提交 d49a4611 编写于 作者: T Travis CI

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......@@ -228,16 +228,6 @@ It looks like there are a lot of arguments. However, most of them are for develo
<td class="left"></td><td class="left"></td><td class="left">√</td><td class="left">√</td>
</tr>
<tr>
<td class="left" rowspan = "2">metric learning</td><td class="left">external</td>
<td class="left">√</td><td class="left">√</td><td class="left">√</td><td class="left">√</td>
</tr>
<tr>
<td class="left">data_server_port</td>
<td class="left"></td><td class="left">√</td><td class="left"></td><td class="left">√</td>
</tr>
<tr>
<td class="left" rowspan = "16">PServer</td><td class="left">start_pserver</td>
<td class="left"></td><td class="left">√</td><td class="left"></td><td class="left">√</td>
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......@@ -184,15 +184,6 @@
- Specify shared dynamic library. It can be defined out of paddle by user.
- type: string (default: "", null).
## Metric Learning
* `--external`
- Whether to use external machine for metric learning.
- type: bool (default: 0).
* `--data_server_port`
- Listening port for dserver (data server), dserver is mainly used in metric learning.
- type: int32 (default: 21134).
## DataProvider
* `--memory_threshold_on_load_data`
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......@@ -351,12 +351,6 @@
<td class="left">diy_beam_search_prob_so</td>
<td class="left"></td><td class="left"></td><td class="left"></td><td class="left"></td>
</tr><tr>
<td class="left" rowspan = "2">metric learning</td><td class="left">external</td>
<td class="left"></td><td class="left"></td><td class="left"></td><td class="left"></td>
</tr><tr>
<td class="left">data_server_port</td>
<td class="left"></td><td class="left"></td><td class="left"></td><td class="left"></td>
</tr><tr>
<td class="left" rowspan = "16">PServer</td><td class="left">start_pserver</td>
<td class="left"></td><td class="left"></td><td class="left"></td><td class="left"></td>
</tr><tr>
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......@@ -181,7 +181,6 @@
<li><a class="reference internal" href="#test">Test</a></li>
<li><a class="reference internal" href="#gpu">GPU</a></li>
<li><a class="reference internal" href="#nlp-rnn-lstm-gru">NLP: RNN/LSTM/GRU</a></li>
<li><a class="reference internal" href="#metric-learning">Metric Learning</a></li>
<li><a class="reference internal" href="#dataprovider">DataProvider</a></li>
<li><a class="reference internal" href="#unit-test">Unit Test</a></li>
<li><a class="reference internal" href="#parameter-server-and-distributed-communication">Parameter Server and Distributed Communication</a></li>
......@@ -463,21 +462,6 @@
</li>
</ul>
</div>
<div class="section" id="metric-learning">
<span id="metric-learning"></span><h2>Metric Learning<a class="headerlink" href="#metric-learning" title="Permalink to this headline"></a></h2>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">--external</span></code><ul>
<li>Whether to use external machine for metric learning.</li>
<li>type: bool (default: 0).</li>
</ul>
</li>
<li><code class="docutils literal"><span class="pre">--data_server_port</span></code><ul>
<li>Listening port for dserver (data server), dserver is mainly used in metric learning.</li>
<li>type: int32 (default: 21134).</li>
</ul>
</li>
</ul>
</div>
<div class="section" id="dataprovider">
<span id="dataprovider"></span><h2>DataProvider<a class="headerlink" href="#dataprovider" title="Permalink to this headline"></a></h2>
<ul class="simple">
