + learning_rate_schedule="manual", + learning_rate_args="1000:1.0,2000:0.9,3000:0.8",) + + 在该示例中，当已训练样本数小于等于1000时，学习率为 :code:`1e-3 * 1.0`；当已训练样本数大于1000小于等于2000时，学习率为 :code:`1e-3 * 0.9`；当已训练样本数大于2000时，学习率为 :code:`1e-3 * 0.8`。 + +* "pass_manual" + + 这是一种按已训练pass数分段取值的学习率退火方法。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： + + .. code-block:: python + + optimizer = paddle.optimizer.Adam( + learning_rate=1e-3, + learning_rate_schedule="manual", + learning_rate_args="1:1.0,2:0.9,3:0.8",) + + 在该示例中，当已训练pass数小于等于1时，学习率为 :code:`1e-3 * 1.0`；当已训练pass数大于1小于等于2时，学习率为 :code:`1e-3 * 0.9`；当已训练pass数大于2时，学习率为 :code:`1e-3 * 0.8`。 + +23. 出现 :code:`Duplicated layer name` 错误怎么办 +-------------------------------------------------- + +出现该错误的原因一般是用户对不同layer的参数 :code:`name` 设置了相同的取值。遇到该错误时，先找出参数 :code:`name` 取值相同的layer，然后将这些layer的参数 :code:`name` 设置为不同的值。 + diff --git a/develop/doc_cn/faq/index_cn.html b/develop/doc_cn/faq/index_cn.html index bd0afac66d..0830eed78a 100644 --- a/develop/doc_cn/faq/index_cn.html +++ b/develop/doc_cn/faq/index_cn.html @@ -186,45 +186,50 @@
-

FAQ

+

FAQ

Contents

-

1. 如何减少内存占用

+

1. 如何减少内存占用

神经网络的训练本身是一个非常消耗内存和显存的工作，经常会消耗数10GB的内存和数GB的显存。 PaddlePaddle的内存占用主要分为如下几个方面:

    @@ -235,7 +240,7 @@ PaddlePaddle的内存占用主要分为如下几个方面:

其中，其他内存杂项是指PaddlePaddle本身所用的一些内存，包括字符串分配，临时变量等等，暂不考虑在内。

-

减少DataProvider缓冲池内存

+

减少DataProvider缓冲池内存

PyDataProvider使用的是异步加载，同时在内存里直接随即选取数据来做Shuffle。即

digraph { rankdir=LR; @@ -255,7 +260,7 @@ PaddlePaddle的内存占用主要分为如下几个方面:</p> <p>这样做可以极大的减少内存占用，并且可能会加速训练过程，详细文档参考 <a class=PyDataProvider2的使用 。

-

神经元激活内存

+

神经元激活内存

神经网络在训练的时候，会对每一个激活暂存一些数据，如神经元激活值等。 在反向传递的时候，这些数据会被用来更新参数。这些数据使用的内存主要和两个参数有关系， 一是batch size，另一个是每条序列(Sequence)长度。所以，其实也是和每个mini-batch中包含 @@ -268,7 +273,7 @@ PaddlePaddle的内存占用主要分为如下几个方面:

-

参数内存

+

参数内存

PaddlePaddle支持非常多的优化算法(Optimizer)，不同的优化算法需要使用不同大小的内存。 例如使用 adadelta 算法，则需要使用等于权重参数规模大约5倍的内存。举例，如果参数保存下来的模型目录 文件为 100M， 那么该优化算法至少需要 500M 的内存。

@@ -276,7 +281,7 @@ PaddlePaddle的内存占用主要分为如下几个方面:

-

2. 如何加速PaddlePaddle的训练速度

+

2. 如何加速PaddlePaddle的训练速度

加速PaddlePaddle训练可以考虑从以下几个方面：

  • 减少数据载入的耗时
    • @@ -284,7 +289,7 @@ PaddlePaddle的内存占用主要分为如下几个方面:

  • 利用分布式训练驾驭更多的计算资源
-

减少数据载入的耗时

+

减少数据载入的耗时

使用pydataprovider时，可以减少缓存池的大小，同时设置内存缓存功能，即可以极大的加速数据载入流程。 DataProvider 缓存池的减小，和之前减小通过减小缓存池来减小内存占用的原理一致。

