From 3f83d0d593816c62153268bd7b5713fb2577b7dd Mon Sep 17 00:00:00 2001
From: Travis CI <paddle-dev@baidu.com>
Date: Fri, 17 Mar 2017 10:44:05 +0000
Subject: [PATCH] Deploy to GitHub Pages:
 2983939b00e517db991156d781ac41fbcbc0184c

---
 .../v1/trainer_config_helpers/layers.rst.txt  |  18 ++-
 .../getstarted/basic_usage/index_en.rst.txt   |   2 +-
 .../api/v1/trainer_config_helpers/layers.html | 114 +++++++++++++-----
 .../doc/getstarted/basic_usage/index_en.html  |   2 +-
 develop/doc/searchindex.js                    |   2 +-
 .../doc/tutorials/rec/ml_regression_en.html   |   5 +-
 .../v1/trainer_config_helpers/layers.rst.txt  |  18 ++-
 .../getstarted/basic_usage/index_cn.rst.txt   |   4 +-
 .../howto/usage/k8s/k8s_distributed_cn.md.txt |   2 +-
 .../api/v1/trainer_config_helpers/layers.html | 114 +++++++++++++-----
 .../getstarted/basic_usage/index_cn.html      |   4 +-
 .../howto/usage/k8s/k8s_distributed_cn.html   |   2 +-
 develop/doc_cn/searchindex.js                 |   2 +-
 .../tutorials/rec/ml_regression_cn.html       |   5 +-
 14 files changed, 200 insertions(+), 94 deletions(-)

diff --git a/develop/doc/_sources/api/v1/trainer_config_helpers/layers.rst.txt b/develop/doc/_sources/api/v1/trainer_config_helpers/layers.rst.txt
index bbea823de4..24389c2d85 100644
--- a/develop/doc/_sources/api/v1/trainer_config_helpers/layers.rst.txt
+++ b/develop/doc/_sources/api/v1/trainer_config_helpers/layers.rst.txt
@@ -432,6 +432,12 @@ multi_binary_label_cross_entropy
     :members: multi_binary_label_cross_entropy
     :noindex:
 
+mse_cost
+---------
+..  automodule:: paddle.trainer_config_helpers.layers
+    :members: mse_cost
+    :noindex:
+
 huber_cost
 ----------
 ..  automodule:: paddle.trainer_config_helpers.layers
@@ -450,6 +456,12 @@ rank_cost
     :members: rank_cost
     :noindex:
 
+sum_cost
+---------
+..  automodule:: paddle.trainer_config_helpers.layers
+    :members: sum_cost
+    :noindex:
+
 crf_layer
 -----------------
 ..  automodule:: paddle.trainer_config_helpers.layers
@@ -486,12 +498,6 @@ hsigmoid
     :members: hsigmoid
     :noindex:
 
-sum_cost
----------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: sum_cost
-    :noindex:
-
 Check Layer 
 ============
 
diff --git a/develop/doc/_sources/getstarted/basic_usage/index_en.rst.txt b/develop/doc/_sources/getstarted/basic_usage/index_en.rst.txt
index c10b897d42..6775da20c2 100644
--- a/develop/doc/_sources/getstarted/basic_usage/index_en.rst.txt
+++ b/develop/doc/_sources/getstarted/basic_usage/index_en.rst.txt
@@ -49,7 +49,7 @@ To recover this relationship between ``X`` and ``Y``, we use a neural network wi
         x = data_layer(name='x', size=1)
         y = data_layer(name='y', size=1)
         y_predict = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b'))
-        cost = regression_cost(input=y_predict, label=y)
+        cost = mse_cost(input=y_predict, label=y)
         outputs(cost)
 
