@@ -3035,6 +3036,55 @@ Input should be a vector of positive numbers, without normalization.
+
+
+
mse_cost
+
+-
+
paddle.trainer_config_helpers.layers.mse_cost(*args, **kwargs)
+
+mean squared error cost:
+
+\[$\]
+
+rac{1}{N}sum_{i=1}^N(t _i- y_i)^2$
+
+
+
+
+
+| param name: | layer name. |
+
|---|
+| type name: | basestring |
+
|---|
+| param input: | Network prediction. |
+
|---|
+| type input: | LayerOutput |
+
|---|
+| param label: | Data label. |
+
|---|
+| type label: | LayerOutput |
+
|---|
+| param weight: | The weight affects the cost, namely the scale of cost.
+It is an optional argument. |
+
|---|
+| type weight: | LayerOutput |
+
|---|
+| param layer_attr: |
|---|
+| | layer’s extra attribute. |
+
+| type layer_attr: |
|---|
+| | ExtraLayerAttribute |
+
+| return: | LayerOutput object. |
+
|---|
+| rtype: | LayerOutput |
+
|---|
+
+
+
+
+
huber_cost
@@ -3170,6 +3220,37 @@ It is an optional argument.
+
+
+
sum_cost
+
+-
+
paddle.trainer_config_helpers.layers.sum_cost(*args, **kwargs)
+A loss layer which calculate the sum of the input as loss
+cost = sum_cost(input=input_layer)
+
+
+
+
+
+| Parameters: |
+- input (LayerOutput.) – The first input layer.
+- name (None|basestring.) – The name of this layers. It is not necessary.
+- layer_attr (ExtraLayerAttribute) – Extra Layer Attribute.
+
+ |
+
|---|
+| Returns: | LayerOutput object.
+ |
+
|---|
+| Return type: | LayerOutput.
+ |
+
|---|
+
+
+
+
crf_layer
@@ -3446,37 +3527,6 @@ False means no bias.
-
-
-
sum_cost
-
--
-
paddle.trainer_config_helpers.layers.sum_cost(*args, **kwargs)
-A loss layer which calculate the sum of the input as loss
-cost = sum_cost(input=input_layer)
-
-
-
-
-
-| Parameters: |
-- input (LayerOutput.) – The first input layer.
-- name (None|basestring.) – The name of this layers. It is not necessary.
-- layer_attr (ExtraLayerAttribute) – Extra Layer Attribute.
-
- |
-
|---|
-| Returns: | LayerOutput object.
- |
-
|---|
-| Return type: | LayerOutput.
- |
-
|---|
-
-
-
-
diff --git a/develop/doc/getstarted/basic_usage/index_en.html b/develop/doc/getstarted/basic_usage/index_en.html
index 87e4f4d605cacfd8667b5b9a0aa20d80f1d5f757..24f62783072949f1946945ae807f7323b1e17b3e 100644
--- a/develop/doc/getstarted/basic_usage/index_en.html
+++ b/develop/doc/getstarted/basic_usage/index_en.html
@@ -256,7 +256,7 @@
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)
diff --git a/develop/doc/searchindex.js b/develop/doc/searchindex.js
index 1c7705c05810954e6c325b7c72341860ff830cfa..04a77864e2cf3451fa764b7f65be32df9b1bd057 100644
--- a/develop/doc/searchindex.js
+++ b/develop/doc/searchindex.js
@@ -1 +1 @@
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Prediction","Activations","Parameter Attributes","DataSources","Evaluators","Layers","Networks","Optimizers","Poolings","Activation","Parameter Attribute","Layers","Networks","Optimizer","Pooling","Datasets","Model Configuration","Training and Inference","PaddlePaddle Design Doc","Python Data Reader Design Doc","Simple Linear Regression","Installing from Sources","PaddlePaddle in Docker Containers","Install and Build","Debian Package installation guide","GET STARTED","RNN Models","RNN Configuration","Contribute Code","Write New Layers","HOW TO","Tune GPU Performance","Run Distributed Training","Argument Outline","Detail Description","Set Command-line Parameters","Use Case","Distributed PaddlePaddle Training on AWS with Kubernetes","Paddle On Kubernetes","<no title>","<no title>","PaddlePaddle Documentation","Chinese Word Embedding Model Tutorial","Generative Adversarial Networks (GAN)","Image Classification Tutorial","Model Zoo - ImageNet","TUTORIALS","Quick 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 ebd810484e9a2cd024dacfc5c7dcbe60cff9c75f..c883b8d575e09588c8db4afff418590f71cabb3d 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 .
user_feature = construct_feature("user")
similarity = cos_sim(a=movie_feature, b=user_feature)
if not is_predict:
- outputs(
- regression_cost(
- input=similarity, label=data_layer(
- 'rating', size=1)))
+ outputs(mse_cost(input=similarity, label=data_layer('rating', size=1)))
define_py_data_sources2(
'data/train.list',
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 bbea823de4d870f8a4384b6a85ebb7e8182797fe..24389c2d8574dfda4bec9298776aa6b1aee51535 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 d01cdaaeb75ec7d02480eb9162cabaad2a947db9..428f58830e0b10c024f31238b7404c6df193eecd 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 2a7a6c8c17882a6f2c95e933e051c4b8f1a8eeee..3121b3f59df650c0a22d0bd305a6f793b202d30e 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 acff9652d0197f4c704919fdc59838cac4d8fb83..f6a29f690db1d41f633e7229771a8cbad4ba99af 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 @@
- cross_entropy
- cross_entropy_with_selfnorm
- multi_binary_label_cross_entropy
+- mse_cost
- huber_cost
- lambda_cost
- rank_cost
+- sum_cost
- crf_layer
- crf_decoding_layer
- ctc_layer
- warp_ctc_layer
- nce_layer
- hsigmoid
-- sum_cost