@provider(min_pool_size=0, ...)
@@ -298,7 +303,7 @@ PaddlePaddle的内存占用主要分为如下几个方面:

 
同时 @provider 接口有一个 cache 参数来控制缓存方法，将其设置成 CacheType.CACHE_PASS_IN_MEM 的话，会将第一个 pass (过完所有训练数据即为一个pass)生成的数据缓存在内存里，在之后的 pass 中，不会再从 python 端读取数据，而是直接从内存的缓存里读取数据。这也会极大减少数据读入的耗时。
-

加速训练速度

+

加速训练速度

PaddlePaddle支持Sparse的训练，sparse训练需要训练特征是 sparse_binary_vector 、 sparse_vector 、或者 integer_value 的任一一种。同时，与这个训练数据交互的Layer，需要将其Parameter设置成 sparse 更新模式，即设置 sparse_update=True

这里使用简单的 word2vec 训练语言模型距离，具体使用方法为:

使用一个词前两个词和后两个词，来预测这个中间的词。这个任务的DataProvider为:

@@ -331,7 +336,7 @@ PaddlePaddle的内存占用主要分为如下几个方面:

-

利用更多的计算资源

+

利用更多的计算资源

利用更多的计算资源可以分为一下几个方式来进行:

  • 单机CPU训练
      @@ -351,17 +356,17 @@ PaddlePaddle的内存占用主要分为如下几个方面:

-

3. 遇到“非法指令”或者是“illegal instruction”

+

3. 遇到“非法指令”或者是“illegal instruction”

PaddlePaddle使用avx SIMD指令提高cpu执行效率，因此错误的使用二进制发行版可能会导致这种错误，请选择正确的版本。

-

4. 如何选择SGD算法的学习率

+

4. 如何选择SGD算法的学习率

在采用sgd/async_sgd进行训练时，一个重要的问题是选择正确的learning_rate。如果learning_rate太大，那么训练有可能不收敛，如果learning_rate太小，那么收敛可能很慢，导致训练时间过长。

通常做法是从一个比较大的learning_rate开始试，如果不收敛，那减少学习率10倍继续试验，直到训练收敛为止。那么如何判断训练不收敛呢？可以估计出如果模型采用不变的输出最小的cost0是多少。

如果训练过程的的cost明显高于这个常数输出的cost，那么我们可以判断为训练不收敛。举一个例子，假如我们是三分类问题，采用multi-class-cross-entropy作为cost，数据中0,1,2三类的比例为 0.2, 0.5, 0.3 , 那么常数输出所能达到的最小cost是 -(0.2*log(0.2)+0.5*log(0.5)+0.3*log(0.3))=1.03 。如果训练一个pass（或者更早）后，cost还大于这个数，那么可以认为训练不收敛，应该降低学习率。

-

5. 如何初始化参数

+

5. 如何初始化参数

默认情况下，PaddlePaddle使用均值0，标准差为 \(\frac{1}{\sqrt{d}}\) 来初始化参数。其中 \(d\) 为参数矩阵的宽度。这种初始化方式在一般情况下不会产生很差的结果。如果用户想要自定义初始化方式，PaddlePaddle目前提供两种参数初始化的方式:

  • 高斯分布。将 param_attr 设置成 param_attr=ParamAttr(initial_mean=0.0, initial_std=1.0)
    • @@ -375,7 +380,7 @@ PaddlePaddle的内存占用主要分为如下几个方面:

    上述代码将bias全部初始化为1.0, 同时将参数初始化为 [1.0, -1.0] 的均匀分布。

-

6. 如何共享参数

+

6. 如何共享参数

PaddlePaddle的参数使用名字 name 作为参数的ID，相同名字的参数，会共享参数。设置参数的名字，可以使用 ParamAttr(name="YOUR_PARAM_NAME") 来设置。更方便的设置方式，是使得要共享的参数使用同样的 ParamAttr 对象。

简单的全连接网络，参数共享的配置示例为:

from paddle.trainer_config_helpers import *
@@ -412,7 +417,7 @@ PaddlePaddle的内存占用主要分为如下几个方面:

 
这里 hidden_a 和 hidden_b 使用了同样的parameter和bias。并且softmax层的两个输入也使用了同样的参数 softmax_param。
-

7. paddlepaddle*.whl is not a supported wheel on this platform.