 Some of the most fundamental usages of PaddlePaddle are demonstrated:
diff --git a/develop/doc/api/v1/trainer_config_helpers/layers.html b/develop/doc/api/v1/trainer_config_helpers/layers.html
index 7db4bdedb9..93a906915a 100644
--- a/develop/doc/api/v1/trainer_config_helpers/layers.html
+++ b/develop/doc/api/v1/trainer_config_helpers/layers.html
@@ -276,16 +276,17 @@
 <li><a class="reference internal" href="#cross-entropy">cross_entropy</a></li>
 <li><a class="reference internal" href="#cross-entropy-with-selfnorm">cross_entropy_with_selfnorm</a></li>
 <li><a class="reference internal" href="#multi-binary-label-cross-entropy">multi_binary_label_cross_entropy</a></li>
+<li><a class="reference internal" href="#mse-cost">mse_cost</a></li>
 <li><a class="reference internal" href="#huber-cost">huber_cost</a></li>
 <li><a class="reference internal" href="#lambda-cost">lambda_cost</a></li>
 <li><a class="reference internal" href="#rank-cost">rank_cost</a></li>
+<li><a class="reference internal" href="#sum-cost">sum_cost</a></li>
 <li><a class="reference internal" href="#crf-layer">crf_layer</a></li>
 <li><a class="reference internal" href="#crf-decoding-layer">crf_decoding_layer</a></li>
 <li><a class="reference internal" href="#ctc-layer">ctc_layer</a></li>
 <li><a class="reference internal" href="#warp-ctc-layer">warp_ctc_layer</a></li>
 <li><a class="reference internal" href="#nce-layer">nce_layer</a></li>
 <li><a class="reference internal" href="#hsigmoid">hsigmoid</a></li>
-<li><a class="reference internal" href="#sum-cost">sum_cost</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#check-layer">Check Layer</a><ul>
@@ -3035,6 +3036,55 @@ Input should be a vector of positive numbers, without normalization.</p>
 </table>
 </dd></dl>
 
+</div>
+<div class="section" id="mse-cost">
+<h3>mse_cost<a class="headerlink" href="#mse-cost" title="Permalink to this headline">¶</a></h3>
+<dl class="function">
+<dt>
+<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">mse_cost</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<dd><blockquote>
+<div><p>mean squared error cost:</p>
+<div class="math">
+\[$\]</div>
+</div></blockquote>
+<p>rac{1}{N}sum_{i=1}^N(t _i- y_i)^2$</p>
+<blockquote>
+<div><table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">param name:</th><td class="field-body">layer name.</td>
+</tr>
+<tr class="field-even field"><th class="field-name">type name:</th><td class="field-body">basestring</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">param input:</th><td class="field-body">Network prediction.</td>
+</tr>
+<tr class="field-even field"><th class="field-name">type input:</th><td class="field-body">LayerOutput</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">param label:</th><td class="field-body">Data label.</td>
+</tr>
+<tr class="field-even field"><th class="field-name">type label:</th><td class="field-body">LayerOutput</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">param weight:</th><td class="field-body">The weight affects the cost, namely the scale of cost.
+It is an optional argument.</td>
+</tr>
+<tr class="field-even field"><th class="field-name">type weight:</th><td class="field-body">LayerOutput</td>
+</tr>
+<tr class="field-odd field"><th class="field-name" colspan="2">param layer_attr:</th></tr>
+<tr class="field-odd field"><td>&#160;</td><td class="field-body">layer&#8217;s extra attribute.</td>
+</tr>
+<tr class="field-even field"><th class="field-name" colspan="2">type layer_attr:</th></tr>
+<tr class="field-even field"><td>&#160;</td><td class="field-body">ExtraLayerAttribute</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">return:</th><td class="field-body">LayerOutput object.</td>
+</tr>
+<tr class="field-even field"><th class="field-name">rtype:</th><td class="field-body">LayerOutput</td>
+</tr>
+</tbody>
+</table>
+</div></blockquote>
+</dd></dl>
+
 </div>
 <div class="section" id="huber-cost">
 <h3>huber_cost<a class="headerlink" href="#huber-cost" title="Permalink to this headline">¶</a></h3>
@@ -3170,6 +3220,37 @@ It is an optional argument.</li>
 </table>
 </dd></dl>
 