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Start","MovieLens Dataset","Regression MovieLens Ratting","Semantic Role labeling Tutorial","Sentiment Analysis Tutorial","Text generation Tutorial"],titleterms:{"case":41,"class":34,"function":47,"new":34,"return":24,AWS:42,Abs:14,DNS:42,EFS:42,For:43,KMS:42,Use:[41,43],Using:[27,33],about:0,absactiv:6,access:42,account:42,activ:[6,14],adadelta:18,adadeltaoptim:12,adagrad:18,adagradoptim:12,adam:18,adamax:18,adamaxoptim:12,adamoptim:12,add:42,address:42,addto:16,addto_lay:10,adversari:48,aggreg:[10,16],algorithm:52,analysi:56,api:[1,4],appendix:52,applic:4,approach:36,architectur:[32,52],argument:[24,38,41,52],asset:42,associ:42,async:39,attent:32,attribut:[7,15],auc_evalu:9,avg:19,avgpool:13,avx:27,aws:42,background:25,base:[9,10],baseactiv:6,basepool:19,basepoolingtyp:13,basesgdoptim:12,batch:24,batch_norm:16,batch_norm_lay:10,batch_siz:24,beam_search:[10,16],between:23,bidirect:56,bidirectional_lstm:[11,17],bilinear_interp:16,bilinear_interp_lay:10,bleu:57,block_expand:16,block_expand_lay:10,book:27,brelu:14,breluactiv:6,bucket:42,build:[26,28,43],built:36,cach:3,cento:26,check:[10,16,34,37],chines:47,choos:42,chunk_evalu:9,cifar:20,classif:[9,49],classification_error_evalu:9,classification_error_printer_evalu:9,clone:33,cloudform:42,cluster:[37,41,42],code:33,column_sum_evalu:9,command:[40,41,52,57],commit:[33,43],common:39,commun:39,compos:24,concat:16,concat_lay:10,concept:42,config:[4,41,54,55],configur:[21,32,35,37,42,52,54],conll05:20,connect:[10,16],contain:[27,43],content:[36,42],context_project:[10,16],contribut:33,conv:[10,16],conv_oper:[10,16],conv_project:[10,16],conv_shift:16,conv_shift_lay:10,convolut:[49,52],core:42,cos_sim:[10,16],cost:[10,16],cpu:[27,41],creat:[24,33,42,43],creator:24,credenti:42,credit:0,crf:16,crf_decod:16,crf_decoding_lay:10,crf_layer:10,cross_entropi:10,cross_entropy_cost:16,cross_entropy_with_selfnorm:10,cross_entropy_with_selfnorm_cost:16,ctc:16,ctc_error_evalu:9,ctc_layer:10,cudnnavg:19,cudnnmax:19,custom:24,dat:53,data:[10,16,20,24,25,32,42,43,47,48,49,52,54,55,56,57],data_lay:10,datafeed:20,dataprovid:[3,4,39],dataset:[20,53,54,57],datasourc:8,datatyp:20,date:33,debian:29,decayedadagrad:18,decayedadagradoptim:12,decor:24,defin:[42,52,56,57],delet:42,delv:49,demo:42,depend:26,deriv:34,descript:[39,48,53,55],design:[23,24],destroi:42,detail:[39,49],develop:[27,35],devic:41,dictionari:[24,47],differ:41,directori:42,distribut:[23,37,39,42],doc:[23,24],docker:[27,43],document:[27,46],dotmul_oper:[10,16],dotmul_project:[10,16],down:42,download:[26,42,43,47,50,54,57],dropout_lay:[11,17],ec2:42,elast:42,embed:[16,47,52],embedding_lay:10,entri:24,eos:16,eos_lay:10,equat:34,evalu:[9,25,54],evalutaion:57,event:[22,23],exampl:[23,47,48],exercis:49,exp:14,expactiv:6,expand:16,expand_lay:10,extern:42,extract:[47,50,54,57],fc_layer:10,featur:[50,53,54,55],field:54,file:[42,43,52,53,54],find:42,first_seq:[10,16],fork:33,format:52,from:[23,26,28],full_matrix_project:[10,16],fulli:[10,16],gan:48,gate:32,gener:[32,48,57],get:[30,43],get_output:16,get_output_lay:10,github:33,gpu:[27,36,39,41],gradient:34,gradient_printer_evalu:9,group:[10,16,42],gru:[11,17,39],gru_group:[11,17],gru_step:16,gru_step_lay:10,gru_unit:[11,17],grumemori:[10,16],guid:29,hand:36,handler:23,hook:33,how:[24,35,36],hsigmoid:[10,16],huber_cost:[10,16],iam:42,ident:14,identity_project:[10,16],identityactiv:6,imag:[10,11,16,17,27,43,49],imagenet:50,imdb:[20,56],img_cmrnorm:16,img_cmrnorm_lay:10,img_conv:16,img_conv_bn_pool:[11,17],img_conv_group:[11,17],img_conv_lay:10,img_pool:16,img_pool_lay:10,imikolov:20,implement:[24,34,48],infer:[22,52],info:50,ingredi:23,init_hook:3,initi:[41,42],input_typ:3,inspect:42,instal:[26,28,29,42,52],instanc:42,integr:42,interfac:[20,24,50],interpol:16,interpolation_lay:10,introduct:[2,47,50,56,57],isn:24,job:[37,42,43],join:[10,16],keep:33,kei:42,kill:37,kube:42,kubectl:42,kubernet:[42,43],label:55,lambda_cost:[10,16],last_seq:[10,16],lastest:33,launch:37,layer:[10,16,23,34,41],layeroutput:10,layertyp:10,line:[40,52],linear:[14,25],linear_comb:16,linear_comb_lay:10,linearactiv:6,list:24,local:[41,42],log:[14,52],logactiv:6,logist:52,lstm:[11,17,39,55,56],lstm_step:16,lstm_step_lay:10,lstmemori:[10,16],lstmemory_group:[11,17],lstmemory_unit:[11,17],map:24,math:[10,16],matrix:39,max:19,maxframe_printer_evalu