+

7. paddlepaddle*.whl is not a supported wheel on this platform.

出现这个问题的主要原因是，没有找到和当前系统匹配的paddlepaddle安装包。最新的paddlepaddle python安装包支持Linux x86_64和MacOS 10.12操作系统，并安装了python 2.7和pip 9.0.1。

更新 pip 包的方法是:

pip install --upgrade pip
@@ -424,7 +429,7 @@ PaddlePaddle的内存占用主要分为如下几个方面:

 如果系统支持 manylinux1_x86_64 而安装包（本地）是 linux_x86_64 ，可以重命名这个whl包为 manylinux1_x86_64 再安装。
-

8. python相关的单元测试都过不了

+

8. python相关的单元测试都过不了

如果出现以下python相关的单元测试都过不了的情况：

24 - test_PyDataProvider (Failed)
 26 - test_RecurrentGradientMachine (Failed)
@@ -455,7 +460,7 @@ Please uninstall paddle package before start unittest. Try to &
-

9. 运行Docker GPU镜像出现 “CUDA driver version is insufficient”

+

9. 运行Docker GPU镜像出现 “CUDA driver version is insufficient”

用户在使用PaddlePaddle GPU的Docker镜像的时候，常常出现 Cuda Error: CUDA driver version is insufficient for CUDA runtime version, 原因在于没有把机器上CUDA相关的驱动和库映射到容器内部。 具体的解决方法是：

$ export CUDA_SO="$(\ls usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')"
@@ -466,7 +471,7 @@ $ docker run ${CUDA_SO更多关于Docker的安装与使用, 请参考 PaddlePaddle Docker 文档 。
-

10. CMake源码编译, 找到的PythonLibs和PythonInterp版本不一致

+

10. CMake源码编译, 找到的PythonLibs和PythonInterp版本不一致

这是目前CMake寻找Python的逻辑存在缺陷，如果系统安装了多个Python版本，CMake找到的Python库和Python解释器版本可能有不一致现象，导致编译PaddlePaddle失败。正确的解决方法是， 用户强制指定特定的Python版本，具体操作如下：

@@ -477,7 +482,7 @@ $ docker run ${CUDA_SO用户需要指定本机上Python的路径：<exc_path>, <lib_path>, <inc_path>

-

11. CMake源码编译，Paddle版本号为0.0.0

+

11. CMake源码编译，Paddle版本号为0.0.0

如果运行 paddle version, 出现 PaddlePaddle 0.0.0；或者运行 cmake ..，出现

CMake Warning at cmake/version.cmake:20 (message):
   Cannot add paddle version from git tag
@@ -486,7 +491,7 @@ $ docker run ${CUDA_SO那么用户需要拉取所有的远程分支到本机，命令为 git fetch upstream，然后重新cmake即可。
-

12. A protocol message was rejected because it was too big

+

12. A protocol message was rejected because it was too big

如果在训练NLP相关模型时，出现以下错误：

[libprotobuf ERROR google/protobuf/io/coded_stream.cc:171] A protocol message was rejected because it was too big (more than 67108864 bytes).  To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.
 F1205 14:59:50.295174 14703 TrainerConfigHelper.cpp:59] Check failed: m->conf.ParseFromString(configProtoStr)
@@ -517,7 +522,7 @@ F1205 14:59:50.295174 14703 Traine
 
完整源码可参考 seqToseq 示例。
-

13. 如何指定GPU设备

+

13. 如何指定GPU设备

例如机器上有4块GPU，编号从0开始，指定使用2、3号GPU：

  • 方式1：通过 CUDA_VISIBLE_DEVICES 环境变量来指定特定的GPU。
    • @@ -533,7 +538,7 @@ F1205 14:59:50.295174 14703 Traine
-

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

+

14. 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:

    @@ -544,14 +549,14 @@ F1205 14:59:50.295174 14703 Traine

    主要的解决办法是减小学习率或者对数据进行归一化处理。

-

15. 编译安装后执行 import paddle.v2 as paddle 报ImportError: No module named v2

+

15. 编译安装后执行 import paddle.v2 as paddle 报ImportError: No module named v2

先查看一下是否曾经安装过paddle v1版本，有的话需要先卸载：

pip uninstall py_paddle paddle

然后安装paddle的python环境, 在build目录下执行

pip install python/dist/paddle*.whl && pip install ../paddle/dist/py_paddle*.whl

-

16. PaddlePaddle存储的参数格式是什么，如何和明文进行相互转化

+

16. PaddlePaddle存储的参数格式是什么，如何和明文进行相互转化

PaddlePaddle保存的模型参数文件内容由16字节头信息和网络参数两部分组成。头信息中，1~4字节表示PaddlePaddle版本信息，请直接填充0；5~8字节表示每个参数占用的字节数，当保存的网络参数为float类型时为4，double类型时为8；9~16字节表示保存的参数总个数。

将PaddlePaddle保存的模型参数还原回明文时，可以使用相应数据类型的 numpy.array 加载具体网络参数，此时可以跳过PaddlePaddle模型参数文件的头信息。若在PaddlePaddle编译时，未指定按照double精度编译，默认情况下按照float精度计算，保存的参数也是float类型。这时在使用 numpy.array 时，一般设置 dtype=float32 。示例如下：

def read_parameter(fname, width):
@@ -574,7 +579,7 @@ F1205 14:59:50.295174 14703 Traine
-

17. 如何加载预训练参数

+

17. 如何加载预训练参数

  • 对加载预训练参数的层，设置其参数属性 is_static=True，使该层的参数在训练过程中保持不变。以embedding层为例，代码如下：
@@ -596,7 +601,7 @@ F1205 14:59:50.295174 14703 Traine
-

18. 集群多节点训练，日志中保存均为网络通信类错误

+

18. 集群多节点训练，日志中保存均为网络通信类错误

集群多节点训练，日志报错为网络通信类错误，比如 Connection reset by peer 等。 此类报错通常是由于某一个节点的错误导致这个节点的训练进程退出，从而引发其他节点无法连接导致，可以参考下面的步骤排查：

    @@ -605,6 +610,112 @@ F1205 14:59:50.295174 14703 Traine
  • 如果当前MPI集群并不支持任务独占模式，可以联系OP是否可以更换集群或升级当前集群。
+
+

19. PaddlePaddle如何输出多个层

+
    +
  • 将需要输出的层作为 paddle.inference.Inference() 接口的 output_layer 参数输入，代码如下：
    • +
+
inferer = paddle.inference.Inference(output_layer=[layer1, layer2], parameters=parameters)
+
+
+
    +
  • 指定要输出的字段进行输出。以输出 value 字段为例，代码如下：
    • +
+
out = inferer.infer(input=data_batch, flatten_result=False, field=["value"])
+
+
+

这里设置 flatten_result=False，得到的输出结果是元素个数等于输出字段数的 list，该 list 的每个元素是由所有输出层相应字段结果组成的 list，每个字段结果的类型是 numpy.array。flatten_result 的默认值为 True，该情况下，PaddlePaddle会分别对每个字段将所有输出层的结果按行进行拼接，如果各输出层该字段 numpy.array 结果的相应维数不匹配，程序将不能正常运行。

+
+
+

20. paddle.layer.memory 的参数 name 如何使用

+
    +
  • paddle.layer.memory 用于获取特定layer上一时间步的输出，该layer是通过参数 name 指定，即，paddle.layer.memory 会关联参数 name 取值相同的layer，并将该layer上一时间步的输出作为自身当前时间步的输出。
    • +
  • PaddlePaddle的所有layer都有唯一的name，用户通过参数 name 设定，当用户没有显式设定时，PaddlePaddle会自动设定。而 paddle.layer.memory 不是真正的layer，其name由参数 memory_name 设定，当用户没有显式设定时，PaddlePaddle会自动设定。paddle.layer.memory 的参数 name 用于指定其要关联的layer，需要用户显式设定。
    • +
+
+
+