+</div>
+<div class="section" id="sum-cost">
+<h3>sum_cost<a class="headerlink" href="#sum-cost" title="Permalink to this headline">¶</a></h3>
+<dl class="function">
+<dt>
+<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">sum_cost</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<dd><p>A loss layer which calculate the sum of the input as loss</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">sum_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">input_layer</span><span class="p">)</span>
+</pre></div>
+</div>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>input</strong> (<em>LayerOutput.</em>) &#8211; The first input layer.</li>
+<li><strong>name</strong> (<em>None|basestring.</em>) &#8211; The name of this layers. It is not necessary.</li>
+<li><strong>layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; Extra Layer Attribute.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput.</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
 </div>
 <div class="section" id="crf-layer">
 <h3>crf_layer<a class="headerlink" href="#crf-layer" title="Permalink to this headline">¶</a></h3>
@@ -3446,37 +3527,6 @@ False means no bias.</li>
 </table>
 </dd></dl>
 
-</div>
-<div class="section" id="sum-cost">
-<h3>sum_cost<a class="headerlink" href="#sum-cost" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">sum_cost</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>A loss layer which calculate the sum of the input as loss</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">sum_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">input_layer</span><span class="p">)</span>
-</pre></div>
-</div>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>input</strong> (<em>LayerOutput.</em>) &#8211; The first input layer.</li>
-<li><strong>name</strong> (<em>None|basestring.</em>) &#8211; The name of this layers. It is not necessary.</li>
-<li><strong>layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; Extra Layer Attribute.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput.</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
 </div>
 </div>
 <div class="section" id="check-layer">
diff --git a/develop/doc/getstarted/basic_usage/index_en.html b/develop/doc/getstarted/basic_usage/index_en.html
index 87e4f4d605..24f6278307 100644
--- a/develop/doc/getstarted/basic_usage/index_en.html
+++ b/develop/doc/getstarted/basic_usage/index_en.html
@@ -256,7 +256,7 @@
 <span class="n">x</span> <span class="o">=</span> <span class="n">data_layer</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 <span class="n">y</span> <span class="o">=</span> <span class="n">data_layer</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 <span class="n">y_predict</span> <span class="o">=</span> <span class="n">fc_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">param_attr</span><span class="o">=</span><span class="n">ParamAttr</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="n">LinearActivation</span><span class="p">(),</span> <span class="n">bias_attr</span><span class="o">=</span><span class="n">ParamAttr</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">))</span>
-<span class="n">cost</span> <span class="o">=</span> <span class="n">regression_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">y_predict</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
+<span class="n">cost</span> <span class="o">=</span> <span class="n">mse_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">y_predict</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
 <span class="n">outputs</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
 </pre></div>
 </div>
diff --git a/develop/doc/searchindex.js b/develop/doc/searchindex.js
index 1c7705c058..04a77864e2 100644
--- a/develop/doc/searchindex.js
+++ b/develop/doc/searchindex.js
@@ -1 +1 @@