:9,maxid:16,maxid_lay:10,maxid_printer_evalu:9,maxout:16,maxout_lay:10,maxpool:13,memori:[10,16],meta:54,mini:24,minibatch:20,misc:[11,17],mix:[10,16,41],mixed_lay:10,mnist:[20,48],model:[3,4,21,23,25,31,32,37,41,47,48,49,50,51,52,57],modifi:43,momentum:18,momentumoptim:12,movi:[53,54],movielen:[20,53,54],multi_binary_label_cross_entropi:10,multi_binary_label_cross_entropy_cost:16,multipl:24,name:42,nce:16,nce_lay:10,need:[24,36],network:[11,17,32,41,48,49,50,52,54,55],neural:[32,49,52,54,55],neuralnetwork:25,nlp:[11,17,39],non:[3,27],norm:[10,16],nvprof:36,nvvp:36,object:54,observ:[47,50],onli:[24,27],optim:[12,18,35,52],option:[26,47],outlin:38,output:[11,37,42],overview:52,packag:29,pad:16,pad_lay:10,paddl:[24,43],paddlepaddl:[23,27,28,42,46,47,57],pair:42,parallel_nn:41,paramet:[7,15,22,23,39,40,42,47,50],pa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\ No newline at end of file
......@@ -228,16 +228,6 @@
<td class="left"></td><td class="left"></td><td class="left">√</td><td class="left">√</td>
</tr>
<tr>
<td class="left" rowspan = "2">度量学习(metric learning)</td><td class="left">external</td>
<td class="left">√</td><td class="left">√</td><td class="left">√</td><td class="left">√</td>
</tr>
<tr>
<td class="left">data_server_port</td>
<td class="left"></td><td class="left">√</td><td class="left"></td><td class="left">√</td>
</tr>
<tr>
<td class="left" rowspan = "16">参数服务器(PServer)</td><td class="left">start_pserver</td>
<td class="left"></td><td class="left">√</td><td class="left"></td><td class="left">√</td>
......
......@@ -180,15 +180,6 @@
 - 用户可以自定义beam search的方法,编译成动态库,供PaddlePaddle加载。 该参数用于指定动态库路径.
- 类型: string (默认: "", null).
## 度量学习(Metric Learning)
* `--external`
- 指示是否使用外部机器进行度量学习.
- 类型: bool (默认: 0).
* `--data_server_port`
- 数据服务器(data server)的监听端口,主要用在度量学习中.
- 类型: int32 (默认: 21134).
## 数据支持(DataProvider)
* `--memory_threshold_on_load_data`
......
......@@ -358,12 +358,6 @@
<td class="left">diy_beam_search_prob_so</td>
<td class="left"></td><td class="left"></td><td class="left"></td><td class="left"></td>
</tr><tr>
<td class="left" rowspan = "2">度量学习(metric learning)</td><td class="left">external</td>
<td class="left"></td><td class="left"></td><td class="left"></td><td class="left"></td>
</tr><tr>
<td class="left">data_server_port</td>
<td class="left"></td><td class="left"></td><td class="left"></td><td class="left"></td>
</tr><tr>
<td class="left" rowspan = "16">参数服务器(PServer)</td><td class="left">start_pserver</td>
<td class="left"></td><td class="left"></td><td class="left"></td><td class="left"></td>
</tr><tr>
......
......@@ -188,7 +188,6 @@
<li><a class="reference internal" href="#">测试</a></li>
<li><a class="reference internal" href="#gpu">GPU</a></li>
<li><a class="reference internal" href="#nlp-rnn-lstm-gru">自然语言处理(NLP): RNN/LSTM/GRU</a></li>
<li><a class="reference internal" href="#metric-learning">度量学习(Metric Learning)</a></li>
<li><a class="reference internal" href="#dataprovider">数据支持(DataProvider)</a></li>
<li><a class="reference internal" href="#">单元测试</a></li>
<li><a class="reference internal" href="#">参数服务器和分布式通信</a></li>
......@@ -470,21 +469,6 @@
</li>
</ul>
</div>
<div class="section" id="metric-learning">
<span id="metric-learning"></span><h2>度量学习(Metric Learning)<a class="headerlink" href="#metric-learning" title="永久链接至标题"></a></h2>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">--external</span></code><ul>
<li>指示是否使用外部机器进行度量学习.</li>
<li>类型: bool (默认: 0).</li>
</ul>
</li>
<li><code class="docutils literal"><span class="pre">--data_server_port</span></code><ul>
<li>数据服务器(data server)的监听端口,主要用在度量学习中.</li>
<li>类型: int32 (默认: 21134).</li>
</ul>
</li>
</ul>
</div>
<div class="section" id="dataprovider">
<span id="dataprovider"></span><h2>数据支持(DataProvider)<a class="headerlink" href="#dataprovider" title="永久链接至标题"></a></h2>
<ul class="simple">
......
因为 它太大了无法显示 source diff 。你可以改为 查看blob
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