21. dropout 使用

+
    +

  • 在PaddlePaddle中使用dropout有两种方式

    +
      +
    • 在相应layer的 layer_atter 设置 drop_rate，以 paddle.layer.fc 为例，代码如下：
      • +
    +
    fc = paddle.layer.fc(input=input, layer_attr=paddle.attr.ExtraLayerAttribute(drop_rate=0.5))
+
    +
    +
      +
    • 使用 paddle.layer.dropout，以 paddle.layer.fc 为例，代码如下：
      • +
    +
    fc = paddle.layer.fc(input=input)
+drop_fc = paddle.layer.dropout(input=fc, dropout_rate=0.5)
+
    +
    +
    • +

  • paddle.layer.dropout 实际上使用了 paddle.layer.add_to，并在该layer里采用第一种方式设置 drop_rate 来使用dropout的。这种方式对内存消耗较大。

    +
    • +

  • PaddlePaddle在激活函数里实现dropout，而不是在layer里实现。

    +
    • +

  • paddle.layer.lstmemory、paddle.layer.grumemory、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 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 paddle.layer.dropout。

    +
    • +
+
+
+

22. 如何设置学习率退火（learning rate annealing）

+

在相应的优化算法里设置learning_rate_schedule及相关参数，以使用Adam算法为例，代码如下：

+
optimizer = paddle.optimizer.Adam(
+    learning_rate=1e-3,
+    learning_rate_decay_a=0.5,
+    learning_rate_decay_b=0.75,
+    learning_rate_schedule="poly",)
+
+
+

PaddlePaddle目前支持8种learning_rate_schedule，这8种learning_rate_schedule及其对应学习率计算方式如下：

+
    +

  • “constant”

    +

    lr = learning_rate

    +
    • +

  • “poly”

    +

    lr = learning_rate * pow(1 + learning_rate_decay_a * num_samples_processed, -learning_rate_decay_b)

    +

    其中，num_samples_processed为已训练样本数，下同。

    +
    • +

  • “caffe_poly”

    +

    lr = learning_rate * pow(1.0 - num_samples_processed / learning_rate_decay_a, learning_rate_decay_b)

    +
    • +

  • “exp”

    +

    lr = learning_rate * pow(learning_rate_decay_a, num_samples_processed / learning_rate_decay_b)

    +
    • +

  • “discexp”

    +

    lr = learning_rate * pow(learning_rate_decay_a, floor(num_samples_processed / learning_rate_decay_b))

    +
    • +

  • “linear”

    +

    lr = max(learning_rate - learning_rate_decay_a * num_samples_processed, learning_rate_decay_b)

    +
    • +

  • “manual”

    +

    这是一种按已训练样本数分段取值的学习率退火方法。使用该learning_rate_schedule时，用户通过参数 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 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：

    +
    optimizer = paddle.optimizer.Adam(
+    learning_rate=1e-3,
+    learning_rate_schedule="manual",
+    learning_rate_args="1000:1.0,2000:0.9,3000:0.8",)
+
    +
    +

    在该示例中，当已训练样本数小于等于1000时，学习率为 1e-3 * 1.0；当已训练样本数大于1000小于等于2000时，学习率为 1e-3 * 0.9；当已训练样本数大于2000时，学习率为 1e-3 * 0.8。

    +
    • +

  • “pass_manual”

    +

    这是一种按已训练pass数分段取值的学习率退火方法。使用该learning_rate_schedule时，用户通过参数 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 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：

    +
    optimizer = paddle.optimizer.Adam(
+    learning_rate=1e-3,
+    learning_rate_schedule="manual",
+    learning_rate_args="1:1.0,2:0.9,3:0.8",)
+
    +
    +

    在该示例中，当已训练pass数小于等于1时，学习率为 1e-3 * 1.0；当已训练pass数大于1小于等于2时，学习率为 1e-3 * 0.9；当已训练pass数大于2时，学习率为 1e-3 * 0.8。

    +
    • +
+
+
+

23. 出现 Duplicated layer name 错误怎么办

+

出现该错误的原因一般是用户对不同layer的参数 name 设置了相同的取值。遇到该错误时，先找出参数 name 取值相同的layer，然后将这些layer的参数 name 设置为不同的值。

+
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