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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],mse_cost:10,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],paraphras:47,pass:41,perform:[36,39],pnpair_evalu:9,point:42,pool:[10,13,16,19],pooling_lay:10,power:16,power_lay:10,pre:33,precision_recall_evalu:9,predict:[5,49,50,54,55,56],prefetch:24,prepar:[25,32,37,42,47,48,49,54,56,57],preprocess:[47,49,52,54,57],prerequisit:37,pretrain:[47,57],print:9,privat:42,problem:25,profil:36,provid:[3,24,52,54,55],pull:33,push:33,pydataprovider2:3,python:[5,24,34,50,52,54],quick:52,randomnumb:39,rank:9,rank_cost:[10,16],rat:54,rate:53,reader:[20,23,24],recurr:[10,11,16,17,32,52],recurrent_group:[10,16],recurrent_lay:10,refer:[3,36,55,56],region:42,regress:[25,52,54],relu:14,reluactiv:6,render:42,repeat:16,repeat_lay:10,request:33,requir:[26,33],reshap:[10,16],resnet:50,result:[37,43,57],revis:[33,47],rmsprop:18,rmspropoptim:12,rnn:[31,32,39],role:55,rotat:16,rotate_lay:10,route53:42,run:[37,43,55],sampl:[10,16],sampling_id:16,sampling_id_lay:10,scale:16,scaling_lay:10,scaling_project:[10,16],script:43,secur:42,selective_fc:16,selective_fc_lay:10,semant:55,sentiment:[20,56],seq_concat:16,seq_concat_lay:10,seq_reshap:16,seq_reshape_lay:10,seqtext_printer_evalu:9,sequenc:32,sequence_conv_pool:[11,17],sequencesoftmax:14,sequencesoftmaxactiv:6,sequenti:3,server:[39,42],servic:42,set:[12,40],setup:[26,42],sgd:39,share:23,shuffl:24,sigmoid:14,sigmoidactiv:6,simpl:[25,32],simple_attent:[11,17],simple_gru:[11,17],simple_img_conv_pool:[11,17],simple_lstm:[11,17],singl:24,slice:[10,16],slope_intercept:16,slope_intercept_lay:10,softmax:14,softmaxactiv:6,softrelu:14,softreluactiv:6,sourc:[26,28],span:26,spars:41,specifi:[41,47],split:54,spp:16,spp_layer:10,squar:14,squareactiv:6,squarerootn:19,squarerootnpool:13,stack:56,standard:52,stanh:14,stanhactiv:6,start:[23,30,42,43,52],startup:43,structur:48,suffici:24,sum:19,sum_cost:[10,16],sum_evalu:9,sum_to_one_norm:16,sum_to_one_norm_lay:10,summar:23,summari:52,sumpool:13,system:42,table_project:[10,16],take:24,tanh:14,tanhactiv:6,tear:42,templat:42,tensor:16,tensor_lay:10,test:[34,39,41,54,55,56],text:57,text_conv_pool:[11,17],timer:36,tip:36,toi:48,tool:36,train:[22,23,24,25,37,39,41,42,43,47,48,49,52,54,55,56,57],trainer:[22,42,54],tran:16,trans_full_matrix_project:[10,16],trans_lay:10,transfer:52,tune:[36,39],tutori:[47,49,51,55,56,57],ubuntu:26,uci_h:20,unit:[34,39],updat:[23,33,42],usag:[24,27,35],use:24,user:[47,53,54,56,57],util:9,value_printer_evalu:9,vector:39,verifi:42,version:33,vgg_16_network:[11,17],visual:50,volum:42,vpc:42,warp_ctc:16,warp_ctc_lay:10,what:36,why:[24,36],wmt14:20,word:[47,52],workflow:57,workspac:37,wrapper:34,write:[34,52],yaml:43,your:33,zoo:[50,51]}})
\ No newline at end of file
diff --git a/develop/doc/tutorials/rec/ml_regression_en.html b/develop/doc/tutorials/rec/ml_regression_en.html
index ebd810484e..c883b8d575 100644
--- a/develop/doc/tutorials/rec/ml_regression_en.html
+++ b/develop/doc/tutorials/rec/ml_regression_en.html
@@ -623,10 +623,7 @@ cp ml-1m/ratings.dat.test .
 <span class="n">user_feature</span> <span class="o">=</span> <span class="n">construct_feature</span><span class="p">(</span><span class="s2">&quot;user&quot;</span><span class="p">)</span>
 <span class="n">similarity</span> <span class="o">=</span> <span class="n">cos_sim</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="n">movie_feature</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">user_feature</span><span class="p">)</span>
 <span class="k">if</span> <span class="ow">not</span> <span class="n">is_predict</span><span class="p">:</span>
-    <span class="n">outputs</span><span class="p">(</span>
-        <span class="n">regression_cost</span><span class="p">(</span>
-            <span class="nb">input</span><span class="o">=</span><span class="n">similarity</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">data_layer</span><span class="p">(</span>
-                <span class="s1">&#39;rating&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">)))</span>
+    <span class="n">outputs</span><span class="p">(</span><span class="n">mse_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">similarity</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">data_layer</span><span class="p">(</span><span class="s1">&#39;rating&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">)))</span>
 
     <span class="n">define_py_data_sources2</span><span class="p">(</span>
         <span class="s1">&#39;data/train.list&#39;</span><span class="p">,</span>
diff --git a/develop/doc_cn/_sources/api/v1/trainer_config_helpers/layers.rst.txt b/develop/doc_cn/_sources/api/v1/trainer_config_helpers/layers.rst.txt
index bbea823de4..24389c2d85 100644
--- a/develop/doc_cn/_sources/api/v1/trainer_config_helpers/layers.rst.txt
+++ b/develop/doc_cn/_sources/api/v1/trainer_config_helpers/layers.rst.txt
@@ -432,6 +432,12 @@ multi_binary_label_cross_entropy
     :members: multi_binary_label_cross_entropy
     :noindex:
 
+mse_cost
+---------
+..  automodule:: paddle.trainer_config_helpers.layers
+    :members: mse_cost
+    :noindex:
+
 huber_cost
 ----------
 ..  automodule:: paddle.trainer_config_helpers.layers
@@ -450,6 +456,12 @@ rank_cost
     :members: rank_cost
     :noindex:
 
+sum_cost
+---------
+..  automodule:: paddle.trainer_config_helpers.layers
+    :members: sum_cost
+    :noindex:
+
 crf_layer
 -----------------
 ..  automodule:: paddle.trainer_config_helpers.layers
@@ -486,12 +498,6 @@ hsigmoid
     :members: hsigmoid
     :noindex:
 
-sum_cost
----------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: sum_cost
-    :noindex:
-
 Check Layer 
 ============
 
diff --git a/develop/doc_cn/_sources/getstarted/basic_usage/index_cn.rst.txt b/develop/doc_cn/_sources/getstarted/basic_usage/index_cn.rst.txt
index d01cdaaeb7..428f58830e 100644
--- a/develop/doc_cn/_sources/getstarted/basic_usage/index_cn.rst.txt
+++ b/develop/doc_cn/_sources/getstarted/basic_usage/index_cn.rst.txt
@@ -55,7 +55,7 @@ PaddlePaddle是源于百度的一个深度学习平台。这份简短的介绍
     # 线性计算网络层: ȳ = wx + b
     ȳ = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b'))
     # 计算误差函数，即  ȳ 和真实 y 之间的距离
-    cost = regression_cost(input= ȳ, label=y)
+    cost = mse_cost(input= ȳ, label=y)
     outputs(cost)
 
 
@@ -69,7 +69,7 @@ PaddlePaddle是源于百度的一个深度学习平台。这份简短的介绍
     
     - **数据层**：数据层 `data_layer` 是神经网络的入口，它读入数据并将它们传输到接下来的网络层。这里数据层有两个，分别对应于变量 `x` 和 `y`。
     - **全连接层**：全连接层 `fc_layer` 是基础的计算单元，这里利用它建模变量之间的线性关系。计算单元是神经网络的核心，PaddlePaddle支持大量的计算单元和任意深度的网络连接，从而可以拟合任意的函数来学习复杂的数据关系。
-    - **回归误差代价层**：回归误差代价层 `regression_cost` 是众多误差代价函数层的一种，它们在训练过程作为网络的出口，用来计算模型的误差，是模型参数优化的目标函数。
+    - **回归误差代价层**：回归误差代价层 `mse_cost` 是众多误差代价函数层的一种，它们在训练过程作为网络的出口，用来计算模型的误差，是模型参数优化的目标函数。
 
 定义了网络结构并保存为 `trainer_config.py` 之后，运行以下训练命令：
 
diff --git a/develop/doc_cn/_sources/howto/usage/k8s/k8s_distributed_cn.md.txt b/develop/doc_cn/_sources/howto/usage/k8s/k8s_distributed_cn.md.txt
index 2a7a6c8c17..3121b3f59d 100644
--- a/develop/doc_cn/_sources/howto/usage/k8s/k8s_distributed_cn.md.txt
+++ b/develop/doc_cn/_sources/howto/usage/k8s/k8s_distributed_cn.md.txt
@@ -213,7 +213,7 @@ I1116 09:10:17.123440    50 Util.cpp:130] Calling runInitFunctions
 I1116 09:10:17.123764    50 Util.cpp:143] Call runInitFunctions done.
 [WARNING 2016-11-16 09:10:17,227 default_decorators.py:40] please use keyword arguments in paddle config.
 [INFO 2016-11-16 09:10:17,239 networks.py:1282] The input order is [movie_id, title, genres, user_id, gender, age, occupation, rating]
-[INFO 2016-11-16 09:10:17,239 networks.py:1289] The output order is [__regression_cost_0__]
+[INFO 2016-11-16 09:10:17,239 networks.py:1289] The output order is [__mse_cost_0__]
 I1116 09:10:17.392917    50 Trainer.cpp:170] trainer mode: Normal
 I1116 09:10:17.613910    50 PyDataProvider2.cpp:257] loading dataprovider dataprovider::process
 I1116 09:10:17.680917    50 PyDataProvider2.cpp:257] loading dataprovider dataprovider::process
diff --git a/develop/doc_cn/api/v1/trainer_config_helpers/layers.html b/develop/doc_cn/api/v1/trainer_config_helpers/layers.html
index acff9652d0..f6a29f690d 100644
--- a/develop/doc_cn/api/v1/trainer_config_helpers/layers.html
+++ b/develop/doc_cn/api/v1/trainer_config_helpers/layers.html
@@ -283,16 +283,17 @@
 <li><a class="reference internal" href="#cross-entropy">cross_entropy</a></li>
 <li><a class="reference internal" href="#cross-entropy-with-selfnorm">cross_entropy_with_selfnorm</a></li>
 <li><a class="reference internal" href="#multi-binary-label-cross-entropy">multi_binary_label_cross_entropy</a></li>
+<li><a class="reference internal" href="#mse-cost">mse_cost</a></li>
 <li><a class="reference internal" href="#huber-cost">huber_cost</a></li>
 <li><a class="reference internal" href="#lambda-cost">lambda_cost</a></li>
 <li><a class="reference internal" href="#rank-cost">rank_cost</a></li>
+<li><a class="reference internal" href="#sum-cost">sum_cost</a></li>
 <li><a class="reference internal" href="#crf-layer">crf_layer</a></li>
 <li><a class="reference internal" href="#crf-decoding-layer">crf_decoding_layer</a></li>
 <li><a class="reference internal" href="#ctc-layer">ctc_layer</a></li>
 <li><a class="reference internal" href="#warp-ctc-layer">warp_ctc_layer</a></li>
 <li><a class="reference internal" href="#nce-layer">nce_layer</a></li>
 <li><a class="reference internal" href="#hsigmoid">hsigmoid</a></li>
-<li><a class="reference internal" href="#sum-cost">sum_cost</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#check-layer">Check Layer</a><ul>
@@ -3042,6 +3043,55 @@ Input should be a vector of positive numbers, without normalization.</p>
 </table>
 </dd></dl>
 
+</div>
+<div class="section" id="mse-cost">
+<h3>mse_cost<a class="headerlink" href="#mse-cost" title="永久链接至标题">¶</a></h3>
+<dl class="function">
+<dt>
+<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">mse_cost</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<dd><blockquote>
+<div><p>mean squared error cost:</p>
+<div class="math">
+\[$\]</div>
+</div></blockquote>
+<p>rac{1}{N}sum_{i=1}^N(t _i- y_i)^2$</p>
+<blockquote>
+<div><table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">param name:</th><td class="field-body">layer name.</td>
+</tr>
+<tr class="field-even field"><th class="field-name">type name:</th><td class="field-body">basestring</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">param input:</th><td class="field-body">Network prediction.</td>
+</tr>
+<tr class="field-even field"><th class="field-name">type input:</th><td class="field-body">LayerOutput</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">param label:</th><td class="field-body">Data label.</td>
+</tr>
+<tr class="field-even field"><th class="field-name">type label:</th><td class="field-body">LayerOutput</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">param weight:</th><td class="field-body">The weight affects the cost, namely the scale of cost.
+It is an optional argument.</td>
+</tr>
+<tr class="field-even field"><th class="field-name">type weight:</th><td class="field-body">LayerOutput</td>
+</tr>
+<tr class="field-odd field"><th class="field-name" colspan="2">param layer_attr:</th></tr>
+<tr class="field-odd field"><td>&#160;</td><td class="field-body">layer&#8217;s extra attribute.</td>
+</tr>
+<tr class="field-even field"><th class="field-name" colspan="2">type layer_attr:</th></tr>
+<tr class="field-even field"><td>&#160;</td><td class="field-body">ExtraLayerAttribute</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">return:</th><td class="field-body">LayerOutput object.</td>
+</tr>
+<tr class="field-even field"><th class="field-name">rtype:</th><td class="field-body">LayerOutput</td>
+</tr>
+</tbody>
+</table>
+</div></blockquote>
+</dd></dl>
+
 </div>
 <div class="section" id="huber-cost">
 <h3>huber_cost<a class="headerlink" href="#huber-cost" title="永久链接至标题">¶</a></h3>
@@ -3177,6 +3227,37 @@ It is an optional argument.</li>
 </table>
 </dd></dl>
 
+</div>
+<div class="section" id="sum-cost">
+<h3>sum_cost<a class="headerlink" href="#sum-cost" title="永久链接至标题">¶</a></h3>
+<dl class="function">
+<dt>
+<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">sum_cost</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<dd><p>A loss layer which calculate the sum of the input as loss</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">sum_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">input_layer</span><span class="p">)</span>
+</pre></div>
+</div>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first simple">
+<li><strong>input</strong> (<em>LayerOutput.</em>) &#8211; The first input layer.</li>
+<li><strong>name</strong> (<em>None|basestring.</em>) &#8211; The name of this layers. It is not necessary.</li>
+<li><strong>layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; Extra Layer Attribute.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">LayerOutput object.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">LayerOutput.</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
 </div>
 <div class="section" id="crf-layer">
 <h3>crf_layer<a class="headerlink" href="#crf-layer" title="永久链接至标题">¶</a></h3>
@@ -3453,37 +3534,6 @@ False means no bias.</li>
 </table>
 </dd></dl>
 
-</div>
-<div class="section" id="sum-cost">
-<h3>sum_cost<a class="headerlink" href="#sum-cost" title="永久链接至标题">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">sum_cost</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>A loss layer which calculate the sum of the input as loss</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">sum_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">input_layer</span><span class="p">)</span>
-</pre></div>
-</div>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first simple">
-<li><strong>input</strong> (<em>LayerOutput.</em>) &#8211; The first input layer.</li>
-<li><strong>name</strong> (<em>None|basestring.</em>) &#8211; The name of this layers. It is not necessary.</li>
-<li><strong>layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; Extra Layer Attribute.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">LayerOutput object.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">LayerOutput.</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
 </div>
 </div>
 <div class="section" id="check-layer">
diff --git a/develop/doc_cn/getstarted/basic_usage/index_cn.html b/develop/doc_cn/getstarted/basic_usage/index_cn.html
index a4a00cbb5c..25d51470cf 100644
--- a/develop/doc_cn/getstarted/basic_usage/index_cn.html
+++ b/develop/doc_cn/getstarted/basic_usage/index_cn.html
@@ -264,7 +264,7 @@ y = data_layer(name=&#39;y&#39;, size=1)
 # 线性计算网络层: ȳ = wx + b
 ȳ = fc_layer(input=x, param_attr=ParamAttr(name=&#39;w&#39;), size=1, act=LinearActivation(), bias_attr=ParamAttr(name=&#39;b&#39;))
 # 计算误差函数，即  ȳ 和真实 y 之间的距离
-cost = regression_cost(input= ȳ, label=y)
+cost = mse_cost(input= ȳ, label=y)
 outputs(cost)
 </pre></div>
 </div>
@@ -279,7 +279,7 @@ outputs(cost)
 <div><ul class="simple">
 <li><strong>数据层</strong>：数据层 <cite>data_layer</cite> 是神经网络的入口，它读入数据并将它们传输到接下来的网络层。这里数据层有两个，分别对应于变量 <cite>x</cite> 和 <cite>y</cite>。</li>
 <li><strong>全连接层</strong>：全连接层 <cite>fc_layer</cite> 是基础的计算单元，这里利用它建模变量之间的线性关系。计算单元是神经网络的核心，PaddlePaddle支持大量的计算单元和任意深度的网络连接，从而可以拟合任意的函数来学习复杂的数据关系。</li>
-<li><strong>回归误差代价层</strong>：回归误差代价层 <cite>regression_cost</cite> 是众多误差代价函数层的一种，它们在训练过程作为网络的出口，用来计算模型的误差，是模型参数优化的目标函数。</li>
+<li><strong>回归误差代价层</strong>：回归误差代价层 <cite>mse_cost</cite> 是众多误差代价函数层的一种，它们在训练过程作为网络的出口，用来计算模型的误差，是模型参数优化的目标函数。</li>
 </ul>
 </div></blockquote>
 </li>
diff --git a/develop/doc_cn/howto/usage/k8s/k8s_distributed_cn.html b/develop/doc_cn/howto/usage/k8s/k8s_distributed_cn.html
index 0996cd7afd..dbc68dd174 100644
--- a/develop/doc_cn/howto/usage/k8s/k8s_distributed_cn.html
+++ b/develop/doc_cn/howto/usage/k8s/k8s_distributed_cn.html
@@ -414,7 +414,7 @@ I1116 <span class="m">09</span>:10:17.123440    <span class="m">50</span> Util.c
 I1116 <span class="m">09</span>:10:17.123764    <span class="m">50</span> Util.cpp:143<span class="o">]</span> Call runInitFunctions <span class="k">done</span>.
 <span class="o">[</span>WARNING <span class="m">2016</span>-11-16 <span class="m">09</span>:10:17,227 default_decorators.py:40<span class="o">]</span> please use keyword arguments in paddle config.
 <span class="o">[</span>INFO <span class="m">2016</span>-11-16 <span class="m">09</span>:10:17,239 networks.py:1282<span class="o">]</span> The input order is <span class="o">[</span>movie_id, title, genres, user_id, gender, age, occupation, rating<span class="o">]</span>
-<span class="o">[</span>INFO <span class="m">2016</span>-11-16 <span class="m">09</span>:10:17,239 networks.py:1289<span class="o">]</span> The output order is <span class="o">[</span>__regression_cost_0__<span class="o">]</span>
+<span class="o">[</span>INFO <span class="m">2016</span>-11-16 <span class="m">09</span>:10:17,239 networks.py:1289<span class="o">]</span> The output order is <span class="o">[</span>__mse_cost_0__<span class="o">]</span>
 I1116 <span class="m">09</span>:10:17.392917    <span class="m">50</span> Trainer.cpp:170<span class="o">]</span> trainer mode: Normal
 I1116 <span class="m">09</span>:10:17.613910    <span class="m">50</span> PyDataProvider2.cpp:257<span class="o">]</span> loading dataprovider dataprovider::process
 I1116 <span class="m">09</span>:10:17.680917    <span class="m">50</span> PyDataProvider2.cpp:257<span class="o">]</span> loading dataprovider dataprovider::process
diff --git a/develop/doc_cn/searchindex.js b/develop/doc_cn/searchindex.js
index 0c6d0f4c51..a46fce4962 100644
--- a/develop/doc_cn/searchindex.js
+++ b/develop/doc_cn/searchindex.js
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\ No newline at end of file
diff --git a/develop/doc_cn/tutorials/rec/ml_regression_cn.html b/develop/doc_cn/tutorials/rec/ml_regression_cn.html
index f48344e621..5ce3c5567f 100644
--- a/develop/doc_cn/tutorials/rec/ml_regression_cn.html
+++ b/develop/doc_cn/tutorials/rec/ml_regression_cn.html
@@ -632,10 +632,7 @@ cp ml-1m/ratings.dat.test .
 <span class="n">user_feature</span> <span class="o">=</span> <span class="n">construct_feature</span><span class="p">(</span><span class="s2">&quot;user&quot;</span><span class="p">)</span>
 <span class="n">similarity</span> <span class="o">=</span> <span class="n">cos_sim</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="n">movie_feature</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">user_feature</span><span class="p">)</span>
 <span class="k">if</span> <span class="ow">not</span> <span class="n">is_predict</span><span class="p">:</span>
-    <span class="n">outputs</span><span class="p">(</span>
-        <span class="n">regression_cost</span><span class="p">(</span>
-            <span class="nb">input</span><span class="o">=</span><span class="n">similarity</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">data_layer</span><span class="p">(</span>
-                <span class="s1">&#39;rating&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">)))</span>
+    <span class="n">outputs</span><span class="p">(</span><span class="n">mse_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">similarity</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">data_layer</span><span class="p">(</span><span class="s1">&#39;rating&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">)))</span>
 
     <span class="n">define_py_data_sources2</span><span class="p">(</span>
         <span class="s1">&#39;data/train.list&#39;</span><span class="p">,</span>
-- 
GitLab